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Space-time adaptive detection (STAD) is an important research aspect of the bistatic multiple-input multiple-output (MIMO) radar system. The signal and clutter space-time models of the bistatic MIMO radar are described in this study. The knowledge-aided (KA) transform, which utilises the structural properties of the target-free data and the sample covariance matrix, is applied to increase the independent secondary data fourfold in the STAD frame. The performance assessment of the KA–STAD detectors with generalised likelihood ratio test (GLRT), adaptive matched filter (AMF), and Rao rules is presented. The simulation experiments show that the detection and constant false alarm rate performances of the presented KA–GLRT, KA–AMF, and KA–Rao detectors outperform the GLRT, AMF, and Rao detectors for the bistatic MIMO radar system STAD frame in clutter background with limited secondary data.

Sources localization is used in radar, sonar, andtelecommunication. Radar has numerous civilian andmilitary applications. Radar system has gone throughmany developments over the last few decades andreached the latest version known as MIMO radar. AMIMO radar is composed of multiple transmitting andreceiving antennas like a conventional phased arrayradar. However, its transmitting antennas transmit linearlyindependent signals so that they can be easilyidentified by the matched filters bank at its receivingend. The matched filtered signals are then processedto extract the ranges, DOAs, DODs, velocities, etc. ofthe targets. A bistatic MIMO radar system provideshigh resolution, spatial diversity, parameter identifiability,etc. which inspired us to use it in this work. Thereare many existing methods to deal with the far field regionof MIMO radar system. However, little work canbe found on the near field region of a bistatic MIMOradar which motivated the work in this thesis. Nearfield targets localization is also important because ofmany indoor applications. Most of the existing nearfield sources localization techniques use Fresnel approximationin which the real spherical wavefront is assumedquadric unlike planar in far field situation. Inthis work we have proposed a novel near field targetslocalization method using Fresnel approximation. TheFresnel approximation leads to a biased estimation ofthe location parameters because the true wavefront isspherical. Consequently, we have proposed two correctionmethods to reduce the effects of Fresnel approximationand other two methods which directly usethe exact signal model based on spherical wavefront., Dans cette thèse, nous considérons la dernière générationdu radar. Il s’agit d’un radar MIMO bistatiquequi est composé de plusieurs antennes d’émission etde réception. Pour ce système, les antennes émettricestransmettent des signaux linéairement indépendantsafin qu’ils puissent être identifiés à l’aide d’unbanc de filtres adaptés au niveau des antennes deréception. Les signaux filtrés sont alors traités pourextraire les paramètres des cibles, tels que les DOA,DOD, vitesse, etc. Un radar MIMO bistatique offre unegrande diversité spatiale et une excellente identifiabilitédes paramètres, etc., ce qui nous a incités à l’utiliserdans ce travail. La situation en champ lointaind’un radar MIMO bistatique est largement traitée dansla littérature. Mais, peu de travaux existe sur la situationen champ proche, c’est ce qui a motivé le travailde cette thèse. La localisation de cibles en champproche est importante en raison de nombreuses applicationsà l’intérieur des constructions. A ce sujet, laplupart des méthodes actuelles utilisent l’approximationde Fresnel dans laquelle le front d’onde sphériquedes signaux reçus est supposé quadrique plutôt queplanaire comme en champ lointain. Dans ce travail dethèse, nous avons proposé une nouvelle méthode delocalisation des cibles en champ proche qui utilise l’approximationde Fresnel. Celle-ci conduit à une estimationbiaisée des paramètres de localisation car en réalitéle front d’onde est sphérique. Nous avons proposéalors deux méthodes de correction pour réduire les effetsde l’approximation de Fresnel et deux autres méthodesqui utilisent directement le modèle exacte basésur le front d’onde sphérique.

International audience; In this paper, we propose an exact model-based method for near-field sources localization with a bistatic multiple input, multiple output (MIMO) radar system, and compare it with an approximated model-based method. The aim of this paper is to propose an efficient way to use the exact model of the received signals of near-field sources in order to eliminate the systematic error introduced by the use of approximated model in most existing near-field sources localization techniques. The proposed method uses parallel factor (PARAFAC) decomposition to deal with the exact model. Thanks to the exact model, the proposed method has better precision and resolution than the compared approximated model-based method. The simulation results show the performance of the proposed method.