Cramér-Rao Bound Analysis on Multiple Scattering in Multistatic Point-Scatterer Estimation

The resolution improvements of time reversal methods through exploiting nonhomogeneous media have attracted much interest recently with broad applications, including underwater acoustics, radar, detection of defects in metals, communications, and destruction of kidney stones. In this paper, we analyze the effect of inhomogeneity generated by multiple scattering among point scatterers under a multistatic sensing setup. We derive the Crameacuter-Rao bounds (CRBs) on parameters of the scatterers and compare the CRBs for multiple scattering using the Foldy-Lax model with the reference case without multiple scattering using the Born approximation. We find that multiple scattering could significantly improve the estimation performance of the system and higher order scattering components actually contain much richer information about the scatterers. For the case where multiple scattering is not possible, e.g., where only a single target scatterer exists in the illuminated scenario, we propose the use of artificial scatterers, which could effectively improve the estimation performance of the target despite a decrease in the degrees of freedom of the estimation problem due to the introduced unknown parameters of the artificial scatterers. Numerical examples demonstrate the advantages of the artificial scatterers

[1]  Gang Shi,et al.  Macrocell multiple-input multiple-output system analysis , 2006, IEEE Transactions on Wireless Communications.

[2]  M. Viberg,et al.  Two decades of array signal processing research: the parametric approach , 1996, IEEE Signal Process. Mag..

[3]  Gang Shi,et al.  Maximum likelihood estimation of point scatterers for computational time-reversal imaging , 2005, Commun. Inf. Syst..

[4]  H. Vincent Poor,et al.  An introduction to signal detection and estimation (2nd ed.) , 1994 .

[5]  Mathias Fink,et al.  Acoustic time-reversal mirrors , 2001 .

[6]  Alfred O. Hero,et al.  Analysis of a Multistatic Adaptive Target Illumination and Detection Approach (MATILDA) to Time Reversal Imaging , 2004 .

[7]  R. Newton Scattering theory of waves and particles , 1966 .

[8]  M Fink,et al.  Random multiple scattering of ultrasound. II. Is time reversal a self-averaging process? , 2001, Physical review. E, Statistical, nonlinear, and soft matter physics.

[9]  P. Maher,et al.  Handbook of Matrices , 1999, The Mathematical Gazette.

[10]  M. Fink,et al.  Acoustic time-reversal through high-order multiple scattering , 1995, 1995 IEEE Ultrasonics Symposium. Proceedings. An International Symposium.

[11]  M. Lax MULTIPLE SCATTERING OF WAVES. II. THE EFFECTIVE FIELD IN DENSE SYSTEMS , 1952 .

[12]  Joe Brewer,et al.  Kronecker products and matrix calculus in system theory , 1978 .

[13]  Mathias Fink,et al.  Time reversal versus phase conjugation in a multiple scattering environment. , 2002, Ultrasonics.

[14]  J. Magnus,et al.  Matrix Differential Calculus with Applications in Statistics and Econometrics (Revised Edition) , 1999 .

[15]  L. Schwartz Cours d'analyse , 1963 .

[16]  B.D. Van Veen,et al.  Beamforming: a versatile approach to spatial filtering , 1988, IEEE ASSP Magazine.

[17]  Mathias Fink,et al.  Time-Reversed Acoustics , 1999 .

[18]  G. Stewart,et al.  Matrix Perturbation Theory , 1990 .

[19]  Hanoch Lev-Ari,et al.  Efficient solution of linear matrix equations with application to multistatic antenna array processing , 2005, Commun. Inf. Syst..

[20]  A. Devaney,et al.  Time-reversal imaging with multiple signal classification considering multiple scattering between the targets , 2004 .

[21]  Mickael Tanter,et al.  Time-reversed acoustics , 2000 .

[22]  Darrell A. Turkington Matrix Calculus and Zero-One Matrices , 2005 .

[23]  M. Lax Multiple Scattering of Waves , 1951 .

[24]  P. Sheng,et al.  Introduction to Wave Scattering, Localization and Mesoscopic Phenomena. Second edition , 1995 .

[25]  C. Tai,et al.  Dyadic green functions in electromagnetic theory , 1994 .

[26]  Emil Wolf,et al.  Principles of Optics: Contents , 1999 .

[27]  Kaare Brandt Petersen,et al.  The Matrix Cookbook , 2006 .

[28]  Simon Haykin,et al.  Detection and estimation: Applications to radar , 1976 .

[29]  B. A. D. H. Brandwood A complex gradient operator and its applica-tion in adaptive array theory , 1983 .

[30]  M. Fink,et al.  Taking advantage of multiple scattering to communicate with time-reversal antennas. , 2003, Physical review letters.

[31]  G. Papanicolaou,et al.  Theory and applications of time reversal and interferometric imaging , 2003 .

[32]  M Fink,et al.  Random multiple scattering of ultrasound. I. Coherent and ballistic waves. , 2001, Physical review. E, Statistical, nonlinear, and soft matter physics.

[33]  G. Papanicolaou,et al.  Imaging and time reversal in random media , 2001 .

[34]  A.J. Devaney Time reversal imaging of obscured targets from multistatic data , 2005, IEEE Transactions on Antennas and Propagation.

[35]  Anthony J. Devaney,et al.  Time-reversal-based imaging and inverse scattering of multiply scattering point targets , 2005 .

[36]  L. Foldy,et al.  The Multiple Scattering of Waves. I. General Theory of Isotropic Scattering by Randomly Distributed Scatterers , 1945 .

[37]  Roux,et al.  Robust Acoustic Time Reversal with High-Order Multiple Scattering. , 1995, Physical review letters.

[38]  P. Morse,et al.  Methods of theoretical physics , 1955 .

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

[40]  L. Scharf,et al.  Statistical Signal Processing: Detection, Estimation, and Time Series Analysis , 1991 .

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

[42]  Arye Nehorai,et al.  Concentrated Cramer-Rao bound expressions , 1994, IEEE Trans. Inf. Theory.

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