Tracking Wide-Band Targets Having Significant Doppler Shift

A method is derived for passively locating wide-band targets (typically acoustic targets) which may be moving at speeds sufficient to produce significant Doppler shift. The method involves a relatively straightforward adaptation of standard beam forming techniques. It is shown that conventional beam forming techniques have less discrimination in the direction of motion of the sources, whereas the proposed technique exhibits no such degradation. The derivative and Hessian of the likelihood function are derived. These may be used for locating the maximum likelihood solution or for deriving a Gaussian approximation to the likelihood function for particle filtering applications. The expressions are applicable for subsonic and supersonic sources. The computation required for implementing a system based on the model is at present prohibitive

[1]  Patrick Pérez,et al.  Sequential Monte Carlo methods for multiple target tracking and data fusion , 2002, IEEE Trans. Signal Process..

[2]  W. Reid Acoustic Tracking of Supersonic Objects , 1970 .

[3]  Henry Leung,et al.  Tracking the direction-of-arrival of multiple moving targets by passive arrays: algorithm , 1999, IEEE Trans. Signal Process..

[4]  Robert Been,et al.  Target Doppler estimation using wideband frequency modulated signals , 2000, IEEE Trans. Signal Process..

[5]  Stuart Perry,et al.  Aircraft flight parameter estimation using acoustical Lloyd's mirror effect , 2002 .

[6]  J. Todd,et al.  Evaluation of the exponential integral for large complex arguments , 1954 .

[7]  Jacob Benesty,et al.  Adaptive eigenvalue decomposition algorithm for real time acoustic source localization system , 1999, 1999 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings. ICASSP99 (Cat. No.99CH36258).

[8]  Michael S. Brandstein,et al.  A practical methodology for speech source localization with microphone arrays , 1997, Comput. Speech Lang..

[9]  Darren B. Ward,et al.  Particle filtering algorithms for tracking an acoustic source in a reverberant environment , 2003, IEEE Trans. Speech Audio Process..

[10]  G. Kitagawa Monte Carlo Filter and Smoother for Non-Gaussian Nonlinear State Space Models , 1996 .

[11]  William Fitzgerald,et al.  A Bayesian approach to tracking multiple targets using sensor arrays and particle filters , 2002, IEEE Trans. Signal Process..

[12]  Gene H. Golub,et al.  The differentiation of pseudo-inverses and non-linear least squares problems whose variables separate , 1972, Milestones in Matrix Computation.

[13]  D. E. Dudgeon Wideband Array Processing For Acoustic Detection and Tracking Of Aircraft/sup */ , 1985, Nineteeth Asilomar Conference on Circuits, Systems and Computers, 1985..

[14]  F. A. Seiler,et al.  Numerical Recipes in C: The Art of Scientific Computing , 1989 .

[15]  L. Kaufman A variable projection method for solving separable nonlinear least squares problems , 1974 .

[16]  J. Krolik Matched‐field minimum variance beamforming in a random ocean channel , 1992 .

[17]  Hong Wang,et al.  Coherent signal-subspace processing for the detection and estimation of angles of arrival of multiple wide-band sources , 1985, IEEE Trans. Acoust. Speech Signal Process..

[18]  K. W. Lo,et al.  Aircraft flight parameter estimation using acoustic multipath delays , 2003 .