<title>Clustering approach to the multitarget multisensor tracking problem</title>

Abstract

Ill a multitarget environment, tracking systems must include methods for associating measurements to targets. The complexity of that task is compounded when data from multiple sensors is available. This paper presents a clustering approach to the multitarget multisensor tracking problem. The measurement set is partitioned into equivalence classes ( clusters) and the data association problem is redefined to be one of associating the cluster centers and the tracks, resulting in a significant reduction in the size of the association problem. Track termination and track initiation are part of system design, therefore allowing the designed system to be tested on elaborate multitarget tracking scenarios involving an unknown and changing number of real aircraft trajectories. Methods for evaluating the performance of the tracking system, as well as the clustering algorithms are introduced. An equivalence relation clustering algorithm is derived and compared by Monte-Carlo simulations to the subtractive clustering algorithm. The tracking system is shown to effectively track seven crossing aircraft trajectories of different duration, in the presence of clutter. Track maintenance is performed by extended Kalman filters.

<title>Group assignment problem</title>

“…The goal was to partition the association problem into list of independent association problems to reduce the size of the measurement-to-track association problem. [8][9][10][11][12] This partitioning of the problem is distinct from the "clustering" considered here that may be termed more appropriately as"grouping". This paper is organized as follows: Section 2 gives a review clustering techniques and illustrates the need for multiple frame cluster tracking, the general cluster assignment problem is formulated in Section 3, the merged measurement problem is briefly discussed in 4, and Section 6 contains a brief summary.…”

Navigation Algorithms and Observability Analysis for Formation Flying Missions

“…Since this bound is based on the Fisher information matrix, literature on this topic is typically problem specific. Other notable examples of state error covariance bounds for Kalman filtering have been noted in regards to relating observability and controllability [16], augmented state applications [15], and multi-target multi-sensor tracking systems [5,41]. In the latter example, Bishop and Nabba [5] examine the use of the least-squares covariance to serve as a reference for the extended Kalman filter performance, and to study optimal sensor placement for a defense corridor guarding against approaching aircraft.…”