This is the first part of a comprehensive and up-to-date survey of the techniques for tracking maneuvering targets without addressing the so-called measurement-origin uncertainty. It surveys various mathematical models of target motion/dynamics proposed for maneuvering target tracking, including 2D and 3D maneuver models as well as coordinate-uncoupled generic models for target motion. This survey emphasizes the underlying ideas and assumptions of the models. Interrelationships among models and insight to the pros and cons of models are provided. Some material presented here has not appeared elsewhere.

Survey of maneuvering target tracking. Part I. Dynamic models

The majority of tactical weapons systems require that manned maneuverable vehicles, such as aircraft, ships, and submarines, be tracked accurately. An optimal Kalman filter has been derived for this purpose using a target model that is simple to implement and that represents closely the motions of maneuvering targets. Using this filter, parametric tracking accuracy data have been generated as a function of target maneuver characteristics, sensor observation noise, and data rate and that permits rapid a priori estimates of tracking performance to be made when maneuvering targets are to be tracked by sensors providing any combination of range, bearing, and elevation measurements.

Estimating Optimal Tracking Filter Performance for Manned Maneuvering Targets

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Written for researchers and engineers working in the field, Radar Systems Analysis and Design Using MATLAB provides fundamental radar principles, advanced topics, and rigorous but easy-to-follow mathematical derivations.

Radar Systems Analysis and Design Using MATLAB

Wireless ATM networks require efficient mobility management to cope with frequent mobile handoff and rerouting of connections. Although much attention has been given in the literature to network architecture design to support wide-area mobility in public ATM networks, little has been done to the important issue of user mobility estimation and prediction to improve the connection reliability and bandwidth efficiency of the underlying system architecture. This paper treats the problem by developing a hierarchical user mobility model that closely represents the movement behavior of a mobile user, and that, when used with appropriate pattern matching and Kalman filtering techniques, yields an accurate location prediction algorithm, HLP, or hierarchical location prediction, which provides necessary information for advance resource reservation and advance optimal route establishment in wireless ATM networks.

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Mobility modeling, location tracking, and trajectory prediction in wireless ATM networks

This paper considers the problem of estimating the position and velocity of an object from noise corrupted bearing measurements obtained by a single moving observation platform. The process is inherently nonlinear and exhibits unusual observability properties that are geometry-dependent. A maximum likelihood estimate (MLE) of the target motion analysis solution is developed and its performance analyzed. A comparison is drawn between the MLE and two previously reported methods, a nonlinear modified-instrumental variable estimate (MIV) and the pseudo-linear estimate (PLE). Both the MIV and PLE are shown to derive from approximations to the nonlinear measurement equation and therefore share some common properties with the MLE. The limits on performance that can be expected from processing bearing data are detailed. Specifically, for long range-to-baseline geometries, approximate expressions for the Cramer-Rao bound are derived. Extension of the results to the practical filters approximately predicts numerically observed behavior. For less restrictive geometries, bounds are presented. Incorporation of a target speed constraint on the MLE results in a transition to a lower dimensional problem as noise level and range increases. Monte Carlo experimental results are presented and the improvements realized by the MLE techniques are evident.

Fundamental properties and performance of conventional bearings-only target motion analysis

Five important tracking filters that are often candidates for implementation in systems that must track maneuvering vehicles are compared in terms of tracking accuracy and computer requirements for tactical applications. A rationale for selecting among these filters, which include a Kalman filter, a simplified Kalman filter, an ?-s filter, a Wiener filter, and a two-point extrapolator, is illustrated by two examples taken from the authors' recent experience.

Real-Time Tracking Filter Evaluation and Selection for Tactical Applications

Tactical air defense systems often require the interchange of target tracking data between two radar sites. This paper presents a systematic and effective procedure, employing correlation gates, for associating , or correlating, track information from one site with that from the other. Using the correlation equation, gate sizes are selected, based on remote and local tracking accuracies, target separation, the number of track coordinates investigated, and the number of successive correlation attempts made, to maximize the selected measure of correlation accuracy. The modeling of physical encounters is discussed and a representative situation is selected for analysis. A variety of correlation procedures are compared in terms of correlation accuracy and ease of implementation.

Robert A. Singer

Papers

Estimating Optimal Tracking Filter Performance for Manned Maneuvering Targets

Real-Time Tracking Filter Evaluation and Selection for Tactical Applications

Correlation of Multiple-Site Track Data