Work

Source Localization and Sensing: A Nonparametric Iterative Adaptive Approach Based on Weighted Least Squares

Year: 2010

Type: article

Abstract: Array processing is widely used in sensing applications for estimating the locations and waveforms of the sources in a given field. In the absence of a large number of snapshots, which is the case in numerous practical applications, such as underwater array processing, it becomes challenging to estimate the source parameters accurately. This paper presents a nonparametric and hyperparameter, free-weighted, least squares-based iterative adaptive approach for amplitude and phase estimation (IAA-APES) in array processing. IAA-APES can work well with few snapshots (even one), uncorrelated, partially correlated, and coherent sources, and arbitrary array geometries. IAA-APES is extended to give sparse results via a model-order selection tool, the Bayesian information criterion (BIC). Moreover, it is shown that further improvements in resolution and accuracy can be achieved by applying the parametric relaxation-based cyclic approach (RELAX) to refine the IAA-APES&BIC estimates if desired. IAA-APES can also be applied to active sensing applications, including single-input single-output (SISO) radar/sonar range-Doppler imaging and multi-input single-output (MISO) channel estimation for communications. Simulation results are presented to evaluate the performance of IAA-APES for all of these applications, and IAA-APES is shown to outperform a number of existing approaches.

Source: IEEE Transactions on Aerospace and Electronic Systems

DOI: 10.1109/taes.2010.5417172

Authors: Tarik Yardibi, Jian Li, Petre Stoica, Ming Xue, Arthur B. Baggeroer

Institutions: University of Florida, Uppsala University, Massachusetts Institute of Technology

Language: English

Topic: Direction-of-Arrival Estimation Techniques

Subfield: Signal Processing

Field: Computer Science

Domain: Physical Sciences

Open Access status: green