Direction-of-arrival estimation of an amplitude-distorted wavefront

In a number of array signal processing applications, such as underwater source localization, the propagation medium is not homogeneous, which causes a distortion of the wavefront received by the array. There has been some interest in the direction-of-arrival (DOA) estimation of such distorted wavefronts. Most works on this problem considered the so-called multiplicative noise scenario based on the rather strong assumption that the distortion is random and can be parameterized by a small number of parameters. To gain robustness against mismodeling, we assume a scenario in which the wavefront amplitude is distorted in a completely arbitrary way. Our main contribution consists of showing how to eliminate all nuisance (distortion) parameters from the likelihood function corresponding to such a scenario and obtain a robust maximum likelihood DOA estimate by means of a simple one-dimensional (1-D) search. Despite its simplicity, it is shown that the estimator has a performance close to the Cramer-Rao Bound (CRB), for which we derive a closed-form expression. Moreover, its accuracy is comparable with that of estimators that require knowledge of the form of amplitude distortions.

[1]  S. Kay Fundamentals of statistical signal processing: estimation theory , 1993 .

[2]  Christoph F. Mecklenbräuker,et al.  Matrix fitting approach to direction of arrival estimation with imperfect spatial coherence of wavefronts , 1997, IEEE Trans. Signal Process..

[3]  Alle-Jan van der Veen,et al.  Direction-of-arrival estimation for constant modulus signals , 1999, IEEE Trans. Signal Process..

[4]  Petre Stoica,et al.  Introduction to spectral analysis , 1997 .

[5]  Vladimir Katkovnik A new concept of adaptive beamforming for moving sources and impulse noise environment , 2000, Signal Process..

[6]  Björn E. Ottersten,et al.  Covariance Matching Estimation Techniques for Array Signal Processing Applications , 1998, Digit. Signal Process..

[7]  Petre Stoica,et al.  MUSIC, maximum likelihood, and Cramer-Rao bound , 1989, IEEE Transactions on Acoustics, Speech, and Signal Processing.

[8]  Mats Bengtsson Antenna array signal processing for high rank data models , 2000 .

[9]  Petre Stoica,et al.  Approximate maximum likelihood estimators for array processing in multiplicative noise environments , 2000, IEEE Trans. Signal Process..

[10]  O. Besson,et al.  Maximum likelihood DOA estimation for constant-modulus signal , 2000 .

[11]  Petre Stoica,et al.  Decoupled estimation of DOA and angular spread for spatially distributed sources , 1999, Conference Record of the Thirty-Third Asilomar Conference on Signals, Systems, and Computers (Cat. No.CH37020).

[12]  Alex B. Gershman,et al.  Experimental results of localization of moving underwater signal by adaptive beamforming , 1995, IEEE Trans. Signal Process..

[13]  Raviv Raich,et al.  Bearing estimation for a distributed source via the conventional beamformer , 1998, Ninth IEEE Signal Processing Workshop on Statistical Signal and Array Processing (Cat. No.98TH8381).

[14]  Björn E. Ottersten,et al.  Estimation of nominal direction of arrival and angular spread using an array of sensors , 1996, Signal Process..

[15]  Petre Stoica,et al.  MUSIC, maximum likelihood, and Cramer-Rao bound: further results and comparisons , 1990, IEEE Trans. Acoust. Speech Signal Process..

[16]  Petre Stoica,et al.  Nonlinear Least-Squares Approach to Frequency Estimation and Detection for Sinusoidal Signals with Arbitrary Envelope , 1999, Digit. Signal Process..

[17]  Petre Stoica,et al.  Performance study of conditional and unconditional direction-of-arrival estimation , 1990, IEEE Trans. Acoust. Speech Signal Process..