The target tracking literature has traditionally been most interested in the "hit" model for the observations process, and the community has developed many techniques for data association. On the other hand, matched field processing (MFP) research has focused on signal processing with the main emphasis on target detection and localization. Treatments of combined tracking/MFP systems are not common, but most concentrate on signal processing, with the idea that a "track" is really a sequence or track-segment of detections that make sense from dynamics considerations. Thus, here we explore the MFP tracking problem, with the key that we attempt to use traditional target-tracking algorithms. In particular, we use an IMMPDAF-AI (interacting multiple-model probabilistic data association filter with amplitude information). It is shown that the use of such an advanced tracking algorithm -plus a number of MFP-specific refinements -produces tracking performance that is far superior to that obtained for a more traditional tracking (a strongest-neighbor Kalman filter), with the added advantage of a significantly reduced numerical load as measured in terms of the number of MFP replicas to be computed.
OVERVIEWTraditional passive sonar target tracking has focused on bearings-only observations (possibly augmented by Doppler measurements if the source is narrowband). This seems reasonable from physical considerations; however, if the receiving antenna has some vertical aperture -even due to its cant -and if there is some knowledge of the sound velocity profile and sea-bottom structure, then it is possible to infer both depth and range measurements as well. This is "matched field processing" (MFP), and from an implementation perspective the idea is that the received array signal is correlated with many received "replica" signals whose forms are based on a point-target's candidate range, bearing, depth and frequency; and of course rely heavily on the propagation model.Target tracking from MFP data is somewhat nontraditional, and there are two reasons (one relating to the goal, and the other to the measurements) for this. The first is that in addition to the usual goals of track accuracy and in-track performance, a key metric in MFP tracking is the number of matched filter replicas that are needed. It is quite computationally expensive to calculate even one, and hence it is of high concern to minimize the total number of matched filters that are needed. The second reason is that the MFP ambiguity function is highly 234 Downloaded From: http://proceedings.spiedigitallibrary.org/ on 06/02/2015 Terms of Use: http://spiedl.org/terms unusual: bearing information is quite good, but depth and range are not well fitted by a simple Gaussian-error model.The target tracking literature has traditionally been most interested in the "hit" model for the observations process: these refer to threshold exceedances by sampled matched filter outputs, and the model is well-suited to radar observations. The community has developed many techniques for dat...