基于标签多伯努利滤波的衍生目标跟踪算法

doi:10.7527/S1000-6893.2015.0103

Abstract

Abstract:

For the problem that the performance of existing random finite set (RFS) based filters serious degrades when tracking spawned targets in low signal-to-noise ratio condition, an improved algorithm based on δ-generalized labeled multi-Bernoulli (δ-GLMB) filter is proposed. According to RFS theory and Bernoulli spawning model, a new predicted formulation is derived. Hypotheses pruning and grouping method along with Bernoulli approximation are adopted to cut down the computational load. To reduce the false alarms introduced by extra hypotheses, the multi-frame smooth method is employed through a new target confirmation step with label information. Simulations show that the proposed method can provide unbiased estimation of cardinality, and significant outperforms the probability hypothesis density (PHD) filter in the low detection probability and dense clutter environment. The computational cost of proposed algorithm grows faster than PHD in the early stage of spawning, while is lower than PHD when targets separate well.

Key words: target tracking, random finite set, labeled multi-Bernoulli, spawned target, sequential Monte Carlo

CLC Number:

V243
TP391

QIU Hao, HUANG Gaoming, ZUO Wei, GAO Jun. Spawned target tracking algorithm based on labeled multi-Bernoulli filtering[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2015, 36(9): 3012-3019.

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Spawned target tracking algorithm based on labeled multi-Bernoulli filtering

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