一种改进的直方图概率多假设多目标跟踪方法

doi:10.7527/S1000-6893.2020.24851

Abstract

Abstract: One of the main disadvantages of the Histogram Probability Multi-Hypothesis Tracking (H-PMHT) method and its variant Poisson distribution Histogram Probability Multi-Hypothesis Tracking (P-HPMHT) method is that their measurement model only considers the background clutter and does not consider the sensor noise, resulting in lower detection probability under low signal-to-noise ratio conditions. To overcome this problem, an improved H-PMHT with the sensor noise model is proposed. By introducing sensor noise into the measurement model, the ability to track and detect targets with low signal-to-noise ratio is significantly improved. The calculation amount of the method proposed maintains a linear relationship with the target number, and it is still suitable for the situation with many targets. Simulation experiments show that when the false tracking ratio is 1/1000, the detection rate of this method can be increased by nearly 20% when the signal-to-noise ratio is 6 dB, and by nearly 10% when it is 3 dB.

Key words: histogram probability multi-hypothesis tracking, low signal-to-noise ratio, multi-target, track before detect, Poisson distribution, expectation maximization

CLC Number:

V556
TN957

ZHANG Yiqun, YIN Lifan, WANG Shuo, SUN Chenggang. An improved histogram PMHT multi-target tracking method[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2021, 42(11): 524851-524851.

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An improved histogram PMHT multi-target tracking method

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