A paradigm for massively parallel processing of matched filters, replica correlators, ambiguity functions, and time-frequency distributions is presented using a SIMD (Single Instruction Multiple Data) programming methodology. It is shown that active sonar detection algorithms, as implemented by frequency domain processing, can be a ''natural match'' to a SIMD methodology. Using the Connection Machine, a 8192 node SIMD parallel processor, this methodology meets the extensive computational needs of enhanced active sonar systems. The decomposition process is presented and examples are given of the output of the computer program OMASP (Connection Machine Ambiguity Surface Processor). CMASP can provide real-time simultaneous multiple beam, Doppler and waveform replica correlations. Synthetic data are processed and the corresponding CMASP outputs are displayed as three-dimensional ambiguity surfaces on networked graphic workstations. Because of efficient problem decomposition, other time-frequency processing can be exploited. Specifically, real-time instantaneous-like time-frequency distributions have been realized (e.g., Rihaczek distributions) in which the data set is presented and processed as time-varying spectral representations.
