# Adaptive channel selection for DOA estimation in MIMO radar

## TL;DR

This paper introduces adaptive antenna selection strategies for improving DoA estimation in MIMO radar, utilizing Bayesian bounds and adaptive sensing to optimize measurement efficiency under noise.

## Contribution

It proposes a novel adaptive sensing framework for antenna selection in MIMO radar based on Bayesian bounds and one-step ahead predictions.

## Key findings

- Adaptive strategies outperform fixed methods in noisy environments.
- Bayesian bound-based policies improve DoA estimation accuracy.
- Simulation results validate the effectiveness of the proposed approach.

## Abstract

We present adaptive strategies for antenna selection for Direction of Arrival (DoA) estimation of a far-field source using TDM MIMO radar with linear arrays. Our treatment is formulated within a general adaptive sensing framework that uses one-step ahead predictions of the Bayesian MSE using a parametric family of Weiss-Weinstein bounds that depend on previous measurements. We compare in simulations our strategy with adaptive policies that optimize the Bobrovsky- Zaka{\i} bound and the Expected Cram\'er-Rao bound, and show the performance for different levels of measurement noise.