Problem
Research questions and friction points this paper is trying to address.
MIMO sensing
multi-target estimation
nuisance parameters
posterior Cramér-Rao bound
variational Bayesian inference
Variational Bayesian Inference Based Progressive Multi-Target MIMO Sensing with Nuisance Parameters
🤖 AI Summary
This study addresses the challenge of multi-target MIMO sensing under unknown angles and interfering reflection coefficients by proposing a progressive Bayesian sensing framework. The framework leverages variational Bayesian inference to efficiently approximate high-dimensional posterior distributions, replacing exponential numerical computations with polynomial complexity. Furthermore, it iteratively updates priors using posteriors to optimize transmit beamforming, thereby minimizing the Posterior Cramér-Rao Bound (PCRB). This research significantly reduces system computational complexity while validating the effectiveness of the proposed framework in progressively refining multi-target sensing performance.
📝 Abstract
This paper proposes a progressive Bayesian multiple-input multiple-output (MIMO) sensing framework for multiple targets over multiple stages. We consider a practical yet challenging scenario where the targets' angles are unknown and random parameters to be estimated, while the targets' reflection coefficients are unknown nuisance parameters. With an initial prior probability density function (PDF) for the targets' angles, we progressively update the prior PDF for each sensing stage as the posterior PDF obtained from the previous stage, based on which Bayesian transmit beamforming optimization is performed to minimize the sum posterior Cramér-Rao bound (PCRB) in estimating the targets' angles and Bayesian sensing is performed with the help of new observations in this stage. To analytically characterize the intractable and high-dimensional posterior PDF with low complexity, we propose a variational Bayesian inference based approach which derives a surrogate posterior PDF in closed form with only polynomial complexity over the number of targets, in sharp contrast to existing numerical calculation approaches with exponential complexity. Numerical results validate the efficacy of our proposed framework in progressively refining sensing performance.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Variational Bayesian Inference
Progressive MIMO Sensing
Nuisance Parameters
Posterior Cramér-Rao Bound
Transmit Beamforming
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