An Improved Doa Estimator Based on Partial Relaxation Approach

Trinh-Hoang, Minh; Viberg, Mats; Pesavento, Marius

doi:10.1109/icassp.2018.8462295

Cited by 11 publications

(3 citation statements)

References 6 publications

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“…which still retains some geometric structure of the sensor array [2]. Applying the PR approach to the conventional DML method and optimizing with respect to the relaxed signal part B yields the concentrated PR-DML cost function [1,2]…”

Section: Partially Relaxed Deterministic Maximum Likelihoodmentioning

confidence: 99%

“…The K DoA estimates are given by the K arguments that correspond to the K deepest local minima of the concentrated objective function in (2). An efficient implementation of the PR-DML method is provided in [1].…”

Section: Partially Relaxed Deterministic Maximum Likelihoodmentioning

confidence: 99%

“…The Partial Relaxation (PR) framework was introduced to overcome the aforementioned disadvantages of the conventional spectral-based DoA methods [1,2]. Instead of ignoring the presence of multiple sources, the PR approach considers both the signal impinging from the current direction of interest as well as the interfering ones.…”

Section: Introductionmentioning

confidence: 99%

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Asymptotic Stochastic Analysis of Partially Relaxed DML

Schenck¹,

Mestre²,

Pesavento³

2020

ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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The Partial Relaxation approach has recently been proposed to solve the Direction-of-Arrival estimation problem [1, 2]. In this paper, we investigate the outlier production mechanism of the Partially Relaxed Deterministic Maximum Likelihood (PR-DML) Direction-of-Arrival estimator using tools from Random Matrix Theory. An accurate description of the probability of resolution for the PR-DML estimator is provided by analyzing the asymptotic stochastic behavior of the PR-DML cost function, assuming that both the number of antennas and the number of snapshots increase without bound at the same rate. The finite dimensional distribution of the PR-DML cost function is shown to be Gaussian in this asymptotic regime and this result is used to compute the probability of resolution.

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Section: Partially Relaxed Deterministic Maximum Likelihoodmentioning

confidence: 99%

Section: Partially Relaxed Deterministic Maximum Likelihoodmentioning

confidence: 99%

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Asymptotic Stochastic Analysis of Partially Relaxed DML

Schenck¹,

Mestre²,

Pesavento³

2020

ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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Probability of Resolution of Partially Relaxed DML an Asymptotic Approach

Schenck

Mestre

Pesavento

2019

2019 IEEE 8th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)

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The Partial Relaxation approach has recently been proposed to solve the Direction-of-Arrival estimation problem [1], [2]. In this paper, we investigate the outlier production mechanism of the Partially Relaxed Deterministic Maximum Likelihood (PR-DML) Direction-of-Arrival estimator using tools from Random Matrix Theory. Instead of applying a single source approximation to multi-source estimation criteria, which is the case for the MUSIC algorithm, the conventional beamformer, or the Capon beamformer, the Partial Relaxation approach accounts for the existence of multiple sources using a non-parametric modification of the signal model. In this paper, an accurate description of the probability of resolution for the PR-DML estimator is provided by analyzing the asymptotic behavior of the PR-DML cost function, assuming that both the number of antennas and the number of snapshots increase without bound at the same rate. The finite dimensional distribution of the PR-DML cost function is shown to be Gaussian in this asymptotic regime and this result is used to compute the probability of resolution.

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