基于交互多模型的分组δ-广义标签多伯努利算法

doi:10.12305/j.issn.1001-506X.2022.04.08

Abstract

Abstract:

When tracking multi-maneuvering targets with jump Markov system generalized labeled multi-Bernoulli filter, there are too many motion mode hypotheses that need to be calculated and pruned frequently, which may increase the computational complexity and negatively affect the tracking accuracy. In order to solve the problem, an interacting multiple model (IMM) based grouping δ-generalized labeled multi-Bernoulli filter is proposed. With this filter, all tracks fall into different groups, association mapping and hypothesis weight calculation run in each individual group, which reduces the computational complexity and enables parallelization. Moreover, IMM algorithm is incorporated into this filter to deal with target maneuver. The time prediction and data update equations are given in detail. Simulation results show that the proposed filter can track multiple maneuvering targets with higher accuracy and lower computational cost.

Key words: random finite set (RFS), δ-generalized labeled multi-Bernoulli (δ-GLMB) filter, multi target tracking (MTT), interacting multiple mode (IMM)

CLC Number:

TN95

Huaisheng XIN, Chen CAO. Interacting multiple model based grouping δ-generalized labeledmulti-Bernoulli algorithm[J]. Systems Engineering and Electronics, 2022, 44(4): 1128-1138.

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References 22

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目标	初始状态/(m, m/s, m, m/s)	出现时刻/s	消失时刻/s
1	[50 000, 200, 50 000, 0]	0	250
2	[50 000, 200, 50 500, 0]	0	300
3	[50 000, 200, 51 000, 0]	0	300
4	[13 000, 200, 1 000, 0]	100	300
5	[80 000, 200, 1 000, 0]	0	300

算法	5目标	10目标	15目标	20目标
δ-GLMB	3.77	7.156	11.45	17.24
JMS-GLMB	7.67	15.602	24.38	35.47
IMMG-δ-GLMB	3.23	4.433	7.455	10.54

其他参数	参数代号
其他参数	P₁	P₂	P₃	P₄
扫描周期/s	1	2	5	10
方位标准差/(°)	0.03	0.1	0.15	0.2
加速度过程方差/(m²/s⁴)	10	50	100	200
距离/km	10	50	100	200

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Interacting multiple model based grouping δ-generalized labeledmulti-Bernoulli algorithm

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