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A modified variable rate particle filter for maneuvering target tracking
- 改进的变速率粒子滤波及其在机动目标跟踪中的应用
- Frontiers of Information Technology & Electronic Engineering Vol. 16, Issue 11, Pages: 985-994(2015)
- Affiliations：
Automation School, Hangzhou Dianzi University, Hangzhou 310018, China
- Author bio：
[ "Yun-fei GUO, E-mail: gyf@hdu.edu.cn" ]
- Funds：
DOI：10.1631/FITEE.1500149
CLC： TN953Received：06 May 2015，
Revised：2015-10-20，
Published：2015-11
- Accepted：
Scan QR Code
GUO Yun-fei, FAN Kong-shuai, PENG Dong-liang, et al. A modified variable rate particle filter for maneuvering target tracking[J]. Frontiers of Information Technology & Electronic Engineering, 2015, 16(11): 985-994.
GUO Yun-fei, FAN Kong-shuai, PENG Dong-liang, et al. A modified variable rate particle filter for maneuvering target tracking[J]. Frontiers of Information Technology & Electronic Engineering, 2015, 16(11): 985-994. DOI： 10.1631/FITEE.1500149.
摘要
目的
2
针对一类特定的机动目标跟踪问题（目标长时间近似匀速直线运动，偶尔强机动运动），提出一种兼顾机动期间跟踪精度与非机动期间系统负担的非线性滤波方法。
创新点
2
提出便于工程应用的基于笛卡尔坐标系的变速率模型和改进的变速率粒子滤波；提出机动检测方法而非多模型方法动态调整变速率粒子滤波的核心参数。通过在改进的变速率粒子滤波中嵌入无味粒子滤波，进一步改进估计精度。
方法
2
首先，提出笛卡尔坐标系下的变速率采样模型。此时状态驻留时间不再与测量周期保持一致，从而可以减少目标非机动期间的状态更新次数。其次，提出一种基于机动检测的改进变速率粒子滤波方法，当目标机动或非机动时，动态调整变速率模型的Gamma分布参数。目标机动时，状态更新更频繁，粒子分布范围更广；目标非机动时，状态更新次数较少，降低系统存储和计算负担。最后，在改进的变速率粒子滤波中采用无味粒子滤波，进一步提高估计精度。
结论
2
改进的变速率粒子滤波方法采用笛卡尔系而非本体坐标系，需要的先验信息更少，更便于工程应用。通过动态调整算法中的分布参数，采用无味粒子滤波方法，可以兼顾目标在非机动和机动期间的跟踪性能，同时降低系统的计算和存储负担。
Abstract
To address the problem of maneuvering target tracking
where the target trajectory has prolonged smooth regions and abrupt maneuvering regions
a modified variable rate particle filter (MVRPF) is proposed. First
a Cartesian-coordinate based variable rate model is presented. Compared with conventional variable rate models
the proposed model does not need any prior knowledge of target mass or external forces. Consequently
it is more convenient in practical tracking applications. Second
a maneuvering detection strategy is adopted to adaptively adjust the parameters in MVRPF
which helps allocate more state points at high maneuver regions and fewer at smooth regions. Third
in the presence of small measurement errors
the unscented particle filter
which is embedded in MVRPF
can move more particles into regions of high likelihood and hence can improve the tracking performance. Simulation results illustrate the effectiveness of the proposed method.
关键词
Keywords
references
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