Treffer: Estimation with Multisensor-Multiscan Detection Fusion

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 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 limitation 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 techniques) for practical high density scenarios, target tracking for the case of glint and multipath; ground target tracking in a Joint STARS scenario; phased array radar resource allocation; track formation of LO targets from EO sensor data; parallelization of assignment algorithms; segmentation of images of targets overlapping in the focal plane and their tracking; radar waveform design for optimized tracking performance; estimation of trajectory parameters for TBM in boost phase; track before detect approach for VLO targets with fluctuating amplitude: passive ranging of LO TBM; unbiased conversion of polar and spherical coordinate radar measurements to Cartesian for long range radars: generalization of the CRLB in the presence of false measurements to non-Gaussian distribution; an efficient estimator for acquisition by an ESA radar of a LO TBM prior to reentry; a Fokker-Planck-Kolmogorov equation based estimator for highly nonlinear systems with large noises; a variable bandwidth estimator for EAW scenarios.