Universal adaptive beamforming: A Bayesian approach

We present a Bayesian universal beamforming framework for adaptive array processing in dynamic underwater acoustic environments with unknown and time-varying propagation geometry. Motivated by ideas from universal prediction and estimation, the proposed approach discretizes the angular domain into a finite set of steering hypotheses and recursively computes posterior probabilities over competing spatial models using observation-dependent likelihood functions. For Gaussian observation models, the posterior update reduces to an exponential-weights recursion driven by hypothesis-dependent beamformer evidence metrics. The resulting framework performs soft spatial inference and adaptive beamforming by continuously redistributing posterior probability across competing steering hypotheses while forming posterior-weighted combinations of branch outputs. The formulation naturally connects to classical adaptive beamformers including matched filtering and minimum mean-square error (MMSE) beamforming. In addition, the framework is extended toward broadband underwater acoustic communication receivers through frequency-domain beamformer synthesis and adaptive equalization. Posterior probabilities are updated according to branch-specific equalization errors, enabling joint spatial-temporal adaptation under multipath propagation, Doppler-induced distortions, and time-varying channel conditions. Experimental results using MACE data demonstrate reliable communication performance with low overhead, low data detection mean-squared error, and zero observed bit errors.

A Switching Beamformer for Highly Non-Stationary Environments

Adaptive beamforming is a cornerstone of array signal processing, yet its performance often collapses in the face of complex, rapidly changing interference. When interferers appear or move unpredictably, conventional estimators encounter a fundamental memory trade-off: short windows enable rapid tracking but suffer from high estimation variance, while long windows provide stable rejection but fail to adapt to shifts. This challenge is resolved by introducing the Universal Switching Beamformer (USB), which integrates competitive sequential prediction into the beamforming architecture. By employing a linear transition diagram, the USB implicitly maintains an exponentially large family of candidate covariance histories and dynamically re-weights them based on their cumulative output power. This mechanism allows the beamformer to automatically vary its effective memory length without explicit change detection or heuristic parameter tuning. A theoretical upper bound is proven on the regret relative to an omniscient oracle that selects the best piecewise-stationary covariance model in hindsight. Extensive simulations and experiments on the SwellEx-96 dataset demonstrate that the USB achieves the agility of short-window estimators and the precision of long-term integration, providing a principled solution for tracking highly non-stationary scenes.

Time Segmented Beamforming via Dynamic Programming: Theory and Implementation

In dynamic acoustic environments with time-varying interferers, effective beamforming requires identifying stationary regions over time. The Capon beamformer, a whitened matched filter constrained to maintain unity gain in the desired direction, theoretically relies on the instantaneous ensemble covariance matrix. Practical implementations rely on the batch Capon (or Sample Matrix Inversion), which estimates the sample covariance matrix (SCM) by averaging over a block of snapshots. This practical approach implicitly assumes that the data within the batch window is stationary and can be coherently combined. In non-stationary settings, a batch approach that averages over fixed or excessively long windows fails, as moving interferers smear the SCM and degrade the beamformer's nulling capabilities. To address this, this paper introduces a temporally segmented distortionless response beamformer. Inspired by the segmented least squares method, which fits piecewise polynomials to data while penalizing excessive segmentation to prevent overfitting, the framework extends practical Capon beamforming by incorporating data-driven temporal segmentation. This formulation minimizes output power while dynamically adapting the SCM estimation windows to local stationarity, offering a principled approach to tracking time-varying interferers.