A general method of continually restructuring an optimum BayesKalman tracking filter is proposed by conceptualizing a growing tree of filters to maintain optimality on a target exhibiting maneuver variables. This tree concept is then constrained from growth by quantizing the continuously sensed maneuver variables and restricting these to a small value from which an average maneuver is calculated. Kalman filters are calculated and carried in parallel for each quantized variable. This constrained tree of several parallel Kalman filters demands only modest om;puter time, yet provides very good performance. This concept is implemented for a Doppler tracking system and the performance is compared to an extended Kalman filter. Simulation results are presented which show dramatic tracking improvement when using the adaptive tracking filter.The Kalman filter has been accepted as one of the best methods of providing the motion analysis of the steadystate parameters of a moving target. Although developed specifically for linear models, the Kalman filter has found wide application for nonlinear observation equations and nonlinear motion through linearization techniques (i.e., extended Kalman filter). Maneuvering target tracking has also received considerable attention. The likelihood of a maneuver can be accommodated in the design by increasing the modeled plant noise, but this, of course, degrades the accuracy during steady conditions. It also makes the estimator very prone to divergence, particularly if nonlinear equations have been approximated by an extended Kalman filter expansion. A number of adaptive approaches have been investigated to resolve this design dilemma by introducing rnodifications to the Kalman filter to get it through the maneuver without diverging or otherwise severely distorting the estimate. Some of these devices are 1) detecting the maneuver and modifying the Kalman filter by reinitializing the covariance matrix, 2) detecting the maneuver and changing the state variables, 3) using a fading memory concept, and 4) using statistical forcing functions to represent target maneuvers. These methods have the limitations of requiring a specific detection mechanism of being expensive to implement and of requiring knowledge of the target not always available.The performance of these techniques also depends upon the target model for the maneuver. For the process proposed here, the target was assumed to experience a very rapid maneuver that occurs only rarely during the course of travel. For the reasons cited above and because of the peculiarity of such a model, the recent literature has explored adaptive parallel Kalman filter structures [1] - [5] . The Bayes-Kalman tree structure proposed here is a variation on those developments.The technique developed in its pure form generates a continuously expanding set of Kalman filters responding to a continuously expanding setof options available to the maneuvering target. The proposed implementation limits the expansion to a discrete set of values for the maneuver var...
The fortunes of signal processors are directly related to progress in integrated circuit technology. This technology has experienced recent advances in logic design density and increased clock rates that will have significant impact on the performance of signal processors for the next 4 to 5 years. The objective of this paper is to develop an architecture that is matched to the computational requirements of a typical RADAR system and show how it can be implemented with these new devices. An analysis has been conducted to determine the relationship beteen multiplications, additions, and memory accesses for those algorithms requiring the highest data throughput including an adaptive array process. An architecture for a signal processing engine is then developed that is matched to the relative ratios for these functions. This signal processing engine is then sized using configurable gate array integrated circuits to implement each of its major functions. Finally, an estimate is made of the number of engines required to process these algorithms.
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