Detection of a sinusoid in white noise by autoregressive spectrum analysis

The problem of detecting a sinusoid of unknown frequency and random phase in white noise is essentially a problem in spectral analysis. The conventional approach is to utilize a Periodogram. This paper examines the merits of a detector based on the auto-regressive spectral estimator. Some advantages of the autoregressive detector are that the performance is independent of the unknown frequency, and the false alarm rate is independent of the noise level. Also, for the first order AR model investigated, the computational and storage requirements needed to compute the test statistic are less than those of the Periodogram. The performance of the AR detector with a model order of one is, however, inferior to that of the Periodogram.