Knowledge‐based design of space–time transmit code and receive filter for a multiple‐input–multiple‐output radar in signal‐dependent interference. Issue 8 (1st October 2015)

• Record Type: Journal Article
• Authors: Karbasi, Seyyed Mohammad; Aubry, Augusto; Carotenuto, Vincenzo; Naghsh, Mohammad Mahdi; Bastani, Mohammad Hassan
• Is Part Of: IET radar, sonar & navigation. Volume 9:Issue 8(2015)
• Page Start: 1124
• Page End: 1135
• Publication Date: 2015-10-01
• Subjects: MIMO radar -- radar receivers -- radar transmitters -- radar interference -- filtering theory -- space‐time codes -- iterative methods -- radar signal processing -- radar clutter -- convex programming -- computational complexity -- polynomials; knowledge‐based design -- space‐time transmit code -- multiple‐input‐multiple‐output radar -- signal‐dependent interference -- STTC -- STRF -- space‐time receive filter -- point‐like target -- dynamic environmental database -- iterative method -- clutter statistics -- signal‐to‐interference‐plus‐noise ratio -- SINR -- convex optimisation problem -- computational complexity -- MIMO system -- single‐input‐single‐output system; Signal processing -- Periodicals; Radar -- Periodicals; Sonar -- Periodicals; Electronics in navigation -- Periodicals; Navigation -- Periodicals; 621.3848
• DOI: 10.1049/iet-rsn.2014.0527
• Languages: English
• ISSNs: 1751-8784

Abstract

Abstract : The authors deal with the robust design of multiple‐input–multiple‐output (MIMO) space–time transmit code (STTC) and space–time receive filter (STRF) for a point‐like target embedded in signal‐dependent interference. Specifically, they assume that the radar exploits knowledge provided by dynamic environmental database, to roughly predict the actual scattering scenario. Then, they devise an iterative method to optimise the (constrained) STTC and the (constrained) STRF which sequentially improves the worst‐case (over interfering scatterers statistics) signal‐to‐interference‐plus‐noise ratio (SINR). Each iteration of the algorithm is handled via solving two (hidden) convex optimisation problems. The resulting computational complexity is linear with the number of iterations and polynomial with the sizes of the STTC and the STRF. At the analysis stage, they assess the performance of the proposed algorithm in terms of the achieved SINR. They show that properly exploiting the spatial degrees of freedom offered by the MIMO system, it is possible to obtain considerable SINR gains with respect to the conventional single‐input–single‐output system. Moreover, their results highlight the capability of the proposed method to robustify the performance of the designed system against possible knowledge inaccuracies.