Experimental results of localization of moving underwater signal by adaptive beamforming

The problem of weak moving signal localization and tracking in the presence of single motionless strong interference is investigated using real data of an underwater experiment in the Baltic sea (Sept. 1990) with a horizontal receiving array of 64 hydrophones and with two independent powerful narrowband sources imitating the signal and interference. Three simple adaptive beamforming methods were employed for the experimental data processing. The first one is based on the well-known projection approach to adaptive beamforming, the second method uses the adaptive canceler approach (also termed the dipole pattern method), and the third method combines these approaches. The signal-to-interference power ratio (SIR) threshold of the signal localization and tracking is evaluated by a special technique, which allows examination of the considered algorithms with change of the SIR in consecutive order. The results of the data processing show the high possibilities of signal localization in the presence of strong interference. The combined method performs better than the methods considered and enables localization of the signal source up to an SIR/spl sime/-25 dB. >

[1]  J. Capon.  High-resolution frequency-wavenumber spectrum analysis , 1969 .

[2]  R. O. Schmidt,et al.  Multiple emitter location and signal Parameter estimation , 1986 .

[3]  Randy L. Haupt,et al.  Introduction to Adaptive Arrays , 1980 .

[4]  W. Gabriel.  Spectral analysis and adaptive array superresolution techniques , 1980, Proceedings of the IEEE.

[5]  Leif Bjørnø.  Underwater Acoustics and Signal Processing , 1981 .

[6]  Eric Hung,et al.  A Fast Beamforming Algorithm for Large Arrays , 1983, IEEE Transactions on Aerospace and Electronic Systems.

[7]  R. Klemm.  Problems of Spatial Signal Processing in Shallow Water , 1983 .

[8]  R. Kumaresan,et al.  Estimating the Angles of Arrival of Multiple Plane Waves , 1983, IEEE Transactions on Aerospace and Electronic Systems.

[9]  Thomas Kailath,et al.  On spatial smoothing for direction-of-arrival estimation of coherent signals , 1985, IEEE Trans. Acoust. Speech Signal Process..

[10]  Jar-Ferr Yang,et al.  Adaptive eigensubspace algorithms for direction or frequency estimation and tracking , 1988, IEEE Trans. Acoust. Speech Signal Process..

[11]  U. Nickel.  Algebraic formulation of Kumaresan-Tufts superresolution method, showing relation to ME and MUSIC methods , 1988 .

[12]  Y. Bar-Ness,et al.  Adaptive antenna arrays using eigenvector methods , 1988, 1988 IEEE AP-S. International Symposium, Antennas and Propagation.

[13]  Benjamin Friedlander,et al.  Performance analysis of a null-steering algorithm based on direction-of-arrival estimation , 1989, IEEE Trans. Acoust. Speech Signal Process..

[14]  E. Kamen,et al.  Multiple target angle tracking using sensor array outputs , 1990 .

[15]  A. I. Malekhanov,et al.  Optimal signal reception in multimode waveguides , 1990 .

[16]  Alexander M. Haimovich,et al.  An eigenanalysis interference canceler , 1991, IEEE Trans. Signal Process..

[17]  K. W. Lo.  Tracking multiple targets in 2-D angular space using sensor array , 1991 .

[18]  A. Gershman,et al.  Synthesis of the weight distribution of an adaptive antenna array with wide dips in the directional pattern , 1991 .

[19]  A. Lee Swindlehurst,et al.  A Performance Analysis ofSubspace-Based Methods in thePresence of Model Errors { Part I : The MUSIC AlgorithmA , 1992 .

[20]  L. Zhang,et al.  Multiple target angle tracking using sensor array outputs , 1993 .

[21]  Alex B. Gershman,et al.  Adaptive detection of moving signal using shallow sea hydroacoustic data , 1994, Proceedings of ICASSP '94. IEEE International Conference on Acoustics, Speech and Signal Processing.