Hoa Van Nguyen, Tran Thien Dat Nguyen, Changbeom Shim, Marzhar Anuar
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6 pages, 5 figures. Presented at The 12th IEEE International Conference on Control, Automation and Information Sciences (ICCAIS 2023), Nov 2023, Hanoi, Vietnam
This paper proposes a smooth-trajectory estimator for the labelled multi-Bernoulli (LMB) filter by exploiting the special structure of the generalised labelled multi-Bernoulli (GLMB) filter. We devise a simple and intuitive approach to store the best association map when approximating the GLMB random finite set (RFS) to the LMB RFS. In particular, we construct a smooth-trajectory estimator (i.e., an estimator over the entire trajectories of labelled estimates) for the LMB filter based on the history of the best association map and all of the measurements up to the current time. Experimental results under two challenging scenarios demonstrate significant tracking accuracy improvements with negligible additional computational time compared to the conventional LMB filter. The source code is publicly available at https://tinyurl.com/ste-lmb, aimed at promoting advancements in MOT algorithms.
A Hybrid Labeled Multi-Bernoulli Filter With Amplitude For Tracking Fluctuating Targets
为了提高跟踪性能，许多跟踪算法都加入了目标回波幅度信息。采用幅度特征的局限性之一是目标的信噪比（SNR），即，通常假定振幅似然的参数是已知的和恒定的。在实际应用中，目标信噪比总是未知的，并且依赖于方位角，因此它会波动。本文提出了一种混合标记多贝努利（LMB）滤波器，它将信号幅度引入LMB滤波器，用于跟踪未知和波动信噪比的目标。目标信噪比的波动由自回归伽马过程建模，并考虑了Swerling 1和3目标的幅度似然。在Rao-Blackwell分解下，提出了一种基于Laplace变换和Markov Chain Monte Carlo方法的近似Gamma估计器来估计目标信噪比，并在目标信噪比的条件下，采用高斯混合滤波器来估计运动状态。通过一个包含三个交叉目标的跟踪场景分析了该混合滤波器的性能。仿真结果验证了所提出的SNR估计器的有效性，并量化了将幅度信息用于多目标跟踪的好处。
Poisson multi-Bernoulli mixture filter for trajectory measurements
Department of Electrical Engineering and Electronics, University of Liverpool（利兹大学电子工程与电子系）；IPTC, ETSI de Telecomunicación, Universidad Politécnica de Madrid（马德里理工大学电信工程系）
