Track Segment Association in Target Selection for Interdiction Using a Single Passive Sensor

In this article, we present an algorithm for the discrimination of the target of interest for the purpose of interdiction in the presence of several spurious targets that are intended to confuse the intercept decision. This is applied to an IR (infrared) detection-based tracking system on a low earth orbit (LEO) satellite and an optical sensor-based tracking system on an interceptor. The spurious targets are released from the target of interest and they move forward at the same speed as the target of interest. They separate due to a release velocity orthogonal to the forward motion. The spurious targets have no distinguishable features so they look the same as the target of interest for the sensor. The observations are noisy and unresolved if the distance between these objects is below the sensor resolution threshold, which degrades the performance of the traditional track-to-measurement association method yielding unreliable results. We analyze the history of the track kinematics as well as the trajectory evolution and associate track segments before and after the spurious target separations to select the track of interest from the other spurious tracks in its vicinity. The novelty of the present work is in generalizing the track segment association to a multitarget environment while accounting for unresolved measurements with a physics-based model. This is accomplished via hypothesis testing to find out which of the tracks after the release corresponds to the track before the release, i.e., which is the target to be interdicted. Also, estimation of the separation times is a novelty-it needs the association based on the finite resolution model because the objects are unresolved after separation for some time. It is shown in the simulations that using the track segment association technique, the percentage of correct target selections has been improved to 95% from 55% for the interceptor scenario and from 85% for the LEO scenario, respectively.