王鑫
陕西科技大学电子信息与人工智能学院 西安 710021
基金项目:国家自然科学基金(61801281)，陕西省教育厅专项科研计划(17JK0084)
详细信息
作者简介:卢锦：女，1984年生，讲师，研究方向为雷达目标检测与状态估计
王鑫：女，1979年生，副教授，研究方向为通信系统及信息安全
通讯作者:卢锦 lj491216@163.com
中图分类号:TN911.72; TN951计量
文章访问数:177
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被引次数:0
出版历程
收稿日期:2021-03-22
修回日期:2021-08-18
网络出版日期:2021-09-16
刊出日期:2021-10-18
Cost-reference Particle Filter Bank Based Track-before-detecting Algorithm
Jin LU,,Xin WANG
College of Electronic Information and Artificial Intelligence, Shaanxi University of Science & Technology, Xi’an 710021, China
Funds:The National Natural Science Foundation of China (61801281), The Scientific Research Project of Shaanxi Provincial Department of Education (17JK0084)
摘要
摘要:基于粒子滤波的检测前跟踪方法是检测和估计非线性调频信号的有效方法之一。但此类方法运算量大，难以并行执行。此外，由于粒子滤波算法收敛较慢，基于粒子滤波的检测前跟踪方法的检测和状态估计能力有待提高。针对上述问题，该文首先提出一种代价参考粒子滤波器组。该滤波器组收敛快速，具有完全的并行结构，可快速准确地估计非线性调频信号的瞬时频率。其次，提出基于代价参考滤波器组的检测前跟踪算法，可在给定虚警率下，在各个时刻检测目标和估计目标状态。两类非线性调频信号检测和估计的仿真结果表明，基于代价参考粒子滤波器组的检测前跟踪算法的检测性能、估计性能和运行速率均优于类似的方法，如基于粒子滤波的检测前跟踪方法，基于Rutten粒子滤波的检测前跟踪方法等。
关键词:非线性调频信号/
检测前跟踪/
瞬时频率估计/
代价参考粒子滤波/
滤波器组
Abstract:Detection and tracking of low signal-to-noise ratio nonlinear frequency modulated signal can be effectively solved by Track-Before-Detecting (TBD) algorithms based on particle filters. However, the algorithms are high in computational complexity and hard to be implemented in parallel. Furthermore, because of the comparatively long convergence processing, the detection and state estimation capabilities of the particle filters based methods are limited. In this paper, a cost-reference particle filter bank is proposed, which does not depend on the distribution of the system and has an entirely parallel structure. Then a detection method based on the cost-reference particle filter bank is proposed. Simulation results of two nonlinear frequency modulated signals detection and estimation illustrate that the propose method has better performance in detection, estimation, and running speed than similar methods, such as particle filter based track-before-detecting algorithm, Rutten particle filter based TBD algorithm.
Key words:Nonlinear frequency modulated signal/
Track-Before-Detecting (TBD)/
Instantaneous frequency curve estimation/
Cost-reference particle filter/
Filter bank
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