基于检测前跟踪技术的多目标跟踪算法研究
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摘要
复杂环境中的微弱目标（如隐身飞机、被地/海/城市杂波淹没的目标等）探测问题是现代雷达面临的严峻挑战。与传统的先检测后跟踪（DBT）技术相比，检测前跟踪（TBD）技术是一种新兴的先进信号处理技术，它通过多帧回波数据积累和联合处理，可以显著提高雷达的微弱目标检测跟踪性能，是国际雷达界研究的前沿热点。TBD作为一项正在发展中的新技术，还需要解决一些新的技术问题，例如，多目标跟踪维数灾难、临近目标相互干扰等，以及处理难度增加和运算量增大带来的新问题。
本论文针对上述问题，研究了基于TBD技术的多目标跟踪算法，以及TBD技术在警戒雷达中的工程应用。本论文的主要工作和贡献如下：
1．在贝叶斯估计理论框架下给出了多目标跟踪问题的数学模型。利用该模型可以将传统的DBT技术和TBD技术统一在同一框架下，为多目标跟踪算法研究奠定了理论基础。
2．提出了新的动态规划（DP）的多目标TBD算法，较好地解决了多目标跟踪中的维数灾难、临近目标相互干扰等问题，比最新的SP-STC-VTA算法虚警率更低、检测性能更高。
3．提出了基于独立分区交叉采样（IJOID）和双层采样结构的粒子滤波多目标跟踪算法，能够适应目标数未知和时变的情况。该算法的计算效率和检测性能优于现有主流粒子滤波算法，在对多个临近目标跟踪时性能更稳定。
4．提出了基于低门限处理的DP-TBD快速算法及其工程应用方案，可大幅度降低计算量，适应警戒雷达的实时性要求。
以上提出的TBD算法以及工程实现方案，已通过仿真实验以及雷达实测数据测试，证明了算法的有效性。
The detection and tracking of low observable targets (e.g. stealth targets, targetsburied in strong clutter) in complex environment are great challenges for modern radarsystems. Different from the traditional detect-before-track (DBT) methods,Track-before-detect (TBD) is a novel and efficient signal processing method which isproposed in recent years to detect low observable targets, and has got much attentioninternationally in radar research area. By jointly processing several data frames, TBD isable to produce more reliable detection and tracking results. As a developing newtechnique, TBD has its own problems and challenges. For example, existing studies onTBD are focusing on the single target tracking problem. Because of the curse ofdimensionality and the performance degradation due to the interference of closelyspaced targets, the multi-target tracking problem is always a big challenge for TBDmethods. Besides, its computational expense and algorithm complexity are muchheavier than DBT methods.
Regarding the challenges mentioned above, in this thesis, the multi-target TBDalgorithm and its application in surveilence radar systems are studied. The maincontributions of the thesis are as follows:
1. The mathematical formulation of multi-target tracking problem is given basedon the Bayesian estimation theory. Then the traditional DBT method and TBD methodare united and discussed under the framework of Bayesian estimation theory. Thischapter makes the theoretical foundation of the subsequent chapters of this thesis.
2. According to the curse of dimensionality and the performance degradation dueto the target interference when targets are in proximity, a novel multi-target Dynamicprogramming (DP) based TBD algorithm is proposed in this chapter. By comparisonwith the existing SP-STC-VTA algorithm, we show that the proposed algorithm has lessfalse tracks, less computational complexity but has better tracking performance.
3. In order to tracking unknown and time-varying number of targets, a novelparticle filtering (PF) based TBD algorithm which uses independent and joint optimalimportance density (IJOID) sampling method and two-layer structure is proposed. This PF tracking algorithm has lower computational complexity, better detectionperformance and can deal with the target interference when targets are near each other.
4. With the purpose of reducing the computational complexity of the DP-TBDalgorithm, a thresholding process based DP-TBD algorithm is proposed. Besides, theapplications of the DP-TBD on the surveillance radar system are also studied.
The above tracking methods and corresponding engineering implementationscheme are tested using both simulated data and real data collected from surveillanceradar. The results demonstrate the efficacy of the proposed algorithm andimplementation scheme.
本论文针对上述问题，研究了基于TBD技术的多目标跟踪算法，以及TBD技术在警戒雷达中的工程应用。本论文的主要工作和贡献如下：
1．在贝叶斯估计理论框架下给出了多目标跟踪问题的数学模型。利用该模型可以将传统的DBT技术和TBD技术统一在同一框架下，为多目标跟踪算法研究奠定了理论基础。
2．提出了新的动态规划（DP）的多目标TBD算法，较好地解决了多目标跟踪中的维数灾难、临近目标相互干扰等问题，比最新的SP-STC-VTA算法虚警率更低、检测性能更高。
3．提出了基于独立分区交叉采样（IJOID）和双层采样结构的粒子滤波多目标跟踪算法，能够适应目标数未知和时变的情况。该算法的计算效率和检测性能优于现有主流粒子滤波算法，在对多个临近目标跟踪时性能更稳定。
4．提出了基于低门限处理的DP-TBD快速算法及其工程应用方案，可大幅度降低计算量，适应警戒雷达的实时性要求。
以上提出的TBD算法以及工程实现方案，已通过仿真实验以及雷达实测数据测试，证明了算法的有效性。
The detection and tracking of low observable targets (e.g. stealth targets, targetsburied in strong clutter) in complex environment are great challenges for modern radarsystems. Different from the traditional detect-before-track (DBT) methods,Track-before-detect (TBD) is a novel and efficient signal processing method which isproposed in recent years to detect low observable targets, and has got much attentioninternationally in radar research area. By jointly processing several data frames, TBD isable to produce more reliable detection and tracking results. As a developing newtechnique, TBD has its own problems and challenges. For example, existing studies onTBD are focusing on the single target tracking problem. Because of the curse ofdimensionality and the performance degradation due to the interference of closelyspaced targets, the multi-target tracking problem is always a big challenge for TBDmethods. Besides, its computational expense and algorithm complexity are muchheavier than DBT methods.
Regarding the challenges mentioned above, in this thesis, the multi-target TBDalgorithm and its application in surveilence radar systems are studied. The maincontributions of the thesis are as follows:
1. The mathematical formulation of multi-target tracking problem is given basedon the Bayesian estimation theory. Then the traditional DBT method and TBD methodare united and discussed under the framework of Bayesian estimation theory. Thischapter makes the theoretical foundation of the subsequent chapters of this thesis.
2. According to the curse of dimensionality and the performance degradation dueto the target interference when targets are in proximity, a novel multi-target Dynamicprogramming (DP) based TBD algorithm is proposed in this chapter. By comparisonwith the existing SP-STC-VTA algorithm, we show that the proposed algorithm has lessfalse tracks, less computational complexity but has better tracking performance.
3. In order to tracking unknown and time-varying number of targets, a novelparticle filtering (PF) based TBD algorithm which uses independent and joint optimalimportance density (IJOID) sampling method and two-layer structure is proposed. This PF tracking algorithm has lower computational complexity, better detectionperformance and can deal with the target interference when targets are near each other.
4. With the purpose of reducing the computational complexity of the DP-TBDalgorithm, a thresholding process based DP-TBD algorithm is proposed. Besides, theapplications of the DP-TBD on the surveillance radar system are also studied.
The above tracking methods and corresponding engineering implementationscheme are tested using both simulated data and real data collected from surveillanceradar. The results demonstrate the efficacy of the proposed algorithm andimplementation scheme.
引文
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