Sparse Bayesian Learning Based Direction-of-Arrival Estimation under Spatially Colored Noise Using Acoustic Hydrophone Arrays

Abstract

Direction-of-arrival (DOA) estimation in a spatially isotropic white noise background has been widely researched for decades. However, in practice, such as underwater acoustic ambient noise in shallow water, the ambient noise can be spatially colored, which may severely degrade the performance of DOA estimation. To solve this problem, this paper proposes a DOA estimation method based on sparse Bayesian learning with the modified noise model using acoustic vector hydrophone arrays. Firstly, an applicable linear noise model is established by using the prolate spheroidal wave functions (PSWFs) to characterize spatially colored noise and exploiting the excellent performance of the PSWFs in extrapolating band-limited signals to the space domain. Then, using the proposed noise model, an iterative method for sparse spectrum reconstruction is developed under a sparse Bayesian learning (SBL) framework to fit the actual noise field received by the acoustic vector hydrophone array. Finally, a DOA estimation algorithm under the modified noise model is also presented, which has a superior performance under spatially colored noise. Numerical results validate the effectiveness of the proposed method.

An Automatic Search and Energy-Saving Continuous Tracking Algorithm for Underwater Targets Based on Prediction and Neural Network

“…This paper sets up two experimental scenarios. The differences between the two scenarios are derived from the parameters in (8). The most important purpose of LA-AIC experiment is to verify its applicability to clock asynchrony.…”

“…This paper sets up two experimental scenarios. The differences between the two scenarios are derived from the parameters in (8). First, if the clock pulse phase difference µ in the clock model of the target object is ignored, the clock model of the target object is converted to T target = t + ε.…”

“…The first and most well-known scheme is the passive detection and localization scheme. As we all know, the first proposed arrival direction scheme (DoA) [8], signal transmission and reception strength scheme (RSSI) [9], arrival time scheme (ToA) [10], and Doppler frequency shift scheme [11] belong to this category. These localization schemes have high location accuracy, but they require high computing power and energy of equipment, and the deployment cost brings economic pressure.…”

Direction-of-Arrival Estimation Method Based on Neural Network with Temporal Structure for Underwater Acoustic Vector Sensor Array

“…Compared with the traditional DOA estimation method based on acoustic pressure sensor array, the DOA estimation method based on AVS array is more diverse and has better performance. It has become one of the research hotspots in the field of array signal processing and marine positioning and recognition [ 2 , 3 , 4 ].…”

Symmetric Connectivity of Underwater Acoustic Sensor Networks Based on Multi-Modal Directional Transducer

“…Underwater acoustic sensor networks are extensively used in marine exploration, disaster warning, sea area surveillance, etc. With the progress of sensor technology and the arousal of marine rights and interests of numerous countries, UASN technology has attracted more and more attention from researchers [ 1 , 2 , 3 , 4 ]. However, due to the disadvantages of high propagation delay, low bandwidth and low communication rate brought by the high complexity of underwater acoustic channel [ 5 ], transmitting/receiving with a traditional half duplex omni-directional transducer will cause serious packet collision.…”