基于DP-TBD的分布式异步迭代滤波融合算法研究

李洋漾; 李雯; 易伟; 孔令讲

doi:10.12000/JR17057

基于DP-TBD的分布式异步迭代滤波融合算法研究

DOI: 10.12000/JR17057

电子科技大学电子工程学院成都 611731

基金项目: 长江学者奖励计划，中央高校基本科研基金（ZYGX2016J031），中国博士后科学基金面上基金（2014M550465）和特别资助基金（2016T90845）

详细信息

作者简介:
李洋漾(1993–)，男，四川人，电子科技大学硕士研究生，研究方向为多传感器数据融合理论、弱小目标检测跟踪技术。E-mail: 575630861@qq.com

李　雯(1993–)，女，陕西人，电子科技大学硕士研究生，研究方向为雷达信号处理、雷达通信一体化波形设计。E-mail: 1757152507@qq.com

易　伟(1983–)，男，四川人，电子科技大学副教授，研究方向为统计信号处理、雷达信号与数据处理、多传感器数据融合理论、弱小目标检测跟踪技术等。E-mail: kussoyi@gmail.com

孔令讲(1974–)，男，河南人，博士，电子科技大学教授，研究方向为宽带雷达系统技术、弱目标检测跟踪技术、雷达协同探测技术、相控阵激光雷达技术，科研概况：主要承担国家863、国防预研、自然基金等科研项目

通讯作者:
李洋漾　575630861@qq.com

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出版历程
- 收稿日期: 2017-06-14
- 修回日期: 2017-07-31
- 网络出版日期: 2018-04-28

A Distributed Asynchronous Recursive Filtering Fusion Algorithm via DP-TBD

School of Electronic Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China

Funds: The Chang Jiang Scholars Program, The Fundamental Research Funds of Central Universities under Grants (ZYGX2016J031), The Chinese Postdoctoral Science Foundation under Grant (2014M550465) and Special Grant (2016T90845)

摘要

摘要: 该文主要运用检测前跟踪动态规划(Dynamic Programming-Track Before Detect)算法解决目标跟踪问题。动态规划(Dynamic Programming, DP)是一种通过对量测空间栅格化处理，然后对离散的量测空间中所有可能的物理路径进行遍历的算法。然而，该算法提供的是一种未经滤波和平滑的点迹序列。随着实际战争环境日益复杂，基于单雷达的DP-TBD算法在信噪比(SNR)较低时跟踪效果不佳。此外，由于DP-TBD算法没有状态误差协方差矩阵，因此无法将不同雷达的点迹序列进行融合。而且由于通信时延和不同的采样周期，不同雷达的数据往往是异步的。为了解决以上问题，该文提出了一种基于DP-TBD的分布式异步迭代滤波融合算法(DynamicProgramming Fuison, DPF)。该算法分为两步，第1步提出了一种迭代滤波方法对DP点迹进行处理；第2步将不同雷达获得的异步状态估计转化为同步的，接着利用几种分布式的融合方法来获取融合之后的状态估计。仿真结果说明，和单雷达相比，该融合算法可以有效提升目标跟踪的性能，同时，该算法也可以降低航迹丢失率和计算量。
- 检测前跟踪 /
- 动态规划 /
- 迭代滤波 /
- 多传感器 /
- 分布式异步融合
Abstract: In this paper, we address target tracking problems by the use of multiple sensors via the Dynamic Programming (DP)-based Track-Before-Detect (TBD) method. Generally, DP-TBD is a grid-based method that estimates target trajectories by searching all the physically admissible paths in a determinate discrete state space. However, this multi-frame detection algorithm provides plot sequences without filtering or smoothing. With the growing complexity of the battle field environment, single radar based on DP-TBD cannot achieve satisfactory results when the Signal-to-Noise Ratio (SNR) is low. Besides, it is very difficult to fuse plot sequences from different radars because they contain no state error covariance matrix. Furthermore, various radars always contain asynchronous data due to the diversity of sampling times and communication delays. To alleviate these problems, we propose a distributed asynchronous recursive filtering fusion (Dynamic Programming Fuison, DPF) algorithm based on DP-TBD, which is divided into two steps. In the first step, we propose an iterative filter algorithm via DP-TBD. Then, we convert the asynchronous evaluation data into synchronous data and implement several distributed fusion algorithms to estimate the target state. Simulation results show that the proposed algorithm can correctly estimate target trajectories and significantly enhance tracking accuracy compared to solo radar. In addition, this algorithm can decrease the track loss rate and calculation burden.
- Track-Before-Detect (TBD) /
- Dynamic Programming (DP) /
- Recursive filtering /
- Multiple sensors /
- Distributed asynchronous fusion

HTML全文

图 1 当N=5时滑窗间多帧关系示意图

Figure 1. The relationships of multiple sliding window frames when N=5

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图 2 DPF算法详细流程图

Figure 2. The detail method flow of DPF algorithm

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图 3 异步数据转换示意图

Figure 3. Asynchronous data conversion figure

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图 4 当SNR=8时的一次蒙特卡洛仿真结果

Figure 4. One time Monte Carlo simulation result when SNR=8

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图 5 从第1帧到第20帧的RMSE

Figure 5. RMSE from the 1st frame to the 20nd frame

下载: 全尺寸图片幻灯片

图 6 信噪比从8 dB到16 dB, DPF算法和TDF算法的航迹丢失率

Figure 6. Track loss rate of DPF algorithm and TDF method from SNR=8 dB to SNR=16 dB

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表 1 DPF算法和CDPF算法一次DP迭代的执行时间(s)

Table 1. One time execution time of DPF algorithm and CDPF algorithm (s)

参数	DPF算法	CDPF算法
传感器1的跟踪时间	4.07	0.052
传感器2的跟踪时间	3.661	0.043
融合时间	0.1	7.74

下载: 导出CSV

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