基于假设传播速度的TDOA联合源定位和传播速度估计

IF 3 3区 工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC Digital Signal Processing Pub Date : 2025-04-01 Epub Date: 2025-01-21 DOI:10.1016/j.dsp.2024.104934
Shaohong Xu, Minghai Yang, Chengyu Li, Beichuan Tang, Yanbing Yang, Liangyin Chen, Yimao Sun
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引用次数: 0

摘要

水声定位(UWAL)在许多水下应用中提出了重大挑战。声音的传播速度通常被视为一个未知参数,它在不同的水下环境中是不同的,因此需要对声源位置和声音的传播速度进行联合估计。在本研究中,我们将声音传播速度建模为假设常数和残差项的和,从而形成了一个新的优化问题来确定声源位置和残差速度。为了解决秩不足问题,我们采用零空间投影,通过加权最小二乘(WLS)对源位置进行粗略估计。为了提高精度,两种策略导致两种方法:第一种利用摄动分析来估计减少误差的修正,而第二种使用最大似然目标函数和泰勒展开来改进粗略估计。性能分析表明,两种方法都能在低噪声条件下实现cram - rao下界(CRLB)。仿真结果验证了这些分析结果,并突出了所提方法的计算效率。
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Joint source localization and propagation speed estimation using TDOA with hypothesized propagation speed
Underwater acoustic localization (UWAL) presents a significant challenge in numerous underwater applications. The speed of sound propagation, often treated as an unknown parameter, varies across different underwater environments, necessitating the joint estimation of both the source position and the sound propagation speed. In this study, we model the sound propagation speed as the sum of a hypothesized constant and a residual term, thereby formulating a new optimization problem to determine the source position and the residual speed. To address the rank-deficient issue, we employ a nullspace projection, enabling an coarse estimate of the source position through weighted least squares (WLS). To enhance accuracy, two strategies lead to two methods: the first utilizes perturbation analysis to estimate a correction that reduces error, while the second refines the coarse estimate using the maximum likelihood objective function and Taylor expansion. Performance analysis demonstrates that both proposed methods can achieve the Cramér–Rao lower bound (CRLB) in low-noise conditions. Simulations validate these analytical results and highlight the computational efficiency of the proposed methods.
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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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