2004 IEEE Radar Conference

Innovative Radar Technologies - Expanding System Capabilities

 
 
 April 26-29, 2004 Wyndham Philadelphia at Franklin Plaza Philadelphia, Pennsylvania
 
 
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Session 4B: Tracking

Tue, 27 April 2004, 4:10 PM - 5:30 PM


4B.1 Application of Reduced State Estimation to Multisensor Fusion with Out-of-Sequence Measurements
4B.2 CoreTracking: An Efficient Approach to Clustering Moving Targets and Tracking Clusters
4B.3 A constrained extended Kalman filter for target tracking
4B.4 Analysis of advanced data association techniques for ASDE radar

4B.1 Application of Reduced State Estimation to Multisensor Fusion with Out-of-Sequence Measurements
By: Purusottam Mookerjee
Lockheed Martin Maritime Systems & Sensors
and: Frank Reifler
Lockheed Martin Maritime Systems & Sensors

In this paper a filtering application of processing multisensor measurements with delays is considered. Because of delays, measurements fed by geographically dispersed sensors to a processing site may arrive out of time sequence. Unlike smoothing or filtering, optimal processing of an out-of-sequence measurement is not a standard problem in filtering theory for which a definitive approach has yet been developed. An optimal reduced state estimator, derived in previous work, is applied to this problem. A simulation example of multisensor fusion is presented, in which one sensor feeds highly accurate but delayed measurements to be fused with a second sensor?s less accurate measurements having no delay. We demonstrate uniform improvement in performance using this algorithm over two traditional approaches.

4B.2 CoreTracking: An Efficient Approach to Clustering Moving Targets and Tracking Clusters
By: Daoying Ma
University at Buffalo, the State University of New York
and: Aidong Zhang
University at Buffalo, the State University of New York

Detecting the activities and predicting the tendencies of large groups of targets in wide battlefields are critical inputs to formulating sound military decisions. Modern airborne radar sensors can provide wide-area surveillance coverage of battlefield ground activities. When obscured by terrain or other factors, some objects may only be detectable at intervals, generating intermittent radar data and creating difficulties for tracking groups over time. In this paper, we present an algorithm, termed CoreTracking, which dynamically groups individual targets into clusters and tracks the clusters over time. Most traditional clustering techniques are static-object-oriented. We propose a ?core member? concept to support dynamic-object-oriented clustering and to mitigate the effects of data intermittence. Observing the movement of the core cluster members, we can track the clusters across frames and predict their future movements. The performance and results of the application of the CoreTracking algorithm to CASTFOREM data sets is also presented.

4B.3 A constrained extended Kalman filter for target tracking
By: Anders Erik Nordsjo
Saab Bofors Dynamics

An Extended Kalman Filter, EKF is proposed for tracking of the position and velocity of a moving target. The suggested method is based on a nonlinear model which, in addition, incorporates means for estimation of possible nonlinearities in the measurements of the target position. In many practical scenarios, the initial estimates of target position and velocity will deviate significantly from the true ones. In order to reduce the impact of erroneous initial conditions and hence, obtain a faster initial convergence to an acceptable trajectory, a certain constrained form of the EKF, named the CEKF, is introduced. Although the original Kalman filter for a purely linear system is inherently stable, there is no guarantee that the linearized model used in the EKF gives a stable algorithm. Hence, it is interesting to note that the proposed CEKF under certain mild conditions renders an exponentially stable algorithm. It is shown that this latter method can conveniently be formulated as a nonlinear minimization problem with a quadratic inequality constraint.

4B.4 Analysis of advanced data association techniques for ASDE radar
By: Jesus Garcia
Universidad Carlos III de Madrid
and: Juan Besada
Universidad Politecnica de Madrid
and: Gonzalo de Miguel
Universidad Politecnica de Madrid
and: Jose M Molina
Universidad Carlos III de Madrid
and: Antonio Berlanga
Universidad Carlos III de Madrid

This paper analyses and evaluates the application of different techniques to the data association problem for ASDE radar. Data association for this sensor requires the removal of the classical one-to-one constraints and should allow tracks be updated by sets of blobs. Different innovative alternatives, based on recent advanced techniques, have been formulated and tried to solve this problem in complex scenarios. Simulation results show the capabilities achieved in terms of tracking robustness, accuracy and required computation

 
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