﻿ 基于统计双门限的中断航迹配对关联算法
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 雷达学报  2015, Vol. 4 Issue (3): 301-308  DOI: 10.12000/JR14077 0

### 引用本文 [复制中英文]

[复制中文]
Qi Lin, Wang Hai-peng, and Liu Yu. Track segment association algorithm based on statistical binary thresholds[J] Journal of Radars, 2015, 4(3): 301-308. DOI: 10.12000/JR14077.
[复制英文]

### 文章历史

, ,
(海军航空工程学院信息融合研究所 烟台 264001

Track segment association algorithm based on statistical binary thresholds
, ,
Institute of Information Fusion, Naval Aeronautical and Astronautically University, Yantai 264001, China
Abstract: The classical Track Segment Association (TSA) algorithm suffers from low accuracy and is impractical to use in concentrated targets, branching, and cross-tracking environment. Thus, a new statistical binary track segment association algorithm is proposed. The new algorithm is more appropriate as it increases the sample size for the ${{\chi }^{2}}$ distribution threshold detection. Simulation results show that in air cross tracking and for ballistic targets, the global correct association rate and the average correct association rate of the proposed algorithm are remarkablyimproved, which proves the good performance of the proposed algorithm.
Key words: Track-to-track association     Track segment     Statistical binary thresholds     Correct association rate
1 引言

TSA算法在理论上具有可行性，然而在实际情况下存在以下问题：(1)由于系统和测量噪声的存在，新航迹的后向预测常常误差较大，造成新老航迹基于单个点的关联配对准确性较差，导致误关联、漏关联经常发生；(2)当目标密集时，因航迹交叉、分岔经常发生错关联[11, 12]，TSA算法性能骤然下降。

2 问题阐述及粗关联 2.1 问题阐述

(1) 老航迹：因缺少量测数据无法进行状态更新的中断航迹。

 ${{\mathbf{T}}^{i}}=\left\{ {{\widehat{\mathbf{x}}}^{i}}\left( k|k \right),k=k_{s}^{i},\cdots ,k_{e}^{i} \right\}, i=1,2,\cdots ,I$ (1)

(2) 新航迹：新起始的航迹段，可能是因各种原因中断的“老航迹”的继续。

 ${{\mathbf{T}}^{j}}=\left\{ {{\widehat{\mathbf{x}}}^{j}}\left( k|k \right),k=k_{s}^{j},\cdots ,k_{e}^{j} \right\}, j=1,2,\cdots ,J$ (2)

 ${{\pi }_{{{i}_{3}}{{j}_{3}}}}={{\pi }_{{{i}_{3}}{{j}_{4}}}}=1$

5 结论

(1) 经仿真验证，在空中飞行目标和弹道目标密集场景下，本文算法的全局正确关联率和平均正确关联率均比TSA算法有显著提高，漏关联率显著下降，表明了本文算法对于航迹预测误差具有更强的适应性，较经典的TSA算法具有更强的关联性能。本文算法在多目标雷达数据处理中对于提高航迹寿命，改善跟踪效果具有较大的参考价值。

(2) 本文设置的关联样本长度分别为5和10，经仿真比较可知，空中飞行目标仿真环境和弹道目标仿真环境下，关联样本长度对关联性能产生的影响不同。具体应用环境下，关联样本长度与关联性能的关系还会受到目标运动模型、随机噪声的大小、中断时间长短等因素的影响。

(3) 文中粗关联部分使用的速度门限检测方法在空中飞行目标和弹道目标场景下具有快速滤除非关联航迹对的能力，在海面慢速目标场景下，由于速度估计不准确，可考虑转弯率门限检测或加速度门限检测。在其它应用背景下，也可考虑基于其它目标属性的简单有效的粗关联方法。

(4) 本文算法适用于目标运动模型较稳定的情况，如文中举例的弹道目标运动场景。当目标发生大机动时，基于航迹预测的中断航迹配对关联算法由于无法准确描述中断区间的机动运动，关联效果恶化严重。关于机动目标的中断航迹关联是下一步研究的重点。

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