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A probabilistic framework for fusing location estimates, which may be biased and inconsistent, is presented. The proposed method, involving Gaussian mixture models (GMMs), utilizes prior information regarding the sensor bias, firstly, to reduce errors in the fused location estimate, and secondly, to produce a fused covariance matrix that better reflects the expected location error. Simulations are...
A three-dimensional whitening filter based on linear prediction is described. It is designed to remove textured backgrounds, with spatial and temporal correlation, in data streams acquired from imaging electro-optic sensors operating in visible, Ultra-Violet (UV) or Infra-Red (IR) bands. The background is modeled as an Auto-Regressive (AR) process. The operation of the filter is examined using real...
The relative performance of multiple hypothesis tracking (MHT) and probabilistic data association (PDA) track initiation is investigated using simulated wide-area-surveillance radar data. The scenarios involve single targets that are low-observable (i.e. a low probability of target detection and a high probability of clutter detection) and non-manoeuvring. Divergent track counts and the receiver operating...
Track coalescence is undesirable when estimating the states of multiple manoeuvring targets, with a less-than-unity probability of detection, in clutter. Simple and compound forms of coalescence are defined and discussed. Simple coalescence is when two or more identical tracks follow a single target; compound coalescence is when two or more identical tracks follow the midpoint (or centroid) of two...
A statistical change detector, implemented as a zero-latency finite-memory filter, is used to identify anomalies in temporal pixel statistics. An F-distributed test statistic is computed for each pixel and used in a hypothesis test. The tracker, with automatic track initiation and termination, uses a low-complexity pairwise joint probabilistic data association (JPDA) algorithm, which has been restricted...
A new measure of similarity, suitable for both signal processing and image processing applications, is presented. The measure of similarity ( ZM ) is an F-distributed test statistic that quantifies the degree of alignment, correlation or coincidence between two or more (time-or space-dependent) waveforms. The test statistic may be used to automate detection, classification, localisation, association...
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