Tracking on Intensity-Modulated Data Streams

Multitarget Tracking of Distributed Targets Using Histogram-PMHT

“…. , N c T } and using independence of the count vectors in N c and Bayes' theorem gives the complete-data PDF at the end of the first stage as p (1) …”

“…The noise component G k (τ ) is assumed known. With these assumptions, it can be shown [1] that for X(k) = {x 0k , x 1k , . .…”

“…, x T k }, the value of X(k) that maximizes the auxiliary function Q kX for each target k is efficiently solved by a recursive Kalman smoothing filter, even when there are truncated cells and the measurement covariance matrices R = {R tk } are to be estimated. The details of this result are omitted here, but the filter steps are listed explicitly in [1,10,11] for the linear Gaussian case and constant background noise.…”

“…The histogram probabilistic multihypothesis tracking (H-PMHT) algorithm [1] uses a synthetic histogram interpretation of the received power in the sensor cells to process all the sensor output data and thus avoids thresholding the data to produce point measurements. The PMHT [2,3] methodology is used to model the superposition of target and noise components in the data, and to link these components across successive scans.…”

Integrated track maintenance for the PMHT via the hysteresis model

“…The standard PMHT restricts the prior to be either constant, or time independent. Existing PMHT research does not address the assignment prior; effort has instead focussed on the target dynamics model [4,8,6,9], more sophisticated measurement models [10][11][12], and matters of practical significance for realistic implementations [7,13,14]. A model is proposed here which allows the assignment prior to be a randomly evolving quantity.…”