The paper considers the problem of estimating the parameters of a complex h e a r FM signal from a finite number of noisy discrete-time observations. An estimation algorithm is proposed, and its asymptotic (large sample) performance is analyzed. The variance of the estimates is shown to be close to the Cramer-Rao lower bound when the signal-to-noise ratio is 0 dB and above. The statistical analysis is verified by some Monte-Carlo simulations.

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Direction finding techniques are usually based on the second order statistics of the received data. In this paper we derive two types of direction finding algorithms which use the fourth order cumulants of the array data. One is a MUSIC-like technique based on eigendecomposition of a suitably defined cumulant matrix. The other is an optimal (asymptotically minimum variance) estimator based on minimization of a certain cost function.

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