【作者】 卢锦；

【导师】 水鹏朗；

【作者基本信息】 西安电子科技大学 ， 信号与信息处理， 2014， 博士

【摘要】 机动微弱目标检测是雷达信号处理领域面临的严峻挑战之一。当目标回波信噪比过低，基于单帧数据的相干或非相干累积方法无法保证可靠检测时，可采用检测前跟踪技术。检测前跟踪技术是一种长时间信号累积方法，通过联合处理多帧观测数据同时实现目标检测和跟踪。但早期的基于动态规划、Hough变换以及最大似然估计的检测前跟踪算法仅适合处理近似直线运动的目标。粒子滤波器(算法)解决统计特性已知的非线性、非高斯问题具有现有算法无可比拟的优势，而代价参考粒子滤波器(算法)具有处理统计特性未知的非线性问题的优势。上述粒子滤波器和代价参考粒子滤波器可有效实现雷达机动微弱目标长时间累积检测和跟踪。因此，研究和设计基于粒子滤波器和代价参考粒子滤波器的检测前跟踪算法对于检测和跟踪低信噪比机动目标具有重要的理论意义和应用价值。本论文的主要工作是研究和设计检测前跟踪算法。提出的方法包括基于辅助粒子滤波器的似然比检验，基于代价参考粒子滤波器的广义似然比检验，基于代价参考粒子滤波器的存在概率检验，和基于前向-后向代价参考粒子滤波器的具有全变差惩罚的广义似然比检验。这些方法能够实现低信噪比条件下机动微弱目标的有效检测和跟踪。本论文内容可概括为以下四个部分：1.粒子滤波算法。通过介绍和分析粒子滤波算法和代价参考粒子滤波算法，提出了两种改进的代价参考粒子滤波算法。粒子滤波算法，如序贯重要性重采样(sequential importance resampling, SIR)和辅助粒子滤波算法(auxiliary particle filter,APF)等，利用大量带有权值的随机样本近似目标状态的后验概率密度函数，基于近似后验概率密度函数可实现多种准则下的目标状态估计。然而粒子率滤波算法要求动态系统的统计特性已知，实际情况往往无法满足。代价参考粒子滤波算法针对系统统计特性未知情况下的状态估计问题，采用用户自定义的风险和代价函数代替粒子滤波中的预测和更新后验概率密度函数实现重采样和更新过程。通过重新定义改进的代价和风险函数，提出了两种改良的代价参考粒子滤波器，提高了统计特性未知时的状态估计性能。2.基于粒子滤波的似然比检测方法。通过分析基于SIR的似然比检测方法的不足，提出了基于APF的似然比检测方法和基于代价参考粒子滤波算法的广义似然比检测方法。采用APF重采样前的未归一化权值构造近似似然比，本文提出了基于APF的似然比检测方法。该方法性能优于基于SIR的似然比检测方法，可有效检测和跟踪统计特性已知时的机动微弱目标。然而，基于SIR和APF的似然比检测方法只适合处理统计特性已知的问题。对于统计特性未知的机动微弱目标检测问题，本文提出了基于代价参考粒子滤波的广义似然比检侧方法。该方法利用代价参考粒子滤波器输出的估计状态序列构造广义似然比，无需系统的统计信息。3.基于粒子滤波的存在概率检测方法。针对统计特性未知时的机动微弱目标检测和跟踪问题，提出了基于代价参考粒子滤波器的存在概率检测方法。在长观测时间情况下，目标常常在观测期间进入和离开一个雷达分辨单元。因此，有时除了要求检测器给出一个分辨单元内是否存在目标外，还要求其报告目标进入和离开分辨单元的时刻。在动态系统的状态向量中引入一个模拟目标存在和消失的存在变量，可得到各个时刻的存在概率。各个时刻的存在概率决定目标是否出现在某一分辨单元内，也可确定目标进入和离开分辨单元的时刻。对于统计特性已知的动态系统，基于SIR的存在概率检验能够很好地实现机动微弱目标检测和跟踪。对于统计特性未知的动态系统，通过引入存在变量和相关系数提高存在概率的估计准确性，本文提出了基于代价参考粒子滤波的存在概率检验方法并用于统计特性未知的机动微弱目标检测和跟踪。另外，基于所有时刻的存在概率构建一个二元判决的检验统计量，通过该统计量可以给出一个分辨单元目标存在的二元判决。4.基于前后-向代价参考粒子滤波的包含全变差惩罚的广义似然比检测。针对噪声背景下未知非线性调频信号检测问题，提出了一种基于前向-后向代价参考粒子滤波的包含全变差惩罚的广义似然比检测方法。上述方法将非线性调频信号建模为分段线性调频信号。每个线性调频片段的中心频率、调频率以及调频率的变化率形成当前时刻的状态向量。将信号和观测随时间的演化建模为统计特性未知的非线性动态系统。通过定义新的代价和风险函数，提出了前向-后向代价参考粒子滤波算法估计信号的状态序列和瞬时频率曲线。基于估计状态序列可构造广义似然比检测统计量，基于估计瞬时频率曲线可构造全变差检测统计量。广义似然比和全变差是检验非线性调频信号存在与否的重要特征。因此，将上述特征融合，提出了具有全变差惩罚的广义似然比检测器。与两种经典的检测方法相比，基于前向-后向代价参考粒子滤波的含有全变差惩罚的广义似然比检测方法明显改善了对未知非线性频率调制信号的检测能力。更多还原

【Abstract】 Weak maneuvering target detection is one of the most important tasks in radar signalprocessing systems. Under the condition of very low signal to noise ratio (SNR), thecoherent or incoherent integrations based on the single frame data are difficult to givereliable detection results. The track-before-detect (TBD) technique is considered to bean effective approach to realize weak maneuvering target detection and tracking. TheTBD technique is a method to implement long time signal integration, which canprocess multiple frames of measurements together and simultaneously perform targetdetection and tracking. However, the early TBD technique is implemented by the Houghtransform, dynamic programming, and maximal likelihood estimation, which prohibit orpenalize deviations from straight line motion. Compared with the now availablemethods, particle filter (PF) has outstanding performance in dealing with thenonlinear/non-Gaussian problem when the statistical information is given. And thecost-reference particle filter (CRPF) performs well in dealing with the nonlinearproblem when the statistical information is unknown. The PF and CRPF are effectivemethods of detection and tracking of weak targets in long integration time. Therefore, itis important to investigate and design the TBD algorithms based upon PF and CRPF forweak maneuvering target detection and tracking.The major works of this dissertation are to investigate and design TBD methods.The proposed methods include the likelihood ratio test based upon the auxiliary particlefilter, the generalized likelihood ratio test based upon the cost-reference particle filter,the existence probability test based on the cost-reference particle filter, and thegeneralized likelihood ratio test with total variation penalty using the forward-backwardcost-reference particle filter. These methods can implement effective detection andtracking of weak maneuvering targets in the case of low SNR.The content of the dissertation is organized as follows:1. Review particle filtering algorithms. By introducing and analyzing the PF andCRPF, two modified versions of the CRPF are presented to improve the capability ofthe state estimation in the case of unknown statistics. In the PFs, such as the sequentialimportance resampling particle filter (SIR) and the auxiliary PF (APF), the posteriorprobability density functions of the states of a dynamic system are approximated by alarge number of weighted particles, based on which the states can be estimated bymeans of different rules. However, the PFs require known statistics of the dynamicsystem including the system noise and measurement noise, which is barely encountered in applications. The CRPF was developed for dynamic systems of unknown statistics,where the user-defined cost and risk functions are used to replace the predicted andupdated posterior probability densities in the PFs for the resampling and updateprocedures. By redefining the improved cost and risk functions, two modified CRPFsare proposed for enhancing the capability of state estimation in dynamic systems ofunknown statistics.2. Likelihood ratio detectors based upon particle filters. By analyzing thedefects of the likelihood ratio detector based on the SIR, the likelihood ratio detectorbased upon the APF and the generalized likelihood ratio detector based upon the CRPFare proposed. Using the unnormalized weights of the APF before resampling toapproximate the likelihood ratio of observation, the APF based likelihood ratio test isproposed, which has better capability in weak maneuvering target detection and trackingthan the SIR based likelihood ratio test. However, the SIR and APF based likelihoodratio test require known statistics of a dynamic system. A CRPF based generalizedlikelihood ratio test is proposed for weak maneuvering target detection and trackingunder the condition of unknown statistics, where the generalized likelihood ratio isconstructed from the estimated state sequence provided by the CRPF.3. Existence probability test based upon particle filter. A CRPF based existenceprobability test is proposed for detection and tracking of weak maneuvering target whenthe dynamic system has unknown statistics. Under the condition of long oberservationtime, a target often enters and leaves a radar resolution cell during the observation. Thus,a detector is sometimes required to report the time instants for a target to enter and leaveexcept for its presence. By adding an existence variable into the state vector to modeltarget presence and absence, the existence probability at each time instant can beestimated, which can be used to measure the target presence or absence in a resolutioncell at each time instant. Moreover, the existence probabilities can show the timeinstants for target to enter and leave the resolution cell. For the dynamic systems ofknown statistics, the existence probability test based upon the SIR performs well inweak maneuvering target detection and tracking. For the dynamic systems of unknownstatistics, by introducing an existence variable and a correlative coefficient to improvethe estimation of the existence probabilities, the existence probability test based uponthe CRPF is proposed for weak target detection and tracking. Moreover, the existenceprobabilities at all the time instant are combined to form a binary test statistic to give abinary decision whether a target is present or absent in a resolution cell.4. Generalized likelihood ratio detector with total variation penalty via the forward-backward CRPF. Aiming at the detection problem of unknown nonlinear FMsignals in noise background, a generalized likelihood ratio test method with the totalvariation penalty based upon the forward-backward CRPF is proposed. In the proposedmethod, a nonlinear FM signal is modeled into piecewise linear FM signals. The centralfrequency, chirp rate, and its change rate of each signal segment form the state vector ofthe signal at this time instant. The evolution of the signal and measurements aremodeled as a nonlinear dynamic system of unknown statistics. By defining new cost andrisk functions, a forward-backward CRPF is proposed to estimate the states and theinstantaneous frequency (IF) curve of a signal. The generalized likelihood ratio teststatistic based upon the estimated states and the total variation of the estimated IF curveare important features to decide whether a nonlinear FM signal exists or not. Therefore,the two features are fused to construct the GLRT detector with total variation penalty.Compared with the two state-of-the-art detectors, the proposed detector providessignificant improvement in detection of unknown nonlinear FM signals.更多还原