Absfrucf-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 in this paper, which provides a means for modeling and tracking signals that may be spread across many sensor cells. This paper will focus on the initial development and testing of this algorithm for one-dimensional sensor data. Elements of the signal model, theory, and algorithm will be presented along with two frequency domain examples.
This paper b u i b on previous presentations of the Histogram Probabilistic Multi-Hypothesis Tracking (H-PMHT) algorithm. The histogram model used in H-PMHT is extended to treat hyper-spectral data, i.e., datain which each spatial cell has spectral content. The general case in which each measurement scan consists of a multidimensional a m y of intensity values (e.g.. the "data-cube" of hyper-spectral imaging systems) is treated here. This data array is interpreted as a spatial-spectral histogram . Direct application of H-PMHT to such data would track local energy peaks in the spatial-spectral domain: however; this approach is sub-optimal when the spatial track is ofprinciple interest and the energy source has signgcant Spectral H-PMHT assumes that the spectral characteristics of the sources are !mown and avaikzble in simple non-parametric f o m . Here the oddirional structure of the spatial-spectral signal model is presented followed by an outline of the Spectral H-PMHT algorithm The improvement in spatial tracking due to the inclusion of spectral information is demonstrated uring simulated intensity data on bearing-frequency cells.
Tracking energy on an intensity-modulated sensor output typically requires windowing, thresholding, and/or interpolation to arrive at "point measurements" to feed the tracking algorithm. Conventional trackers are point trackers, and point measurement estimation procedures pose problems for tracking signal energy that is distributed across many sensor cells. Such signals are sometimes termed "over-resolved." Large arrays provide greater resolution with the potential for improved detection and classification performance, but higher resolution is in direct conflict with tracking over-resolved signals. The Histogram-Probabilistic Multi-Hypothesis Tracker (H-PMHT) algorithm addresses these issues and provides a means for modeling and tracking signals that are spread across several contiguous sensor cells. H-PMHT models the cell responses as a received energy histogram, and the probability density underlying this histogram is modeled by a mixture density. Elements of the H-PMHT signal model, theory, and algorithm are presented for linear Gauss-Markov targets. Tracking examples using simulated azimuth beam data are presented.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.