X. Rong Li

Survey of maneuvering target tracking. Part I. Dynamic models

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 V. Multiple-model methods

This is the fifth part of a series of papers that provide a comprehensive survey of techniques for tracking maneuvering targets without addressing the so-called measurement-origin uncertainty. Part I and Part II deal with target motion models. Part III covers measurement models and associated techniques. Part IV is concerned with tracking techniques that are based on decisions regarding target maneuvers. This part surveys the multiple-model methods

Survey of maneuvering target tracking: dynamic models

the use of multiple models (and filters) simultaneously - which is the prevailing approach to maneuvering target tracking in recent years. The survey is presented in a structured way, centered around three generations of algorithms: autonomous, cooperating, and variable structure. It emphasizes the underpinning of each algorithm and covers various issues in algorithm design, application, and performance.

Technical Communique: The optimality for the distributed Kalman filtering fusion with feedback

This is the first part of a series of papers that provide a comprehensive and up-to-date survey of the problems and techniques of tracking maneuvering targets in the absence of the so-called measurement-origin uncertainty. It surveys the various mathematical models of target dynamics proposed for maneuvering target tracking, including 2D and 3D maneuver models as well as coordinate-uncoupled generic models for target dynamics. This survey emphasizes the underlying ideas and assumptions of the models. Interrelationships among the models surveyed and insight to the pros and cons of the models are provided. Some material presented here has not appeared elsewhere.

Survey of maneuvering target tracking: II. Ballistic target models

A rigorous performance analysis is dedicated to the distributed Kalman filtering fusion with feedback for distributed recursive state estimators of dynamic systems. It is shown that the Kalman filtering track fusion formula with feedback is, like the track fusion without feedback, exactly equivalent to the corresponding centralized Kalman filtering formula. Moreover, the so-called P matrices in the feedback Kalman filtering at both local trackers and fusion center are still the covariance matrices of tracking errors. Although the feedback here cannot improve the performance at the fusion center, the feedback does reduce the covariance of each local tracking error. The above results can be extended to a hybrid track fusion with feedback received by a part of the local trackers.

Best linear unbiased filtering with nonlinear measurements for target tracking

This paper is the second part in a series that provides a comprehensive survey of the problems and techniques of tracking maneuvering targets in the absence of the so-called measurement-origin uncertainty. It surveys motion models of ballistic targets used for target tracking. Models for all three phases (i.e., boost, coast, and reentry) of motion are covered.

Multiple-model estimation with variable structure- part VI: expected-mode augmentation

In tracking applications, target dynamics are usually modeled in the Cartesian coordinates, while target measurements are directly available in the original sensor coordinates. Measurement conversion is widely used such that the Kalman filter can be applied in the Cartesian coordinates. A number of improved measurement-conversion techniques have been proposed recently. However, they have fundamental limitations, resulting in performance degradation, as pointed out in a recent survey conducted by the authors. A filter is proposed here that is theoretically optimal in the sense of minimizing the mean-square error among all linear unbiased filters in the Cartesian coordinates. The proposed filter is free of the fundamental limitations of the measurement-conversion approach. Results of an approximate, recursive implementation are compared with those obtained by two state-of-the-art conversion techniques. Simulation results are provided.

Comments on "Unbiased converted measurements for tracking"

A new class of variable-structure (VS) algorithms for multiple-model (MM) estimation is presented, referred to as expected-mode augmentation (EMA). In the EMA approach, the original set of models is augmented by a variable set of models intended to match the expected value of the unknown true mode. These models are generated adaptively in real time as (globally or locally) probabilistically weighted sums of mode estimates over the model set. This makes it possible to cover a large continuous mode space by a relatively small number of models at a given accuracy level. The paper presents new theoretical results for model-set design, a general formulation of the EMA approach, along with theoretical analysis and justification, and three algorithms for its practical implementation. The performance of the proposed EMA algorithms is evaluated via simulation of a generic maneuvering target tracking problem.

Survey of maneuvering target tracking: III. Measurement models

We show that there exists a compatibility problem in the derivation of the mean and covariance of the converted measurement errors in L. Mo et al. (ibid., vol.34, no.3, p.1023-7, 1998), and then present a modification to the computation of them, in which both the mean and the covariance are computed strictly conditioned on the measurements.

Multiple-model estimation with variable structure. IV. Design and evaluation of model-group switching algorithm

This is the third part of a series of papers that provide a comprehensive survey of the techniques for tracking maneuvering targets without addressing the so-called measurement-origin uncertainty. Part I and Part II deal with general target motion models and ballistic target motion models, respectively. This part surveys measurement models, including measurement model-based techniques, used in target tracking. Models in Cartesian, sensor measurement, their mixed, and other coordinates are covered. The stress is on more recent advances - topics that have received more attention recently are discussed in greater details.