No abstract

The ability to provide an accurate view of a region of interest is standard among existing tracking systems. The extension of this capability to include accurate projections of the targets to future times increases the complexity of the problem. However, this ability to predict future locations is an important problem due to the inherent time latency that is present from "sensor to shooter." Because of this, a major requirement of the tracking system is that it must be able to use available information to accurately predict future target locations. The focus of this paper is to describe an improved state estimation technique that incorporates road information by using a Variable Structure 1MM. Furthermore, this approach will identify and account for stopped or stationary moving targets. Finally, some preliminary performance results will be presented.

This paper. describes uti approach to sensor hius modeling arid estiniotioii ,for. groirnri target tracking applications rising nirrltiple ciirhorxe Ground Moving Target indicator (GMTI) radur sensors. This upproach was developed as part of tlrr i'recisioti Firecontrol Tracking (PFCT) segnient of the DARPA AffordaDle Moving Surjiace Target Engagenleiit (4MSTE) program For airborne sensors. slowli. vai?.itig platforni location, heading and veloci!ij errors lead to tinie-dependent nieasurenient biases. Track accuracv caii be improved bv using a Kalman j l t e r to estimate and correct the biases in real tinie. based on j x e d reference points. The reference point location can be known a priori or estiniated online as part of the bias correction algorithm.When the reference locations are known a prior, bias effects can be riearlv coriipletelv eliniinated, When the reference point is estiniated online, signrjcant perfor-niance iniprovenient is ohtairied relative to uncorrected nieasrrrenients.