Direction of Arrival Estimation Based on DDOA and Self-Organizing Map

Xiuhui Tan,Hongping Hu,Rong Cheng,Yanping Bai

doi:10.1155/2015/231307

Xiuhui Tan, Hongping Hu + Show 2 more

Open Access

https://doi.org/10.1155/2015/231307

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Abstract

An effective two-level self-organizing map (SOM) neural network for direction of arrival (DOA) of sound signals estimation is proposed. The approach is based on the distance difference of arrival (DDOA) and a uniform linear sensor array in a 2D plane; it performs a nonlinear mapping between the DDOA vectors and angles of arrival (AOA). We found that the topological order of DDOA vectors and AOAs of same signals is uniform; thus, the topological order preserving of SOM network makes it valid to estimate AOA through DDOA. From the results of simulations and lake experiments, it is shown that the network has the advantage of accuracy and robustness, can be trained in advance, and is easy to implement.

Highlights

Target detection and localization are important problems in sonar, radar, radio emitter tracking, and mobile communications, and estimation of sound signal source direction is one of the basic issues
Take 50 × 50 uniform distribution points in region [0, 20] × [0, 20] as the location of 50 × 50 signals, and the distance difference of arrival (DDOA) vectors d of them are taken as sample vectors, where the angle of arrival (AOA) are assumed to be from 0∘ to 90∘
A scheme based on DDOA vectors and two-level self-organizing map (SOM) is set up for estimation of angles of arrival

Summary

Introduction

Target detection and localization are important problems in sonar, radar, radio emitter tracking, and mobile communications, and estimation of sound signal source direction is one of the basic issues. In the past few decades, varieties of approaches have been proposed for solving the direction of arrival (DOA) of signal source, such as the multiple signal classification (MUSIC) algorithm [1], the estimation of signal parameters via rotational invariance technique (ESPRIT) [2] These algorithms are known for the resolution of high accuracy and well performance in case of low signal-to-noise ratios. Xun et al [11] proposed a self-organizing map scheme for mobile location estimation, and the network is set up between the strengths of signals and user’s location All these neural networks are efficient, blind, and easy to implement in practice. We proposed a two-level SOM network to approximate the relationship between the distance difference of arrival (DDOA) and DOA when there is only single signal waveform, and a 2D DOA problem with a uniform linear sensor array y.

Background

DOA Estimation with SOM

Simulation Results

Conclusion and Discussion

Conflict of Interests