Raytheon Company

Gillian K Groves

Lloyd J Lewins

John E Albus

Julie R Schacht

Larisa A Stephan

In accordance with one aspect of the present invention, a system for tracking a target is provided that includes an image sensor mounted to a gimbal for acquiring an image, wherein the image includes a plurality of pixels representing the target and a background. The system further includes a motor for rotating the gimbal and an autotracker electrically coupled to the image sensor and the motor. The autotracker includes a probability map generator for computing a probability that each of the plurality of pixels having a particular intensity is either a portion of the target or a portion of the background, a pixel processor in communicative relation with the probability map generator for calculating a centroid of the target based upon the probabilities computed by the probability map generator, and a controller in communicative relation with the pixel processor for generating commands to the motor based upon the centroid.

Probability weighted centroid tracker

An imaging target tracker and a method for determining thresholds that are used to optimally distinguish a target from background. The present invention is an imaging target tracker that comprises a likelihood-based threshold selection method that uses knowledge of the location of a target to build up temporally-smoothed grey-level distribution or likelihood maps. Inner and outer histograms are formed that comprise inner and outer regions of an image, respectively, using inner, buffer and outer gates that segment the image. The inner and outer histograms are then recursively smoothed. A lookup table of actually observed pixel values is formed. A target likelihood value for actually observed pixel values is then computed. Legal start and end points of histogram segments are identified. Valid histogram segments are identified. The longest histogram segment is identified. Finally, two optimal thresholds are selected 8 and are set at respective ends of histogram segments. The likelihood maps adapt over time to the signature of the target. The grey-level distribution of the target is used to select thresholds that pass a band of grey levels whose likelihood that they belong to the target is high.

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Torrance, CA, United States of America

Gillian K Groves