Novel Bayesian smoothing algorithms for improved track initiation and maintenance in clutter

University of Technology, Sydney. Faculty of Engineering.Target tracking is a well established field with over fifty years of intense research. While in its core, it deals with estimating targets dynamic states, it is also a critical component of all ” Situation Awareness” and threat assessment systems. These higher layer applications take decisions on important questions like number of targets, positions of them, the instant and position of their initiation, the instant and position of their maneuvers and above all, which of them are threatening and/or friendly. The lower level target tracking algorithms feed the necessary information to these decision taking systems. There are a number of target tracking algorithms to cater for the need of such systems. Most of these available algorithms are based on filtering theory. But it is established that smoothing increases the accuracy of the systems at the expense of a slight lag between the instant of estimation and the instant at which the parameter of interest is being estimated. Hence smoothing is not widely used for practical target tracking applications. However, the situation awareness system is expected to perform better if more precise information is obtained about initiation and termination of the targets along with improved discrimination of true/false targets. This thesis addresses the problem of improved track initiation and maintenance with the smoothing framework to provide better information. It first reviews target tracking and filtering literature. It introduces the concept of random set smoother and derives the IPDA smoother under linear Gaussian assumption. IPDA smoother is also derived by extending the PDA smoother. Finally a theoretical link is established between Random Set smoothing and IPDA smoothing framework. To extend the domain into multiple sensor scenario, the problem of out-of-sequence measurements is also addressed in this thesis under target existence uncertainty. Several realistic scenarios are simulated and the results are verified

A Modified Bayesian Framework for Multi-Sensor Target Tracking with Out-of-Sequence-Measurements

Target detection and tracking is important in military as well as in civilian applications. In order to detect and track high-speed incoming threats, modern surveillance systems are equipped with multiple sensors to overcome the limitations of single-sensor based tracking systems. This research proposes the use of information from RADAR and Infrared sensors (IR) for tracking and estimating target state dynamics. A new technique is developed for information fusion of the two sensors in a way that enhances performance of the data association algorithm. The measurement acquisition and processing time of these sensors is not the same; consequently the fusion center measurements arrive out of sequence. To ensure the practicality of system, proposed algorithm compensates the Out of Sequence Measurements (OOSMs) in cluttered environment. This is achieved by a novel algorithm which incorporates a retrodiction based approach to compensate the effects of OOSMs in a modified Bayesian technique. The proposed modification includes a new gating strategy to fuse and select measurements from two sensors which originate from the same target. The state estimation performance is evaluated in terms of Root Mean Squared Error (RMSE) for both position and velocity, whereas, track retention statistics are evaluated to gauge the performance of the proposed tracking algorithm. The results clearly show that the proposed technique improves track retention and and false track discrimination (FTD)

Efficient Kalman Filtering and Smoothing

The Kalman filter and Kalman smoother are important components in modern multitarget tracking systems. Their application are vast which include guidance, navigation and control of vehicles. On top of that, when the motion model is uncertain, MultipleModel approach can be combined with the filtering and smoothing method. However, with large amount of retrodiction window size, number of motion models and large number of targets, this process can become very computationally intensive and thus time consuming. Very often, real-time processing is needed in the world of tracking and therefore, this computational bottleneck become a problem. This is the motivation behind this thesis, to reduce the computational complexity when multi-target, multiwindow or multi-model applications are used. This thesis presents several approaches to tackle this multi-dimensional problems in terms of complexity while maintaining satisfactory precision. A natural step forward will be in leveraging the modern multi-core architectures. However, in order to parallelize such process, these algorithms have to be reformulated to be fitted into the parallel processors. In order to parallelise multi-target and multi-window scenario, this thesis introduce nested parallelism and prefix-sum algorithm to tackle the problem and realised this on Intel Knights Landing (KNL) Processor and OpenMP memory model. On the other hand, in the case of limited parallel resources, this thesis also develop alternatives called Fast Kalman smoother (FRTS) to lower the computation complexity due to multi-window problem. Specifically the smoother algorithm is reformulated such that it is computationally independent of number of window size in the fixed-lag configuration. Although the underlying mathematics is the same as the conventional approach, FRTS introduced numerical stability issue which makes the smoother unstable. Therefore, this thesis introduce the idea of condition number to monitor the deterioration rate in order to correct the numerical error once the pre-set threshold is breached. In addition to the large number of targets and retrodiction window size mentioned earlier, the number of models running simultaneously make the problem even more challenging in the perspective of real-time performance. Since such algorithms are the fundamental backbone of a large amount of multi-frame tracking algorithms, it would be beneficial to have a multi-model algorithm that is computationally independent to number of model utilised. Consequently, this thesis extend the FRTS concept to fixed-lag Multiple-Model smoothing method to achieve this goal. The proposed algorithms are compared and tested through an extensive and exhaustive set of evaluations against the literature, and discuss the relative merits. These evaluations show that these contributions pave a way to secure substantial performance gains for multi-dimensional tracking algorithms over conventional approaches

Multiple Object Trajectory Estimation Using Backward Simulation

This paper presents a general solution for computing the multi-object posterior for sets of trajectories from a sequence of multi-object (unlabelled) filtering densities and a multi-object dynamic model. Importantly, the proposed solution opens an avenue of trajectory estimation possibilities for multi-object filters that do not explicitly estimate trajectories. In this paper, we first derive a general multi-trajectory backward smoothing equation based on random finite sets of trajectories. Then we show how to sample sets of trajectories using backward simulation for Poisson multi-Bernoulli filtering densities, and develop a tractable implementation based on ranked assignment. The performance of the resulting multi-trajectory particle smoothers is evaluated in a simulation study, and the results demonstrate that they have superior performance in comparison to several state-of-the-art multi-object filters and smoothers.Comment: Accepted for publication in IEEE Transactions on Signal Processin

World Modeling for Intelligent Autonomous Systems

The functioning of intelligent autonomous systems requires constant situation awareness and cognition analysis. Thus, it needs a memory structure that contains a description of the surrounding environment (world model) and serves as a central information hub. This book presents a row of theoretical and experimental results in the field of world modeling. This includes areas of dynamic and prior knowledge modeling, information fusion, management and qualitative/quantitative information analysis

World Modeling for Intelligent Autonomous Systems

The functioning of intelligent autonomous systems requires constant situation awareness and cognition analysis. Thus, it needs a memory structure that contains a description of the surrounding environment (world model) and serves as a central information hub. This book presents a row of theoretical and experimental results in the field of world modeling. This includes areas of dynamic and prior knowledge modeling, information fusion, management and qualitative/quantitative information analysis

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

World Modeling for Intelligent Autonomous Systems

Within the scope of this work, we have attained a row of theoretical and experimental results in the field of world modeling as well as gathered significant experience and expertise. The covered topics include concepts and approaches for dynamic and prior knowledge modeling, information association, fusion and management as well as their practical realization and experimental evaluation