Optical data association neural net
David P. Casasent, Mark L. Yee · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 1993
ABSTRACT This paper presents new results of our data association (DA) neural net (NN) on measurement-to-estimate data(rather than measurement-to-measurement data). It also uses our detection unit. Our new jitter model is discussed andinitial results are presented. This also includes tests of our fixed coefficient estimator and initial optical laboratoryresults.We find that: the use of an estimator improves DA MN results (since no clutter is present in the estimate frame),reduction of jitter by using the detection system in tracking helps, our system handles measurement noise and jitterand clutter with no loss of track, and our fixed coefficient estimator suffices. 1. INTRODUCTION Our DA NN algorithm was previously described [1 1 and is thus only reviewed in Section 2. Our prior DA NNresults on measurement-to-measurement data are reviewed in Section 3. We then advance our new models (Section 4)and present our new results (Section 5) and initial Kalman filter estimator comparisons (Section 6).