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We present an algorithm for adaptive selection of pulse repetition frequencyor antenna activations for Doppler and DoA estimation. The adaptation isperformed sequentially using a Bayesian filter, responsible for updating thebelief on parameters, and a controller, responsible for selecting transmissionvariables for the next measurement by optimizing a prediction of the estimationerror. This selection optimizes the Weiss-Weinstein bound for amulti-dimensional frequency estimation model based on array measurements of anarrow-band far-field source. A particle filter implements the update of theposterior distribution after each new measurement is taken, and this posterioris further approximated by a Gaussian or a uniform distribution for whichcomputationally fast expressions of the Weiss-Weinstein bound are analyticallyderived. We characterize the controller's optimal choices in terms of SNR andvariance of the current belief, discussing their properties in terms of theambiguity function and comparing them with optimal choices of otherWeiss-Weinstein bound constructions in the literature. The resulting algorithmsare analyzed in simulations where we showcase a practically feasible real-timeevaluation based on look-up tables or small neural networks trained off-line.

Adaptive transmission for radar arrays using Weiss-Weinstein bounds