多传感器多目标跟踪中的概率数据互联

通过在两维雷达和红外搜索跟踪两种不同传感器观测空间上建立多目标运动状态的投影，单传感器的ＪＰＤＡ算法被推广到此种多传感器数据融合系统之中，实现了其中的多目标数据互联和多传感器数据融合，从而提高了跟踪性能。     

视场非完全重叠的分布式雷达多目标跟踪方法

在探测能力、波形设计及天线指向等因素制约下,分布式雷达视场并非完全重合,由此造成的观测信息差异给后续信息融合带来了巨大挑战。该文基于高斯混合实现的集势概率假设密度(CPHD)滤波器,提出了一种视场部分重叠下的分布式雷达多目标跟踪方法。首先,利用多目标密度乘积切分出概率假设密度(PHD)中表征共同观测信息的部分;之后,标准的分布式融合(算术平均或几何平均融合)方法作用于切分出的共同观测目标信息以提升跟踪性能,补偿融合则作用于雷达单独观测目标信息以扩展视场范围。该文方法无须视场先验信息,能够适应雷达视场未知时的分布式融合多目标跟踪场景。仿真实验验证了所提出方法在未知、时变雷达视场下跟踪多目标的性能,表明了该文方法比基于高斯混合的聚类方法性能更好。      

PHD filter based track-before-detect for MIMO radars

In this paper a Probability Hypothesis Density (PHD) filter based track-before-detect (TBD) algorithm is proposed for Multiple-Input-Multiple-Output (MIMO) radars. The PHD filter, which propagates only the first-order statistical moment of the full target posterior, is a computationally efficient solution to multitarget tracking problems with varying number of targets. The proposed algorithm avoids any assumption on the maximum number of targets as a result of estimating the number of targets together with target states. With widely separated transmitter and receiver pairs, the algorithm utilizes the Radar Cross Section (RCS) diversity as a result of target illumination from ideally uncorrelated aspects. Furthermore, a multiple sensor TBD is proposed in order to process the received signals from different transmitter-receiver pairs in the MIMO radar system. In this model, the target observability to the sensor as a result of target RCS diversity is taken in to consideration in the likelihood calculation. In order to quantify the performance of the proposed algorithm, the Posterior Cramer-Rao Lower Bound (PCRLB) for widely separated MIMO radars is also derived. Simulation results show that the new algorithm meets the PCRLB and provides better results compared with standard Maximum Likelihood (ML) based localizations.     

Parameter identifiability of space-time MIMO radar

In multiple-input multiple-output (MIMO) radar, the maximum number of targets which can be uniquely identified, also known as the parameter identifiability, is one of the most basic issues. In this paper, the parameter identifiability is extended from the space-only MIMO radar to the space-time MIMO radar. According to the parameter identifiability conditions derived, the maximum number of targets which can be identified by the space-time MIMO radar is presented. Comparing with space-only MIMO radar, the parameter identifiability of space-time MIMO radar is significantly enhanced by exploiting both the spatial and temporal characteristics of the transmitted waveforms. The results are validated by several numerical examples.     

相控阵雷达的自适应调度和多目标数据处理技术

本文全面阐述了适用于多功能相控阵雷达的自适应调度和多目标数据处理技术，给出了相控阵雷达调度及数据处理的软件和硬件体系结构。并用Ｃ语言实现了调度和数据处理的功能。测试结果表明自适应调度方法及多目标数据处理算法是令人满意的。     

长波红外导引头空间自动目标识别智能系统(二)

根据红外导引头信息处理流程，系统研究了红外图像处理流程中各级的功能和所采用的算法，给出了系统运行的部分实验结果。这是精确制导自动目标识别的一个实现例子。最后对本文做了概括的小结。     

一种新的多波门跟踪系统

本文从波门形成的原理出发,着重介绍一种新的多波门对多目标跟踪的电视跟踪系统,并给出控制流程图和原理图及实验结果。     

PERFORMANCE EVALUATION OF THE DOUBLE-THRESHOLD CFAR DETECTOR IN MULTIPLE-TARGET SITUATIONS

In radar systems of automatic detection, an estimate of background clutter power is used to set the detection threshold. An interference saturated environment is frequently encountered in these systems （multiple target situations）. Therefore, the detection of signals in such an environment becomes one of the most important problems to be solved. The double-threshold algorithm is one of the more interesting detectors used in these situations. While the first threshold operation ensures that the calculation of the detection （second） threshold is based on a set of samples which is free of strong interferers and is therefore much more representative of the noise level, the second threshold is used to declare the presence or the absence of the radar target. The object of the present paper is to analyze the performance of such type of CFAR schemes when the radar receiver contains a noncoherent integrator amongst its basic elements. It is found that the processor detectability loss is very low and the performance degradation, caused by interferers is quite small even if the number of outlying targets is large, given that the first threshold is properly chosen.     

分布式多传感器数据融合中的双门限航迹相关算法

本文提出两种适合于分布式多传感器数据融合的双门限航迹相关算法.文中对这两种双门限航迹相关准则进行了详细的描述,研究了航迹相关质量设计、多义性处理和漏相关概率的估计方法,并通过仿真将它们与两个经典方法进行了比较.仿真结果表明,在密集目标环境下和/或交叉、分岔和机动航迹较多的场合,双门限航迹相关算法的性能明显优于传统方法,其正确相关率比传统方法提高了大约35％.     

A novel all-neighbor fuzzy association approach for multitarget tracking in a cluttered environment

This paper proposes a novel all-neighbor fuzzy association approach for multitarget tracking in a cluttered environment. It performs data association with a little prior knowledge and updates the predicted target state estimate using a fuzzy weighted sum of innovations. Unlike the joint probabilistic data association filter, in which the similarity measures are determined in terms of the conditional probability for all feasible data association hypothesis, the proposed fuzzy association approach determines the similarity measures between measurements and tracks in terms of possibility weights based on a partition matrix. The possibility weights are determined according to the fuzzy clustering algorithm. The proposed approach is able to perform all-neighbor association with a lower computational complexity in the expense of a little lower performance compared to the standard joint probabilistic data association filter. Computer simulation shows the feasibility and the efficiency of the proposed all-neighbor fuzzy association approach.