Estimation With Multisensor Fusion
Final rept. 1 Dec 1999-30 NOv 2000
CONNECTICUT UNIV STORRS
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The research effort reported here focused on the development of practical advanced algorithms for optimal processing of the information obtained from various remote sensing devices radar, ESM or electro-optical for surveillance and tracking targets. The processing consists of integrationfiltering of the sensor data across time and fusion across sensors with the main goal being overcoming the inherent limitations of real-world sensors accuracy and reliability due to noise, which cause false alarms, and other factors, such as low observable LO targets, which lead to low detection probability. We developed algorithms for association and fusion of measurements from multiple, asynchronous heterogeneous sensors based on discrete mathematical optimization techniques multidimensional matchingassignment techniques for practical high density scenario target tracking for the case of multipath phased array radar resource allocation for the case of unresolved targets track formation of LO targets from EQ sensor latd radar waveform design for optimized tracking i.e., system level performance track before detect approach for VLO targets with fluctuating amplitude generalization of the CRLB iii the presence of false measurements to ion-Gaussian distribution an efficient estimator that meets the CRI-B for acquisition by an ESA radar of a LQ TBM prior to reentry SAM identification for timely countermeasures bias estimation for multiple radars using targets of opportunity exact incorporation of target classification into multidimensional assignment.
- Numerical Mathematics
- Miscellaneous Detection and Detectors