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 $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.

Survey of maneuvering target tracking. Part V. Multiple-model methods

The feasibility of a moving-bank multiple model adaptive estimator/controller is examined. Compared to a conventional full-bank multiple model adaptive algorithm, a significant reduction in the number of required elemental filters is accomplished through a dynamic re-declaration of the positions in parameter space that the elemental filters occupy. Critical to the performance of the moving-bank estimator is the decision method that governs movement of the bank of elemental filters. Five such methods are developed and their performances are compared to each other and to a benchmark filter with artificial knowledge of the "true" parameter value. Three adaptive controller algorithms are also generated and evaluated. A simple but physically motivated example is used to gain insights into relative performance potential of the proposed algorithms.

Investigation of moving-bank multiple model adaptive algorithms

A multiple-model adaptive controller that provides for reconfiguration in response to sensor and/or actuator failures is developed for an approach and landing profile for the short-take-off-and-landing (STOL) F-15 aircraft. Each elemental controller within the multiple model controller is based on a command-generator-tracker/proportional-plus-integral/Kalman-filter design employing reduced-order models, and each assumes a particular system status: no failures or a single failed surface or sensor. Filter residual monitoring is used as the mechanism for selecting the appropriate weighting coefficients for blending the outputs of the elemental controllers. The entire controller is evaluated against a higher order truth model with a selected failure, and the process is repeated for all failure modes of interest. >

Multiple model adaptive controller for the STOL F-15 with sensor/actuator failures

A new approach, referred to as best model augmentation (BMA), for variable-structure multiple-model (VSMM) estimation is presented. Here the original set of models is augmented by a variable set of models intended to best match the unknown true mode. Based on the Kullback-Leiber (KL) information, two versions of the criterion serving as a metric of the closeness between candidate models and the true mode are derived in the space of states and measurements, respectively. The model set adaptation (MSA) in BMA turns out to be an online optimization problem based on the KL criterion, which can be solved easily. The performance of the proposed BMA approach is evaluated via several scenarios for maneuvering target tracking. Simulation results demonstrated the effectiveness of BMA compared with the interacting multiple-model (IMM) algorithm and the expected-mode augmentation (EMA) algorithm.

Best Model Augmentation for Variable-Structure Multiple-Model Estimation

A new combination of mathematical programming methods and partitioned adaptive filters is proposed for the simultaneous real-time estimation of the parameters and state of an unknown linear system. The algorithm uses a version of the simplex method of non-linear programming to direct parameter selection for a bank of Kidman filters, thus combining the known convergence characteristics of partitioned adaptive filters with the robustness of this search technique. Motivation for the approach is discussed in light of recently published results concerning convergence of the decision function of the partitioned filters, and a simple example is given.

P. R. Lamb

Papers

Simplex-directed partitioned adaptive filters