  We present an algorithm for adaptive selection of pulse repetition frequency
or antenna activations for Doppler and DoA estimation. The adaptation is
performed sequentially using a Bayesian filter, responsible for updating the
belief on parameters, and a controller, responsible for selecting transmission
variables for the next measurement by optimizing a prediction of the estimation
error. This selection optimizes the Weiss-Weinstein bound for a
multi-dimensional frequency estimation model based on array measurements of a
narrow-band far-field source. A particle filter implements the update of the
posterior distribution after each new measurement is taken, and this posterior
is further approximated by a Gaussian or a uniform distribution for which
computationally fast expressions of the Weiss-Weinstein bound are analytically
derived. We characterize the controller's optimal choices in terms of SNR and
variance of the current belief, discussing their properties in terms of the
ambiguity function and comparing them with optimal choices of other
Weiss-Weinstein bound constructions in the literature. The resulting algorithms
are analyzed in simulations where we showcase a practically feasible real-time
evaluation based on look-up tables or small neural networks trained off-line.
