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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 accountsfor 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. ; Grant numbers : This work was supported in part by the Catalan Government under grant 2017-SGR-01479.

Precision medicine is a new health care concept intended to hasten progress toward individualized treatment and, in so doing, to improve everyone's opportunity to enjoy good health. Yet, this concept pays scant attention to opportunities for change in the social determinants that are the major driv­ers of health. Precision medicine research is likely to generate improvements in medical care but may have the unintended conse­quence of worsening existing disparities in health care access. For prevention, precision medicine emphasizes comprehensive risk prediction and individual efforts to accom­plish risk reduction. The application of the precision medicine vision to type 2 diabe­tes, a growing threat to population health, fails to acknowledge collective responsibility for a health-promoting society.Ethn Dis. 2019;29(Suppl 3):669-674;doi:10.18865/ed.29.S3.669

The so-called Partial Relaxation approach has recently been proposed to solve the Direction-of-Arrival estimation problem. In this paper, we extend the previous work by applying Covariance Fitting with a data model that includes the noise covariance. Instead of applying a single source approximation to multi-source estimation criteria, which is the case for MUSIC, 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 the Partial Relaxation framework, the structure of the desired direction is kept, whereas the sensor array manifold corresponding to the remaining signals is relaxed [1], [2]. This procedure allows to compute a closed-form solution for the relaxed signal part and to come up with a simple spectral search with a significantly reduced computational complexity. Unlike in the existing Partial Relaxed Covariance Fitting approach, in this paper we utilize more prior-knowledge on the structure of the covariance matrix by also considering the noise covariance. Simulation results show that, the proposed method outperforms the existing Partial Relaxed Covariance Fitting method, especially in difficult conditions with small sample size and low Signal-to-Noise Ratio. Its threshold performance is close to that of Deterministic Maximum Likelihood, but at significantly lower cost. ; Grant numbers : This work was supported in part by the Catalan Government under grant 2017-SGR-01479.© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.