Tracking the Tracker from its Passive Sonar ML-PDA Estimates

Target motion analysis with wideband passive sonar has received much attention. Maximum likelihood probabilistic data-association (ML-PDA) represents an asymptotically efficient estimator for deterministic target motion, and is especially well-suited for low-observable targets; the results presented here apply to situations with higher signal to noise ratio as well, including of course the situation of a deterministic target observed via clean measurements without false alarms or missed detections. Here we study the inverse problem, namely, how to identify the observing platform (following a two-leg motion model) from the results of the target estimation process, i.e. the estimated target state and the Fisher information matrix, quantities we assume an eavesdropper might intercept. We tackle the problem and we present observability properties, with supporting simulation results.Comment: To appear in IEEE Transactions on Aerospace and Electronic System

Intelligent Automatic Interpretation of Active Marine Sonar

This dissertation explores the problems raised by the design and construction of a real-time sonar interpreter operating in a three dimensional marine context, and then focusses on two major research issues inherent in sonar interpretation: the treatment of observer and object motion, and the efficient exploitation of the specularity of acoustic reflection. The theoretical results derived in these areas have been tested where appropriate by computer simulation. In the context of mobile marine robotics, the registration of sensory data obtained from differing viewpoints is of paramount importance. Small marine vehicles of the type considered here do not carry sophisticated navigational equipment, and cannot be held stationary in the water for any length of time. The viewpoint registration problem is defined and analysed in terms of the new problem of motion resolution: the task of resolving the apparent motion of objects into that part due to the movement of the observer and that due to the objects' proper motion. Two solutions to this under constrained problem are presented. The first presupposes that the observer orientation is known ~ priori so that only the translational observer motion must be determined. It is applicable to two and three-dimensional situations. The second solution determines both the translational and the rotational motion of the observer, but is restricted to a two-dimensional situation. Both solutions are based on target extensively tested in two tracking techniques, and have dimensions by computer simulation. been The necessary extensions to deal with full three-dimensional motion are also discussed. The second major research issue addressed in this thesis is the efficient use of specularity. Specular echoes have a high intrinsic information content because of the alignment conditions necessary for their generation. In the marine acoustic context they provide a significant proportion of the information available from an acoustic ranger. I suggest a new method that uses directly the information present in specular reflections and the history of the vehicle motion to classify the specular echo sources and infer the local structure of the objects bearing them. The method builds on the output of a motion resolution system. Six distinct types of specular echo source are described and three properties useful for their discrimination are discussed. A suitable inference system for the analysis and classification of specular echo sources is also proposed

Poisson multi-Bernoulli mixture filtering with an active sonar using BELLHOP simulation

This paper examines the use of Poisson multi-Bernoulli mixture (PMBM) filters with realistic signal propagation models for tracking of targets with active sonar systems. In particular, the paper considers application of BELLHOP simulation to model the spatial dependence of the target probability of detection. The intention is to develop practical approaches to the problem of accurately representing sonar propagation within an advanced tracking filter

Integrated perception, modeling, and control paradigm for bistatic sonar tracking by autonomous underwater vehicles

Thesis (Sc. D.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2012.Cataloged from PDF version of thesis.Includes bibliographical references (p. 357-364).In this thesis, a fully autonomous and persistent bistatic anti-submarine warfare (ASW) surveillance solution is developed using the autonomous underwater vehicles (AUVs). The passive receivers are carried by these AUVs, and are physically separated from the cooperative active sources. These sources are assumed to be transmitting both the frequency-modulated (FM) and continuous wave (CW) sonar pulse signals. The thesis then focuses on providing novel methods for the AUVs/receivers to enhance the bistatic sonar tracking performance. Firstly, the surveillance procedure, called the Automated Perception, is developed to automatically abstract the sensed acoustical data from the passive receiver to the track report that represents the situation awareness. The procedure is executed sequentially by two algorithms: (i) the Sonar Signal Processing algorithm - built with a new dual-waveform fusion of the FM and CW signals to achieve reliable stream of contacts for improved tracking; and (ii) the Target Tracking algorithm - implemented by exploiting information and environmental adaptations to optimize tracking performance. Next, a vehicular control strategy, called the Perception-Driven Control, is devised to move the AUV in reaction to the track report provided by the Automated Perception. The thesis develops a new non-myopic and adaptive control for the vehicle. This is achieved by exploiting the predictive information and environmental rewards to optimize the future tracking performance. The formulation eventually leads to a new information-theoretic and environmental-based control. The main challenge of the surveillance solution then rests upon formulating a model that allows tracking performance to be enhanced via adaptive processing in the Automated Perception, and adaptive mobility by the Perception-Driven Control. A Unified Model is formulated in this thesis that amalgamates two models: (i) the Information-Theoretic Model - developed to define the manner at which the FM and CW acoustical, the navigational, and the environmental measurement uncertainties are propagated to the bistatic measurement uncertainties in the contacts; and (ii) the Environmental-Acoustic Model - built to predict the signal-to-noise power ratios (SNRs) of the FM and CW contacts. Explicit relationships are derived in this thesis using information theory to amalgamate these two models. Finally, an Integrated System is developed onboard each AUV that brings together all the above technologies to enhance the bistatic sonar tracking performance. The system is formulated as a closed-loop control system. This formulation provides a new Integrated Perception, Modeling, and Control Paradigm for an autonomous bistatic ASW surveillance solution using AUVs. The system is validated using the simulated data, and the real data collected from the Generic Littoral Interoperable Network Technology (GLINT) 2009 and 2010 experiments. The experiments were conducted jointly with the NATO Undersea Research Centre (NURC).by Raymond Hon Kit Lum.Sc.D

Application of the EM algorithm for the multitarget/multisensor tracking problem

Caption title.Includes bibliographical references (p. 27-29).Supported by the Laboratory for Information and Decision Systems, Massachusetts Institute of Technology. DAAH04-95-1-0103Karl J. Molnar, James W. Modestino

Wi-Fi based people tracking in challenging environments

People tracking is a key building block in many applications such as abnormal activity detection, gesture recognition, and elderly persons monitoring. Video-based systems have many limitations making them ineffective in many situations. Wi-Fi provides an easily accessible source of opportunity for people tracking that does not have the limitations of video-based systems. The system will detect, localise, and track people, based on the available Wi-Fi signals that are reflected from their bodies. Wi-Fi based systems still need to address some challenges in order to be able to operate in challenging environments. Some of these challenges include the detection of the weak signal, the detection of abrupt people motion, and the presence of multipath propagation. In this thesis, these three main challenges will be addressed. Firstly, a weak signal detection method that uses the changes in the signals that are reflected from static objects, to improve the detection probability of weak signals that are reflected from the person’s body. Then, a deep learning based Wi-Fi localisation technique is proposed that significantly improves the runtime and the accuracy in comparison with existing techniques. After that, a quantum mechanics inspired tracking method is proposed to address the abrupt motion problem. The proposed method uses some interesting phenomena in the quantum world, where the person is allowed to exist at multiple positions simultaneously. The results show a significant improvement in reducing the tracking error and in reducing the tracking delay

Tracking interacting targets in multi-modal sensors

PhDObject tracking is one of the fundamental tasks in various applications such as surveillance, sports, video conferencing and activity recognition. Factors such as occlusions, illumination changes and limited field of observance of the sensor make tracking a challenging task. To overcome these challenges the focus of this thesis is on using multiple modalities such as audio and video for multi-target, multi-modal tracking. Particularly, this thesis presents contributions to four related research topics, namely, pre-processing of input signals to reduce noise, multi-modal tracking, simultaneous detection and tracking, and interaction recognition. To improve the performance of detection algorithms, especially in the presence of noise, this thesis investigate filtering of the input data through spatio-temporal feature analysis as well as through frequency band analysis. The pre-processed data from multiple modalities is then fused within Particle filtering (PF). To further minimise the discrepancy between the real and the estimated positions, we propose a strategy that associates the hypotheses and the measurements with a real target, using a Weighted Probabilistic Data Association (WPDA). Since the filtering involved in the detection process reduces the available information and is inapplicable on low signal-to-noise ratio data, we investigate simultaneous detection and tracking approaches and propose a multi-target track-beforedetect Particle filtering (MT-TBD-PF). The proposed MT-TBD-PF algorithm bypasses the detection step and performs tracking in the raw signal. Finally, we apply the proposed multi-modal tracking to recognise interactions between targets in regions within, as well as outside the cameras’ fields of view. The efficiency of the proposed approaches are demonstrated on large uni-modal, multi-modal and multi-sensor scenarios from real world detections, tracking and event recognition datasets and through participation in evaluation campaigns

Mathematical Models and Monte-Carlo Algorithms for Improved Detection of Targets in the Commercial Maritime Domain

Commercial Vessel Traffic Monitoring Services (VTMSs) are widely used by port authorities and the military to improve the safety and efficiency of navigation, as well as to ensure the security of ports and marine life as a whole. Technology based on the Kalman Filtering framework is in widespread use in modern operational VTMS systems. At a research level, there has also been a significant interest in Particle Filters, which are widely researched but far less widely applied to deliver an operational advantage. The Monte-Carlo nature of Particle Filters places them as the ideal candidate for solving the highly non-linear, non-Gaussian problems encountered by modern VTMS systems. However, somewhat counter-intuitively, while Particle Filters are best suited to exploit such non-linear, non-Gaussian problems, they are most frequently used within a context that is mostly linear and Gaussian. The engineering challenge tackled by the PhD project reported in this thesis was to study and experiment with models that are well placed to capitalise on the abilities of Particle Filters and to develop solutions that make use of such models to deliver a direct operational advantage in real applications within the commercial maritime domain