Parameter tracking method for polarization-sensitive arrays based on the generalized labeled multi-Bernoulli filter

IF 2.9 3区 工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC
Jingchao You , Zhikun Chen , Xue Liu , Zhibin Chen
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引用次数: 0

Abstract

Current methods for the joint estimation of direction of arrival (DOA) and polarization parameters are generally optimized for stationary scenarios. In dynamic environments where signal sources move rapidly, these methods frequently encounter estimation errors. To overcome this challenge, we introduce a novel approach for the joint tracking of DOA and polarization parameters in dynamic scenarios. This method utilizes a polarization-sensitive array to capture incoming wave signals and employs the generalized labeled multi-Bernoulli (GLMB) framework to update the DOA and polarization parameters. Initially, an enhanced MUSIC function is deployed as a pseudo-likelihood function to improve particle distribution in areas of high-likelihood. Subsequently, a novel measurement separation (NMSS) strategy is developed to create a one-to-one correspondence between measurements and signal sources. The implementation of this algorithm through sequential Monte Carlo (SMC) techniques aims to approximate the posterior density accurately. Simulation results indicate that our proposed method surpasses existing algorithms, particularly in environments characterized by low signal-to-noise ratios (SNR) and limited snapshots.
基于广义标记多重伯努利滤波器的偏振敏感阵列参数跟踪方法
目前联合估计到达方向和极化参数的方法一般都是针对静止情况进行优化的。在信号源快速移动的动态环境中,这些方法经常会遇到估计误差。为了克服这一挑战,我们提出了一种动态情况下DOA和极化参数联合跟踪的新方法。该方法利用极化敏感阵列捕获入射波信号,并采用广义标记多伯努利(GLMB)框架更新DOA和极化参数。首先,将增强的MUSIC函数作为伪似然函数部署,以改善高似然区域的粒子分布。随后,开发了一种新的测量分离(NMSS)策略,在测量和信号源之间创建一对一的对应关系。该算法通过序贯蒙特卡罗(SMC)技术实现,目的是精确地逼近后验密度。仿真结果表明,我们提出的方法优于现有算法,特别是在低信噪比(SNR)和有限快照的环境中。
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来源期刊
Digital Signal Processing
Digital Signal Processing 工程技术-工程:电子与电气
CiteScore
5.30
自引率
17.20%
发文量
435
审稿时长
66 days
期刊介绍: Digital Signal Processing: A Review Journal is one of the oldest and most established journals in the field of signal processing yet it aims to be the most innovative. The Journal invites top quality research articles at the frontiers of research in all aspects of signal processing. Our objective is to provide a platform for the publication of ground-breaking research in signal processing with both academic and industrial appeal. The journal has a special emphasis on statistical signal processing methodology such as Bayesian signal processing, and encourages articles on emerging applications of signal processing such as: • big data• machine learning• internet of things• information security• systems biology and computational biology,• financial time series analysis,• autonomous vehicles,• quantum computing,• neuromorphic engineering,• human-computer interaction and intelligent user interfaces,• environmental signal processing,• geophysical signal processing including seismic signal processing,• chemioinformatics and bioinformatics,• audio, visual and performance arts,• disaster management and prevention,• renewable energy,
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