A Suboptimal Estimation Algorithm with Probabilistic Editing for False Measurements with Applications to Target Tracking with Wake Phenomena,

摘要

The purpose of this paper is to describe a suboptimal techique called probabilistic edit, which can be used for state variable estimation in conjunction with Kalman filtering techniques when the underlying noisy measurement process can contain false measurements, i.e., measurements containing no information about the state variables are certain random periods of time. The basic probabilistic edit algorithm is modified to accomodate real-time considerations and is incorpoarted into a seven-state extended Kalman filter which: (1) tracks ballistic re-entry vehicles, and (2) estimates their ballistic coefficient. In estimation problems of this kind, the radar measurements of the re-entry vehicles position are corrupted due to contamination of the hard body return with that of the wake. Consequently, a degradation in the performance of the basic tuned extended Kalman filter occurs. Thus, measurements that appear (in a probabilistic sense) to be highly contaminated by wake are modeled as false measurements. The paper includes a discussion of the effect of wake, a description of the basic tracking algorithm, and modifications of the basic tracking algorithm to compensate for the wake corrupted measurements. Finally, the performance of three distinct algorithms: (1) unmodified ballistic tracking filter, (2) modified ballistic tracking filter using a chi-square test to reject bad measurements, and (3) modified ballistic tracking filter using the probabilistic edit algorithm, using actual data will be presented.