Algorithms and architectures are described for optical matrix-matrix and matrix-vector processing. Implementations are based on quadratic residue number system coding which has the remarkable property that computations in the real and imaginary channels are performed in parallel without any interactions. This approach significantly reduces the complexity of implementation. Residue-based processing provides increased dynamic range and a reduced number of computations with high speed and parallelism of the optics fully utilized. Several architectures of optical processors described in this paper include systolic and data flow arrays.
When multiple radar targets are close to each other, the return signals from these targets are overlapped in time. Therefore, by applying conventional motion compensation algorithms designed for single target, the multiple targets cannot be resolved, and each individual target cannot be clearly imaged. However, each individual target may have its own velocity and direction different from others. These different Doppler histories can be utilized to separate target from each other. By taking time-frequency transforms, different Doppler changing rates can be estimated. Using each estimated Doppler changing rate and making phase correction, each individual target may be imaged. In this paper, we first review algorithms for radar imaging of multiple moving targets, then, analyze the performance of these algorithms, and, finally, discuss the advantages and limitations of these algorithms by using simulated radar data.
Techniques are described for implementing an optical matrix‐vector processor for complex signals. A representation of complex numbers based on modular algebra is utilized to decouple real and imaginary channels, thereby simplifying the implementations. The pipelined architecture of an optical matrix‐vector processor is described.
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