<title>Noncooperative target sensor registration in a multiple-hypothesis tracking architecture</title>

Abstract

In a distributed track-level level fusion system, it is a well-known fact that successful fusion of tracks from offboard sources requires that these tracks do not contain underlying biases or offsets. Unfortunately, this lack of bias or offset is often not the case, because the offboard tracking system references its tracks to a coordinate system that is offset and misaligned with respect to truth due to navigational drift and sensor misalignment.In this paper, we present a technical approach for both detecting and correcting for these biases in a noncooperative target sense. Furthermore, the algorithms are configured to operate in a multiple-hypothesis tracking environment.These algorithms have been implemented in a simulated air threat environment, and performance improvements have been noted of up to an order of magnitude in target/track miss distance.

Control Loop Sensor Calibration Using Neural Networks for Robotic Control

“…This can wreak havoc with the performance of the involved tracking systems. When calibration and alignment are the main factors, the problem is referred to as sensor registration [1,2]. Since the real-time activities of a tracking problem cannot be postponed, the correction of the registration or the mismodeling must be performed online.…”