arXiv subjects

David Mateos-Núñez

Publications and source records attributed to David Mateos-Núñez.

4 recordsLinked to original sources

arXiv2019

Height estimation for automotive MIMO radar with group-sparse reconstruction

A method is developed for sequential azimuth and height estimation of small objects at far distances in front of a moving vehicle using coherent or mutually incoherent MIMO arrays. The model considers phases and amplitudes for near-field multipath signals produced by specular non-diffusive ground-reflections where the reflection phase shift and power attenuation due to the interaction with the ground is assumed unknown. Group-sparsity allows combining measurements along the trajectory of the vehicle provided that the road is flat as well as measurements from multiple incoherent sensors at different locations in the vehicle. It is shown in simulations that the proposed approach significantly increases estimation accuracy and decreases false alarms, both crucial for the detection of small objects at far distances. This model is suitable for non-uniform sparse arrays and can be used for height estimation using efficient methods such as block orthogonal matching pursuit.

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arXiv2018

Adaptive transmission for radar arrays using Weiss-Weinstein bounds

We present an algorithm for adaptive selection of pulse repetition frequency or antenna activations for Doppler and DoA estimation. The adaptation is performed sequentially using a Bayesian filter, responsible for updating the belief on parameters, and a controller, responsible for selecting transmission variables for the next measurement by optimizing a prediction of the estimation error. This selection optimizes the Weiss-Weinstein bound for a multi-dimensional frequency estimation model based on array measurements of a narrow-band far-field source. A particle filter implements the update of the posterior distribution after each new measurement is taken, and this posterior is further approximated by a Gaussian or a uniform distribution for which computationally fast expressions of the Weiss-Weinstein bound are analytically derived. We characterize the controller's optimal choices in terms of SNR and variance of the current belief, discussing their properties in terms of the ambiguity function and comparing them with optimal choices of other Weiss-Weinstein bound constructions in the literature. The resulting algorithms are analyzed in simulations where we showcase a practically feasible real-time evaluation based on look-up tables or small neural networks trained off-line.

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arXiv2017

Adaptive channel selection for DOA estimation in MIMO radar

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- Zakaı bound and the Expected Cramér-Rao bound, and show the performance for different levels of measurement noise.

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arXiv2016

Distributed saddle-point subgradient algorithms with Laplacian averaging

We present distributed subgradient methods for min-max problems with agreement constraints on a subset of the arguments of both the convex and concave parts. Applications include constrained minimization problems where each constraint is a sum of convex functions in the local variables of the agents. In the latter case, the proposed algorithm reduces to primal-dual updates using local subgradients and Laplacian averaging on local copies of the multipliers associated to the global constraints. For the case of general convex-concave saddle-point problems, our analysis establishes the convergence of the running time-averages of the local estimates to a saddle point under periodic connectivity of the communication digraphs. Specifically, choosing the gradient step-sizes in a suitable way, we show that the evaluation error is proportional to $1/\sqrt{t}$, where $t$ is the iteration step. We illustrate our results in simulation for an optimization scenario with nonlinear constraints coupling the decisions of agents that cannot communicate directly.

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