For highly correlated received sequences, the convergence rate of the equalizer is a strongly limiting factor. The structure of the input autocorrelation matrix in Volterra second-order adaptive filters for general colored Gaussian input processes has been analyzed to determine how to best formulate a computationally efficient, fast adaptive algorithm. It was shown that when the input signal samples are ordered properly within the input data vector, the autocorrelation matrix of quadratic filter inherits a block diagonal structure, with some of the subblocks also having diagonal structure. In particular, this project emphasizes passive imaging and characterization of aircraft using reflected commercial TV and radio signals. This project investigates how the learning process in adaptive digital filters is disturbed by hardware failures and how to design filters and adaptive algorithms that can continue operating in the presence of such failures.
Adaptive systems are capable of adjusting parameters to reduce a specified error criterion.
It has been shown that whenever a hardware failure occurs that increases the error, the system will attempt to compensate for this failure by further self-adjustment. Recently this research has concentrated on compensating broader classes of hardware errors and on applying adaptive fault tolerance to adaptive filters of the infinite impulse response class. Tomography is the principle underlying most of the key diagnostic imaging modalities and many other imaging techniques, including synthetic aperture radar. These techniques are orders of magnitude faster than FBP for typical images, and promise to overcome the computational bottleneck created by new imaging technologies that acquire large quantities of data in real time. Similar developments are pursued for iterative and for 3-D reconstruction.