基于椭圆方向和半轴长度的多伯努利扩展目标跟踪

孙可; 胡青霜; 郑翔飞; 吴孙勇

doi:10.13700/j.bh.1001-5965.2022.0869

基于椭圆方向和半轴长度的多伯努利扩展目标跟踪

doi: 10.13700/j.bh.1001-5965.2022.0869

孙可¹,
胡青霜¹,
郑翔飞¹,
吴孙勇^{1, 2, ,}

1.
桂林电子科技大学数学与计算科学学院，桂林 541004
2.
广西密码学与信息安全重点实验室，桂林 541004

基金项目: 国家自然科学基金(62263007,62061010,62161007)；广西科技厅项目(AA19182007)；广西密码学与信息安全重点实验室研究课题资助(GCIS202132)；桂林电子科技大学数学与计算科学学院研究生创新项目(2022YJSCX02)；桂林电子科技大学研究生教育创新计划(2022YCXS145)

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通讯作者:
E-mail：wusunyong121991@163.com

中图分类号: V221⁺.3；TN953
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出版历程
- 收稿日期: 2022-10-30
- 录用日期: 2023-02-24
- 网络出版日期: 2023-04-14
- 整期出版日期: 2024-11-30

Multi-Bernoulli extended target tracking based on orientation and half axes lengths of an ellipse

1.
School of Mathematics and Computing Science，Guilin University of Electronic Science and Technology，Guilin 541004，China
2.
Guangxi Key Laboratory of Cryptography and Information Security，Guilin 541004，China

Funds: National Natural Science Foundation of China (62263007,62061010,62161007); Guangxi Science and Technology Department Project (AA19182007); Supported by Guangxi Key Laboratory of Cryptography and Information Security (GCIS202132); Graduate Innovation Program of School of Mathematics and Computing Science, Guilin University of Electronic Science and Technology (2022YJSCX02); Innovation Project of Guilin University of Electronic Science and Technology Graduate Education (2022YCXS145)

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Corresponding author: E-mail：wusunyong121991@163.com

摘要

摘要:
针对杂波环境下扩展目标外形难以估计、新生目标先验信息未知等问题，在扩展目标势均衡多目标多伯努利(ET-CBMeMBer)滤波器的基础上，开展基于椭圆方向和半轴长度(OAL)的参数化多扩展目标跟踪研究。借助乘性噪声构造考虑目标空间信息的显式量测方程，并推导OAL-CBMeMBer滤波器高斯混合实现的封闭形式解；基于显式的量测方程，利用已知的量测数据自适应构造考虑了质心位置和外形状态的新生目标先验信息，并提出自适应新生OAL-CBMeMBer滤波器。仿真实验结果表明：所提OAL-CBMeMBer滤波器提高了目标数目和状态的估计精度，能够有效地对多扩展目标进行跟踪。
- 多扩展目标 /
- 势平衡多目标多伯努利滤波器 /
- 椭圆方向 /
- 椭圆半轴长度 /
- 自适应新生
Abstract:
The problem that the shape of the extended target is hard to estimate and the newborn target's prior information is unknown in the clutter environment is solved in this paper by using the extended targets-cardinality balance multi-target and multi-Bernoulli (ET-CBMeMBer) filter. This parameterized multi-extended target tracking algorithm is based on the orientation and half axes lengths of an ellipse (OAL). Considering the spatial information of the target, an explicit measurement equation is constructed by multiplicative noise, and the closed-form solution implemented by the Gaussian mixture of the OAL-CBMeMBer filter is deduced. Based on the explicit measurement equation, the prior information of the newborn target that considers the position of the centroid and the shape state is adaptively constructed by employing the known measurement data, and the adaptive OAL-CBMeMBer filter is proposed. The proposed OAL-CBMeMBer filter can effectively track numerous extended targets and increases the estimation accuracy of target number and state, according to simulation findings.
- multi-extended target /
- cardinality balance multi-target multi-Bernoulli filter /
- elliptic orientation /
- half axes lengths of an ellipse /
- adaptive birth distributions

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图 1 利用已知量测构建的椭圆

Figure 1. Ellipse constructed with known measurements

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图 2 真实运动轨迹

Figure 2. Real motion trajectory

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图 3 平均目标个数估计

Figure 3. Average target number estimation

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图 4 平均PMOSPA误差

Figure 4. Average PMOSPA error

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图 5 平均目标个数估计

Figure 5. Average target number estimation

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图 6 平均PMOSPA误差

Figure 6. Average PMOSPA error

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表 1 场景目标初始状态及存活时间

Table 1. Initial state and survival time of the scenario target

目标	质心状态	外形状态	出现时刻/s	消失时刻/s
目标1	[−1000,−1000,14,10]	[0,15,35]	1	90
目标2	[−1000,1000,15,−20]	[0,15,30]	10	100
目标3	[1000,−1000,−11,−15]	[0,15,35]	20	70
目标4	[1000,1000,18,−13]	[0,15,30]	30	100
目标5	[1000,1000,−10,−13]	[0,10,20]	40	80
目标6	[1000,−1000,18,11]	[0,15,30]	50	100

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