초록 ▼

The present invention is in general related to target type estimation in target tracking systems. The invention enables a low complexity estimation of target type, using, e.g. ESM sensor data, by the introduction of an ambiguity restoring procedure in certain likelihood calculations. The invention f

The present invention is in general related to target type estimation in target tracking systems. The invention enables a low complexity estimation of target type, using, e.g. ESM sensor data, by the introduction of an ambiguity restoring procedure in certain likelihood calculations. The invention further enables the systematic use of target type probability information in the calculation of strobe track crosses and their associated quality which is particularly useful for deghosting purposes. Methods for utilisation of target type probability information in the processes of strobe tracking, association, track quality evaluation and multiple hypothesis tracking are also disclosed.

대표청구항 ▼

[ What is claimed is:] [1.]1. A target type estimation method for estimation of target types in a tracking system, using discrete information from measurements, said target type estimation method operating in a target type system comprising a number N.sub.T of target types and a number N.sub.M of me

[ What is claimed is:] [1.]1. A target type estimation method for estimation of target types in a tracking system, using discrete information from measurements, said target type estimation method operating in a target type system comprising a number N.sub.T of target types and a number N.sub.M of measurement outcomes, where each target type has a relation to at least one of said measurement outcomes and where each measurement outcome has a relation to at least one of said target types, said target type estimation method comprises an update step including a time recursive calculation of target type probabilities, comprising the steps of:calculating probabilities (P(t.sub.q,T.sub.i.vertline.Z(t.sub.q-1))) at a certain time (t.sub.q) that a track is of said target type (T.sub.i, i=1, . . . ,N.sub.T), using measurements (Z(t.sub.q-1)) at a previous time (t.sub.q-1);calculating the likelihood (P(t.sub.q,z(t.sub.q).vertline.T.sub.1,Z(t.sub.q-1))) for a certain measurement (z(t.sub.q)) given the target type and the measurements up to said previous time; andcalculating probabilities (P(t.sub.q,T.sub.i.vertline.Z(t.sub.q))) that a track is of said target type at the present time from said probabilities (P(t.sub.q,T.sub.i.vertline.Z(t.sub.q-1))) that a track is of said target type at a previous time (t.sub.q-1) and said likelihood (P(t.sub.q,z(t.sub.q).vertline.T.sub.i,Z(t.sub.q-1))); characterised in that said update step further comprises the steps of:approximating said likelihood (P(t.sub.q,z(t.sub.q).vertline.T.sub.i,Z(t.sub.q-1))) from a reduced number of parameters; andrestoring ambiguity in said likelihood approximation in cases where at least two target types have relations to a different number of measurement outcomes and at least one measurement outcome has a relation to at least two of said target types.