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Study of multi-modal and non-Gaussian probability density functions in target tracking with applications to dim target tracking.

机译：研究目标跟踪中的多模态和非高斯概率密度函数及其在昏暗目标跟踪中的应用。

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摘要

The majority of deployed target tracking systems use some variant of the Kalman filter for their state estimation algorithm. In order for a Kalman filter to be optimal, the measurement and state equations must be linear and the process and measurement noises must be Gaussian random variables (or vectors). One problem arises when the state or measurement function becomes a multi-modal Gaussian mixture. This typically occurs with the interactive multiple model (IMM) technique and its derivatives and also with probabilistic and joint probabilistic data association (PDA/JPDA) algorithms. Another common problem in target tracking is that the target's signal-to-noise ratio (SNR) at the sensor is often low. This situation is often referred to as the dim target tracking or track-before-detect (TBD) scenario. When this occurs, the probability density function (PDF) of the measurement likelihood function becomes non-Gaussian and often has a Rayleigh or Ricean distribution. In this case, a Kalman filter variant may also perform poorly. The common solution to both of these problems is the particle filter (PF). A key drawback of PF algorithms, however, is that they are computationally expensive. This dissertation, thus, concentrates on developing PF algorithms that provide comparable performance to conventional PFs but at lower particle costs and presents the following four research efforts. (1) A multirate multiple model particle filter (MRMMPF) is presented in Section-3. The MRMMPF tracks a single, high signal-to-noise-ratio, maneuvering target in clutter. It coherently accumulates measurement information over multiple scans via discrete wavelet transforms (DWT) and multirate processing. This provides the MRMMPF with a much stronger data association capability than is possible with a single scan algorithm. In addition, its particle filter nature allows it to better handle multiple modes that arise from multiple target motion models. Consequently, the MRMMPF provides substantially better root-mean-square error (RMSE) tracking performance than either a full-rate or multirate Kalman filter tracker or full-rate multiple model particle filter (MMPF) with a same particle count. (2) A full-rate multiple model particle filter for track-before-detect (MMPF-TBD) and a multirate multiple model particle filter for track-before-detect (MRMMPF-TBD) are presented in Section-4. These algorithms extend the areas mentioned above and track low SNR targets which perform small maneuvers. The MRMMPF-TBD and MMPF-TBD both use a combined probabilistic data association (PDA) and maximum likelihood (ML) approach. The MRMMPF-TBD provides equivalent RMSE performance at substantially lower particle counts than a full-rate MMPF-TBD. In addition, the MRMMPF-TBD tracks very dim constant velocity targets that the MMPF-TBD cannot. (3) An extended spatial domain multiresolutional particle filter (E-SD-MRES-PF) is developed in Section-5. The E-SD-MRES-PF modifies and extends a recently developed spatial domain multiresolutional particle filter prototype. The prototype SD-MRES-PF was only demonstrated for one update cycle. In contrast, E-SD-MRES-PF functions over multiple update cycles and provides comparable RMSE performance at a reduced particle cost under a variety of PDF scenarios. (4) Two variants of a single-target Gaussian mixture model particle filter (GMMPF) are presented in Section-6. The GMMPF models the particle cloud as a Gaussian finite mixture model (FMM). MATLAB simulations show that the GMMPF provides performance comparable to a particle filter but at a lower particle cost.
机译：大多数部署的目标跟踪系统使用卡尔曼滤波器的某些变体进行状态估计算法。为了使卡尔曼滤波器最佳，测量和状态方程必须是线性的，过程和测量噪声必须是高斯随机变量（或矢量）。当状态或测量函数变为多峰高斯混合时，就会出现一个问题。这通常发生在交互式多模型（IMM）技术及其派生类以及概率和联合概率数据关联（PDA / JPDA）算法中。目标跟踪中的另一个常见问题是传感器处目标的信噪比（SNR）通常很低。这种情况通常称为昏暗目标跟踪或检测前跟踪（TBD）场景。发生这种情况时，测量似然函数的概率密度函数（PDF）变为非高斯分布，并且通常具有瑞利或莱斯分布。在这种情况下，卡尔曼滤波器的变体也可能表现不佳。解决这两个问题的通用方法是使用粒子过滤器（PF）。但是，PF算法的主要缺点是计算量大。因此，本论文着重于开发可提供与传统PF相当的性能但具有较低颗粒成本的PF算法，并提出了以下四项研究工作。 （1）第3节介绍了一种多速率多模型粒子滤波器（MRMMPF）。 MRMMPF在杂波中跟踪单个高信噪比的机动目标。它通过离散小波变换（DWT）和多速率处理，在多次扫描中一致地累积测量信息。与单次扫描算法相比，这为MRMMPF提供了更强大的数据关联能力。此外，它的粒子过滤器特性使它能够更好地处理由多个目标运动模型产生的多种模式。因此，与具有相同粒子数的全速率或多速率Kalman滤波器跟踪器或全速率多模型粒子滤波器（MMPF）相比，MRMMPF提供了更好的均方根误差（RMSE）跟踪性能。 （2）在第4节中介绍了一种用于检测前跟踪的全速率多模型粒子滤波器（MMPF-TBD）和一种用于检测前跟踪的多速率多模型粒子滤波器（MRMMPF-TBD）。这些算法扩展了上面提到的区域，并跟踪执行较小操作的低SNR目标。 MRMMPF-TBD和MMPF-TBD都使用组合的概率数据关联（PDA）和最大似然（ML）方法。与全速率MMPF-TBD相比，MRMMPF-TBD的颗粒数要低得多，可提供同等的RMSE性能。另外，MRMMPF-TBD跟踪MMPF-TBD无法跟踪的非常暗的恒速目标。 （3）在第5节中开发了扩展的空间域多分辨率粒子滤波器（E-SD-MRES-PF）。 E-SD-MRES-PF修改并扩展了最近开发的空间域多分辨率粒子滤波器原型。原型SD-MRES-PF仅演示了一个更新周期。相反，E-SD-MRES-PF在多个更新周期内运行，并在各种PDF方案下以降低的颗粒成本提供了可比的RMSE性能。 （4）第6节介绍了单目标高斯混合模型粒子滤波器（GMMPF）的两个变体。 GMMPF将粒子云建模为高斯有限混合模型（FMM）。 MATLAB仿真显示，GMMPF提供的性能可与颗粒过滤器媲美，但颗粒成本较低。

著录项

  • 作者

    Hlinomaz, Peter V.;

  • 作者单位

    Wright State University.;

  • 授予单位 Wright State University.;
  • 学科 Engineering Electronics and Electrical.
  • 学位 Ph.D.
  • 年度 2008
  • 页码 143 p.
  • 总页数 143
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

  • 入库时间 2022-08-17 11:38:35

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