Robust detection by autoregressive spectrum analysis
Steven Kay · IEEE Transactions on Acoustics Speech and Signal Processing · 1982
The problem of detecting a signal with an unknown Doppler shift and random phase in white noise is essentially a problem in spectral analysis. This paper examines the merits of a detector based upon the autoregressive spectral estimator. Some advantages of the auto-regressive detector are that the detection performance is independent of Doppler shift and phase and the false alarm rate is independent of noise level. Also, the performance does not depend upon the exact signal form but only upon its autocorrelation function, leading to a robust detector. For the first order autoregressive model investigated, the computational and storage requirements of the autoregressive detector are less than that for a conventional bank of matched filters detector. It is shown by example that when the actual received signal departs appreciably from the signal assumed in a conventional detector, i.e., a bank of matched filters, the AR detection performance exceeds that of the conventional detector.