Extended kalman filters generally provide very good performance against even highly maneuvering targets, but the processing to implement such a filter is extremely high. By contrast, many a-b filters can often be simply implemented in either hardware or firmware, but at some cost in performance. Numerous improvements to a-b filters have been proposed to increase performance, but the attendant increase in processing is often ignored. For both kalman and a-b filters, there have been few attempts to combine the tailored improvements to produce a filter that is precisely matched to its requirements, and which includes consideration of processor require- ments in the processing of mixing and matching filter design con- siderations. This research effort will provide a summary of filter design options that emphasize the "what" is being accomplished over the "how" of its derivation. Simulations of different filters will be accomplished using existing codes to provide the tracking data. A methodology is proposed that will add processor requirements as an additional measure of merit, which will be useful in selecting options for inclusion in a design.
Keywords: algorithms tracker kalman filters a-b filters tracking filter maneuvering tar computers radar
