Source Enumeration using the Distribution of Angles: A Robust and Parameter-Free Approach
Source enumeration, the task of estimating the number of sources from the signal received by the array of antennas, is a critical problem in array signal processing. Numerous methods have been proposed to estimate the number of sources under white or colored Gaussian noise. However, their performance degrades significantly in the presence of a limited number of observations and/or a large number of sources. In this work, we propose a method based on the distribution of angles that performs well in (a) independent Gaussian, (b) spatially colored Gaussian, and (c) heavy-tailed noise, even when the number of sources is large. We support the supremacy of our algorithm over state-of-the-art methods with extensive simulation results.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Long-Term Sequential Decision Making under Risk
We study finite-horizon MDP planning under \emph{root-based} (resolute) risk objectives that apply a rank-dependent functional to the distribution of total returns. Such objectives are non-linear in the return distributi…
Decision MakingRobust Bayesian Cluster Enumeration Based on the $t$ Distribution
A major challenge in cluster analysis is that the number of data clusters is mostly unknown and it must be estimated prior to clustering the observed data. In real-world applications, the observed data is often subject t…
ClusteringPolicy Gradient Approach to Compilation of Variational Quantum Circuits
We propose a method for finding approximate compilations of quantum unitary transformations, based on techniques from policy gradient reinforcement learning. The choice of a stochastic policy allows us to rephrase the op…
Phase Retrieval via Model-Free Power Flow Jacobian Recovery
Phase retrieval is a prevalent problem in digital signal processing and experimental physics that consists of estimating a complex signal from magnitude measurements. This paper expands the classical phase retrieval fram…
RetrievalUniform random generation of large acyclic digraphs
Directed acyclic graphs are the basic representation of the structure underlying Bayesian networks, which represent multivariate probability distributions. In many practical applications, such as the reverse engineering …