Method for non-stationary jammer suppression in noise radar systems

Noise radars represent a rapidly growing research topic owing to numerous advantages over the conventional radars. This study proposes a method for strong non-stationary jammer suppression in noise radar systems. The corrupted received signal is divided into non-overlapping segments so that the instantaneous frequency (IF) of the jammer can be approximated by a parabola within each segment. To that end, an adaptive recursive procedure is proposed. The procedure uses the polynomial-phase transform to estimate the parabola coefficients. The jammer suppression is done for each segment separately. The simulations, performed for various types of FM interferences, prove the effectiveness of the proposed method even for highly non-stationary jammers with non-polynomial phase.

[1]  M.G. Amin,et al.  A self-coherence anti-jamming GPS receiver , 2005, IEEE Transactions on Signal Processing.

[2]  Ram M. Narayanan,et al.  Design, performance, and applications of a coherent ultra-wideband random noise radar , 1998 .

[3]  Ljubisa Stankovic,et al.  Time-frequency-based non-stationary interference suppression for noise radar systems , 2008 .

[4]  Athanasios Papoulis,et al.  Probability, Random Variables and Stochastic Processes , 1965 .

[5]  Boualem Boashash,et al.  Estimating and interpreting the instantaneous frequency of a signal. II. A/lgorithms and applications , 1992, Proc. IEEE.

[6]  Sune R. J. Axelsson.  Noise radar using random phase and frequency modulation , 2004, IEEE Transactions on Geoscience and Remote Sensing.

[7]  B. Friedlander,et al.  Multicomponent signal analysis using the polynomial-phase transform , 1996, IEEE Transactions on Aerospace and Electronic Systems.

[8]  Moeness G. Amin,et al.  Interference mitigation in spread spectrum communication systems using time-frequency distributions , 1997, IEEE Trans. Signal Process..

[9]  Ram M. Narayanan,et al.  Theoretical aspects of radar imaging using stochastic waveforms , 2001, IEEE Trans. Signal Process..

[10]  Benjamin Friedlander,et al.  The discrete polynomial-phase transform , 1995, IEEE Trans. Signal Process..

[11]  E. Walton,et al.  Ultrawide-band noise radar in the VHF/UHF band , 1999 .

[12]  Moeness G. Amin,et al.  Short-time Fourier transform receiver for nonstationary interference excision in direct sequence spread spectrum communications , 2001, IEEE Trans. Signal Process..

[13]  Peter O'Shea,et al.  A fast algorithm for estimating the parameters of a quadratic FM signal , 2004, IEEE Transactions on Signal Processing.

[14]  Sune R. J. Axelsson,et al.  Noise Radar for range/Doppler processing and digital beamforming using low-bit ADC , 2003, IEEE Trans. Geosci. Remote. Sens..

[15]  I. Miller.  Probability, Random Variables, and Stochastic Processes , 1966 .

[16]  Bernard Mulgrew,et al.  Iterative frequency estimation by interpolation on Fourier coefficients , 2005, IEEE Transactions on Signal Processing.

[17]  Boaz Porat,et al.  Estimation and classification of polynomial-phase signals , 1991, IEEE Trans. Inf. Theory.

[18]  R. Narayanan,et al.  Doppler estimation using a coherent ultrawide-band random noise radar , 2000 .

[19]  LJubisa Stankovic,et al.  Order adaptive local polynomial FT based interference rejection in spread spectrum communication systems , 2005, IEEE Transactions on Instrumentation and Measurement.

[20]  Anna Scaglione,et al.  Adaptive time-varying cancellation of wideband interferences in spread-spectrum communications based on time-frequency distributions , 1999, IEEE Trans. Signal Process..

[21]  P. O'Shea.  A new technique for instantaneous frequency rate estimation , 2002, IEEE Signal Processing Letters.