Cette thèse s'intéresse à l'étude et au développement de méthodes de pistage mono et multicible en contexte Track-Before-Detect (TBD) par filtrage particulaire. Contrairement à l'approche classique qui effectue un seuillage préalable sur les données avant le pistage, l'approche TBD considère directement les données brutes afin de réaliser conjointement la détection et le pistage des différentes cibles. Il existe plusieurs solutions à ce problème, néanmoins cette thèse se restreint au cadre bayésien des Modèles de Markov Cachés pour lesquels le problème TBD peut être résolu à l'aide d'approximations particulaires. Dans un premier temps, nous nous intéressons à des méthodes particulaires monocibles existantes pour lesquels nous proposons différentes lois instrumentales permettant l'amélioration des performances en détection et estimation. Puis nous proposons une approche alternative du problème monocible fondée sur les temps d'apparition et de disparition de la cible; cette approche permet notamment un gain significatif au niveau du temps de calcul. Dans un second temps, nous nous intéressons au calcul de la vraisemblance en TBD -- nécessaire au bon fonctionnement des filtres particulaires -- rendu difficile par la présence des paramètres d'amplitudes des cibles qui sont inconnus et fluctuants au cours du temps. En particulier, nous étendons les travaux de Rutten et al. pour le calcul de la vraisemblance au modèle de fluctuations Swerling et au cas multicible. Enfin, nous traitons le problème multicible en contexte TBD. Nous montrons qu'en tenant compte de la structure particulière de la vraisemblance quand les cibles sont éloignées, il est possible de développer une solution multicible permettant d'utiliser, dans cette situation, un seule filtre par cible. Nous développons également un filtre TBD multicible complet permettant l'apparition et la disparition des cibles ainsi que les croisements. / This thesis deals with the study and the development of mono and multitarget tracking methods in a Track-Before-Detect (TBD) context with particle filters. Contrary to the classic approach that performs before the tracking stage a pre-detection and extraction step, the TBD approach directly works on raw data in order to jointly perform detection and tracking. Several solutions to this problem exist, however this thesis is restricted to the particular Hidden Markov Models considered in the Bayesian framework for which the TBD problem can be solved using particle filter approximations.Initially, we consider existing monotarget particle solutions and we propose several instrumental densities that allow to improve the performance both in detection and in estimation. Then, we propose an alternative approach of the monotarget TBD problem based on the target appearance and disappearance times. This new approach, in particular, allows to gain in terms of computational resources. Secondly, we investigate the calculation of the measurement likelihood in a TBD context -- necessary for the derivation of the particle filters -- that is difficult due to the presence of the target amplitude parameters that are unknown and fluctuate over time. In particular, we extend the work of Rutten et al. for the likelihood calculation to several Swerling models and to the multitarget case. Lastly, we consider the multitarget TBD problem. By taking advantage of the specific structure of the likelihood when targets are far apart from each other, we show that it is possible to develop a particle solution that considers only a particle filter per target. Moreover, we develop a whole multitarget TBD solution able to manage the target appearances and disappearances and also the crossing between targets.

abstract: In this thesis, an adaptive waveform selection technique for dynamic target tracking under low signal-to-noise ratio (SNR) conditions is investigated. The approach is integrated with a track-before-detect (TBD) algorithm and uses delay-Doppler matched filter (MF) outputs as raw measurements without setting any threshold for extracting delay-Doppler estimates. The particle filter (PF) Bayesian sequential estimation approach is used with the TBD algorithm (PF-TBD) to estimate the dynamic target state. A waveform-agile TBD technique is proposed that integrates the PF-TBD with a waveform selection technique. The new approach predicts the waveform to transmit at the next time step by minimizing the predicted mean-squared error (MSE). As a result, the radar parameters are adaptively and optimally selected for superior performance. Based on previous work, this thesis highlights the applicability of the predicted covariance matrix to the lower SNR waveform-agile tracking problem. The adaptive waveform selection algorithm's MSE performance was compared against fixed waveforms using Monte Carlo simulations. It was found that the adaptive approach performed at least as well as the best fixed waveform when focusing on estimating only position or only velocity. When these estimates were weighted by different amounts, then the adaptive performance exceeded all fixed waveforms. This improvement in performance demonstrates the utility of the predicted covariance in waveform design, at low SNR conditions that are poorly handled with more traditional tracking algorithms. / Dissertation/Thesis / M.S. Electrical Engineering 2011

The traditional tracking approach of forming detections and then associating these detections together is known to perform poorly at low signal-to-noise ratios (SNR). Track-before-detect (TBD) approaches, where the sensor data is used directly as opposed to forming detections, has been shown to perform better than traditional approaches at low SNRs. One recently introduced TBD algorithm is the Quanta Tracking Algorithm that is formed by applying maximum likelihood estimation to the histogram probabilistic multi-target tracker (HPMHT). Quanta has shown promising performance for low SNR scenarios, but the body of literature is small and the evaluations that have been done so far do not consider several practical aspects of using the algorithm in real applications and are difficult to compare to other algorithms due to the SNR definitions used. This paper seeks to address these deficiencies in the literature. A re-derivation of Quanta that corrects some issues present in the original derivation while also integrating extensions from the HPMHT literature will also be presented. These extensions are shown to make Quanta able to correct for errors in the assumed size targets as well as improve estimating the SNR of fluctuating targets. / Thesis / Master of Applied Science (MASc)

Detection and tracking of signals used in sonar applications in noisy environment is the focus of this thesis. We have concentrated on the low Signal-to-Noise Ratio (SNR) case where the conventional detection methods are not applicable. Furthermore, it is assumed that the duty cycle is relatively low. In the problem that is of concern the carrier frequency, pulse repetition interval (PRI) and the existence of the signal are not known. The unknown character of PRI makes the problem challenging since it means that the signal exists at some unknown intervals. A recursive, Bayesian track-before-detect (TBD) filter using particle filter based methods is proposed to solve the concerned problem. The data used by the particle filter is the magnitude of a complex spectrum in complex Gaussian noise. The existence variable is added in the design of the filter to determine the existence of the signal. The evolution of the signal state is modeled by a linear stochastic process. The filter estimates the signal state including the carrier frequency and PRI. Simulations are done under different scenarios where the carrier frequency, PRI and the existence of the signal varies. The results demonstrate that the algorithm presented in this thesis can detect signals which cannot be detected by conventional methods. Besides detection, the tracking performance of the filter is satisfying.

In this study Track Before Detect (TBD) approach will be analysed for tracking of dim moving targets. First, a radar setup is presented in order to introduce the radar range equation and signal models. Then, preliminary information is given about particle filters. As the main algorithm of this thesis, a multi-model particle filter method is developed in order to solve the non-linear non-Gaussian Bayesian estimation problem. Probability of target existence and RMS estimation accuracy are defined as the performance parameters of the algorithm for very low SNR targets. Simulation results are provided and performance analysis is presented as a conclusion.

abstract: In this thesis, an integrated waveform-agile multi-modal tracking-beforedetect sensing system is investigated and the performance is evaluated using an experimental platform. The sensing system of adapting asymmetric multi-modal sensing operation platforms using radio frequency (RF) radar and electro-optical (EO) sensors allows for integration of complementary information from different sensors. However, there are many challenges to overcome, including tracking low signal-to-noise ratio (SNR) targets, waveform configurations that can optimize tracking performance and statistically dependent measurements. Address some of these challenges, a particle filter (PF) based recursive waveformagile track-before-detect (TBD) algorithm is developed to avoid information loss caused by conventional detection under low SNR environments. Furthermore, a waveform-agile selection technique is integrated into the PF-TBD to allow for adaptive waveform configurations. The embedded exponential family (EEF) approach is used to approximate distributions of parameters of dependent RF and EO measurements and to further improve target detection rate and tracking performance. The performance of the integrated algorithm is evaluated using real data from three experimental scenarios. / Dissertation/Thesis / M.S. Electrical Engineering 2012

In recent years, the particle filter has gained prominence in the area of target tracking because it is robust to non-linear target motion and non-Gaussian additive noise. Traditional track filters, such as the Kalman filter, have been well studied for linear tracking applications, but perform poorly for non-linear applications. The particle filter has been shown to perform well in non-linear applications. The particle filter method is computationally intensive and advances in processor speed and computational power have allowed this method to be implemented in real-time tracking applications. This thesis explores the use of particle filters to detect and track stealthy targets in noisy imagery. Simulated point targets are applied to noisy image data to create an image sequence. A particle filter method known as Track-Before-Detect is developed and used to provide detection and position tracking estimates of a single target as it moves in the image sequence. This method is then extended to track multiple moving targets. The method is analyzed to determine its performance for targets of varying signal-to-noise ratio and for varying particle set sizes. The simulation results show that the Track-Before-Detect method offers a reliable solution for tracking stealthy targets in noisy imagery. The analysis shows that the proper selection of particle set size and algorithm improvements will yield a filter that can track targets in low signal-to-noise environments. The multi-target simulation results show that the method can be extended successfully to multi-target tracking applications. This thesis is a continuation of automatic target recognition and target tracking research at Cal Poly under Dr. John Saghri and is sponsored by Raytheon Space and Airborne Systems.

<p>Integrated tracking and detection, based on unthresholded measurements, also referred to as track before detect (TBD) is a hard nonlinear and non-Gaussian dynamical estimation and detection problem. However, it is a technique that enables the user to track and detect targets that would be extremely hard to track and detect, if possible at all with ''classical'' methods. TBD enables us to be better able to detect and track weak, stealthy or dim targets in noise and clutter and particles filter have shown to be very useful in the implementation of TBD algorithms. </p><p>This Master's thesis has investigated the use of particle filters on radar measurements, in a TBD approach.</p><p>The work has been divided into two major problems, a time efficient implementation and new functional features, as estimating the radar cross section (RCS) and the extension of the target. The later is of great importance when the resolution of the radar is such, that specific features of the target can be distinguished. Results will be illustrated by means of realistic examples.</p>

Integrated tracking and detection, based on unthresholded measurements, also referred to as track before detect (TBD) is a hard nonlinear and non-Gaussian dynamical estimation and detection problem. However, it is a technique that enables the user to track and detect targets that would be extremely hard to track and detect, if possible at all with ''classical'' methods. TBD enables us to be better able to detect and track weak, stealthy or dim targets in noise and clutter and particles filter have shown to be very useful in the implementation of TBD algorithms. This Master's thesis has investigated the use of particle filters on radar measurements, in a TBD approach. The work has been divided into two major problems, a time efficient implementation and new functional features, as estimating the radar cross section (RCS) and the extension of the target. The later is of great importance when the resolution of the radar is such, that specific features of the target can be distinguished. Results will be illustrated by means of realistic examples.

abstract: Tracking a time-varying number of targets is a challenging dynamic state estimation problem whose complexity is intensified under low signal-to-noise ratio (SNR) or high clutter conditions. This is important, for example, when tracking multiple, closely spaced targets moving in the same direction such as a convoy of low observable vehicles moving through a forest or multiple targets moving in a crisscross pattern. The SNR in these applications is usually low as the reflected signals from the targets are weak or the noise level is very high. An effective approach for detecting and tracking a single target under low SNR conditions is the track-before-detect filter (TBDF) that uses unthresholded measurements. However, the TBDF has only been used to track a small fixed number of targets at low SNR. This work proposes a new multiple target TBDF approach to track a dynamically varying number of targets under the recursive Bayesian framework. For a given maximum number of targets, the state estimates are obtained by estimating the joint multiple target posterior probability density function under all possible target existence combinations. The estimation of the corresponding target existence combination probabilities and the target existence probabilities are also derived. A feasible sequential Monte Carlo (SMC) based implementation algorithm is proposed. The approximation accuracy of the SMC method with a reduced number of particles is improved by an efficient proposal density function that partitions the multiple target space into a single target space. The proposed multiple target TBDF method is extended to track targets in sea clutter using highly time-varying radar measurements. A generalized likelihood function for closely spaced multiple targets in compound Gaussian sea clutter is derived together with the maximum likelihood estimate of the model parameters using an iterative fixed point algorithm. The TBDF performance is improved by proposing a computationally feasible method to estimate the space-time covariance matrix of rapidly-varying sea clutter. The method applies the Kronecker product approximation to the covariance matrix and uses particle filtering to solve the resulting dynamic state space model formulation. / Dissertation/Thesis / Doctoral Dissertation Electrical Engineering 2015