The main objective of this dissertation is to develop mean-squared error performance predictions for multiple target tracking. Envisioned as an approximate Cramer-Rao lower bound, these performance predictions allow a tracking system designer to quickly and efficiently predict the general performance trends of a tracking system. The symmetric measurement equation (SME) approach to multiple target tracking (MTT) lies at the heart of our method. The SME approach, developed by Kamen et al., offers a unique solution to the data association problem. Rather than deal directly with this problem, the SME approach transforms it into a nonlinear estimation problem. In this way, the SME approach sidesteps report-to-track associations. Developing performance predictions using the SME approach requires work in several areas: (1) extending SME tracking theory, (2) developing nonlinear filters for SME tracking, and (3) understanding techniques for computing Cramer-Rao error bounds in nonlinear filtering. First, on the SME front, we extend SME tracking theory by deriving a new set of SME equations for motion in two dimensions. We also develop the first realistic and efficient method for SME tracking in three dimensions. Second, we apply, for the first time, the unscented Kalman filter (UKF) and the particle filter to SME tracking. Using Taylor series analysis, we show how different SME implementations affect the performance of the EKF and UKF and show how Kalman filtering degrades for the SME approach as the number of targets rises. Third, we explore the Cramer-Rao lower bound (CRLB) and the posterior Cramer-Rao lower bound (PCRB) for computing MTT error predictions using the SME. We show how to compute performance predictions for multiple target tracking using the PCRB, as well as address confusion in the tracking community about the proper interpretation of the PCRB for tracking scenarios.

El compuesto PF9601N es un derivado de propargilamina y un potente inhibidor irreversible de la enzima monoamino oxidasa B (IMAO-B) el cual fue identificado por nuestro grupo tras una extensiva búsqueda de potenciales IMAOs. Además de su potente capacidad inhibidora, el PF9601N posee varias propiedades neuroprotectoras demostradas en varios modelos animales y celulares de la enfermedad de Parkinson (EP). Estos efectos, los cuales han sido relacionados con la presencia de la propargilamina en su estructura, están mediados por acciones en vías involucradas en la neurodegeneración observada en otras enfermedades neurodegenerativas como la enfermedad de Alzheimer (EA). Así, para estudiar más en detalle las propiedades beneficiosas del PF9601N investigamos sus efectos en un modelo in vivo de excitotoxicidad, un mecanismo implicado en el daño neuronal observado en las enfermedades neurodegenerativas. El hallazgo de que el PF9601N era capaz de evitar el daño excitotóxico mediante la disminución de la liberación inducida de glutamato y aspartato, y el aumento de la liberación de taurina así como mediante la prevención de la activación glial y la apoptosis proporcionó un valor añadido a este compuesto para ser considerado en la terapia de estas enfermedades. El tratamiento actual para la EA se basa principalmente en el uso de inhibidores de las enzimas colinesterasas (IChEs). Sin embargo, estos fármacos no son capaces de disminuir la progresión de la enfermedad y sólo producen una mejora temporal de los síntomas. Actualmente está ampliamente aceptado que la EA es una enfermedad multifactorial. En este contexto, la aproximación farmacológica más novedosa, conocida como aproximación de los MTDL (de las siglas en inglés “ligandos dirigidos hacia múltiples dianas”), propone el uso de compuestos multifuncionales capaces de abrazar varias propiedades biológicas. Esta tesis se centra en el estudio de la relación estructura-actividad (REA) así como la evaluación biológica de varios compuestos híbridos especialmente diseñados y sintetizados para actuar sobre múltiples factores involucrados en la EA. Los compuestos híbridos combinan la porción de bencilpiperidina de Donepecilo, un anticolinesterásico ampliamente utilizado en el tratamiento de la enfermedad, con el grupo propargilamina o indolil propargilamina presente en PF9601N, con el objetivo de obtener un compuesto capaz de retener la capacidad inhibidora de MAO así como las propiedades neuroprotectoras y antiapoptóticas de PF9601N. El trabajo presentado en esta tesis demuestra que algunos de los compuestos híbridos son potentes IMAOs (rango nM) y moderadamente potentes IChEs (rango subM). De entre todos los compuestos evaluados, ASS234 resultó ser un potente inhibidor de la agregación del péptido β−amiloide (A) y fue capaz de ejercer una acción protectora frente a la toxicidad inducida por Aβ y H2O2 en células neuronales. En resumen, los datos presentados en esta tesis doctoral sugieren que el compuesto ASS234 es un compuesto multidiana muy prometedor que podría tener un papel modificador en la EA dada su demostrada capacidad de interactuar con varias dianas involucradas en la patogénesis de esta enfermedad. / PF9601N is a propargylamine-containing irreversible monoamine oxidase B inhibitor (MAOBI) previously identified by our group in an extensive screen of potential MAOIs. Besides its potent inhibitory capacity, it possesses several neuroprotective properties demonstrated in different animal and cellular models of Parkinson’s disease (PD). The beneficial effects of PF9601N, which have been related to the propargylamine group present in the molecule, are mediated through actions in pathways that are commonly involved in the neurodegeneration observed in other neurodegenerative disorders such as Alzheimer’s disease (AD), thus making this molecule a promising agent in the therapy of this disease as well. Thus, to study the beneficial properties of PF9601N in depth, we investigated its effects against an in vivo model of excitotoxicity, an important mechanism involved in the neuronal damage observed in neurodegenerative diseases. The finding that PF9601N was able to prevent the induced excitotoxic damage by decreasing the evoked release of excitatory neurotransmitters and decreasing the output of the inhibitory and neuroprotective taurine as well as preventing the induced glial activation and apoptosis gave more value to this compound to be considered in the therapy. The current treatment for AD is the use of cholinesterase inhibitors (ChEIs) although there is also a NMDA receptor antagonist. However, far from stopping the disease’s progression, these drugs only produce a temporary symptomatic benefit, thus highlighting an urgent need to provide real disease-modifying drugs. At present, the most accepted notion is that AD is a multifactorial disease caused by many different factors and thus drug therapy with multifunctional compounds, the so-called multi-target-directed ligand (MTDL) approach, embracing diverse biological properties will have noticeable advantages over individual-target drugs or cocktails of drugs. In this context, this thesis focuses on the structure-activity relationship (SAR) study and the biological evaluation of different hybrid compounds specifically designed and synthesised to target multiple factors involved in AD. The hybrid molecules combine the benzyl piperidine moiety of Donepezil, a commonly used anticholinesterasic for the treatment of AD, with the propargylamine or the indolyl propargylamine substructure of PF9601N, with the aim of retaining the MAO inhibitory capacity as well as the neuroprotective and antiapoptotic properties observed for this compound. The work presented in this thesis demonstrates that some hybrid compounds are potent MAOIs (nM range) and moderately potent ChEIs (submicroM range). Among them, ASS234 has also been shown to reduce Αβ fibrillogenesis, and to protect neuronal cells from A and H2O2 toxicity. Thus, this compound has proved to be able to block the Aβ-induced cell death in two ways: by preventing caspase cleavage and activation and blocking LDH release. Overall, the present data suggest ASS234 as a promising MTDL that may have a potential disease-modifying role in the treatment of AD since it is able to interact with diverse targets involved in the pathogenesis underlying AD.

<p> In this thesis, we address the problem of sensor management with particular application to using unmanned aerial vehicles (U AV s) for multi target tracking. Also, we present a decision based approach for controlling information flow in decentralized multi-target multi-sensor data fusion.</p> <p> Considering the problem of sensor management for multitarget tracking, we study the problem of decision based control of a group of UAVs carrying out surveillance over a region that includes a number of moving targets. The objective is to maximize the information obtained and to track as many targets as possible with the maximum possible accuracy. Uncertainty in the information obtained by each UAV regarding the location of the ground targets are addressed in the problem formulation. We propose an altered version of a classical Value Iteration algorithm, one of the most commonly used techniques to calculate the optimal policy for Markov Decision Processes (MDPs) based on Dynamic Element Matching (DEM) algorithms. DEM algorithms, widely used for reducing harmonic distortion in Digital-to-Analog converters, are used as a core element in the modified algorithm. We introduce and demonstrate a number of new performance metrics, to verify the effectiveness of an MDP policy, especially useful for quantifying the impact of the modified DEM-based Value Iteration algorithm on an MDP policy. Also, we introduce a multi-level hierarchy of MDPs controlling each of the UAV s. Each level in the hierarchy solves a problem at a different level of abstraction. Simulation results are presented on a representative multisensor-multitarget tracking problem showing a significant improvement in performance compared to the classical algorithm. The proposed method demonstrated robust performance while guaranteeing polynomial computational complexity.</p> <p> Decentralized multisensor-multitarget tracking has numerous advantages over singlesensor or single-platform tracking. In this thesis, we present a solution for one of the main problems in decentralized tracking, namely, distributed information transfer and fusion among the participating platforms. We present a decision mechanism for collaborative distributed data fusion that provides each platform with the required data for the fusion process while substantially reducing redundancy in the information flow in the overall system. We consider a distributed data fusion system consisting of platforms that are decentralized, heterogenous, and potentially unreliable. The proposed approach, which is based on Markov Decision Processes with introduced hierarchial structure will control the information exchange and data fusion process. The information based objective function is based on the Posterior Cramer-Rao lower bound and constitutes the basis of a reward structure for Markov decision processes which are used, together with decentralized lookup substrate, to control the data fusion process. We analyze three distributed data fusion algorithms - associated measurement fusion, tracklet fusion and track-to-track fusion. The thesis also provides a detailed analysis of communication and computational load in distributed tracking algorithms. Simulation examples demonstrate the operation and the performance results of the system.</p> <p> In this thesis, we also present the development of a multisensor-multitarget tracking testbed for simulating large-scale distributed scenarios, capable of handling multiple, heterogeneous sensors, targets and data fusion methods</p>. / Thesis / Doctor of Philosophy (PhD)

<p> In this thesis new joint target tracking and classification techniques for aspect-dependent measurements are developed. Joint target tracking and classification methods can result in better tracking and classification performance than those treating these as two separate problems. Significant improvement in state estimation and classification performance can be achieved by exchanging useful information between the tracker and the classifier. Target classification in many target tracking algorithms is not typically done by taking into consideration the target-to-sensor orientation. However, the feature information extracted from the signal that originated from the target is generally a strong function of the target-to-sensor orientation. Since sensor returns are sensitive to this orientation, classification from a single sensor may not give exact target classes. Better classification results can be obtained by fusing feature measurements from multiple views of a target. In multitarget scenarios, handling the classification becomes more challenging due to the identifying the feature information corresponding to a target. That is, it is difficult to identify the origin of measurements. In this case, feature measurement origin ambiguities can be eliminated by integrating the classifier into multiframe data association. This technique reduces the ambiguity in feature measurements while improving track purity. </p> <p> A closed form expression for multiaspect target classification is not feasible. Then, training based statistical modeling can be used to model the unknown feature measurements of a target. In this thesis, the Observable Operator Model (OOM), a better alternative to the Hidden Markov Model (HMM), is used to capture unknown feature distribution of each target and thus can be used as a classifier. The proposed OOM based classification technique incorporates target-to-sensor orientation with a sequence of feature information from multiple sensors. Further, the multi-aspect classifier can be modeled using the OOM to handle unknown target orientation. The target orientation estimation using OOM can also be used to find improved estimates of the states of highly maneuverable targets with noisy kinematic measurements. One limiting factor in obtaining accurate estimates of highly maneuvering target states is the high level of uncertainty in velocity and acceleration components. The target orientation information is helpful in alleviating this problem to accurately determine the velocity and acceleration components. </p> <p> Various simulation studies based on two-dimensional scenarios are presented in this thesis to demonstrate the merits of the proposed joint target tracking and classification algorithms that use aspect-dependent feature measurements.</p> / Thesis / Doctor of Philosophy (PhD)

Data fusion is the methodology of efficiently combining the relevant information from different sources. The goal is to achieve estimates and inferences with better confidence than those achievable by relying on a single source. Initial data fusion applications were predominantly in defense: target tracking, threat assessment and land mine detection. Nowadays, data fusion is applied to robotics (e.g., environment identification for navigation), medicine (e.g., medical diagnosis), geoscience (e.g., data integration from different sources) and industrial engineering (e.g., fault detection). This thesis focuses on data fusion for distributed multisensor tracking systems. In these systems, each sensor can provide the information as measurements or local estimates, i.e., tracks. The purpose of this thesis is to advance the research in the fusion of local estimates for multisensor multitarget tracking systems, namely, track fusion. This study also proposes new methods for track-to-track association, which is an implicit subproblem of track fusion. The first contribution is for the case where local sensors perform tracking using particle filters (Monte Carlo based methods). A method of associating tracks estimated through labeled particle clouds is developed and demonstrated with subsequent fusion. The cloud-to-cloud association cost is devised together with computation methods for the general and specialized cases. The cost introduced is proved to converge (with increasing clouds cardinality) toward the corresponding distance between the underlying distributions. In order to simulate the method introduced, a particle filter labeled at particle level was developed, based on the Probability Hypothesis Density (PHD) particle filter. The second contribution is for the case where local sensors produce tracks using Kalman filter-type estimators, in the form of track state estimate and track state covariance matrix. For this case the association and fusion is improved in both terms of accuracy and identity, by introducing at each fusion time the prior information (both estimate and identity) from the previous fusion time. The third contribution is for the case where local sensors produce track estimates under the form of MHT, therefore where each local sensor produces several hypotheses of estimates. A method to use the information from other sensors in propagating each sensor's internal hypotheses over time is developed. A practical fusion method for real world local tracking sensors, i.e., asynchronous and with incomplete information available, is also developed in this thesis. / Thesis / Doctor of Philosophy (PhD)

In this dissertation, we seek to improve and advance the use of the finite-set statistics (FISST) approach to multitarget tracking. We consider a subsea multitarget tracking application that poses several challenges due to factors, such as, clutter/environmental noise, joint target and sensor state dependent measurement uncertainty, target-measurement association ambiguity, and sub-optimal sensor placement. The specific application that we consider is that of an underwater mobile sensor that measures the relative angle (i.e., bearing angle) to sources of acoustic noise in order to track one or more ships (targets) in a noisy environment. However, our contributions are generalizable for a variety of multitarget tracking applications. We build upon existing algorithms and address the problem of improving tracking performance for multiple maneuvering targets by incorporation several target motion models into a FISST tracking algorithm known as the probability hypothesis density filter. Moreover, we develop a novel method for associating measurements to targets using the Bayes factor, which improves tracking performance for FISST methods as well as other approaches to multitarget tracking. Further, we derive a novel formulation of Bayes risk for use with set-valued random variables and develop a real-time planner for sensor motion that avoids local minima that arise in myopic approaches to sensor motion planning. The effectiveness of our contributions are evaluated through a mixture of real-world and simulated data. / Doctor of Philosophy / In this dissertation, we seek to improve the accuracy of multitarget tracking algorithms based on finite-set statistics (FISST). We consider a subsea tracking application where a sensor seeks to estimate the position of nearby ships using measurements of the relative sensor-ship angle. Several challenges arise in our application due to factors such as environmental noise and limited resolution of measurements. Our work advances FISST algorithms by expanding upon existing methods and deriving novel solutions to mitigate challenges. We address the non-trivial question of improving tracking accuracy by planning of future sensor motion. We show that our contributions greatly improve tracking accuracy by evaluating algorithm performance using a mixture of real-world and simulated data.

This thesis presents a clustering-based approach to decrease the computational cost of data association in multi-target tracking. This is achieved by clustering the sensor tracks using approximate distance functions, thereby decreasing the number of possible associations and the need to calculate expensive statistical distances between tracks. The studied tracking problem includes passive and active sensors with built-in filters. Statistical and non-statistical distance functions were designed to account for the characteristics of the different combinations of sensors. The computational cost and accuracy of these distance functions were evaluated and compared. Analysis is done in a simulated environment with randomly positioned targets and sensors. Simulations show that there are approximate distances with a cost of calculation ten times cheaper than the true statistical distance, with only minor drops in accuracy. Spectral clustering is used on these distances to divide complex association problems into sub-problems. This algorithm is evaluated on a large number of random scenarios. The mean size of the largest sub-problem is 40 % of the original, and the mean number of errors in the clustering is 5 %.

No description available.

Tracking multiple targets with uncertain target dynamics is a difficult problem, especially with nonlinear state and/or measurement equations. Random finite set theory provides a rigorous foundation to multitarget tracking problems. It provides a framework to represent the full multitarget posterior in contrast to other conventional approaches. However, the computational complexity of performing multitarget recursion grows exponentially with the number of targets. The Probability Hypothesis Density (PHD) filter, which only propagates the first moment of the multitarget posterior, requires much less computational complexity. This thesis addresses some of the essential issues related to practical multitarget tracking problems such as tracking target maneuvers, stealthy targets, multitarget tracking in a distributed framework. With maneuvering targets, detecting and tracking the changes in the target motion model also becomes important and an effective solution for this problem using multiple-model based PHD filter is proposed. The proposed filter has the advantage over the other methods in that it can track a timevarying number of targets in nonlinear/ non-Gaussian systems. Recent developments in stealthy military aircraft and cruise missiles have emphasized the need to t rack low SNR targets. The conventional approach of thresholding the measurements throws away potential information and thus results in poor performance in tracking dim targets. The problem becomes even more complicated when multiple dim targets are present in the surveillance region. A PHD filter based recursive track-before-detect approach is proposed in this thesis to track multiple dim targets in a computationally efficient way. This thesis also investigates multiple target tracking using a network of sensors. Generally, sensor networks have limited energy, communication capability and computational power. The crucial consideration is what information needs to be transmitted over the network in order to perform online estimation of the current state of the monitored system, whilst attempting to minimize communication overhead. Finally, a novel continuous approximation approach for nonlinear/ non-Gaussian Bayesian tracking system based on spline interpolation is presented. The resulting filter has the advantages over the widely-known discrete particle based approximation approach in that it does not suffer from degeneracy problems and retains accurate density over the state space. The filter is general enough to be applicable to nonlinear/non-Gaussian system and the density could even be multi-modal. / Thesis / Candidate in Philosophy

<p>Multi-Target Tracking (MTT), where the number of targets as well as their states are time-varying, concerns with the estimation of both the number of targets and the individual states from noisy sensor measurements, whose origins are unknown. Filtering typically produces the best estimates of the target state based on all measurements up to current estimation time. Smoothing or retrodiction, which uses measurements beyond the current estimation time, provides better estimation of target states. This thesis proposes smoothing methods for various estimation methods that produce delayer, but better, estimates of the target states.</p> <p>First, we propose a novel smoothing method for the Probability Hypothesis Density (PHD) estimator. The PHD filer, which propagates the first order statistical moment of the multitarget state density, a computationally efficient MTT algorithm. By evaluating the PHD, the number of targets as well as their individual states can be extracted. Recent Sequential Monte Carlo (SMC) implementations of the PHD filter have paved the way to its application to realistic nonlinear non-Gaussian problems. The proposed PHD smoothing method involves forward multitarget filtering using the standard PHD filter recursion followed by backward smoothing recursion using a novel recursive formula.</p> <p>Second, we propose a Multiple Model PH (MMPHD) smoothing method for tracking of maneuvering targets. Multiple model approaches have been shown to be effective for tracking maneuvering targets. MMPHD filter propagates mode-conditioned PHD recursively. The proposed backward MMPHD smoothing algorithm involves the estimation of a continuous state for target dynamic as well as a discrete state vector for the mode of target dynamics.</p> <p>Third, we present a smoothing method for the Gaussian Mixture PHD (GMPHD) state estimator using multiple sensors. Under linear Gaussian assumptions, the PHD filter can be implemented using a closed-form recursion, where the PHD is represented by a mixture of Gaussian functions. This can be extended to nonlinear systems by using the Extended Kalman Filter (EKF) or the Unscented Kalman Filter (UKF). In the case of multisenor systems, a sequential update of the PHD has been suggested in literature. However, this sequential update is susceptible to imperfections in the last sensor. In this thesis, a parallel update for GMPHD filter is proposed. The resulting filter outputs are further improved using a novel closed-form backward smoothing recursion.</p> <p>Finally, we propose a novel smoothing method for Kalman based Interacting Multiple Model (IMM) estimator for tracking agile targets. The new method involves forwarding filtering followed by backward smoothing while maintaining the fundamental spirit of the IMM. The forward filtering is performed using the standard IMM recursion, while the backward smoothing is performed using a novel interacting smoothing recursion. This backward recursion mimics the IMM estimator in the backward direction, where each mode conditioned smoother uses standard Kalman smoothing recursion.</p> / Thesis / Doctor of Philosophy (PhD)