A Fast Multiple-Source Detection and Localization Array Signal Processing Algorithm Using the Spatial Filtering and ML Approach

• Published in: IEEE transactions on signal processing Vol. 55; no. 5; pp. 1815 - 1827
• Main Authors: Tadaion, A.A., Derakhtian, M., Gazor, S., Aref, M.R.
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 01.05.2007; Institute of Electrical and Electronics Engineers; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Additive white noise; Algorithms; Applied sciences; Array signal processing; AWGN; Computational complexity; detection; Detection, estimation, filtering, equalization, prediction; Detectors; Direction of arrival estimation; Exact sciences and technology; Filtering; Information, signal and communications theory; Intervals; invariant test; Invariants; Localization; maximum-likelihood detection; Miscellaneous; Noise; Nonlinear filters; orthogonal transformation; Position (location); Signal and communications theory; Signal processing; Signal processing algorithms; Signal to noise ratio; Signal, noise; source detection; source localization; source separation; Studies; Telecommunications and information theory; Testing; Upper bound; Upper bounds; Performance evaluation; Spatial frequency filtering; Direction-of-arrival estimation; Source separation; AWGN; Array signal processing; Fast Fourier transformation; Signal estimation; invariant test; Hypothesis test; Linear antenna arrays; Target detection; Signal detection; detection; source detection; Algorithm; Computational complexity; Upper bound; Algorithm performance; Simulation; Source localization; Narrow band signal; Likelihood ratio test; Signal processing; orthogonal transformation; Maximum likelihood; maximum-likelihood detection; Signal to noise ratio; direction-of- arrival estimation
• ISSN: 1053-587X; 1941-0476
• DOI: 10.1109/TSP.2006.890819

We propose a computationally efficient algorithm for detection of multiple signals that also gives a rough estimation of their direction of arrivals (DOAs). The narrowband received signals from a uniform linear array are first filtered by a set of orthogonal filters, e.g., by a fast Fourier transformation, in order to separate the sources into multiple spatial intervals. This transformation converts the complicated multihypothesis problem of source detection and localization into multiple binary hypothesis testing problems. For an additive white Gaussian noise (AWGN) environment, the maximum-likelihood (ML) solution of these interrelated tests requires substantially less computational complexity than that of the multihypothesis problem. For each spatial interval, a binary test detects the presence of a single source and thus gives a rough localization. We employ generalized-likelihood ratio (GLR) tests as the detection criterion, assuming that the number of sources, their power, and the noise variance are all unknown. We also show that the optimal uniformly most powerful invariant (UMPI) detector does not exist. However, we derive a UMPI detector that uses some extra information and as a result provides an upper bound performance for evaluation of any invariant detector. Simulations illustrate that the proposed noniterative GLR test performs efficiently for various number of observed data snapshots and signal-to-noise-ratios (SNR)s, and its performance is comparable to the upper bound performance. We used the proposed algorithm for the initialization of the iterative implementation of the standard ML localization. This combination is a high-resolution localization algorithm with a low computational complexity