Le concept de radar passif aéroporté repose sur l’utilisation de plusieurs antennes réseau, disposées sur une plateforme en vue de couvrir un angle solide large de détection, en s’appuyant sur l’utilisation de signaux d’opportunité provenant d’émetteurs au sol. La détection aéroportée à partir de signaux d’opportunité est intéressante, notamment pour assurer l’autoprotection d’un avion ou d’un hélicoptère ; en revanche elle constitue un défi technique notamment en raison du niveau des signaux interférents, en provenance de l’émetteur et des trajets multiples indirects (le fouillis), bien supérieur au niveau de signal utile diffusé par la cible à détecter. D’autres effets, tels que la structure arbitraire des signaux (forme d’onde non-radar) et sa conséquence sur les lobes secondaires en distance, contribuent à la complexité du traitement à mettre en œuvre.Le point de départ des recherches se situe à l’intersection des techniques de radar passif (utilisant la corrélation entre un signal de référence non connu a priori et les signaux diffus renvoyés par l’environnement) et les techniques de type STAP (Space Time Adaptive Processing) utilisées pour la détection des cibles mobiles par les radars aéroportés conventionnels. Dans ce contexte, les travaux de thèse permettent d’étendre d’une part la caractérisation et la qualification des signaux « radar passif » à une configuration aéroportée, d’autre part les techniques STAP à une configuration bistatique et à des signaux de forme arbitraire et non structurés comme des signaux radar. Les recherches mettent en évidence l’importance primordiale du trajet direct et des premiers échos de fouillis qui parasitent la caractérisation spatio-temporelle des échos reçus dans la case distance de la cible sous test. La caractéristique du fouillis, habituellement tracée dans le plan Doppler-angle, se trouve affectée par ces interférences qu’il faut éliminer au préalable. Pour cela, un premier filtre à réponse finie est mis en œuvre sur chaque capteur, puis le traitement STAP est appliqué à l’ensemble du réseau d’antennes.Les traitements proposés sont simulés et les performances en détection sont analysées. Une expérimentation est conduite, à l’aide d’un réseau de 4 antennes mobiles au sol. Les conditions sont réunies pour collecter des signaux de fouillis étalés en Doppler et analyser l’effet d’une forme d’onde non-radar. Les traitements d’élimination des interférences sont mis en œuvre et ainsi qualifiés expérimentalement. / The novel concept for the airborne passive radar is to have multiple passive receiving arrays covering a 4 steradian angle around the platform which makes use of the ground-based stationary transmitter as the illuminator of opportunity. This challenging passive radar configuration would well find application for localized covert surveillance on an airborne platform such as an unmanned aerial vehicle, helicopter, etc. For the airborne passive radar, during moving target detections, it encounters the effects of strong interfering signal returns against the weak target returns where this severe interfering environment is usually characterized by the high levels of direct path and clutter against the thermal noise background. Due to the continuous wave, random and aperiodic nature of the passive signal and given the strong direct path and clutter signals, their random range sidelobes couplings into further range cells will seriously exacerbate the background interference, making target detections a big challenge. Moreover, owing to the platform motion, the clutter received by the airborne passive radar is not only extended in both range and angle, it is also spread over a region in Doppler frequency which further complicates the problem.This research work is focused on identifying and analyzing the critical issues faced by the airborne passive radar on moving target detections and to develop effective signal processing schemes for improved performance. As a first step, it is important to accurately derive the model for the received passive signals and consequently, efficient signal processing schemes can be studied to mitigate and to improve detections performance. The signal processing schemes for the airborne passive radar can be segregated into a two-step interference cancellation process where the direct path and strong clutter coupling components (and their corresponding random range sidelobes) present in the received signal at each antenna element can first be effectively suppressed by the adaptive interference cancellation algorithm prior to matched filter processing. Further cancellation on the residual random range sidelobes couplings and on the spatial-Doppler dependent clutter can be achieved using reduced-dimension STAP. Trials based on the ground-based moving passive radar experiments are conducted as the final part of this research work to validate and evaluate the signal processing schemes which is a major progress towards implementing an operational airborne passive radar.

Thesis (Master').

This thesis contains design of beamforming network designed for passive radar antennas. The first part contains theory of passive radars and beamforming networks. The next part implies design of beamforming network at the block digram level. Then are choosed circuits for amplitude and phase control, including the design of control communication. It follows by realization of IQ phase shifter and his automatic measurement. Based on this results is phase shifter adjusted and PCB of 2x2 beamforming network is designed. Last part includes design of control application (Matlab) and control program for STM32F407VG microcontroller.

The multi-target tracking problem essentially involves the recursive joint estimation of the state of unknown and time-varying number of targets present in a tracking scene, given a series of observations. This problem becomes more challenging because the sequence of observations is noisy and can become corrupted due to miss-detections and false alarms/clutter. Additionally, the detected observations are indistinguishable from clutter. Furthermore, whether the target(s) of interest are point or extended (in terms of spatial extent) poses even more technical challenges. An approach known as random finite sets provides an elegant and rigorous framework for the handling of the multi-target tracking problem. With a random finite sets formulation, both the multi-target states and multi-target observations are modelled as finite set valued random variables, that is, random variables which are random in both the number of elements and the values of the elements themselves. Furthermore, compared to other approaches, the random finite sets approach possesses a desirable characteristic of being free of explicit data association prior to tracking. In addition, a framework is available for dealing with random finite sets and is known as finite sets statistics. In this thesis, advanced signal processing techniques are employed to provide enhancements to and develop new random finite sets based multi-target tracking algorithms for the tracking of both point and extended targets with the aim to improve tracking performance in cluttered environments. To this end, firstly, a new and efficient Kalman-gain aided sequential Monte Carlo probability hypothesis density (KG-SMC-PHD) filter and a cardinalised particle probability hypothesis density (KG-SMC-CPHD) filter are proposed. These filters employ the Kalman- gain approach during weight update to correct predicted particle states by minimising the mean square error between the estimated measurement and the actual measurement received at a given time in order to arrive at a more accurate posterior. This technique identifies and selects those particles belonging to a particular target from a given PHD for state correction during weight computation. The proposed SMC-CPHD filter provides a better estimate of the number of targets. Besides the improved tracking accuracy, fewer particles are required in the proposed approach. Simulation results confirm the improved tracking performance when evaluated with different measures. Secondly, the KG-SMC-(C)PHD filters are particle filter (PF) based and as with PFs, they require a process known as resampling to avoid the problem of degeneracy. This thesis proposes a new resampling scheme to address a problem with the systematic resampling method which causes a high tendency of resampling very low weight particles especially when a large number of resampled particles are required; which in turn affect state estimation. Thirdly, the KG-SMC-(C)PHD filters proposed in this thesis perform filtering and not tracking , that is, they provide only point estimates of target states but do not provide connected estimates of target trajectories from one time step to the next. A new post processing step using game theory as a solution to this filtering - tracking problem is proposed. This approach was named the GTDA method. This method was employed in the KG-SMC-(C)PHD filter as a post processing technique and was evaluated using both simulated and real data obtained using the NI-USRP software defined radio platform in a passive bi-static radar system. Lastly, a new technique for the joint tracking and labelling of multiple extended targets is proposed. To achieve multiple extended target tracking using this technique, models for the target measurement rate, kinematic component and target extension are defined and jointly propagated in time under the generalised labelled multi-Bernoulli (GLMB) filter framework. The GLMB filter is a random finite sets-based filter. In particular, a Poisson mixture variational Bayesian (PMVB) model is developed to simultaneously estimate the measurement rate of multiple extended targets and extended target extension was modelled using B-splines. The proposed method was evaluated with various performance metrics in order to demonstrate its effectiveness in tracking multiple extended targets.