This thesis focuses on the problem of underwater target tracking with consideration for realistic conditions using active sonar. This thesis addresses the following specific problems: 1) underwater detection in three dimensional (3D) space using multipath detections and an uncertain sound speed profile in heavy clutter, 2) tracking a group of divers whose motion is dependent on each other using sonar detections corrupted by unknown structured background clutter, 3) extended target tracking (ETT) with a high-resolution sonar in the presence of multipath detection and measurement origin uncertainty. Unrealistic assumptions about the environmental conditions may degrade the performance of underwater tracking algorithms. Hence, underwater target tracking with realistic conditions is addressed by integrating the environment-induced uncertainties or constraints into the trackers. First, an iterated Bayesian framework is formulated using the ray-tracing model and an extension of the Maximum Likelihood Probabilistic Data Association (ML-PDA) algorithm to make use of multipath information. With the ray-tracing model, the algorithm can handle more realistic sound speed profile (SSP) instead of using the commonly-assumed constant velocity model or isogradient SSP. Also, by using the iterated framework, we can simultaneously estimate the SSP and target state in uncertain multipath environments. Second, a new diver dynamic motion (DDM) model is integrated into the Probability Hypothesis Density (PHD) to track the dependent motion diver targets. The algorithm is implemented with Gaussian Mixtures (GM) to ensure low computational complexity. The DDM model not only includes inter-target interactions but also the environmental influences (e.g., water flow). Furthermore, a log-Gaussian Cox process (LGCP) model is seamlessly integrated into the proposed filter to distinguish the target-originated measurement and false alarms. The final topic of interest is to address the ETT problem with multipath detections and clutter, which is practically relevant but barely addressed in the literature. An improved filter, namely MP-ET-PDA, with the classical probabilistic data association (PDA) filter and random matrices (RM) is proposed. The optimal estimates can be provided by MP-ET-PDA filter by considering all possible association events. To deal with the high computational load resulting from the data association, a Variational Bayesian (VB) clustering-aided MP-ET-PDA is proposed to provide near real-time processing capability. The traditional Cramer-Rao Lower Bound (CRLB), which is the inverse of the Fisher information matrix (FIM), quantifies the best achievable accuracy of the estimates. For the estimation problems, the corresponding theoretical bounds are derived for performance evaluation under realistic underwater conditions. / Thesis / Doctor of Philosophy (PhD)

The focus of this thesis is to implement factor graphs into the problem of detection, classification, and localization (DCL) of underwater objects using active SOund Navigation And Ranging (SONAR). A factor graph is a bipartite graphical representation of the decomposition of a particular function. Messages are passed along the edges connecting factor and variable nodes, on which, a message passing algorithm is applied to compute the posterior probabilities at a particular node. This thesis addresses two issues. In the first section, the formulation of factor graphs for each section of the DCL chain required followed by their closed-form solutions. For the detector, the factor graph determines if the signal is a detection or simply noise. In the classifier, it outputs the probability for the elements in the class. Last, when using a factor graph for the tracker, it gives the estimated state of the object being tracked. The second part concentrates on the application to Continuous Active SONAR (CAS). When using CAS, a bistatic configuration is used allowing for a more rapid update rate where two unmanned underwater vehicles (UUVs) are used as the receiver and transmitter. The goal is to evaluate CAS's effectiveness to determine if the tracking accuracy improves as the transmit interval decreases. If CAS proves to be more efficient in target tracking, the next objective is to determine which messages sent between the two UUVs are most beneficial. To test this, a particle filter simulation is used. / Master of Science

This work investigates the ideal distribution of sensors in networked arrays. MATLAB models these arrays and simulates the results these networks obtain using active and passive sonar. These results determine the ideal sensor placement for optimal parameter detection and estimation of targets. This work's first part focuses on active sonar networks with a fixed number of sensors in a differing number of arrays. MATLAB simulates the data of these sensors taking into account the geometries and velocities of the arrays and targets, then estimates the parameters of the targets using an elliptical filter, a conventional beamformer, a matched filter and one of three fusion methods. This work compares the performance of each network and fusion method. This work shows that the adding more arrays, regardless of size, enhances the overall performance of the network. It also shows the larger arrays obtain more robust parameter estimation. The second part of this work investigates the effects of uncertainty of the array position and orientation using passive sonar. Two networks, one with 2 32-channel arrays and one with 8 2-channel arrays, estimate a sound source's location using a conventional beamformer. MATLAB simulates the data taking into account the geometries of the arrays and the sound source. The results of these simulations show that when uncertainty of position and orientation increases, the better the smaller arrays estimate the location of the sound source compared to the larger arrays. / Master of Science

La connaissance de l'environnement marin est nécessaire pour un grand nombre d'applications dans le domaine de l'acoustique sous-marine comme la communication, la localisation et détection sonar et la surveillance des mammifères marins. Il constitue le moyen principal pour éviter les interférences néfastes entre le milieu naturel et les actions industriels et militaires conduites en zones côtières.Notre travail de thèse se place dans un contexte de sonar actif avec des fréquences allant de 1 kHz à 10 kHz pour des distances de propagations allant de 1 km à plusieurs dizaines de kilomètres. Nous nous intéressons particulièrement aux environnements de propagation grands fonds, à l'utilisation des antennes industrielles comme les antennes de flancs, les antennes cylindriques et les antennes linéaires remorquées, et à l'utilisation de signaux large bande afin de travailler avec des résolutions en distance et en vitesse très élevées. Le travail de recherche présenté dans ce mémoire est dédié à la recherche de nouveaux paramètres discriminants pour la classification de cible sous-marine en sonar actif et notamment à l'estimation de l'immersion instantanée.Cette étude présente : (1) les calculs de nouvelles bornes de Cramer-Rao pour la position d'une cible en distance en et en profondeur, (2) l'estimation conjointe de la distance et de l'immersion d'une cible à partir de la mesure des temps d'arrivées et des angles d'élévations sur une antenne surfacique et (3) l'estimation conjointe de la distance, de l'immersion et du gisement d'une cible à partir de la mesure des temps d'arrivées et des pseudo-gisements sur une antenne linéaire remorquée.Les méthodes développées lors de cette étude ont été validées sur des simulations, des données expérimentales à petite échelle et des données réelles en mer. / The knowledge of the marine environment is required for many underwater applications such as communications, sonar localization and detection, and marine mammals monitoring. It enables preventing harmful interference between the natural environment and industrial and military actions in coastal areas.This thesis work concentrates upton the context of active sonar with frequencies from 1 kHz to 10 kHz and long propagation ranges from 1 km to several tens of kilometers. We also concentrates upon deep water environment, the use of industrial arrays such as cylindrical arrays, flank arrays and linear towed arrays, and the use of large time-bandwidth signals in order to obtain high distance and speed resolutions. This research work is dedicated to the research of new features for the underwater target classification in active sonar, and specifically to the instantaneous target-depth estimation.This thesis presents: (1) calculations of new Cramer-Rao bounds for the target-position in range and in depth, (2) the joint estimation of the target-depth and the target-range from the arrival time and elevation angle measures with a surface array, (3) the joint estimation of the target-depth, the target-range and the target-bearing from the arrival time and pseudo-bearing angle measures with a linear towed array.The methods presented in this manuscript have been benchmarked on simulation, on reduced-scale experimental data and real marine data.

<p>The domain below the surface in maritime security is hard to monitor with conventional methods, due to the often very noisy environment. In conventional methods the measurements are thresholded in order to distinguish potential targets. This is not always a feasible way of treating measurements. In this thesis a system based on raw measurements, that are not thresholded, is presented in order to track and classify divers with an active sonar. With this system it is possible to detect and track weak targets, even with a signal to noise ratio that often goes below 0 dB.</p><p>The system in this thesis can be divided into three parts: the processing of measurements, the association of measurements to targets and the classification of targets. The processing of measurements is based on a particle filter using Track Before Detect (TBD). Two algorithms for association of measurements, Joint Probabilistic Data Association (JPDA) and Highest Probability Data Association (HPDA), have been implemented. The classification of targets is done using an assumed novel approach. The system is evaluated by doing simulations with approximately 8 hours of recorded data, where divers are present at nine different times. The simulations are done a number of times to catch The classification rate is high and the false alarm rate is low.</p> / <p>Undervattensdomänen är svår att övervaka i marina säkerhetssystem med sedvanliga metoder, på grund av den brusiga miljön. I traditionella metoder trösklas mätningarna för att urskilja potentiella mål. Detta är inte alltid ett godtagbart sätt att behandla mätningar på. I den här rapporten presenteras ett system baserat på behandling av rå mätdata, som inte trösklas, för att spåra och klassificera dykare med en aktiv sonar. Med detta system är det möjligt att detektera och spåra svaga mål, trots att signal till brus förhållandet ofta går under 0 dB.</p><p>Systemet i den här rapporten kan delas upp i tre delar: behandling av mätningar, association av mätningar till mål samt klassificering av mål. Behandlingen av mätningarna görs med ett partikelfilter som använder Track Before Detect (TBD). Två algoritmer för associering av mätningar, Joint Probabilistic Data Association (JPDA) och Highest Probability Data Association (HPDA), har implementerats. Klassificeringen av mål görs med en egenutvecklad metod som inte har hittats i existerande dokumentation. Systemet utvärderas genom att simuleringar görs på ungefär 8 timmar inspelad data, där dykare är närvarande vid nio olika tillfällen. Simuleringarna görs ett antal gånger för att fånga upp stokastiska beteenden. Andelen lyckade klassificeringar är hög och andelen falsklarm är låg.</p>

The domain below the surface in maritime security is hard to monitor with conventional methods, due to the often very noisy environment. In conventional methods the measurements are thresholded in order to distinguish potential targets. This is not always a feasible way of treating measurements. In this thesis a system based on raw measurements, that are not thresholded, is presented in order to track and classify divers with an active sonar. With this system it is possible to detect and track weak targets, even with a signal to noise ratio that often goes below 0 dB. The system in this thesis can be divided into three parts: the processing of measurements, the association of measurements to targets and the classification of targets. The processing of measurements is based on a particle filter using Track Before Detect (TBD). Two algorithms for association of measurements, Joint Probabilistic Data Association (JPDA) and Highest Probability Data Association (HPDA), have been implemented. The classification of targets is done using an assumed novel approach. The system is evaluated by doing simulations with approximately 8 hours of recorded data, where divers are present at nine different times. The simulations are done a number of times to catch The classification rate is high and the false alarm rate is low. / Undervattensdomänen är svår att övervaka i marina säkerhetssystem med sedvanliga metoder, på grund av den brusiga miljön. I traditionella metoder trösklas mätningarna för att urskilja potentiella mål. Detta är inte alltid ett godtagbart sätt att behandla mätningar på. I den här rapporten presenteras ett system baserat på behandling av rå mätdata, som inte trösklas, för att spåra och klassificera dykare med en aktiv sonar. Med detta system är det möjligt att detektera och spåra svaga mål, trots att signal till brus förhållandet ofta går under 0 dB. Systemet i den här rapporten kan delas upp i tre delar: behandling av mätningar, association av mätningar till mål samt klassificering av mål. Behandlingen av mätningarna görs med ett partikelfilter som använder Track Before Detect (TBD). Två algoritmer för associering av mätningar, Joint Probabilistic Data Association (JPDA) och Highest Probability Data Association (HPDA), har implementerats. Klassificeringen av mål görs med en egenutvecklad metod som inte har hittats i existerande dokumentation. Systemet utvärderas genom att simuleringar görs på ungefär 8 timmar inspelad data, där dykare är närvarande vid nio olika tillfällen. Simuleringarna görs ett antal gånger för att fånga upp stokastiska beteenden. Andelen lyckade klassificeringar är hög och andelen falsklarm är låg.

Detection of an underwater target with active sonar in shallow waters such as the Baltic sea is a big challenge. This since the sound beams from the sonar will be reflected on the surfaces, sea surface and sea bottom, and the water volume itself which generates reverberation. Reverberation which will be reflected back to the receiver, is strong in intensity which give rise to many false targets in terms of classifying a target in a surveillance area. These false targets are unwanted and a real target might benefit from these miss-classifications in terms of remaining undetected. It is especially hard if the signal-to-noise ratio (SNR) is approaching zero, i.e. the target strength and the reverberation strength are equal in magnitude. The classical approach to a target detection problem is to assign a threshold value to the measurement, and the data point exceeding the threshold is classified as a target. This approach does not hold for low levels of SNR, since a threshold would not have a statistical significance and could lead to neglecting important data. Track-before-detect (TrBD) is a proposed method for low-SNR situations which tracks and detects a target based on unthresholded data. TrBD enables tracking and detecting of weak and/or stealthy targets. Due to the issues with target detection in shallow waters, the hypothesis of this thesis is to investigate the possibility to implement TrBD, and evaluate the performance of it, when applied on a low-SNR target. The TrBD is implemented with a particle filter which is a recursive Bayesian solution to the problem of integrated tracking and detection. The reverberation data was generated by filtering white noise with an Autoregressive filter of order 1. The target is assigned to propagate according to a constant velocity state space model. Two types of TrBD algorithms are implemented, one which is trained on the background and one which is not. The untrained TrBD is able to track and detect the target but only for levels of SNR down to 4dB. Lower SNR leads to the algorithm not being able to distinguish the target signal from the reverberation. The trained TrBD on the other hand, is able to perform very well for levels of SNR down to 0dB, it is able to track and detect the target and neglect the reverberation. For trajectories passing through areas with high reverberation, the target was lost for a short period of time until it could be retracked again. Overall, the TrBD was successfully implemented on the self-generated data and has a good performance for various target trajectories.