High performance parallel-DSP computing in model-based spectral estimation

Doppler blood flow spectral estimation is a technique for non-invasive card iovascular disease detection. Blood flow velocity and disturbance may be de termined by measuring the spectral mean frequency and bandwidth, respective ly. The work presented here, evaluates a high performance parallel-Doppler Signal Processing architecture (SHARC) for the computation of a parametric model-based spectral estimation method known as the modified covariance alg orithm. The model-based method incorporates improvement in frequency resolu tion when compared with Fast Fourier Transform (FFT)-based methods. However , the computational complexity and the need for real-time response of the a lgorithm, makes necessary the use of high performance processing in order t o fulfil such demands. Sequential and parallel implementations of the algor ithm are introduced, A performance analysis of the implementations is also presented, demonstrating the effectiveness of the algorithm and the feasibi lity for real-time response of the system. The results open a greater scope for utilising this architecture in implementing new and more complex metho ds. The results are applied to the development of a real-time spectrum anal yser for pulsed Doppler blood flow instrumentation. (C) 1999 Elsevier Scien ce B.V. All rights reserved.