HICK'S PROBABILISTIC DATA ASSOCIATION METHOD

A process, known as the Hick's Probabilistic Data Association Algorithm, correlates sensor measurement to target tracks under condition in which there are numerous false measurements. It accomplishes this by forming multiple hypotheses and computing a probabilistic score for each. The hypothesis with the high score is then used as a probability vector to update each target's track. The result is accomplished by combining the attributes of Joint Probabilistic Data Association (JPDA) and Nearest Neighbor Standard Filter (NNSF). This combination provides an improved algorithm which yields improved performance ever both prior art methods under high clutter conditions with crossing targets.