Estimation With Multisensor Fusion
Abstract
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 integration/filtering 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 matching/assignment 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.
Document Details
- Document Type
- Technical Report
- Publication Date
- Jul 23, 2003
- Accession Number
- ADA416565
Entities
People
- Krishna R. Pattipati
- P. K. Willett
- Yaakov Bar-Shalom
Organizations
- University of Connecticut