Novel methods for multi-target tracking with applications in sensor registration and fusion

Maintaining surveillance over vast volumes of space is an increasingly important capability for the defence industry. A clearer and more accurate picture of a surveillance region could be obtained through sensor fusion between a network of sensors. However, this accurate picture is dependent on the sensor registration being resolved. Any inaccuracies in sensor location or orientation can manifest themselves into the sensor measurements that are used in the fusion process, and lead to poor target tracking performance. Solutions previously proposed in the literature for the sensor registration problem have been based on a number of assumptions that do not always hold in practice, such as having a synchronous network and having small, static registration errors. This thesis will propose a number of solutions to resolving the sensor registration and sensor fusion problems jointly in an efficient manner. The assumptions made in previous works will be loosened or removed, making the solutions more applicable to problems that we are likely to see in practice. The proposed methods will be applied to both simulated data, and a segment of data taken from a live trial in the field

Tracking an air target in multistatic radar networks

The first radars used in military scenarios to detect enemies were bistatic because the technology that would allow a transmitter and a receiver to use the same antenna had not been developed. Then, with the development of monostatic radars, there was almost no interest in the bistatic radars subject. Nowadays, due to the fact that monostatic radars alone have reached its limits in terms of performance and because of the existence of new threats, the interest in bistatic and multistatic radars should last longer. Bistatic and multistatic radars are particularly interesting in military scenarios where it is important to be able to detect and track stealth targets and also to be able to operate with minimized risks of being affected by jamming attacks. This thesis investigates how much multistatic radars can surpass stand alone monostatic radars when attempting to track a target. Simulations with different geometries and different target trajectories are performed in order to assess the tracking performance in each scenario. Tracking performance is assessed in terms of estimated position, velocity and acceleration accuracies. Different geometries include monostatic radar, netted monostatic radars, bistatic radars with target crossing and not crossing the baseline, multistatic radars with only 1 TX and many RXs, multistatic radars with many TXs and only 1 RX and multistatic radars with many TXs and RXs. Simulations are performed using real radar characteristics in order to assess whether it is possible to use navigation radars to track targets with low RCS. The research herein presented shows that it is possible to achieve a good accuracy configuring a geometry that is suitable for the requirements of a system. Also, from the results of the simulations it is possible to understand why multistatic radars can still work with acceptable accuracy if a TXs is lost/destroyed

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

Sensor management for multi-target tracking using random finite sets

Sensor management in multi-target tracking is commonly focused on actively scheduling and managing sensor resources to maximize the visibility of states of a set of maneuvering targets in a surveillance area. This project focuses on two types of sensor management techniques: - controlling a set of mobile sensors (sensor control), and - scheduling the resources of a sensor network (sensor selection).​ In both cases, agile sensors are employed to track an unknown number of targets. We advocate a Random Finite Set (RFS)-based approach for formulation of a sensor control/selection technique for multi-target tracking problem. Sensor control/scheduling offers a multi-target state estimate that is expected to be substantially more accurate than the classical tracking methods without sensor management. Searching for optimal sensor state or command in the relevant space is carried out by a decision-making mechanism based on maximizing the utility of receiving measurements.​ In current solutions of sensor management problem, the information of the clutter rate and uncertainty in sensor Field of View (FoV) are assumed to be known in priori. However, accurate measures of these parameters are usually not available in practical situations. This project presents a new sensor management solution that is designed to work within a RFS-based multi-target tracking framework. Our solution does not require any prior knowledge of the clutter distribution nor the probability of detection profile to achieve similar accuracy. Also, we present a new sensor management method for multi-object filtering via maximizing the state estimation confidence. Confidence of an estimation is quantified by measuring the dispersion of the multi-object posterior about its statistical mean using Optimal Sub-Pattern Assignment (OSPA). The proposed method is generic and the presented algorithm can be used with any statistical filter

THEORETICAL ASPECTS AND REAL ISSUES IN AN INTEGRATED MULTIRADAR SYSTEM

In the last few years Homeland Security (HS) has gained a considerable interest in the research community. From a scientific point of view, it is a difficult task to provide a definition of this research area and to exactly draw up its boundaries. In fact, when we talk about the security and the surveillance, several problems and aspects must be considered. In particular, the following factors play a crucial role and define the complexity level of the considered application field: the number of potential threats can be high and uncertain; the threat detection and identification can be made more complicated by the use of camouflaging techniques; the monitored area is typically wide and it requires a large and heterogeneous sensor network; the surveillance operation is strongly related to the operational scenario, so that it is not possible to define a unique approach to solve the problem [1]. Information Technology (IT) can provide an important support to HS in preventing, detecting and early warning of threats. Even though the link between IT and HS is relatively recent, sensor integration and collaboration is a widely applied technique aimed to aggregate data from multiple sources, to yield timely information on potential threats and to improve the accuracy in monitoring events [2]. A large number of sensors have already been developed to support surveillance operations. Parallel to this technological effort in developing new powerful and dedicated sensors, interest in integrating a set of stand-alone sensors into an integrated multi-sensor system has been increasing. In fact, rather than to develop new sensors to achieve more accurate tracking and surveillance systems, it is more useful to integrate existing stand-alone sensors into a single system in order to obtain performance improvements In this dissertation, a notional integrated multi-sensor system acting in a maritime border control scenario for HS is considered. In general, a border surveillance system is composed of multiple land based and moving platforms carrying different types of sensors [1]. In a typical scenario, described in [1], the integrated system is composed of a land based platform, located on the coast, and an airborne platform moving in front of the coast line. In this dissertation, we handle two different fundamental aspects. In Part I, we focus on a single sensor in the system, i.e. the airborne radar. We analyze the tracking performance of such a kind of sensor in the presence of two different atmospheric problems: the turbulence (in Chapter 1) and the tropospheric refraction (in Chapter 2). In particular, in Chapter 1, the losses in tracking accuracy of a turbulence-ignorant tracking filter (i.e. a filter that does not take into account the effects of the atmospheric turbulences) acting in a turbulent scenario, is quantified. In Chapter 2, we focus our attention on the tropospheric propagation effects on the radar electromagnetic (em) signals and their correction for airborne radar tracking. It is well known that the troposphere is characterized by a refractive index that varies with the altitude and with the local weather. This variability of the refractive index causes an error in the radar measurements. First, a mathematical model to describe and calculate the em radar signal ray path in the troposphere is discussed. Using this mathematical model, the errors due to the tropospheric propagation are evaluated and the corrupted radar measurements are then numerically generated. Second, a tracking algorithm, based on the Kalman Filter, that is able to mitigate the tropospheric errors during the tracking procedure, is proposed. In Part II, we consider the integrated system in its wholeness to investigate a fundamental prerequisite of any data fusion process: the sensor registration process. The problem of sensor registration (also termed, for naval system, the grid-locking problem) arises when a set of data coming from two or more sensors must be combined. This problem involves a coordinate transformation and the reciprocal alignment among the various sensors: streams of data from different sensors must be converted into a common coordinate system (or frame) and aligned before they could be used in a tracking or surveillance system. If not corrected, registration errors can seriously degrade the global system performance by increasing tracking errors and even introducing ghost tracks. A first basic distinction is usually made between relative grid-locking and absolute grid-locking. The relative grid-locking process aligns remote data to local data under the assumption that the local data are bias free and that all biases reside with the remote sensor. The problem is that, actually, also the local sensor is affected by bias. Chapter 3 of this dissertation is dedicated to the solution of the relative grid-locking problem. Two different estimation algorithms are proposed: a linear Least Squares (LS) algorithm and an Expectation-Maximization-based (EM) algorithm. The linear LS algorithm is a simple and fast algorithm, but numerical results have shown that the LS estimator is not efficient for most of the registration bias errors. Such non-efficiency could be caused by the linearization implied by the linear LS algorithm. Then, in order to obtain a more efficient estimation algorithm, an Expectation-Maximization algorithm is derived. In Chapter 4 we generalize our findings to the absolute grid-locking problem. Part III of this dissertation is devoted to a more theoretical aspect of fundamental importance in a lot of practical applications: the estimate of the disturbance covariance matrix. Due to its relevance, in literature it can be found a huge quantity of works on this topic. Recently, a new geometrical concept has been applied to this estimation problem: the Riemann (or intrinsic) geometry. In Chapter 5, we give an overview on the state of the art of the application of the Riemann geometry for the covariance matrix estimation in radar problems. Particular attention is given for the detection problem in additive clutter. Some covariance matrix estimators and a new decision rule based on the Riemann geometry are analyzed and their performance are compared with the classical ones. [1] Sofia Giompapa, “Analysis, modeling, and simulation of an integrated multi-sensor system for maritime border control”, PhD dissertation, University of Pisa, April 2008. [2] H. Chen, F. Y. Wang, and D. Zeng, “Intelligence and security informatics for Homeland Security: information, communication and transportation,” Intelligent Transportation Systems, IEEE Transactions on, vol. 5, no. 4, pp. 329-341, December 2004

Aeronautical Engineering: A continuing bibliography, supplement 116

This bibliography lists 550 reports, articles, and other documents introduced into the NASA scientific and technical information system in November 1979

Data bases and data base systems related to NASA's Aerospace Program: A bibliography with indexes

This bibliography lists 641 reports, articles, and other documents introduced into the NASA scientific and technical information system during the period January 1, 1981 through June 30, 1982. The directory was compiled to assist in the location of numerical and factual data bases and data base handling and management systems