<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.

<p>This thesis discusses model based tracking of radar echoes in tanks with liquid content. The errors in the measurements are not always random in these cases, and the interference that occurs when nearby echoes moves in relation to each other makes tracking difficult. </p><p>The tracking problem can be broken down to four parts; track initiation, track destruction, association of the measured echoes to the tracks and to update the tracks with the associated echoes. </p><p>The radar measurement is primarily made on the surface. However, additional echoes from the tank floor or other structures and double bounces are normally present. There are often linear relations between the positions of the measured echoes. </p><p>Kalman filters are typically used to estimate the positions of the echoes. Best performance is achieved if a model of the entire tank is used; however that model must be tailored to match the exact conditions in the tank. It is also possible to consider the echoes to be independent and track their positions using separate models. </p><p>Some of the possiblemethods to automatically build a model for a specific tank have been explored.</p>

This thesis discusses model based tracking of radar echoes in tanks with liquid content. The errors in the measurements are not always random in these cases, and the interference that occurs when nearby echoes moves in relation to each other makes tracking difficult. The tracking problem can be broken down to four parts; track initiation, track destruction, association of the measured echoes to the tracks and to update the tracks with the associated echoes. The radar measurement is primarily made on the surface. However, additional echoes from the tank floor or other structures and double bounces are normally present. There are often linear relations between the positions of the measured echoes. Kalman filters are typically used to estimate the positions of the echoes. Best performance is achieved if a model of the entire tank is used; however that model must be tailored to match the exact conditions in the tank. It is also possible to consider the echoes to be independent and track their positions using separate models. Some of the possiblemethods to automatically build a model for a specific tank have been explored.

Ces travaux se placent dans le cadre général de l'assistance au conducteur et plus particulièrement de la sécurité. L'objectif est ici de surveiller l'environnement d'un véhicule grâce à un capteur télémétrique à balayage et d'informer le conducteur de situations potentiellement dangereuses. Ce dispositif permet alors d'envisager une manoeuvre d'évitement ou d'atténuation de collision. Deux points particuliers ont retenu notre attention : la détection d'objets qui occupe une place privilégiée car elle conditionne directement les performances globales de la méthode, et le processus d'association/suivi qui doit permettre d'associer efficacement les mesures disponibles à chaque objet suivi. Les données télémétriques utilisées nécessitent de passer par une étape de détection afin d'estimer le nombre d'objets présents dans la scène et leur distance au capteur, en procédant à une agrégation des mesures liées au même objet. Nous proposons en particulier dans ce mémoire une méthode de détection d'objets qui exploite non seulement la nature des mesures disponibles mais également les caractéristiques géométriques particulières liées au contexte applicatif. L'approche retenue pour l'étape d'association repose sur les méthodes d'association probabiliste de données qui permettent notamment de considérer le fait qu'une mesure disponible puisse ne pas être liée à un objet, en exploitant donc directement les notions de probabilité de détection et de fausse alarme. Ces probabilités, et notamment la probabilité de détection, demeurent non seulement fortement liées au détecteur, mais également au contexte de la scène : contexte capteur/objet et contexte objet/objet. Pour pouvoir intégrer ces informations globales de contexte, nous proposons une méthode d'association-suivi basée sur les réseaux bayésiens qui autorise l'intégration de paramètres liés aux caractéristiques des objets et du capteur dans la détermination de la probabilité de détection.

Multiple target tracking (MTT) is the process of identifying the number of targets present in a surveillance region and the state estimates, or track, of each target. MTT remains a challenging problem due to the NP-hard data association step, where unlabeled measurements are identified as either a measurement of an existing target, a new target, or a spurious measurement called clutter. Existing techniques suffer from at least one of the following drawbacks: divergence in clutter, underlying assumptions on the number of targets, high computational complexity, time-consuming implementation, poor performance at low detection rates, and/or poor track continuity. Our goal is to develop an efficient MTT algorithm that is simple yet effective and that maintains track continuity enabling persistent tracking of an unknown number of targets. A related field to tracking is regression analysis, where the parameters of static signals are estimated from a batch or a sequence of data. The random sample consensus (RANSAC) algorithm was developed to mitigate the effects of spurious measurements, and has since found wide application within the computer vision community due to its robustness and efficiency. The main concept of RANSAC is to form numerous simple hypotheses from a batch of data and identify the hypothesis with the most supporting measurements. Unfortunately, RANSAC is not designed to track multiple targets using sequential measurements.To this end, we have developed the recursive-RANSAC (R-RANSAC) algorithm, which tracks multiple signals in clutter without requiring prior knowledge of the number of existing signals. The basic premise of the R-RANSAC algorithm is to store a set of RANSAC hypotheses between time steps. New measurements are used to either update existing hypotheses or generate new hypotheses using RANSAC. Storing multiple hypotheses enables R-RANSAC to track multiple targets. Good tracks are identified when a sufficient number of measurements support a hypothesis track. The complexity of R-RANSAC is shown to be squared in the number of measurements and stored tracks, and under moderate assumptions R-RANSAC converges in mean to the true states. We apply R-RANSAC to a variety of simulation, camera, and radar tracking examples.