Turbo Parametric Spectral Estimation Method of Clutter Profile for Adaptive Radar Detection

Identification of unwanted components of a received echo is critical to improving the radar detection performance. Previously proposed parametric parameter estimation methods, such as MUSIC, ESPRIT, and Burg were implemented to estimate moments of radar clutter. Since none of these methods could estimate the value of Doppler spread and have sufficient accuracy, the Stochastic Maximum Likelihood (SML) technique was implemented. Since its estimation accuracy was profoundly initial point dependent and computationally costly, a novel estimation method (Turbo SML) is proposed. The proposed method outperformed the strategies proposed in the literature with its high Doppler resolution, accuracy, and low computational complexity. Besides, Turbo SML performance was optimized by using Burg estimates for starting point choice. After accomplishing nearly optimal estimation, its estimates were utilized to implement an approximately Max-Normalized Signal to Interference plus Noise Ratio (SINR) filter. Superior to detection filters in literature, the proposed filter can maximize its output Signal to Interference plus Noise Ratio (SINR) with a few numbers of secondary data.

[1]  Danilo Orlando,et al.  A Unifying Framework for Adaptive Radar Detection in Homogeneous Plus Structured Interference— Part II: Detectors Design , 2015, IEEE Transactions on Signal Processing.

[2]  E. J. Kelly.  An Adaptive Detection Algorithm , 1986, IEEE Transactions on Aerospace and Electronic Systems.

[3]  J. Cadzow.  Maximum Entropy Spectral Analysis , 2006 .

[4]  Tariq Pervez Sattar,et al.  Comparison between MUSIC and ESPRIT direction of arrival estimation algorithms for wireless communication systems , 2012, The First International Conference on Future Generation Communication Technologies.

[5]  Dirk T. M. Slock,et al.  Efficient Maximum Likelihood Joint Estimation of Angles and Times of Arrival of Multiple Paths , 2015, 2015 IEEE Globecom Workshops (GC Wkshps).

[6]  Ralph Otto Schmidt,et al.  A signal subspace approach to multiple emitter location and spectral estimation , 1981 .

[7]  José M. N. Leitão,et al.  Nonparametric estimation of mean Doppler and spectral width , 2000, IEEE Trans. Geosci. Remote. Sens..

[8]  Tom J. Moir,et al.  Speech enhancement using Maximum A-Posteriori and Gaussian Mixture Models for speech and noise Periodogram estimation , 2016, Comput. Speech Lang..

[9]  Gökhan M. Güvensen,et al.  On the impact of fast-time and slow-time preprocessing operations on adaptive target detectors , 2018, 2018 IEEE Radar Conference (RadarConf18).

[10]  Hakan A. Çirpan,et al.  Maximum likelihood blind channel estimation in the presence of Doppler shifts , 1999, IEEE Trans. Signal Process..

[11]  Pascal Larzabal,et al.  Parametric spectral moments estimation for wind profiling radar , 2003, IEEE Trans. Geosci. Remote. Sens..

[12]  Gokhan M. Guvensen,et al.  A Subspace-Aware Kelly's Detector using Reduced Secondary Data with Fast and Slow Time Preprocessing , 2019, 2019 IEEE Radar Conference (RadarConf).

[13]  V. Venkatesh,et al.  A frequency diversity pulse-pair algorithm for extending Doppler radar velocity Nyquist range , 2016, 2016 IEEE Radar Conference (RadarConf).

[14]  Pooja Gupta,et al.  MUSIC and improved MUSIC algorithm to estimate direction of arrival , 2015, 2015 International Conference on Communications and Signal Processing (ICCSP).