Tracking on intensity-modulated sensor data streams

Conventional trackers are point trackers. Tracking energy on a field of sensor cells requires windowing, thresholding, and interpolating to arrive at data points to feed the tracker. This scheme poses problems when tracking energy that is distributed across many cells. Such signals are sometimes termed “over-resolved.” It has been suggested that tracking could be improved by decreasing the resolution of the signal processor, so that the cells are large enough to encompass the bulk of the energy, and better match the point tracker assumptions. Larger arrays provide greater resolution at lower frequencies, with the potential for improved detection and classification performance, but in direct conflict with tracking “over-resolved” signals. These issues are addressed by the histogram-based probabilistic multi-hypothesis tracking (PMHT) method discussed, which provides a means for modeling and tracking signals that may be spread across many sensor cells. This paper focuses on the initial development and testing of this algorithm for one-dimensional sensor data. Elements of the signal model, theory, and algorithm are presented along with two frequency domain examples