Richard L. Moose

Modeling and Estimation for Tracking Maneuvering Targets

A new approach to the three-dimensional airborne maneuvering target tracking problem is presented. The method, which combines the correlated acceleration target model of Singer [3] with the adaptive semi-Markov maneuver model of Gholson and Moose [8], leads to a practical real-time tracking algorithm that can be easily implemented on a modern fire-control computer. Preliminary testing with actual radar measurements indicates both improved tracking accuracy and increased filter stability in response to rapid target accelerations in elevation, bearing, and range.

Maneuvering Target Tracking Using Adaptive State Estimation

Two approaches to a nonlinear state estimation problem are presented. The particular problem addressed is that of tracking a maneuvering target in three-dimensional space using spherical observations (radar data). Both approaches rely on semi-Markov modeling of target maneuvers and result in effective algorithms that prevent the loss of track that often occurs when a target makes a sudden, radical change in its trajectory. Both techniques are compared using real and simulated radar measurements with emphasis on performance and computational burden.

Adaptive underwater target tracking using passive multipath time-delay measurements

This paper examines the problem of adaptively tracking in range and velocity an underwater maneuvering target using passive time delay measurements. The target can make large scale random velocity and depth changes at times which are unknown to the observer. Tracking is accomplished by making use of the basic linearized polar model of target and observer motion previously developed [1]. Now, however, a nonlinear system block has been added to the tracking system [2], [3], which leads to two major benefits. First, the need for extended Kalman filters is eliminated making the passive tracking system more robust than it was previously [3]. The second benefit is a partial decoupling of depth estimation from the polar range estimator, which considerably reduces the computational level of the adaptive tracking system.

Digital Control of High Performance Aircraft Using Adaptive Estimation Techniques

Since the early 1960s, a rapid advance in signal processing, including filtering and estimation techniques, has been evident. In contrast, applied feedback control, particularly for aircraft, is currently based on technology available prior to 1960, i. e., primarily either constant gain feedback or at most a standard gain-scheduling. In this paper, an adaptive signal processing algorithm is joined with gain-scheduling to produce an effective scheme for controlling the dynamics of high performance aircraft. A technique is presented for a reduced-order model (the longitudinal dynamics) of a high performance short-takeoff-and-landing (STOL) aircraft. The actual controller views the nonlinear behavior of the aircraft as being equivalent to a randomly switching sequence of linear models taken from a preliminary piecewise-linear fit of the system nonlinearities. The adaptive nature of the estimator is necessary to select the proper sequence of linear models along the flight trajectory. From the analysis of the reduced-order model the nonlinear behavior has been found to be well approximated by assuming an effective switching of the linear models at random times, the durations of which reflect the motion of the aircraft in response to pilot commands.

A modified Gaussian sum approach to estimation of non-Gaussian signals

A Gaussian sum estimation algorithm has previously been developed to deal with noise processes that are non-Gaussian. Inherent in this algorithm is a serious growing memory problem that causes the number of terms in the Gaussian sum to increase exponentially at each iteration. A modified Gaussian sum estimation algorithm using an adaptive filter is developed that avoids the growing memory problem of the previous algorithm while providing effective state estimation. The adaptive filter is comprised of a fixed set of estimators operating in parallel with each individual estimate possessing its own corresponding weighting term. A simulation example illustrates the new non-Gaussian estimation technique. >

Applications of adaptive state estimation theory

Two main areas of application of adaptive state estimation theory are presented. Following a review of the basic estimation approach, its application to both the control of nonlinear plants and to the problem of tracking maneuvering targets is presented. Results are brought together from these two areas of investigation to provide insight into the wide range of possible applications of the general estimation method.

A computationally efficient technique for state estimation of nonlinear systems

Abstract An estimation technique is presented for the class of nonlinear systems consisting of memoryless nonlinearities embedded in a dynamic linear system. The approach is based on a useful sampled-data nonlinear system simulation method, which involves the addition of an extra state variable for each nonlinear element. The nonlinear estimator is developed along the lines of the basic Kalman state estimation, using quasilinearization instead of the Taylor series linearization used in extended Kalman filters. It is demonstrated that this new method out performs the extended Kalman filter in terms of the mean-square error of the state estimate. This estimator was used effectively for state estimation in cases where the extended Kalman filter does not converge. Moreover the new method is directly applicable to feedback systems with multiple nonlinearities and stochastic disturbances.

Adaptive Estimation for a System with Unknown Measu rement Bias

An adaptive state estimator for passive underwater tracking of maneuvering targets is developed. The state estimator is designed specifically for a system containing unknown or randomly switching biased measurements. In modeling the stochastic system, it is assumed that the bias sequence dynamics can be modeled by a semi-Markov process. By incorporating the semi-Markovian concept into a Bayesian estimation technique, an estimator consisting of a bank of parallel, adaptively weighted, Kalman filters has been developed. Despite the large and randomly varying measurement biases, the proposed estimator, provides an accurate estimate of the system states.

Adaptive State Estimation for a System With Unknown Input and Measurement Bias

An adaptive state estimator for passive underwater tracking of maneuvering targets is developed. The state estimator is designed specifically for a system containing independent unknown or randomly switching input and measurement biases. In modeling the stochastic system, it is assumed that the bias sequence dynamics for both input and measurement can be modeled by a semi-Markov process. By incorporating the semi-Markovian concept into a Bayesian estimation technique, an estimator consisting of a bank of parallel adaptively weighted Kalman filters has been developed. Despite the large and randomly varying biases, the proposed estimator provides an accurate estimate of the system states.

Modeling and control of nonlinear plants

A method of modeling nonlinear plants is presented which uses established piecewise-linear approximations to nonlinear systems, but provides a reduction to the number of approximations needed. The reduction in the number of approximations is achieved with the addition of time-correlated plant noise, similar to the method of correlated acceleration applied to the problem of tracking a maneuvering target.