APPLYING IMAGE PROCESSING AND NEURAL NETWORK TECHNIQUES TO DATA ASSOCIATION ALGORITHM

被引：0

作者：

Chung, Yi-Nung ^{[1
]}

机构：

[1] Natl Changhua Univ Educ, Dept Elect Engn, Changhua 500, Taiwan

来源：

INTERNATIONAL JOURNAL OF INNOVATIVE COMPUTING INFORMATION AND CONTROL | 2011年 / 7卷 / 5A期

关键词：

Multiple-target tracking; Data association; Competitive Hopfield neural network; TRACKING; OPTIMIZATION;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Multiple-target tracking (MTT) is a prerequisite step for radar surveillance systems. Data association is the key technique used in radar MTT systems. This paper presents a new approach for data association that uses both quantity data and image information. In order to combine these two attributes, a fusion algorithm based on the competitive Hopfield neural network (CHNN) is developed to match radar measurements with existing target tracks. When target maneuvering problems are detected, an adaptive maneuvering estimator is applied. Computer simulation results indicate that the proposed approach is suitable for multiple-target tracking problems and has good performance.

引用

页码：2427 / 2439

页数：13

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