Detection and estimation of multiple weak signals in non-Gaussian noise

We address the problem of efficient resolution, detection and estimation of weak tones in a potentially massive amount of data. Our goal is to produce a relatively small reduced data set characterizing the signals in the environment in time and frequency, The requirements for this problem are that the process must be computationally, efficient, high gain and able to resolve signals and efficiently compress the signal information into a form that may be easily displayed and further processed. To meet these requirements, we propose a concentrated peak representation (CPR,) in which the spectral energy is concentrated in spectral peaks, and only the magnitudes and locations of the peaks are retained. We base our process on the cross spectral representation we have previously applied to other problems. In selecting this method, we have considered other representations and estimation methods such as the Wigner distribution and Welch's method. We compare our method to these methods. The spectral estimation method we propose is a variation of Welch's method and the cross-power spectral (CPS) estimator which was first applied to signal estimation and detection in the mid 1980's. The CPS algorithm arid the method we present here are based on the principles first described by Kodera et al.