单脉冲雷达未分辨目标波达方向(direction of arrival,DOA)估计是跟踪雷达抗干扰领域的一个难点问题,现有方法通常假定干信比已知,限制了其在实际雷达系统中的应用。针对密集假信号存在下的单脉冲雷达未分辨目标DOA估计问题,提出了一种...单脉冲雷达未分辨目标波达方向(direction of arrival,DOA)估计是跟踪雷达抗干扰领域的一个难点问题,现有方法通常假定干信比已知,限制了其在实际雷达系统中的应用。针对密集假信号存在下的单脉冲雷达未分辨目标DOA估计问题,提出了一种无需干信比先验信息的未分辨目标DOA估计方法。首先通过对密集假信号聚类估计出诱饵DOA,然后推导了以诱饵DOA为参数的目标DOA估计解析表达式。仿真结果表明,所提算法能在密集假信号干扰下准确估计干扰和目标DOA。展开更多
The key challenge of the extended target probability hypothesis density (ET-PHD) filter is to reduce the computational complexity by using a subset to approximate the full set of partitions. In this paper, the influen...The key challenge of the extended target probability hypothesis density (ET-PHD) filter is to reduce the computational complexity by using a subset to approximate the full set of partitions. In this paper, the influence for the tracking results of different partitions is analyzed, and the form of the most informative partition is obtained. Then, a fast density peak-based clustering (FDPC) partitioning algorithm is applied to the measurement set partitioning. Since only one partition of the measurement set is used, the ET-PHD filter based on FDPC partitioning has lower computational complexity than the other ET-PHD filters. As FDPC partitioning is able to remove the spatially close clutter-generated measurements, the ET-PHD filter based on FDPC partitioning has good tracking performance in the scenario with more clutter-generated measurements. The simulation results show that the proposed algorithm can get the most informative partition and obviously reduce computational burden without losing tracking performance. As the number of clutter-generated measurements increased, the ET-PHD filter based on FDPC partitioning has better tracking performance than other ET-PHD filters. The FDPC algorithm will play an important role in the engineering realization of the multiple extended target tracking filter.展开更多
在雷达导引头末制导阶段,低信噪比(Signal to Noise Ratio,SNR)导致对目标的检测和定位性能恶化。为此,本文提出一种基于随机有限集的联合检测与DOA(Direction of Arrival)估计算法。该算法在单目标伯努利滤波器框架下,基于点目标扩展...在雷达导引头末制导阶段,低信噪比(Signal to Noise Ratio,SNR)导致对目标的检测和定位性能恶化。为此,本文提出一种基于随机有限集的联合检测与DOA(Direction of Arrival)估计算法。该算法在单目标伯努利滤波器框架下,基于点目标扩展函数对经过低门限判决后的数据构建目标观测方程,在天线和视线混合坐标系下建立状态变量描述,求解状态向量微分方程并对其离散化得到离散时间差分方程,经过状态误差分析得到状态转移模型,再经过粒子递归实现联合检测与状态估计。通过仿真实验,验证了该算法的有效性。与传统跟踪前检测方法对比,该算法能在低信噪比下提高检测性能和DOA估计精度。展开更多
The ability to extract state-estimates for each target of a multi-target posterior, referred to as multi-estimate extraction（MEE）, is an essential requirement for a multi-target filter, whose key performance assessm...The ability to extract state-estimates for each target of a multi-target posterior, referred to as multi-estimate extraction（MEE）, is an essential requirement for a multi-target filter, whose key performance assessments are based on accuracy, computational efficiency and reliability. The probability hypothesis density（PHD） filter, implemented by the sequential Monte Carlo approach,affords a computationally efficient solution to general multi-target filtering for a time-varying number of targets, but leaves no clue for optimal MEE. In this paper, new data association techniques are proposed to distinguish real measurements of targets from clutter, as well as to associate particles with measurements. The MEE problem is then formulated as a family of parallel singleestimate extraction problems, facilitating the use of the classic expected a posteriori（EAP） estimator, namely the multi-EAP（MEAP） estimator. The resulting MEAP estimator is free of iterative clustering computation, computes quickly and yields accurate and reliable estimates. Typical simulation scenarios are employed to demonstrate the superiority of the MEAP estimator over existing methods in terms of faster processing speed and better estimation accuracy.展开更多
文摘单脉冲雷达未分辨目标波达方向(direction of arrival,DOA)估计是跟踪雷达抗干扰领域的一个难点问题,现有方法通常假定干信比已知,限制了其在实际雷达系统中的应用。针对密集假信号存在下的单脉冲雷达未分辨目标DOA估计问题,提出了一种无需干信比先验信息的未分辨目标DOA估计方法。首先通过对密集假信号聚类估计出诱饵DOA,然后推导了以诱饵DOA为参数的目标DOA估计解析表达式。仿真结果表明,所提算法能在密集假信号干扰下准确估计干扰和目标DOA。
基金supported by the National Natural Science Foundation of China(61401475)
文摘The key challenge of the extended target probability hypothesis density (ET-PHD) filter is to reduce the computational complexity by using a subset to approximate the full set of partitions. In this paper, the influence for the tracking results of different partitions is analyzed, and the form of the most informative partition is obtained. Then, a fast density peak-based clustering (FDPC) partitioning algorithm is applied to the measurement set partitioning. Since only one partition of the measurement set is used, the ET-PHD filter based on FDPC partitioning has lower computational complexity than the other ET-PHD filters. As FDPC partitioning is able to remove the spatially close clutter-generated measurements, the ET-PHD filter based on FDPC partitioning has good tracking performance in the scenario with more clutter-generated measurements. The simulation results show that the proposed algorithm can get the most informative partition and obviously reduce computational burden without losing tracking performance. As the number of clutter-generated measurements increased, the ET-PHD filter based on FDPC partitioning has better tracking performance than other ET-PHD filters. The FDPC algorithm will play an important role in the engineering realization of the multiple extended target tracking filter.
文摘在雷达导引头末制导阶段,低信噪比(Signal to Noise Ratio,SNR)导致对目标的检测和定位性能恶化。为此,本文提出一种基于随机有限集的联合检测与DOA(Direction of Arrival)估计算法。该算法在单目标伯努利滤波器框架下,基于点目标扩展函数对经过低门限判决后的数据构建目标观测方程,在天线和视线混合坐标系下建立状态变量描述,求解状态向量微分方程并对其离散化得到离散时间差分方程,经过状态误差分析得到状态转移模型,再经过粒子递归实现联合检测与状态估计。通过仿真实验,验证了该算法的有效性。与传统跟踪前检测方法对比,该算法能在低信噪比下提高检测性能和DOA估计精度。
基金partly supported by the Marie SklodowskaCurie Individual Fellowship (No. 709267)under the European Union’s Framework Programme for ResearchInnovation Horizon 2020 and National Natural Science Foundation of China (No. 51475383)
文摘The ability to extract state-estimates for each target of a multi-target posterior, referred to as multi-estimate extraction（MEE）, is an essential requirement for a multi-target filter, whose key performance assessments are based on accuracy, computational efficiency and reliability. The probability hypothesis density（PHD） filter, implemented by the sequential Monte Carlo approach,affords a computationally efficient solution to general multi-target filtering for a time-varying number of targets, but leaves no clue for optimal MEE. In this paper, new data association techniques are proposed to distinguish real measurements of targets from clutter, as well as to associate particles with measurements. The MEE problem is then formulated as a family of parallel singleestimate extraction problems, facilitating the use of the classic expected a posteriori（EAP） estimator, namely the multi-EAP（MEAP） estimator. The resulting MEAP estimator is free of iterative clustering computation, computes quickly and yields accurate and reliable estimates. Typical simulation scenarios are employed to demonstrate the superiority of the MEAP estimator over existing methods in terms of faster processing speed and better estimation accuracy.
