An improved track segment association algorithm using MM-GNN method

Shengsen Pan, Qinglong Bao, Weibing Hou, Zengping Che
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引用次数: 2

Abstract

Track breakages are common due to target maneuver, Doppler radar blind spot, long sampling interval and low detection probabilities. Note that the existed classic association algorithms have low correlation accuracy and poor practicability in dense environment or in track crossing situation. In this paper, we aim to present a new track segment association (TSA) technique to improve track continuity. An improved Multiple Model (MM) method with Global Nearest Neighbor (GNN) algorithm for track segment association is put forward, that is MM-GNN method. Simulation experiments show that proposed algorithm enhances the track maintenance performance and the track life of tracking result while ensuring the accuracy of tracking. Verified by real measured data, it confirms that the algorithm is suitable for multiple target tracking in practical radar system with the improvement of track continuity.
基于MM-GNN方法的航迹段关联改进算法
由于目标机动、多普勒雷达盲点、采样间隔长和探测概率低,航迹破坏是常见的问题。需要注意的是,现有的经典关联算法在密集环境或轨道交叉情况下的关联精度较低,实用性较差。本文提出了一种新的航迹分段关联(TSA)技术来提高航迹的连续性。提出了一种基于全局最近邻(GNN)算法的航迹段关联改进多模型(MM)方法,即MM-GNN方法。仿真实验表明,该算法在保证跟踪精度的同时,提高了跟踪结果的航迹维护性能和航迹寿命。通过实测数据的验证,证明该算法适用于实际雷达系统中的多目标跟踪,提高了跟踪的连续性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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