A NEW APPROACH FOR ESTIMATING SPECTRA FROM RANDOMLY SAMPLED SEQUENCES

Random sampling is one of the methods that can overcome the Nyquist li mit when evaluating a frequency spectrum of a signal. However, the com putational complexity becomes N-2 as the FFT cannot be used. A new app roach, called hybrid additive random sampling, is proposed. This new s cheme is devised by concatenating random sampling sequences in such a way that symmetry is created in the transform kernel for reducing the computational effort while the anti-alias property is maintained, A sa vings of the least 75% in computation is achieved. The sampling scheme is also found to be suitable for parallel implementation. In this pap er, the algorithms for generating the sampling sequence and evaluating the spectrum are described in detail. The performances of the scheme in terms of noise, accuracy, etc., are compared with genuine random sa mpling and another approach proposed previously. The advantages and li mitations are included.