Maximum-likelihood source localization and unknown sensor location estimation for wideband signals in the near-field

• Published in: IEEE transactions on signal processing Vol. 50; no. 8; pp. 1843 - 1854
• Main Authors: Chen, J.C., Hudson, R.E., Kung Yao
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 01.08.2002; Institute of Electrical and Electronics Engineers; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Acoustic arrays; Acoustic sensors; Applied sciences; Cramer-Rao bounds; Detection, estimation, filtering, equalization, prediction; Estimates; Estimators; Exact sciences and technology; Information, signal and communications theory; Least squares method; Maximum likelihood estimation; Multiple signal classification; Narrowband; Near fields; Position (location); Position measurement; Radar tracking; Sensor arrays; Sensors; Signal and communications theory; Signal processing algorithms; Signal, noise; Studies; Telecommunications and information theory; Wideband; Performance evaluation; Projection method; Wide band signal; Array signal processing; Simulation; Signal estimation; Iterative method; Maximum likelihood; Localization; Algorithm; Beam forming; Cramer Rao inequality
• ISSN: 1053-587X; 1941-0476
• DOI: 10.1109/TSP.2002.800420

In this paper, we derive the maximum-likelihood (ML) location estimator for wideband sources in the near field of the sensor array. The ML estimator is optimized in a single step, as opposed to other estimators that are optimized separately in relative time-delay and source location estimations. For the multisource case, we propose and demonstrate an efficient alternating projection procedure based on sequential iterative search on single-source parameters. The proposed algorithm is shown to yield superior performance over other suboptimal techniques, including the wideband MUSIC and the two-step least-squares methods, and is efficient with respect to the derived Cramer-Rao bound (CRB). From the CRB analysis, we find that better source location estimates can be obtained for high-frequency signals than low-frequency signals. In addition, large range estimation error results when the source signal is unknown, but such unknown parameter does not have much impact on angle estimation. In some applications, the locations of some sensors may be unknown and must be estimated. The proposed method is extended to estimate the range from a source to an unknown sensor location. After a number of source-location frames, the location of the uncalibrated sensor can be determined based on a least-squares unknown sensor location estimator.