Demonstrating significant passive radar performance increase through using known communication signal format

In order to determine if passive radar performance can be good enough to allow passive radars to take the place of active radars, we seek the ultimate limits of passive radar performance. The focus is on the case where the radars are employed for estimating the position and velocity of a target. Further, the passive radar employs several dispersed existing communication transmitters, which could be cellular base stations. We compare the Cramer-Rao bound (CRB) for an active radar with the CRB for a passive radar under various amounts of prior information concerning the transmitted signals. The mean-square-error of maximum likelihood estimation is presented to provide the threshold at which the estimation performance approaches the CRB. One important result shows that if we know the employed transmitted signals are of known form, for example those coming from GSM base stations, but contain unknown bits, then under reasonable models the passive radar can estimate the bits and ultimately achieve target estimation performance that is very close to that of the active radar. Estimation performance for a number of other interesting cases with less information shows considerable performance loss.

[1]  Qian He,et al.  Noncoherent versus coherent MIMO radar: Performance and simplicity analysis , 2012, Signal Process..

[2]  Hongbin Li,et al.  Joint Delay and Doppler Estimation for Passive Sensing With Direct-Path Interference , 2016, IEEE Transactions on Signal Processing.

[3]  B. C. Ng,et al.  On the Cramer-Rao bound under parametric constraints , 1998, IEEE Signal Processing Letters.

[4]  Jianbin Hu,et al.  Generalized Cramér–Rao Bound for Joint Estimation of Target Position and Velocity for Active and Passive Radar Networks , 2015, IEEE Transactions on Signal Processing.

[5]  Braham Himed,et al.  Detection in Passive MIMO Radar Networks , 2014, IEEE Transactions on Signal Processing.

[6]  Brian M. Sadler,et al.  Constrained Cramer-Rao bounds on source separation , 2001, Proceedings. 2001 IEEE International Symposium on Information Theory (IEEE Cat. No.01CH37252).

[7]  Peter Swerling,et al.  Probability of detection for fluctuating targets , 1960, IRE Trans. Inf. Theory.

[8]  Brian M. Sadler,et al.  Maximum-Likelihood Estimation, the CramÉr–Rao Bound, and the Method of Scoring With Parameter Constraints , 2008, IEEE Transactions on Signal Processing.

[9]  Israel Korn,et al.  GMSK with Frequency-Selective Rayleigh Fading and Cochannel Interference , 1992, IEEE J. Sel. Areas Commun..

[10]  Peter B. Luh,et al.  The MIMO Radar and Jammer Games , 2012, IEEE Transactions on Signal Processing.

[11]  Visa Koivunen,et al.  MIMO radar filterbank design for interference mitigation , 2014, 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[12]  Michael A. Saville,et al.  Centralized Passive MIMO Radar Detection Without Direct-Path Reference Signals , 2014, IEEE Transactions on Signal Processing.

[13]  Brian M. Sadler,et al.  On the performance of source separation with constant modulus signals , 2002, 2002 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[14]  Brian D. Rigling,et al.  Cramér-Rao Bounds for UMTS-Based Passive Multistatic Radar , 2014, IEEE Transactions on Signal Processing.

[15]  Yonina C. Eldar,et al.  Spatial compressive sensing in MIMO radar with random arrays , 2012, 2012 46th Annual Conference on Information Sciences and Systems (CISS).

[16]  Steven Kay,et al.  Fundamentals Of Statistical Signal Processing , 2001 .

[17]  Rick S. Blum,et al.  The Significant Gains From Optimally Processed Multiple Signals of Opportunity and Multiple Receive Stations in Passive Radar , 2014, IEEE Signal Processing Letters.

[18]  M. Skolnik,et al.  Introduction to Radar Systems , 2021, Advances in Adaptive Radar Detection and Range Estimation.

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

[20]  Hongbin Li,et al.  A Parametric Moving Target Detector for Distributed MIMO Radar in Non-Homogeneous Environment , 2013, IEEE Transactions on Signal Processing.

[21]  Hongbin Li,et al.  Signal detection with noisy reference for passive sensing , 2015, Signal Process..