基于收-发联合优化的认知雷达序贯信号检测

在扩展目标条件假设下,提出了一种联合优化发射端和接收端资源配置的认知雷达序贯信号检测方法.该方法在假设检验的基础上,根据系统之前时刻获得的目标先验信息在发射端进行自适应波形设计,在接收端进行自适应检测门限值调整,以提高系统的检测性能.理论分析和仿真实验表明:通过收-发联合优化处理后,系统的估计性能和检测性能都得到明显提高.同时,随着循环次数的增加,目标冲激响应的估计精度越来越高,使得发射波形也越来越逼近最优波形,估计检测概率和虚警概率也越来越逼近各自的真实值,从而在提高系统性能的同时也保证了决策的可靠性.     

基于AWD和EBD的认知雷达序贯信号检测

研究了认知雷达在扩展目标假设下,目标冲激响应未知时的信号检测问题。根据认知雷达闭环结构的特点,基于最大似然(ML)准则完成了对目标冲激响应的序贯估计,并结合自适应波形设计(AWD)和检测前估计(EBD)方法,提出了一种迭代更新目标冲激响应估计和发射波形的序贯检测算法。由理论分析和仿真结果可知,该算法对目标冲激响应的估计精度以及在近似恒虚警检测(CFAR)准则下的实际检测概率,均随着认知雷达循环次数的增加而提高,且估计的检测概率随着目标冲激响应估计精度的提高逐渐逼近实际检测概率,从而有效提高了决策的可靠性。     

认知MIMO雷达目标分类波形自适应方法

针对认知多输入多输出(MIMO)雷达系统,研究了基于序贯假设检验的扩展目标分类波形自适应方法.采用基于信息论的波形设计方法,通过最大化雷达回波与扩展目标冲激响应之间的互信息获得每一类目标的最优雷达波形,进而根据多类目标的当前概率加权合成最优MIMO雷达信号.在多次雷达观测中,根据贝叶斯准则不断更新每一类目标可能出现的概率,最优波形自适应变化,最终趋于真实目标类别的最优波形.仿真结果表明,相比较于传统的固定波形,采用自适应最优波形的认知MIMO雷达系统进行目标分类的平均观测次数显著降低.     

一种利用随机矩阵调制实现共享通信方法

本文提出了一种利用随机矩阵调制-OFDM雷达成像波形实现共享通信的方法。首先,利用基础波形和随机矩阵在OFDM波形上构造随机矩阵调制-OFDM波形库。然后,在发射端根据不同的通信方式确定不同的发射方式,并根据雷达性能和通信传输要求选取相应的参数。最后,在雷达接收端,利用发射信号的伪正交性分离出回波信号实现雷达成像;在通信接收端,利用接收信号在与发射波形相对应的匹配滤波器的输出端有较高的目标尖峰,来解调出通信信息。数值仿真表明,该方法在保留该波形优异的雷达成像性能的同时,可有效实现共享通信。基于随机矩阵调制-OFDM波形数量的丰富性以及良好的正交性,可以保证较大的数据通信速率及较高的数据通信质量。     

一种综合考虑目标检测与估计的波形设计方法

讨论了在综合考虑目标检测与估计性能的条件下,雷达发射波形的优化设计问题.由于衡量目标检测与估计性能的直接指标是信噪比(SNR)和互信息(MI),文中采用将SNR作为约束条件下最大化MI的准则,通过引入发射信号的自相关序列将优化目标转发为凸优化问题,进而优化设计发射波形.仿真实验的结果表明,利用该方法所设计的波形能够满足输出SNR约束(对于目标检测性能)且同时能够最大化MI(对应目标估计性能).     

雷达目标检测的最优波形设计综述

充分利用雷达目标和环境特性,设计最优发射波形,能够从本质上提高雷达目标检测性能,具有重要的研究价值。该文将近几年发表的雷达目标检测的最优波形设计文献进行总结和归纳,为面向目标检测的波形优化设计研究提供方法和依据,具有一定的参考价值。      

认知雷达的未知目标检测

认知雷达可以在探测过程中不断优化自身系统参数,实现与当前探测环境的匹配,从而能够改善雷达的探测性能。针对未知目标的探测问题,利用当前回波数据更新目标分量的估计值及其协方差矩阵,基于目标相关信息优化下一次探测所需的发射机波形和接收机滤波器,并构成一个闭环处理过程。该文提出了两种优化途径,第1种途径利用目标分量的估计仅优化下一次探测波形,在接收端采用广义匹配滤波器;第2种途径将估计误差等效为信号依赖的噪声,联合优化发射波形与接收机滤波器。计算机仿真分析表明,采用闭环迭代优化的方法是渐进等效的,并可以在相干累积获得的性能增益基础上,进一步改善雷达的探测性能。     

波形具有任意相关性时MIMO雷达的检测性能

基于Neyman-Pearson准则,研究了空间分集MIMO(多输入多输出)雷达发射波形相关时的自、互相关旁瓣对MIMO雷达检测性能的影响.首先,分析了波形的相关性,导致了在空间上和时间上的白高斯噪声变为相关色噪声,同时,使得接收的回波信号具有一定的相关性,影响了空间分集的效果;然后,通过理论分析得到了MIMO雷达在波形具有任意相关性时的检测概率和虚警概率的解析表达式.通过数值计算,得到如下结论:与发射正交波形相比,在高信噪比(SNR)工作时,M1MO雷达波形相关时的自、互相关旁瓣使得MIMO雷达的检测性能下降;在低SNR时,波形的相关性能够改善检测性能;波形的低自、互相关旁瓣对MIMO雷达的检测性能影响不大.     

基于压缩感知的认知雷达多目标跟踪方法

该文提出了一种基于压缩感知的认知雷达跟踪方法,该方法将压缩感知理论引入到认知雷达跟踪的问题中。通过对回波信号的稀疏表示,完成稀疏变换矩阵和观测矩阵的设计,实现了降采样条件下量测信号的重构。在系统的接收端,考虑到传统的粒子滤波容易陷入局部最优,对粒子数目要求大等问题,采用了粒子群优化的粒子滤波来对目标状态进行实时估计。在系统的发射端,采用优化后验克拉美罗界(Posterior Cramr-Rao Bounds, PCRB)设计了雷达发射波形参数,降低了对目标跟踪精度的PCRB。仿真表明,相比于传统跟踪方法,该文所提跟踪方法不仅有效地减少了雷达的数据量,而且较大地提高了目标的跟踪性能。      

分辨率约束下毫米波雷达波形参数及接收权联合设计

针对自动驾驶中有限平台空间及发射功率导致毫米波雷达目标检测性能较低的问题,该文提出一种距离及速度分辨率约束下提升毫米波雷达目标检测概率的波形参数及接收权联合设计方法.首先,基于调频连续波(FMCW)信号,所提方法建立了毫米波相控阵阵列检测模型;其次,通过分析距离及速度分辨率与发射波形参数关系,构建考虑距离及速度分辨率的发射波形参数约束;然后,基于最大化输出信杂噪比(SCNR)准则,建立具有距离及速度分辨率约束的发射波形参数及接收权值联合优化模型以改善毫米波雷达目标检测及距离速度分辨性能;最后,所提方法基于交替迭代方法求解所得复杂非线性优化问题.仿真结果表明,所提方法可自适应调整发射波形参数和接收权以提升目标检测性能同时满足距离及速度分辨率需求.     

Range-spread target detecting for cognitive radar based on track-before-detect

The problem of cognitive radar detecting for range-spread target with unknown target impulse response (TIR) is studied. A detecting algorithm which updates the estimate of TIR and transmitted waveform iteratively is proposed based on track-before-detect method. In the algorithm, the Kalman filter is used to track the range-spread target for estimating the TIR, and the signal-to-noise rate criterion is used to design the transmitted waveform. The simulation results show that if the proposed algorithm is used to detect moving target, the performance of tracking and detecting will be improved when the number of loops increases. And the estimated detecting probability will approach the true detecting probability when the tracking accuracy increases, which would improve the reliability of decision.     

Information theory and radar waveform design

The use of information theory to design waveforms for the measurement of extended radar targets exhibiting resonance phenomena is investigated. The target impulse response is introduced to model target scattering behavior. Two radar waveform design problems with constraints on waveform energy and duration are then solved. In the first, a deterministic target impulse response is used to design waveform/receiver-filter pairs for the optimal detection of extended targets in additive noise. In the second, a random target impulse response is used to design waveforms that maximize the mutual information between a target ensemble and the received signal in additive Gaussian noise. The two solutions are contrasted to show the difference between the characteristics of waveforms for extended target detection and information extraction. The optimal target detection solution places as much energy as possible in the largest target scattering mode under the imposed constraints on waveform duration and energy. The optimal information extraction solution distributes the energy among the target scattering modes in order to maximize the mutual information between the target ensemble and the received radar waveform     

检测性能约束下目标估计的波形优化设计

为了提高杂波背景下检测性能约束的扩展目标估计性能,提出一种基于凸优化和随机化方法的波形优化设计算法。文中考虑了实际雷达系统应用对目标检测性能的要求,定义了随机目标场景下的信杂噪比,并将之作为约束条件,以最大化接收回波与目标随机冲激响应之间的互信息作为优化准则,利用凸优化和随机化方法进行恒包络相位编码信号的设计。仿真实验结果表明,该算法获得的发射信号能够满足系统最低检测信杂噪比的要求,最大化互信息,提高了目标的估计性能,将扩展目标估计性能和检测性能有效统一起来。     

基于不确定集的稳健MIMO雷达波形设计算法

针对雷达目标回波信号存在不确定性导致MIMO雷达波形优化设计性能下降问题,该文提出一种扁平椭球不确定集约束下的稳健自适应发射-接收波形联合优化设计方法。首先将目标脉冲响应的误差推广到更为一般的扁平椭球不确定集约束条件,并利用Lagrange乘子法对优化过程进行推导,得出扁平椭球不确定集约束下的闭式表达式。其次为了提高目标脉冲响应不确定集范围较大时的优化性能,采用了迭代鲁棒最小方差法进行求解(IRMVB),求得更为精确的目标脉冲响应,提高了SINR改善性能。然后进一步分析了基于扁平椭球不确定集约束条件与球体约束条件优化问题的内在联系,推导得出该文求解过程为广义对角加载方法。最后通过仿真实验表明所提算法对于目标回波信号不确定性具有更高的稳健性。     

Minimax robust transmission waveform and receiving filter design for extended target detection with imprecise prior knowledge

It is well known that optimal extended target detection for wideband radar in the presence of colored Gaussian noise and signal-dependent interference can be implemented, based on the prior information of target impulse response, by transmit-receiver design via maximizing the output signal-to-interference plus noise ratio (SINR). However, the prior knowledge of the target is usually imprecise. The target impulse response is very sensitive to target-radar orientation, and the initial phase of target echo is a function of target-radar distance, namely, the exact target impulse response cannot be obtained in transmission waveform design. Additionally, the transmission waveform is desired to be of constant modulus for power efficiency. In this paper, we propose a robust method to jointly design the transmission waveform with constant modulus constraint and the receiving filters. The cost function is established by maximizing the worst-case output SINR and an iterative procedure is presented based on modified sequential quadratic programming. Numerical results show that the proposed method can increase the worst-case output SINR significantly.     

最大互信息准则下的认知MIMO雷达波形设计

针对杂波背景下对认知多输入多输出(MIMO)雷达波形设计研究较少的问题,提出了一种基于最大互信息准则的波形设计方法。该方法结合一般认知雷达波形设计模型,以雷达发射功率作为约束条件,以最大化目标与接收回波间的互信息为目标,对认知MIMO雷达的波形设计问题进行建模,并采用卡尔曼滤波一步预测的方法对快速运动扩展目标的冲激响应进行预测,实现对目标的精准估计;同时,基于上述分析采用最大能量分配的方法进行波形设计。仿真实验表明,与传统的线性调频信号(LFM)相比,文中所提方法可以获得更多的信息量。     

Optimal mismatched filter design for radar ranging, detection, and resolution

In a multiple-target environment a radar signal processor often uses weighting filters that are not matched to the transmitted waveform. In this paper the mean-square range-estimation error, the detection Signal-to-noise ratio (SNR), and the effects of sidelobes are expressed in terms of the impulse response of an arbitrary mismatched filter. It is desired to find that impulse response that results in the minimum range-estimate variance subject to preassigned constraints on the side-lobes and the detection SNR. It is shown that for detecting the radar target and estimating its range, the form of the optimum filter is a modified transversal equalizer. If only detection is required, the optimum filter reduces to the transversal equalizer. Certain parameters upon which the solution depends can be found by solving a nonlinear programming problem. Numerical results are given for an interesting class of transmitted waveforms.     

基于WLS-TIR的多目标识别认知雷达波形自适应方法

 针对多个雷达目标的识别问题,研究认知雷达的波形自适应方法,对目标冲激响应线性加权求和获得多个目标的加权表示,基于互信息准则,通过最大化接收到的信号与目标冲激响应线性加权和(WLS-TIR)的互信息获得了针对多目标识别的最优波形,给出了权值的计算方法和多目标识别的自适应机制.仿真实验表明,本文提出的方法相对于传统的线性调频信号(LFM)对多目标的识别性能有明显的提高,并具有较强的实用性和可行性.     

集中式MIMO雷达研究综述

多输入多输出(MIMO)雷达作为一种新体制雷达,利用其发射波形分集的特点,在目标检测、参数估计、射频隐身及抗干扰等诸多方面展现出了突出的性能,经过学者们近20年的深入研究,基于正交波形的MIMO雷达相关理论日臻完善,并在汽车辅助驾驶、安全防卫等领域得到广泛应用。近年来,随着电磁环境感知及知识辅助等概念的引入,基于波形优化的MIMO雷达主动抗干扰、射频隐身、以及探测-通信一体化等技术受到学者们的关注并得到深入研究。该文力图对学者们近20年来围绕MIMO雷达的研究工作进行归纳与综述,内容主要包括:正交波形MIMO雷达原理、目标探测性能分析、典型应用;正交波形MIMO雷达波形设计与特点;基于知识的认知MIMO波形设计与算法;基于MIMO的探测-通信一体化波形设计与算法;MIMO雷达信号处理、数据处理及资源管理。论文最后对MIMO雷达在机载应用中的空时处理(STAP)、MIMO雷达在成像中的信号处理、以及基于时分多波形分集的线性调频毫米波MIMO雷达信号处理等进行了讨论。      

MIMO Radar Waveform Optimization With Prior Information of the Extended Target and Clutter

The concept of multiple-input multiple-output (MIMO) radar allows each transmitting antenna element to transmit an arbitrary waveform. This provides extra degrees of freedom compared to the traditional transmit beamforming approach. It has been shown in the recent literature that MIMO radar systems have many advantages. In this paper, we consider the joint optimization of waveforms and receiving filters in the MIMO radar for the case of extended target in clutter. A novel iterative algorithm is proposed to optimize the waveforms and receiving filters such that the detection performance can be maximized. The corresponding iterative algorithms are also developed for the case where only the statistics or the uncertainty set of the target impulse response is available. These algorithms guarantee that the SINR performance improves in each iteration step. Numerical results show that the proposed methods have better SINR performance than existing design methods.