GMTI STAP performance under space‐time impaired clutter environment. Issue 10 (1st October 2019)

• Record Type: Journal Article
• Authors: Bhamre, Pooja; Gupta, Shilpi
• Is Part Of: IET radar, sonar & navigation. Volume 13:Issue 10(2019)
• Page Start: 1836
• Page End: 1841
• Publication Date: 2019-10-01
• Subjects: array signal processing -- radar detection -- radar clutter -- space‐time adaptive processing -- matrix inversion -- Doppler radar -- radar interference -- principal component analysis -- numerical analysis; signal‐to‐interference‐plus‐noise ratio loss -- principal component canceller -- high decorrelated clutter environment -- MDV -- GMTI space‐time adaptive processing performance -- space‐time impaired clutter environment -- minimum detectable velocity -- independent channel mismatch -- moving target discrimination -- extrusive beamforming methods -- sample matrix inversion -- clutter impairment model -- ground moving target indicator RADAR system -- GMTI RADAR system -- foliage penetration -- angle independent channel mismatch -- Doppler clutter impairments -- mathematical modelling -- numerical simulations; Signal processing -- Periodicals; Radar -- Periodicals; Sonar -- Periodicals; Electronics in navigation -- Periodicals; Navigation -- Periodicals; 621.3848
• DOI: 10.1049/iet-rsn.2019.0083
• Languages: English
• ISSNs: 1751-8784

Abstract

Abstract : Ground moving target indicator (GMTI) RADAR system frequently incorporates space‐time adaptive processing for successful moving targets discrimination in a highly cluttered environment. The minimum detectable velocity (MDV) of the target gets affected due to the added clutter impairments such as foliage penetration and angle independent channel mismatch. A novel clutter model is postulated in this work that includes assessment enacted in view of both angle and Doppler clutter impairments at a time instead of their individual analysis as in the reported literature. The performance of the GMTI RADAR in terms of the signal‐to‐interference‐plus‐noise ratio loss is evaluated using the extrusive beamforming methods namely sample matrix inversion and principal component canceller. In high decorrelated clutter environment, the later outperforms in terms of the MDV, making use of a lesser number of training snapshots. The approach is validated with mathematical modelling and numerical simulations.