认知雷达波形设计

认知雷达波形设计，用于目标检测的波形设计，取自IEEE文献。

认知波形设计 文献摘要大全

• 1. Cognitive radar waveform design for multiple targets based on information theory
  • 基于信息论的多目标认知雷达波形设计
  • In order to make received radar echo take maximum targets information under multiple targets' circumstance, the information theoretical approach for designing transmit waveform is utilized. Firstly, it deduces the relationship between transmitting waveform and multitarget mutual information in two conditions. One condition is radar echo with only noise background and the other is with clutter in consideration. Then it obtains the optimal transmitting waveform based on maximum mutual information criterion. Transmitting waveform is designed by utilizing prior information including target spectral variance, noise power spectrum and clutter power spectrum. Simulation results demonstrate that compared with LFM signal, designed waveform based on maximum mutual information criterion can make radar echoes contain more multi-targets' information and improve radar performance as a result. Finally, correlative parameters which influence mutual information are analyzed and the conclusion is gained that mutual information is directly proportion to observation time of transmitting waveform and approximately logarithmic to transmit power. Mutual information would be raised when clutter is feebler and the highest peak of target spectral variance is more near.
  • 为了使接收雷达回波在多目标环境下获得最大目标信息，利用信息理论方法设计发射波形。首先，推导了两种情况下发射波形与多目标互信息的关系。一种是只有噪声背景的雷达回波，另一种是有杂波背景的雷达回波。然后根据最大互信息准则得到最优发射波形。利用目标谱方差、噪声功率谱和杂波功率谱等先验信息设计发射波形。仿真结果表明，与线性调频信号相比，基于最大互信息准则设计的波形能够使雷达回波包含更多的多目标信息，从而提高雷达性能。最后分析了影响互信息的相关参数，得出互信息与发射波形观测时间成正比，与发射功率成近似对数的结论。杂波越弱，目标光谱方差的峰值越近，就会产生互信息。
  • 目的：在多目标环境下获得最大目标信息
  • 方法：利用最大互信息准则获得最优发射波形，分析影响互信息的相关参数。
  • 优势：互信息与发射波形观测时间成正比，与发射功率成近似对数的结论。杂波越弱，目标光谱方差的峰值越近，就会产生互信息。 能够使雷达回波包含更多的多目标信息，从而提高雷达性能。
• 2. MIMO clutter discrete probing for cognitive radar
  • 认知雷达多输入多输出杂波离散探测
  • A new airborne radar mode is introduced that addresses the problem of high false alarm rates due to strong clutter discretes in the radar field of regard. The new mode takes advantage of emerging cognitive and fully adaptive radar (CoFAR) architectures that support rapid adaptation of the radar space-time transmit waveform. The new mode exploits this flexibility to both rapidly characterize strong clutter discretes and minimize their impact on target detection performance, while minimizing impact to radar timeline. The new mode leverages a MIMO probing approach that rapidly characterizes the clutter discretes in the scene and uses the received signals to form an appropriate space-time waveform response that minimizes their radar return and impact on radar performance during the processing of subsequent radar pulses. The paper provides details about the processing algorithms and presents a performance assessment based on a simulation of an airborne GMTI radar system.
  • 摘要提出了一种新的机载雷达模式，解决了雷达领域内由于强杂波离散而导致虚警率高的问题。新模式利用了新兴的认知和全自适应雷达(CoFAR)架构，支持对雷达空时传输波形的快速适应。新的模式利用了这种灵活性，既可以快速地描述强杂波离散，又可以将其对目标检测性能的影响最小化，同时将对雷达时间轴的影响最小化。新模式利用MIMO探测方法，快速表征场景中的杂波离散点，并使用接收到的信号形成适当的空时波形响应，将其雷达回波和后续雷达脉冲处理期间对雷达性能的影响降至最低。本文详细介绍了处理算法，并给出了基于机载GMTI雷达系统仿真的性能评估。
  • 目的： 1、机载雷达 2、解决了雷达领域内由于强杂波离散而导致虚警率高的问题
  • 方法：利用MIMO探测方法，快速表征场景中的杂波离散点，并使用接收到的信号形成适当的空时波形响应。
  • 优势：可以快速地描述强杂波离散，又可以将其对目标检测性能的影响最小化，同时将对雷达时间轴的影响最小化。
• 3. Cognitive radar waveforms for frequency dense environments
  • 频率密集环境下的认知雷达波形
  • A wideband cognitive radar operating in spectrally dense environments needs to use special adaptive waveforms, in order to mitigate the effect of the interferences caused by a number of concurrent narrow band communication services. In the literature, the use of adaptive waveforms with spectral nulls has been proposed. This solution, however, presents some drawbacks in case of high level of spectrum congestion. In fact, due to the high number of frequency nulls, the zero Doppler uncertainty function of the received waveform can result to be corrupted by a high number of sidelobes. This paper proposes and evaluates the use of a new family of waveforms, defined as Adaptive Frequency Modulated Waveforms (AFMWs), whose structure is the combination of a Linear Frequency Modulated Waveform with an Adaptive Stepped Frequency Waveform. A practical implementation of a Cognitive Radar tailored to a specific case study of interest is discussed and the relative performance evaluation results are presented, with regard to detection and recognition capabilities. The results demonstrate that the AFMWs are much better suited to the tasks of detection and recognition than other types of waveforms.
  • 在频谱密集环境下工作的宽带认知雷达需要使用特殊的自适应波形，以减轻并发的窄带通信业务造成的干扰的影响。在文献中，已提出使用自适应波形与频谱零值。然而，这种解决方案在频谱拥塞程度高的情况下存在一些缺陷。事实上，由于高频零值的存在，接收波形的零多普勒不确定度函数可能会被大量的旁瓣所破坏。本文提出并评估了一种新的波形族，定义为自适应调频波形(AFMWs)，其结构是线性调频波形与自适应步进频率波形的组合。本文讨论了一种适用于具体案例研究的认知雷达的实际实现，并给出了有关检测和识别能力的相对性能评估结果。结果表明，与其他波形相比，AFMWs更适合于检测和识别任务。
  • 目的：在频率密集的环境中，为了减轻并发的窄带通信造成的干扰影响，设计特殊的自适应波形。 以前的解决方法在频谱拥塞程度高的情况下存在缺陷。
  • 方法：线性调频波形与自适应步进频率波形的组合
  • 优势：AFMWs更适合于检测和识别任务
• 4. Optimal waveform design for target tracking and estimation in cognitive radar
  • 认知雷达目标跟踪与估计的最优波形设计
  • In this paper, the problem of optimal waveform design for target tracking and estimation in cognitive radar (CR) is investigated. This problem is analyzed in signal-dependent interference, as well as additive channel noise for extended target with unknown target impulse response (TIR). An improved online waveform optimization design method for target tracking and estimation is proposed. In this method, Kalman filter is used to accurately estimate the TIR. At each Kalman filtering (KF) iteration, the online waveform spectrum optimization design based on mutual information (MI) criterion is modeled in Fourier domain. Unlike most existing CR waveform optimization design with constant prior information, unknown and dynamic TIR is considered in this method. Simulation results demonstrate that CR with the proposed waveform performs better than traditional radar system with fixed waveform, and offers more flexibility.
  • 研究了认知雷达中目标跟踪与估计的最优波形设计问题。分析了未知目标脉冲响应(TIR)的扩展目标的信号相关干扰和加性信道噪声问题。提出了一种改进的目标跟踪与估计在线波形优化设计方法。该方法利用卡尔曼滤波对红外光谱进行精确估计。在每次卡尔曼滤波(KF)迭代时，基于互信息(MI)准则的在线波形谱优化设计在傅里叶域中建模。与现有的基于恒定先验信息的CR波形优化设计不同，该方法考虑了未知的动态TIR。仿真结果表明，采用该波形的雷达系统比传统固定波形的雷达系统性能更好，具有更大的灵活性。
  • 目的：提出了一种改进的目标跟踪与估计在线波形优化设计方法。
  • 方法：分析了未知目标脉冲响应(TIR)的扩展目标的信号相关干扰和加性信道噪声问题。利用卡尔曼滤波对红外光谱进行精确估计。在每次卡尔曼滤波(KF)迭代时，基于互信息(MI)准则的在线波形谱优化设计在傅里叶域中建模。
  • 优势：采用该波形的雷达系统比传统固定波形的雷达系统性能更好，具有更大的灵活性。
• 5. Constant modulus waveforms with restraining spectral interferences for cognitive MIMO radar
  • 认知MIMO雷达抑制频谱干扰的常模波形
  • Apart from basic orthogonality, cognitive multiple-input multiple-output (MIMO) radar waveforms should have the capacity of restraining spectral interferences in complex environment. Constant-modulus waveforms are also desired for the radio frequency (RF) amplifiers. Based on above considerations, this paper proposes an optimization method to design cognitive MIMO radar waveforms with constant modulus and spectral interference constraints. The optimal waveform design problem is converted into standard semi-definite programs (SDP), which are resolved with the semi-definite relaxation (SDR) technique. Simulation results indicate that the proposed approach can effectively design the desired cognitive MIMO radar waveforms with constant modulus and restraining spectral interferences.
  • 认知多输入多输出(MIMO)雷达波形除了具有基本的正交性外，还应具有在复杂环境中抑制光谱干扰的能力。射频(RF)放大器也需要恒模波形。基于以上考虑，本文提出了一种优化设计常模量和频谱干扰约束的认知MIMO雷达波形的方法。将最优波形设计问题转化为标准半定程序(SDP)，用半定松弛(SDR)技术求解。仿真结果表明，该方法能有效地设计出理想的认知MIMO等模雷达波形，抑制频谱干扰。
  • 目的：使MIMO雷达在复杂环境中具有抑制光谱干扰的能力。
  • 方法：将最优波形设计问题转化为标准半定程序(SDP)，用半定松弛(SDR)技术求解
  • 优势：该方法能有效地设计出理想的认知MIMO等模雷达波形，抑制频谱干扰
• 6. Robust waveform design for multi-target detection in cognitive MIMO radar
  • 认知MIMO雷达多目标检测的鲁棒波形设计
  • This paper considers the robust waveform design for improving performance of multi-target detection in cognitive multiple-input multiple-output radar. The goal of our robust waveform design is to maximize the worst-case detection probability of multiple targets based on the prior informations obtained from previous observations, under constraint of transmitted waveform energy. For accomplishing the goal, a two-step process is proposed. First, covariance matrix of probing signals is designed. Then, waveform is synthesized from achieved covariance matrix. The effectiveness of our proposed method is illustrated through numerical examples.
  • 本文研究了认知多输入多输出雷达中提高多目标检测性能的鲁棒波形设计。我们的鲁棒波形设计的目标是在发射波形能量的约束下，根据先前观测得到的先验信息，最大限度地提高多个目标的最坏情况检测概率。为了实现这一目标，提出了一个两步过程。首先，设计了探测信号的协方差矩阵。然后，根据得到的协方差矩阵合成波形。数值算例表明了本文方法的有效性。
  • 目的：在发射波形能量的约束下，根据先前观测得到的先验信息，最大限度地提高多个目标的最坏情况检测概率。
  • 方法：设计探测信号的协方差矩阵，根据协方差矩阵合成波形
  • 优势：提高多个目标的最坏情况检测概率
• 7. The Impact of Nonlinear Power Amplifier Load Impedance on Notched Waveforms for Cognitive Radar Spectrum Sharing
  • 认知雷达频谱共享中非线性功率放大器负载阻抗对缺口波形的影响
  • High-bandwidth waveforms are required to obtain good range resolution in radar applications, yet contiguous bandwidth is often not readily available. To that end, there has been significant effort involved with the design of spectrally notched radar waveforms. However, maintaining the desired notch depth for these optimized waveforms is challenging in actual transmission due to third- and other odd-order nonlinearities in the transmitter power amplifier (PA) Here the effect of the PA is examined for a particular class of transmittercompatible notched waveforms. Load-pull measurements show that the impedance needed to maximize the power-added efficiency (PAE) and range of the radar system while ensuring a desired minimum notch depth can change significantly for different notch positions and widths. This illustrates the need for reconfigurable power amplifier circuitry in radars for waveform notching applications.
  • 在雷达应用中，为了获得良好的距离分辨率，需要高带宽的波形，但通常无法获得连续的带宽。为此，在设计频谱缺口雷达波形方面进行了大量的努力。然而，在实际传输中，由于发射机功率放大器(PA)中的三阶和其他奇阶非线性，为这些优化波形保持所需的陷波深度具有挑战性。负载-拉力测量表明，在确保所需的最小陷波深度的同时，最大化功率附加效率(PAE)和雷达系统的范围所需的阻抗会因不同的陷波位置和宽度而发生显著变化。这说明了在波形切口应用的雷达中，需要可重构的功率放大器电路
  • 目的：为了获得良好的距离分辨率
  • 方法：
  • 优势：
• 8. Cognitive radar: Waveform design for target detection
  • 认知雷达:目标检测的波形设计
  • 综述
  • Cognitive radar is an emergent technique in modern radar system development. Cognitive radar achieves new levels of radar performance by leveraging mechanisms present in biological systems and incorporating them into the function and operation of the radar system. Here recent developments and future directions of cognitive radar are presented with a focus on the detection of radar targets. These studies require a deeper examination into both the nature of the operating environment and the characteristics of targets themselves. Additionally, sources of interference which serve to impact radar performance are examined under the framework of cognitive radar and promising interference mitigation techniques are reviewed.
  • 认知雷达是现代雷达系统发展中的一项新兴技术。认知雷达利用生物系统中存在的机制，并将其融入雷达系统的功能和操作中，从而实现雷达性能的新水平。本文介绍了认知雷达的最新发展和未来发展方向，重点介绍了雷达目标的检测。这些研究需要对经营环境的性质和目标本身的特征进行更深入的审查。此外，在认知雷达的框架下，对影响雷达性能的干扰源进行了研究，并对有前景的干扰抑制技术进行了综述。
  • 目的：
  • 方法：
  • 优势：
• 9. Cognitive radar waveform design with low range sidelobes and high Doppler tolerance
  • 低距离副瓣高多普勒容忍度的认知雷达波形设计
  • This paper mainly considers the unimodular Linear-FM synthesized (LFM-Syn) waveform design for cognitive radar. To achieve its low range sidelobes and high Doppler tolerance together, a novel template-based objective function is formulated with constraint on both the quadratic phase and random phase of the waveform. In addition, a novel Alternating Projection Phase Control method (APPC) is proposed to optimize this function. Simulations show some performance of APPC over several existing techniques, and also our waveforms optimized by APPC 1) has Doppler tolerance similar to conventional LFM one and 2) has Low Probability of Intercept (LPI) similar to the random noise one, as well as 3) has low range sidelobes.
  • 本文主要研究认知雷达的单模线性调频合成(LFM-Syn)波形设计。为了同时实现低距离旁瓣和高多普勒容限，提出了一种基于模板的目标函数，该函数同时约束波形的二次相位和随机相位。此外，提出了一种新的交变投影相位控制方法来优化该函数。仿真结果表明，APPC在几种现有技术的基础上具有一定的性能，优化后的波形具有与常规LFM相似的多普勒容忍度，具有与随机噪声相似的低截获概率，具有较低的旁瓣。
  • 目的：为了同时实现低距离旁瓣和高多普勒容限
  • 方法：约束波形的二次相位和随机相位，通过一种新的交变投影相位控制方法来优化该函数
  • 优势：优化后的波形具有与常规LFM相似的多普勒容忍度，具有与随机噪声相似的低截获概率，具有较低的旁瓣。
• 10. Adaptive spectrum controlled waveforms for cognitive radar
  • 认知雷达的自适应频谱控制波形
  • This paper presents and evaluates, by simulation, the feasibility of a new type of spectrum controlled waveforms for use in a cognitive radar operating in a spectrally dense environment. The addressed scenario is constituted of a wideband radar with cognitive functionalities and a number of narrow band communication services working in frequency hopping mode. The paper proposes the use of a special type of waveform, namely Adaptive Spectrum Controlled Waveform (ASCW), which can carry out the cognitive functionalities of the wideband radar, by using the frequency nulling technique to minimize the effect of the interference of the coexisting communication services.
  • 本文提出并通过仿真评估一种新型频谱控制波形用于在频谱密集环境下操作的认知雷达的可行性。该方案由一个具有认知功能的宽带雷达和一些以跳频模式工作的窄带通信业务组成。本文提出利用一种特殊类型的波形，即自适应频谱控制波形(ASCW)，利用频率零化技术来减小共存通信业务干扰的影响，实现宽带雷达的认知功能。
  • 目的：一种新型频谱控制波形用于在频谱密集环境下操作的认知雷达的可行性
  • 方法：自适应频谱控制波形(ASCW)，利用频率零化技术来减小共存通信业务干扰的影响
  • 优势：实现宽带雷达的认知功能。
• 11. Waveform Design of Cognitive Radar Based on Maximum SINR and MI
  • 基于最大信噪比和MI的认知雷达波形设计
  • In real battlefield, the prior information of radar target is not easy to obtain. The waveform based on the prior information of radar target can not meet the needs of detection and parameter estimation. This paper considers the optimal waveform design method under different task energy constraints. In order to improve the detection performance of radar system, the signal-to-interference-to-noise ratio (SINR) of known and random extended targets can be maximized; in order to improve the performance of parameter estimation, the mutual information (MI) between radar echo and spectrum response of random targets can be maximized, and the optimal waveform design and simulation are carried out.
  • 在真实的战场中，雷达目标的先验信息不易获取。基于雷达目标先验信息的波形不能满足雷达目标检测和参数估计的需要。研究了不同任务能量约束下的最优波形设计方法。为了提高雷达系统的探测性能，可以将已知和随机扩展目标的信噪比(SINR)最大化;为了提高参数估计性能，最大化雷达回波与随机目标频谱响应之间的互信息，并进行了最优波形设计和仿真。
  • 目的：在不易获取目标的先验信息的环境中，提高雷达系统的探测性能
  • 方法：将已知和随机扩展目标的信噪比(SINR)最大化， 最大化雷达回波与随机目标频谱响应之间的互信息
  • 优势：提高雷达系统的探测性能，提高参数估计性能
• 12. Optimal sequential waveform design for cognitive radar
  • 认知雷达序列波形优化设计
  • This paper addresses the problem of adaptive sequential waveform design for system parameter estimation. This problem arises in several applications such as radar, sonar, or tomography. In the proposed technique, the transmit/input signal waveform is optimally determined at each step, based on the measurements in the previous steps. The waveform is determined to minimize the Bayesian Cramér-Rao bound (BCRB) for estimation of the unknown system parameter at each step. The algorithm is tested for spatial transmit waveform design in multiple-input multiple-output radar target angle estimation at very low signal-to-noise ratio. The simulations show that the proposed adaptive waveform design achieves significantly higher rate of performance improvement as a function of the pulse index, compared to identical signal transmission.
  • 本文讨论了系统参数估计的自适应序列波形设计问题。这个问题出现在几个应用，如雷达，声纳，或层析成像。在所提出的技术中，发射/输入信号波形是根据前面步骤中的测量结果在每一步中确定的最佳波形。该波形以最小贝叶斯Cramer-Rao界(BCRB)为目标，在每一步对未知系统参数进行估计。该算法在低信噪比下的多输入多输出雷达目标角估计的空间发射波形设计中得到了验证。仿真结果表明，与相同的信号传输相比，所提出的自适应波形设计在脉冲指数的作用下获得了更高的性能改进率。
  • 目的：
  • 方法：以最小贝叶斯Cramer-Rao界(BCRB)为目标，在每一步对未知系统参数进行估计
  • 优势：在低信噪比下的多输入多输出雷达目标角估计的空间发射波形设计中得到了验证，与传统信号传输相比，该自适应波形设计在【脉冲指数】的作用下获得了更高的性能改进率。
• 13. A novel waveform selection method for cognitive radar during target tracking based on the wind driven optimization technique
  • 基于风驱动优化技术的认知雷达目标跟踪波形选择新方法
  • In recent years, the cognitive radar (CR) with waveform diversity has exhibited significant performance improvements over the traditional fixed waveform radar and has become an area of vigorous research and development. A novel waveform selection strategy based on wind driven optimization (WDO) technique is proposed in this paper. Firstly, the predicted tracking Cramér-Rao Lower Bounds (CRLB) model is built based the Interacting Multiple Model Particle Filter (IMMPF) method. Then the waveform selection model between the waveform parameters and the tracking performance is introduced. Finally, the WDO technique is used to minimize the predicted CRLB for the waveform selection. The tracking accuracy is demonstrated in the Monte Carlo simulations. The results are validated through the comparison with other methods.
  • 近年来，具有波形多样性的认知雷达(CR)在性能上比传统的固定波形雷达有了显著的提高，已成为一个活跃的研究和开发领域。提出了一种基于风力优化(WDO)技术的波形选择策略。首先，基于交互多模型粒子滤波(IMMPF)方法建立了预测跟踪crer - rao下界模型;然后介绍了波形参数与跟踪性能之间的波形选择模型。最后，利用WDO技术将预测的CRLB值最小化，用于波形选择。通过蒙特卡罗仿真验证了该方法的跟踪精度。通过与其它方法的比较，验证了计算结果的正确性。
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• 14. Adaptive single-tone waveform design for target recognition in Cognitive Radar
  • Adaptive single-tone waveform design for target recognition in Cognitive Radar
  • Cognitive radar is a recently proposed system concept, one of whose most important characteristics is the closed-loop operation. The feedback structure from the receiver to the transmitter enables the optimization of transmission waveforms based on the latest knowledge about targets and environments. In this paper, we propose an improved waveform design method for target recognition in cognitive radar by restricting the waveforms to be single-tone with constant envelope which are commonly used in practical transmitters. Comparing with a previously proposed method using arbitrary waveforms, our method provides almost the same performance with highly reduced computational cost in sequential transmissions.
  • 认知雷达是最近提出的一个系统概念，其最重要的特点之一是闭环操作。从接收机到发射机的反馈结构能够基于目标和环境的最新知识优化发射波形。本文提出了一种用于认知雷达目标识别的改进波形设计方法，将实际发射机常用的目标识别波形限制为不变包络的单音波形。与先前提出的使用任意波形的方法相比，我们的方法在连续传输中提供了几乎相同的性能，同时大大降低了计算成本。
  • 目的：降低成本
  • 方法：将实际发射机常用的目标识别波形限制为不变包络的单音波形
  • 优势：与先前提出的使用任意波形的方法相比，我们的方法在连续传输中提供了几乎相同的性能，同时大大降低了计算成本
• 15. Demonstration of Real-time Cognitive Radar using Spectrally-Notched Random FM Waveforms
  • 用频谱陷波随机调频波形演示实时认知雷达
  • With the reality of increasing radio frequency (RF) spectral congestion, radar systems capable of dynamic spectrum sharing are needed. Recent work has demonstrated a real-time cognitive capability on a software defined radio (SDR) by generating pulse-agile LFM chirps that vary their center frequency and bandwidth to avoid dynamic interference on a per-pulse basis. Separately, spectral notching of random FM waveforms was developed and experimentally evaluated as another means with which to mitigate emulated interference, though real-time operation had not yet been demonstrated. Here the operational framework of the former is combined with the waveform agility of the latter to facilitate real-time generation of notched, random FM waveforms as part of an integrated cognitive SDR architecture. This implementation supports pulse repetition frequencies up to 2.2 kHz for on-the-fly waveform synthesis, can incorporate multiple spectral notches per waveform, and can achieve notch depths of 25 dB relative to peak power (with greater depth possible given greater computational resources). Performance examples are illustrated along with implementation decisions and design trade-offs.
  • 随着射频频谱拥塞的日益严重，需要能够动态频谱共享的雷达系统。最近的工作已经证明了软件无线电的实时认知能力，它通过产生脉冲捷变的LFM来改变其中心频率和带宽，以避免每个脉冲的动态干扰。另外，随机调频波形的频谱陷波被开发出来，并作为减轻仿真干扰的另一种手段进行了实验评估，尽管实时操作尚未得到证实。在这里，前者的操作框架与后者的波形灵活性相结合，以促进陷波、随机调频波形的实时生成，作为集成认知软件无线电架构的一部分。这种实现支持高达2.2千赫的脉冲重复频率，用于动态波形合成，每个波形可以包含多个频谱陷波，并且可以实现相对于峰值功率25分贝的陷波深度(在给定更大计算资源的情况下，可以实现更大的深度)。性能示例与实现决策和设计权衡一起说明。
  • 目的：减小频谱之间的干扰
  • 方法：通过产生脉冲捷变的LFM来改变其中心频率和带宽，以避免每个脉冲的动态干扰
  • 优势：支持高达2.2千赫的脉冲重复频率，用于动态波形合成，每个波形可以包含多个频谱陷波，并且可以实现相对于峰值功率25分贝的陷波深度
• 16. Clutter-compensating Adaptive Waveforms with Cognitive Radar for Target Classification Using EM-simulated Ground-based RCS Responses
  • 认知雷达杂波补偿自适应波形，利用em模拟地基RCS响应进行目标分类
  • We investigate the ground-based classification performance of a cognitive radar (CRr) using high-fidelity target models with their RCS responses in the presence of transmit waveform-dependent clutter. Moreover, we consider a proper grazing angle for clutter scenarios. The CRr utilizes real-time adaptive waveform technique called probability-weighted energy (PWE) technique. In this work, the radar tries to classify any of the four ground-based vehicles at a look angle of 30° where clutter can definitely be a major interference. As such, we design clutter-compensating adaptive waveforms for CRr to improve classification in the presence of both narrowband and wideband clutter.
  • 我们研究了认知雷达(CRr)的地基分类性能，使用高保真目标模型及其RCS响应，在存在发射波形相关杂波的情况下。此外，我们考虑了适当的掠射角，以适应杂波情况。CRr采用实时自适应波形技术，称为概率加权能量(PWE)技术。在这项工作中，雷达试图以30°的角度对四个地面车辆中的任何一个进行分类，因为杂波肯定是主要干扰。因此，我们设计了杂波补偿自适应波形，以提高在窄带和宽带杂波情况下的分类能力。
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• 17. Improved waveform design for non-Gaussian target classification in cognitive radar
  • 认知雷达非高斯目标分类的改进波形设计
  • Because the performance of traditional Gaussian cognitive radar(CR) system for target response varying from Gaussian will be degraded, this paper extended the Gaussian target response to arbitrary non-Gaussian target distribution. In this paper, the CR multiple hypothesis classification algorithm was used for non-Gaussian targets, and the sparse spectrum of correlated narrowband target responses was utilized. In addition, we simulated the multiple nonGaussian distribution targets, and the simulation results demonstrate the non-Gaussian target classification algorithm is very effective.
  • 由于传统的高斯认知雷达系统对不同高斯分布的目标响应性能下降，本文将高斯目标响应扩展到任意非高斯分布。本文对非高斯目标采用CR多重假设分类算法，利用相关窄带目标响应的稀疏谱。此外，我们还对多个非高斯分布目标进行了仿真，仿真结果表明非高斯目标分类算法是非常有效的。
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• 18. An adaptive waveform design method for OFDM cognitive radar
  • OFDM认知雷达的自适应波形设计方法
  • With fixed emitted waveforms, traditional radars could not achieve optimal performance when the target and environment change. Cognitive radar can apperceive changing information outside, feed back to the transmitter intelligently, and then adjust waveforms to achieve better performance. Therefore, cognitive radar can fit the fast changing environment of modern battlefield better than the traditional radar. The advantage of OFDM signals for cognitive radar is analyzed in this paper firstly. And then, a new architecture of OFDM cognitive radar is proposed. The crucial component - adaptive waveform design for the OFDM cognitive radar is addressed to solve the key problem of “learning” and “feedback” in cognitive radar. The main idea is to establish the “estimation-optimization” mechanism to handle the changing of the target and the surrounding environment. Simulation results demonstrate that the performance of OFDM cognitive radar is better than that of traditional radar.
  • 传统雷达在发射波形固定的情况下，当目标和环境发生变化时，无法达到最佳性能。认知雷达能够感知外界变化的信息，智能地反馈给发射机，然后调整波形以达到更好的性能。因此，认知雷达比传统雷达更能适应快速变化的现代战场环境。本文首先分析了OFDM信号在认知雷达中的优势。在此基础上，提出了一种新的OFDM认知雷达体系结构。为了解决认知雷达中的“学习”和“反馈”的关键问题，提出了OFDM认知雷达的关键元件自适应波形设计。其主要思想是建立“估计-优化”机制，以应对目标和周围环境的变化。仿真结果表明，OFDM认知雷达的性能优于传统雷达。
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• 19. Adaptive Waveform Selection Algorithm based on Reinforcement Learning for Cognitive Radar
  • 基于强化学习的认知雷达自适应波形选择算法
  • Cognitive radar is a newly emerging intelligent radar that can adaptively change the transmitted signal waveform according to changes in the target and environment to improve the accuracy of target state estimation. In this paper, the running process of cognitive radar adaptive transmission is analyzed, the tracking waveform parameter selection is correlated with the target state estimation and the reinforcement learning model is established. The problem of unknown target state space is solved by the “prioritized sweeping” method and the computational efficiency is improved by replacing “eligibility trace”. The simulation results show that the indirect reinforcement learning method is better than the fixed waveform and the waveform selection algorithm based on the minimum mean square error for the tracking accuracy and state estimation error of the target.
  • 认知雷达是一种新兴的智能雷达，它可以根据目标和环境的变化自适应地改变发射信号波形，以提高目标状态估计的准确性。本文分析了认知雷达自适应传输的运行过程，将跟踪波形参数选择与目标状态估计相关联，建立了强化学习模型。采用“优先扫描”方法解决了目标状态空间未知的问题，并通过替代“合格跟踪”提高了计算效率。仿真结果表明，间接强化学习方法在目标跟踪精度和状态估计误差方面优于固定波形和基于最小均方误差的波形选择算法。
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• 20. Cognitive Radar Waveform Diversity for Anti-Passive False Target Jamming in an Active Radar Seeker
  • 主动雷达导引头抗无源伪目标干扰的认知雷达波形多样性
  • Based on the principle of cognitive radar, this paper proposes a waveform diversity method to resolve the passive false target jamming. Different signal leads to different one-dimensional scattering centers of the ship and passive false target, the presented method focuses on measuring the target and passive false target jamming one-dimensional scattering centers Distinction Degree (DD). Using the Distinction Degree as adaptive waveform criterion and according to the criterion selected the optimal waveform for anti-passive false target jamming. Finally, according to the recognition rate, the method verified the optimal waveform.
  • 基于认知雷达的原理，提出了一种解决被动假目标干扰的波形分集方法。不同的信号会导致舰船的一维散射中心与被动假目标的不同，该方法着重于测量干扰目标与被动假目标的一维散射中心区分度。以区别度为自适应波形准则，根据该准则选择抗无源伪目标干扰的最优波形。最后，根据识别率，验证了最优波形。
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• 21. Target recognition with adaptive waveforms in cognitive radar using practical target RCS responses
  • 认知雷达中使用实际目标RCS响应的自适应波形目标识别
  • In this paper, we utilize high-fidelity electromagnetic-simulated RCS responses in a cognitive radar (CRr) platform performing target recognition. Previous works used arbitrarily generated target responses consisting of a few frequency resonances which are distinct across different targets. However, realistic target responses contain rich frequency components characterized by physical scattering centers of the target. It is therefore imperative to build on prior works by considering practical target responses. We utilize an improved waveform design technique known as probability-weighted energy (PWE) over classical spectral variance methods such as probability-weighted spectral variance (PWSV). Our results showed an improvement in classification performance of SNR and mutual information (MI)-based waveforms used in conjunction with PWE and PWSV update methods over receiver-adaptive wideband pulsed waveform using a CRr platform. In this work, we also consider a more complex case where the target's azimuth angle has some deviation such that the response from that target is not deterministic but rather from an ensemble of different responses as dictated by aspect angle uncertainty.
  • 在本文中，我们利用一个认知雷达(CRr)平台中的高保真电磁模拟RCS响应来进行目标识别。以前的研究使用任意生成的目标响应，这些响应由几个不同目标的不同频率共振组成。然而，现实的目标响应中含有丰富的以目标物理散射中心为特征的频率分量。因此，必须在之前的工作基础上考虑实际的目标响应。我们利用改进的波形设计技术称为概率加权能量(PWE)在经典的频谱方差方法，如概率加权谱方差(PWSV)。我们的结果显示，使用CRr平台，结合PWE和PWSV更新方法，对基于信噪比和互信息(MI)的波形的分类性能得到了改善。在这项工作中，我们还考虑了一个更复杂的情况，即目标的方位角有一些偏差，使得目标的响应不是确定的，而是由不同响应的集合决定的，这是由方向角的不确定性决定的。
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• 22. Range-doppler and anti-interference performance of Cognitive Radar detection waveform
  • 认知雷达探测波形的距离多普勒与抗干扰性能
  • Cognitive Radar could adjust its transmit waveform to get the best outperform in the alternative targets and environments with a close-loop feedback. It has been regarded as one of the tendency of radar. Optimization waveform design method is one of the critical technologies of radar cognition. This article analyses the range-doppler and anti-interference performance of a most common cognitive radar detection waveform. Firstly, the signal model and waveform solved process are reviewed, and the ambiguity function of the waveform is calculated and analyzed; secondly, the decrease of the output SNR of the cognitive radar caused by the white and colored interference are respectively derived; Finally, the MTLAB numerical simulation results are given.
  • 认知雷达可以通过闭环反馈来调整其发射波形，以在可选目标和环境中获得最佳性能。它已成为雷达发展的趋势之一。优化波形设计方法是雷达认知的关键技术之一。本文分析了一种最常见的认知雷达探测波形的距离多普勒和抗干扰性能。首先，回顾了信号模型和波形求解过程，并对波形的模糊函数进行了计算和分析;其次，分别推导了白干扰和彩色干扰对认知雷达输出信噪比的影响;最后给出了MTLAB的数值模拟结果。
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• 23. Cognitive waveform design for anti-velocity deception jamming with adaptive initial phases
  • 具有自适应初始相位的反速度欺骗干扰认知波形设计
  • A new cognitive radar waveform design strategy is outlined to counter the velocity deception jamming based on adaptive initial phases. The kernel is to adapt the Doppler spectrum of radar echo with jamming power suppressed in some stopbands, where the true targets exist. Meanwhile, a multi-channel processing trick with different integral multiple pulse repetition interval (PRI) delay is employed to estimate the parameters of true and false targets, especially the lagged PRI number of DRFM repeat-back jammer which fakes the false targets. Simulation results of the jamming suppression and multi-channel processing-based parameter cognition demonstrate the effectiveness and feasibility of our proposed scheme.
  • 针对速度欺骗干扰，提出了一种基于自适应初始相位的认知雷达波形设计策略。其核心是对雷达回波的多普勒频谱进行调整，将干扰功率抑制在一定的阻带内。同时，利用不同积分多脉冲重复间隔(PRI)的多通道处理技巧估计真、假目标的参数，特别是DRFM回调干扰机伪造假目标的滞后PRI数。干扰抑制和多通道处理参数认知的仿真结果验证了该方案的有效性和可行性。
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• 24. Parameter estimation and waveform design for cognitive radar by minimal free-energy principle
  • 基于最小自由能原理的认知雷达参数估计与波形设计
  • In this paper we develop a new framework for Bayesian parameter estimation using adaptive waveforms by the minimal free energy (FE) principle in the context of cognitive radar. Unlike conventional approaches, the new method utilizes the minimal FE principle as a unifying criterion for optimal estimator design and waveform design. The FE principle seeks to approximate the true density of the unknown parameters in response to sequential measurement data. In the case of a single unknown parameter we show that the estimators based on the FE principle and the conventional Bayesian estimator are identical. Moreover, the waveform design based on the FE principle results in similar water-filling solution as the traditional mutual information method.
  • 在本文中，我们开发了一个新的贝叶斯参数估计框架，使用自适应波形，通过最小自由能(FE)原理在认知雷达的背景下。与传统方法不同，新方法利用最小有限元原理作为最优估计器设计和波形设计的统一准则。FE原理寻求近似未知参数的真实密度响应序列测量数据。在单个未知参数的情况下，我们证明了基于FE原理的估计量与传统贝叶斯估计量是相同的。此外，基于有限元原理的波形设计与传统互信息法得到的充水解相似。
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• 25. Cognitive Waveform Optimization for Phase-Modulation-Based Joint Radar-Communications System
  • 基于相位调制的联合雷达通信系统的认知波形优化
  • A dual-function radar communication (DFRC) system enables the implementation of a primary radar operation and a secondary communication function concurrently. A bank of transmit beamforming weight vectors are guaranteed to have the same transmitted radiation pattern to satisfy in the target detection requirements, while the phase symbol is selected from a preset dictionary so that communication information can be embedded. However, as the radar channel is time-variant due to the fluctuation in the radar cross-section (RCS) of the target and the Doppler shift that results from the relative motion of the target, it is necessary for a successive waveform design and selection scheme to continually obtain target information. Our work aims at enhancing the target detection performance by maximizing the relative entropy (RE) between two hypotheses (in the first hypothesis we assume the target is not present in the echoes while in the second hypothesis we assume the target exists in the echoes) and by minimizing the mutual information (MI) between successive target echoes. The proposed scheme overcomes the coexisting communication and radar detection problems in intelligent transportation systems (ITSs), where it is necessary to extract the features of target information that is obtained from a vehicle-mounted sensor. Our simulation results demonstrate an improvement in the target detection performance by the proposed two-stage approach. In addition, the system can transmit data of several Mbps with low symbol error rates.
  • 一种双功能雷达通信(DFRC)系统可同时实现一次雷达操作和二次通信功能。保证一组发射波束形成权值向量具有相同的发射辐射模式以满足目标检测要求，同时从预设字典中选择相位符号以嵌入通信信息。然而，由于目标的雷达横截面(RCS)的波动以及目标相对运动产生的多普勒频移导致的雷达通道是时变的，需要一个连续的波形设计和选择方案来不断获取目标信息。我们的工作,目的是提高目标探测性能最大化之间的相对熵(重新)两个假设(在第一个假说假设目标不存在的回声在第二个假设我们假定目标存在于回声),通过最小化之间的互信息(MI)连续目标回声。该方案克服了智能交通系统(ITSs)中同时存在的通信和雷达检测问题，需要从车载传感器中提取目标信息的特征。我们的仿真结果表明，提出的两阶段方法改善了目标检测性能。此外，该系统可以以低误码率传输数兆比特每秒的数据。
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• 26. Integrated range-Doppler map and extended target identification with adaptive waveform for cognitive radar
  • 认知雷达综合距离-多普勒图和自适应波形扩展目标识别
  • In this paper, we propose an integrated scheme to identify an extended moving target while trying to correctly locate its peak range-Doppler cell. The scheme is extended to handle multiple target responses. The cognitive radar platform uses adaptive waveform based on the eigenwaveform. Two real-time adaptive waveforms called maximum a posteriori probability weighted eigenwaveform (MAP-PWE) and match-filtered PWE (MF-PWE) are combined with range-Doppler map (RDM) technique to execute target type identification for moving extended targets. Joint RDM cell localization and target identification performance comparison between a traditional pulsed wideband waveform, MAP-PWE, and MF-PWE techniques are shown. It is noted the MF-PWE performs better than the wideband and MAP-PWE.
  • 在本文中，我们提出了一个综合的方案来识别扩展运动目标，同时尝试正确定位其峰值距离-多普勒单元。该方案被扩展到处理多个目标响应。认知雷达平台采用基于特征波形的自适应波形。将最大后验概率加权特征波形(map -PWE)和匹配滤波特征波形(MF-PWE)两种实时自适应波形与距离-多普勒图(RDM)技术相结合，对运动扩展目标进行目标类型识别。比较了传统的脉冲宽带波形、MAP-PWE和MF-PWE技术的RDM单元定位和目标识别性能。值得注意的是，MF-PWE的性能优于宽带和MAPPWE。
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• 27. Novel System Architecture and Waveform Design for Cognitive Radar Radio Networks
  • 认知雷达无线电网络的系统结构与波形设计
  • A novel approach to combining communication and radar functionalities in a single waveform design for cognitive radar radio (CRR) networks is proposed. This approach aims at extracting the target parameters from the radar scene, as well as facilitating high-data-rate communications between CRR nodes, by adopting a single waveform optimization solution. The system design technique addresses the coexisting communication and radar detection problems in mission-critical services, where there is a need of integrating the knowledge about the target scene gained from distinct radar entities functioning in tandem with each other. High spatial resolution and immunity to multipath fading make ultrawideband (UWB) signals an appropriate choice for such applications. The proposed solution is achieved by applying the mutual-information-based strategy to design the sequence of UWB transmission pulses and embed into them the communication data with the pulse position modulation scheme. With subsequent iterations of the algorithm, simulation results demonstrate an improvement in extraction of the parameters from the radar scene, such as target position and impulse response, while still maintaining high-throughput radio links with low bit error rates between CRR nodes.
  • 提出了一种将通信功能与雷达功能相结合的认知雷达无线电(CRR)网络单波形设计新方法。该方法采用单波形优化方案，旨在从雷达场景中提取目标参数，促进CRR节点之间的高数据率通信。系统设计技术解决了在关键任务服务中共存的通信和雷达检测问题，其中有一个需要集成从不同的雷达实体在彼此串联运作中获得的目标场景的知识。超宽带信号具有较高的空间分辨率和抗多径衰落的能力，是此类应用的理想选择。该解决方案采用互信息策略设计超宽带传输脉冲序列，并采用脉冲位置调制方案将通信数据嵌入其中。随着算法的后续迭代，仿真结果表明，在从雷达场景中提取参数，如目标位置和脉冲响应，同时仍然保持高吞吐量无线电链路与CRR节点之间的低误码率。
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• 28. Robust Waveform Design Based on Jammer Games in Cognitive Radar
  • 认知雷达中基于干扰机博弈的鲁棒波形设计
  • Due to the uncertainties of radar target prior information in the actual scene, the waveform designed based on radar target prior information cannot meet the needs of parameter estimation. In order to ensure the performance of parameter estimation for the complex target model, a mutual information (MI)-based waveform design method which is robust to the model uncertainties under the hierarchical game model of radar and jammer is proposed in this paper. Simulation results indicate that the robust waveform design method ensures the parameter estimation performance effectively and provides useful guidance for waveform energy allocation.
  • 由于实际场景中雷达目标先验信息的不确定性，基于雷达目标先验信息设计的波形不能满足参数估计的需要。摘要为了保证复杂目标模型的参数估计性能，提出了一种在雷达与干扰机层次博弈模型下，基于互信息(MI)的对模型不确定性具有鲁棒性的波形设计方法。仿真结果表明，鲁棒波形设计方法有效地保证了参数估计性能，为波形能量分配提供了有用的指导。
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• 29. Design and simulation of the cognitive radar for extended target using MATLAB
  • 基于MATLAB的扩展目标认知雷达的设计与仿真
  • The cognitive radar is intelligent radar. In this paper, optimization of received waveform for the cognitive radar to estimate the extended targets in the presence of the clutter is discussed. The echo signal back to the radar is convolution between transmitted waveform and each extended Target Impulse Response (TIR) and convolution between transmitted waveform and clutter in the environment. A two step method is used to minimize the echo signal. The first step includes minimization of mutual Coherence individually for every extended target. The second step includes weight vector optimization by maximum iteration method. Finally optimized transmitted Waveform generated gives an accurate results and with better detection.
  • 认知雷达就是智能雷达。本文讨论了认知雷达接收波形的优化问题，以在杂波存在的情况下估计扩展目标。返回到雷达的回波信号是发射波形与每个扩展目标脉冲响应(TIR)的卷积以及发射波形与环境杂波的卷积。采用两步法对回波信号进行最小化。第一步包括对每个扩展目标分别进行相互相干性的最小化。第二步是最大迭代法的权向量优化。最后优化后的发射波形产生的结果准确，具有较好的检测效果。
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• 30. Multi-Target Detection and Adaptive Waveform Design for Cognitive MIMO Radar
  • 认知MIMO雷达多目标检测与自适应波形设计
  • Multi-target detection is the main function of radar. Cognitive radar, which can adaptively investigate the radar scene and determine the next action based on previous measurements, has better performance. In this paper, a multi-target detection method and an adaptive waveform design algorithm for cognitive MIMO radar are proposed. In this method, the multi-target detection is modeled as a multi-hypothesis testing. The multi-hypothesis testing is investigated according to sequentially received data. Along with the multi-target detection method, an adaptive waveform design algorithm based on information theory is proposed to improve the efficiency of the multi-hypothesis testing. We adopt semi-definite relaxation technique and semi-definite programming to tackle the nonconvex design problem. Numerical examples demonstrate that the proposed multi-target detection method has better performance than the classical target detection method, and the proposed adaptive waveform design algorithm can significantly improve the performance of the proposed multi-target detection method.
  • 多目标检测是雷达的主要功能。认知雷达具有较好的性能，它能够自适应地调查雷达场景，并根据之前的测量结果确定下一步的行动。本文提出了一种认知MIMO雷达多目标检测方法和自适应波形设计算法。该方法将多目标检测建模为多假设检验。根据连续接收的数据进行多假设检验。在多目标检测方法的基础上，提出了一种基于信息理论的自适应波形设计算法，以提高多假设检验的效率。我们采用半定松弛技术和半定规划来解决非凸设计问题。数值算例表明，所提出的多目标检测方法比经典目标检测方法具有更好的性能，所提出的自适应波形设计算法可以显著提高所提出的多目标检测方法的性能。
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• 31. Unimodular Waveform Design With Desired Ambiguity Function for Cognitive Radar
  • 认知雷达的模糊功能的单模波形设计
  • In this correspondence, we target the problem of radar waveform design based on the ambiguity function. The problem is formulated as an optimization, where the nonconvex unimodularity constraint is also considered. The problem is solved by successive application of majorization minimization (MM) and projected gradient descent algorithm (PGD). The proposed method has the unprecedented ability to synthesize nonzero subregions. The superiority of the proposed algorithm in achieving zero subregions is confirmed through simulation, where a suppression superiority of at least 6 dB is evident compared to the best state-of-the-art benchmark.
  • 在该通信中，我们针对基于模糊函数的雷达波形设计问题。该问题被表述为一个优化问题，其中也考虑了非凸单模性约束。采用优化最小化算法(MM)和投影梯度下降算法(PGD)求解该问题。该方法具有前所未有的合成非零子区域的能力。通过仿真验证了该算法在实现零子区域方面的优越性，与最先进基准相比，该算法的抑制优势至少为6db。
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• 32. A Method of Cognitive Transmitting Waveform Design Based on Mutual Information
  • 一种基于互信息的认知传输波形设计方法
  • In order to solve the problems that the adaptive processing of radar is mainly focused on the receiving end and the signal processing technology of receiver has been more and more perfect, a time domain waveform design method based on prior information is proposed to identify the targets in the clutter environment. The cost function for power spectrum of transmitting waveform is established by maximizing the mutual information between a random extended target and the received signal. Then, the time domain waveform is got by a kind of iterative algorithm. The simulation results show that the method improves the target recognition performance compared to the traditional chirp, and the synthesized power spectrum of time domain can be a good approximation to the optimal power spectrum.
  • 为了解决雷达的自适应处理的问题主要集中在接收端,接收机的信号处理技术已经越来越完美,时域波形设计方法提出了基于先验信息确定目标杂波环境中。通过最大化随机扩展目标与接收信号之间的互信息，建立了发射波形功率谱的代价函数。然后，通过一种迭代算法得到时域波形。仿真结果表明，与传统的啁啾方法相比，该方法提高了目标识别性能，合成的时域功率谱可以很好地逼近最优功率谱。
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• 33. Optimal cognitive radar transmit-receiver design for extended target with unknown target impulse response
  • 未知目标脉冲响应扩展目标的认知雷达发接收机优化设计
  • In this paper, the problem of joint transmit waveform and receive filter design for cognitive radar (CR) is investigated. The problem is analyzed in signal-dependent interference, as well as additive channel noise for extended target with unknown target impulse response (TIR). An improved online waveform optimization design method is employed for target detection by maximizing the average signal to interference plus noise ratio (SINR) of the received echo on the premise of ensuring the TIR estimation precision. In the proposed method, the transmit waveform and receive filter are optimally determined at each step based on the observations in the previous steps. Simulation results demonstrate that CR with the proposed waveform achieve significantly higher rate of estimation accuracy and detection performance improvement compared to traditional radar system with fixed waveform, and offers more flexibility.
  • 本文研究了认知雷达(CR)的联合发射波形与接收滤波器设计问题。分析了未知目标脉冲响应(TIR)扩展目标的信号相关干扰和加性信道噪声问题。采用改进的在线波形优化设计方法，在保证红外估计精度的前提下，最大化接收回波的平均信噪比(SINR)来检测目标。在所提出的方法中，发射波形和接收滤波器是根据前面步骤中的观测结果在每一步中最优地确定的。仿真结果表明，与固定波形的传统雷达系统相比，该波形具有更高的估计准确率和检测性能，具有更大的灵活性。
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• 34. Adaptive Waveform Optimization for MIMO Radar Imaging Based on Sparse Recovery
  • 基于稀疏恢复的MIMO雷达成像自适应波形优化
  • Multiple-input multiple-output (MIMO) radar imaging is a new technique to obtain the radar image of aerospace targets. Orthogonal waveform design is one of the important issues for MIMO radar imaging. However, the fully orthogonal waveforms in the same frequency and with the arbitrary time delay do not exist in practice. Thus, the imaging result using nonorthogonal waveforms based on matched filtering (MF) method is usually unsatisfactory if further processing like digital beam forming (DBF) is not used. Sparse recovery (SR) method is possible to restrain the mutual interference of nonorthogonal waveforms by exploiting the sparsity of targets and improve the imaging quality. In this article, waveform design issue in SR-based MIMO imaging method is studied. The difference in the designs of waveforms in MF method and SR method is discussed. Based on requirements analysis, a comprehensive optimization model is built for waveform design and the existing cycle algorithm (CA) is modified to solve the model. Considering the fact that the target scene is always changing, waveforms should be adjusted along with the dynamic scene. Therefore, an adaptive waveform optimization method is further proposed based on the cognition of target scene. The dimension of SR model is reduced and the waveforms are optimized according to the cognitive target length. Moreover, based on the reconstructed target range profiles, transmitting waveforms together with recovery algorithm are further optimized to match the target better. Simulation results show that the waveforms after optimization are better than the nonoptimized waveforms and the proposed adaptive optimization method is valid and robust for the dynamic target scene.
  • 多输入多输出(MIMO)雷达成像技术是一种获取航空航天目标雷达图像的新技术。正交波形设计是MIMO雷达成像的重要问题之一。然而，在实际中并不存在相同频率和任意时延的全正交波形。因此，如果不使用诸如数字波束形成(DBF)等进一步处理，使用基于匹配滤波(MF)方法的非正交波形的成像结果通常是不令人满意的。稀疏恢复(SR)方法可以利用目标的稀疏性抑制非正交波形的相互干扰，提高成像质量。本文研究了基于sr的MIMO成像方法的波形设计问题。讨论了MF法和SR法在波形设计上的差异。在需求分析的基础上，建立了波形设计的综合优化模型，并对现有的周期算法(CA)进行了改进以求解该模型。考虑到目标场景是不断变化的，波形应随着动态场景的变化而调整。为此，提出了一种基于目标场景认知的自适应波形优化方法。根据认知目标长度对SR模型进行了降维和波形优化。在重建目标距离像的基础上，进一步优化发射波形和恢复算法，以更好地匹配目标。仿真结果表明，优化后的波形优于未优化的波形，所提出的自适应优化方法对动态目标场景是有效和鲁棒的。
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• 35. A Cognitive Radar Waveform Optimization Approach Based on Deep Reinforcement Learning
  • 基于深度强化学习的认知雷达波形优化方法
  • The focus of this paper is on the deep reinforcement learning (DRL) based cognitive radar waveform optimization problem, by utilizing the prior knowledge of the environment and adaptively adjusting the system parameters. We first design deep reinforcement learning-based waveform optimization framework, where agent is radar, state is entropy of environment state and action is transmit waveform. Based on the fact that the continuous changing in cognitive radar tracking system, resulting in infinite states of environment and targets, we design a new deep Q-network to map the state-action pair to its Q-values. After training, radar in DRL system will obtain a policy with which it can select the optimal parameter of waveform to transmit according to the entropy of environment state, which is the comentropy of the posterior probability of state. Simulation results illustrate that by using the proposed approach, the precision of target tracking can be improved clearly.
  • 本文的研究重点是基于深度强化学习(DRL)的认知雷达波形优化问题，利用环境的先验知识，自适应地调整系统参数。首先设计了基于深度强化学习的波形优化框架，其中agent为雷达，state为环境状态熵，action为发射波形。摘要针对认知雷达跟踪系统不断变化，导致环境和目标状态无限的事实，设计了一种新的深度q -网络，将状态-行为对映射到其q值。训练后，DRL系统中的雷达会根据环境状态的熵，即状态的后验概率的com熵，得到一种策略来选择最优的波形参数进行发射。仿真结果表明，该方法能明显提高目标跟踪的精度。
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• 36. Cognitive radar waveform optimization for stealth target RCS estimation
  • 认知雷达波形优化隐身目标RCS估计
  • In this paper, the radar cross section (RCS) estimation performance is improved by cognitive radar waveform optimization in both angle and frequency domain. F-35 stealth target is modeled by physical optics (PO) approximation method. The recognition of target characteristics by cognitive radar is carried out from the perspective of maximum observation of RCS. The waveform optimization problem is formulated using maximum a posteriori probability (MAP) and Kalman filtering (KF) to estimate RCS of the stealth target. The minimum mean square error (MMSE) is taken as the objective function which is solved by different nature inspired waveform optimization (NIWO). It is demonstrated through computer simulations that the proposed method can reconstruct RCS of the stealth target and outperforms the traditional semi-definite relaxation (SDR) technique, showing a promising method of waveform optimization for stealth target RCS reconstruction in cognitive radar.
  • 本文通过认知雷达波形在角域和频域的优化，提高了雷达横截面(RCS)的估计性能。采用物理光学近似法对F-35隐身目标进行建模。认知雷达对目标特征的识别是从RCS最大观测的角度进行的。利用最大后验概率(MAP)和卡尔曼滤波(KF)对隐身目标的RCS进行估计，提出了波形优化问题。以最小均方误差(MMSE)为目标函数，采用不同性质波形优化(NIWO)方法求解。计算机仿真结果表明，该方法能较好地重构隐身目标的RCS，优于传统的半定松弛(SDR)方法，为认知雷达隐身目标的RCS重构提供了一种波形优化方法。
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• 37. Cognitive Radar Performance Analysis with different Types of Targets
  • 认知雷达对不同类型目标的性能分析
  • In this paper, experimental results with the CODIR cognitive radar testbed are presented. Different optimization objectives for the tracking performance and/or the radar resource usage have been considered to test the optimization capability under various conditions. For that, a new generalized cost function has been defined that can be parameterized to the different objectives. The radar resource usage and the tracking performance has been analyzed using DGPS ground truth data. The system is able to adapt its radar parameter in real-time to meet one or a combinations of objectives. In the case of optimizing multiple conflicting objectives the relative prioritization can be adjusted and the system adapts its radar parameters accordingly.
  • 本文给出了CODIR认知雷达实验台的实验结果。对跟踪性能和/或雷达资源使用的不同优化目标进行了考虑，以测试在不同条件下的优化能力。为此，定义了一种新的广义代价函数，该函数可以参数化到不同的目标。利用DGPS地面真值数据分析了雷达资源的使用情况和跟踪性能。该系统能够实时调整其雷达参数以满足一个或一个目标的组合。在对多个冲突目标进行优化的情况下，可以调整相对优先级，并相应地调整雷达参数。
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• 38. Radar Adaptive Waveform Selection Behavior Recognition Based on Neural Network
  • 基于神经网络的雷达自适应波形选择行为识别
  • Cognitive radar has the ability to perceive the environment and target information, and complete the work state optimization through analysis and decision. Radar's behavior is defined as all the characteristics of a radar's performance and the process of changing its state according to the law of change and the external influences. In this paper, the adaptive selection behavior principle of radar emission waveform is firstly described. Then, for the waveform adaptive selection behavior, a black box model is constructed by using neural network. The environment, target characteristics and waveform parameters are used as input training data, and the waveform selection result is taken as output. By learning the training waveform selection process of the radar, we will obtain a neural network model with the same capabilities as the radar adaptive waveform selection system, thus completing the identification of the radar adaptive waveform selection behavior. Finally, through simulation analysis, the method has good results.
  • 认知雷达能够感知环境和目标信息，并通过分析和决策完成工作状态优化。雷达行为定义为雷达性能的所有特征，以及雷达状态根据变化规律和外界影响而发生变化的过程。本文首先阐述了雷达发射波形的自适应选择行为原理。然后，针对波形自适应选择行为，利用神经网络建立了黑盒模型。将环境、目标特征和波形参数作为输入训练数据，将波形选择结果作为输出。通过学习雷达的训练波形选择过程，得到与雷达自适应波形选择系统具有相同能力的神经网络模型，从而完成对雷达自适应波形选择行为的识别。最后，通过仿真分析，该方法取得了良好的效果。
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• 39. Cognitive Target Tracking via Angle-Range-Doppler Estimation With Transmit Subaperturing FDA Radar
  • 利用发射子孔径FDA雷达进行角度-距离-多普勒估计的认知目标跟踪
  • Cognitive radar is an intelligent active sensing technique, which can learn the interactions between radar and its surrounding environment and adaptively adjust the transmit waveforms or parameters for improved performance. In this paper, we propose a cognitive target tracking scheme via angle-range-Doppler estimation with transmit subaperturing frequency diverse array (TS-FDA) radar. FDA is an emerging array technique that employs a small frequency increment across its array elements to produce a range-angle-dependent beampattern, which provides promising applications for joint angle-range-Doppler estimation of targets. In order to jointly enjoy the advantages of FDA localization in angle-range dimension and phased-array in coherent gain, we divide the FDA elements into multiple subarrays and propose two optimization criteria, respectively, based on signal-to-noise ratio and Cramér-Rao bound, to adaptively design the transmit weight matrix according to the prior knowledge extracted from the cognitive observation data at each transmission updating for improved tracking performance. All proposed approaches are verified by numerical results.
  • 认知雷达是一种智能主动感知技术，它可以学习雷达与其周围环境之间的相互作用，并自适应地调整发射波形或参数以提高性能。本文提出了一种基于发射子孔径频率分集阵列雷达的角度-距离-多普勒估计的认知目标跟踪方案。美国食品和药物管理局是一种新兴的阵列技术，它采用一个小的频率增量在其阵列元素，以产生一个距离-角度依赖的波束图，这提供了有前途的应用联合角度-距离-多普勒估计的目标。为了联合利用频域分析在角度距离维定位和相控阵在相干增益方面的优势，将频域分析单元划分为多个子阵，并分别基于信噪比和克拉美-拉奥边界提出了两种优化准则，在每次传输更新时，根据从认知观测数据中提取的先验知识自适应地设计传输权重矩阵，以提高跟踪性能。数值结果验证了所有提出的方法。
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• 40. Cognitive radar waveform design for spectral coexistence in signal-dependent interference
  • 信号相关干扰中频谱共存的认知雷达波形设计
  • In this paper, we deal with cognitive design of the transmit signal and receive filter optimizing the radar detection performance without affecting spectral compatibility with some licensed overlaid electromagnetic radiators. We assume that the radar is embedded in a highly reverberating environment and exploit cognition provided by Radio Environmental Map (REM), to induce spectral constraints on the radar waveform, by a dynamic environmental database, to predict the actual scattering scenario, and by an Electronic Support Measurement (ESM) system, to acquire information about hostile active jammers. At the design stage, we develop an optimization procedure which sequentially improves the Signal to Interference plus Noise Ratio (SINR). Moreover, we enforce a spectral energy constraint and a similarity constraint between the transmitted signal and a known radar waveform. At the analysis stage, we assess the effectiveness of the proposed technique to optimizing SINR while providing spectral coexistence.
  • 在本文中，我们讨论了发射信号和接收滤波器的认知设计，以优化雷达检测性能，同时不影响与某些许可的叠加电磁辐射器的频谱兼容性。我们假设雷达嵌入在一个高度混响的环境中，并利用无线电环境图(REM)提供的认知，通过动态环境数据库对雷达波形进行频谱约束，预测实际的散射情况，并通过电子支持测量(ESM)系统获取关于敌对活动干扰机的信息。在设计阶段，我们开发了一个优化程序，从而提高了信号干扰噪声比(SINR)。此外，我们在发射信号和已知雷达波形之间实施频谱能量约束和相似性约束。在分析阶段，我们评估所提出的技术在提供频谱共存的同时优化SINR的有效性。
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• 泛化能力
  • 泛化能力指算法模型对未知数据的预测能力。
  • 学习的目的是学到隐含在数据背后的规律，对具有同一规律的学习集以外的数据经过训练的网络也能给出合适的输出，该能力称为泛化能力。
• 鲁棒性：
  • 鲁棒是Robust的音译，也就是健壮和强壮的意思。它是在异常和危险情况下系统生存的关键。
  • 在深度学习中常用于形容算法模型，当说算法模型具有鲁棒性时，表明对这个算法模型而言，一些异常的数据对整体的性能影响不大或者基本没有影响。

认知雷达：用于目标检测的波形设计

• 西蒙·海金（Simon Haykin）于2006年首次提出以来，认知雷达作为一个令人感兴趣的领域已经迅速发展
• 动机
  • 执行规范的雷达功能的需求
  • 例如在日趋复杂，竞争激烈的动态雷达环境中执行检测和跟踪功能，这些环境具有严重的杂波和一种或多种形式的有意和无意干扰。
  • 为了在这些环境中有效运行，现代雷达必须在其发送和接收过程中都具有自适应能力。
  • 常规雷达具有预先编程的波形和接收处理功能，这些信号是为在预定义环境中针对特定类型的目标而有目的地选择的，因此不适合现代雷达环境。
• 特点
  • 认知雷达是雷达体系结构的新范式，它协同地结合了信号处理，环境建模和硬件功能方面的最新进展，即使在具有挑战性的操作环境和条件下也能达到峰值雷达性能。
• SLA系统
  • 适应环境是生命的基础。作为人类，我们处理周围的世界，并根据我们对观察结果的个人解释来适应我们的行为。
  • 认知雷达将从生物系统的适应能力中汲取的经验教训应用于雷达系统的设计和操作。
  • Guerci等提出了一种认知完全自适应雷达（CoFAR）的体系结构。作者采用这种方法将一种认知系统定义为“感知，学习和适应（SLA）”的系统。
• 认知波形设计
  • Haykin提出了最佳波形设计的最初认知框架。从那时起，在设计用于认知雷达的最佳波形和接收器处理方面的研究蓬勃发展。
  • 几十年来，目标检测一直是雷达研究的关键主题。通常，检测方法侧重于设计最佳波形和接收器处理，以在特定的，明确定义的场景中获得检测结果。波形生成仅限于基本波形，以便满足早期雷达系统中的处理和硬件限制。
  • 认知目标检测的最基本形式是利用来自环境的反馈来调整雷达的发射波形或雷达的接收处理，从而提高复杂雷达环境中目标检测的性能。
  • 雷达系统现在可以传输更为复杂的波形，并具有根据需要自动调整这些波形的能力。认知雷达利用这种新兴能力来适应波形，以提高系统性能并改善目标检测。
  • 自适应性很关键
• 认知目标检测
  • 优化目标探测的雷达波形需要能够处理目标和环境知识的不确定性的方案。波形设计的最新进展集中在选定的不确定性领域，以开发认知方案。例如，运动目标与静止目标需要不同的处理。像雷达干扰这样的有意干扰与环境杂波这样的无意干扰相比，对波形设计提出了不同的挑战。
  • 结果表明，距离-多普勒分辨率较差，是设计认知雷达波形时必须考虑的因素
  • The paper presents a new adaptive waveform called match-filtered probability weighted eigenwaveform (MFPWE) technique.
    • 研究了运动扩展目标检测与识别的最优波形。本文提出了一种新的自适应波形——匹配滤波概率加权特征波形(MFPWE)技术。作者将其MF-PWE技术与距离-多普勒图(RDM)技术相结合，得到了一个确定最佳发射波形的完整闭环方案。该技术的仿真证明了最优波形设计方案的成功实现。在这篇论文中，同样的作者将他们的研究更进一步，将问题扩展到检测多个移动扩展目标。另一种方法的运动目标检测问题提出。作者利用内曼-皮尔逊检测器准则推导出最优波形。检测器的输入包括杂波频率响应的先验知识、目标速度和一般的加性噪声。仿真结果表明，所得到的最优波形在运动目标情况下表现明显更好。作者确实注意到，获取目标和杂波的先验知识是具有挑战性的，反馈过程是一个必要的认知雷达元素，以帮助在不确定知识的情况下。
  • 动目标
  • 无意干扰
  • 有意干扰
•  认知波形设计过程
  • 认知波形设计过程如图所示。请注意，该过程中的某些步骤是双向的，因此可以在步骤之间进行反馈。发射的波形与包含所需目标，杂波和干扰的复杂环境相互作用。使用受先验知识数据库以及专业系统影响的智能信号处理来处理返回信号。专业系统通过与信号处理例程和先验知识数据库进行交互来优化波形。最后，选择并发送优化的波形，重新开始该过程。
• SLA系统模型
  • 该图说明了此定义在雷达中的应用。通过发送波形并处理返回值来获得感测特性。通过利用专业系统的监督学习，基于规则的推理，自适应算法或其组合来实现学习属性。