Title
Maximum-Likelihood Doa Estimation At Low Snr In Laplace-Like Noise
Abstract
We consider the estimation of the direction of arrivals (DOAs) of plane waves hidden in additive, mutually independent, complex circularly symmetric noise at very low signal to noise ratio (SNR). The maximum-likelihood estimator (ML) for the DOAs of deterministic signals carried by plane waves hidden in noise with a Laplace-like distribution is derived. This leads to a DOA estimator based on the Least Absolute Deviation (LAD) criterion. We prove analytically that a weighted phase-only beamformer (which evaluates the scalar product between the steering vector and the complex signum function of the observed array data) is an approximation to a beamformer based on the Least Absolute Deviation (LAD) criterion. The root mean squared error of DOA estimators versus SNR is compared in a simulation study: the conventional beamformer (CBF), the weighted phase-only beam former, and sparse Bayesian learning (SBL3). This shows show that the ML estimator and weighted phase-only beamformer are well performing DOA estimators at low SNR for additive homoscedastic and heteroscedastic Gaussian noise, as well as Laplace-like noise.
Year
DOI
Venue
2019
10.23919/EUSIPCO.2019.8902711
2019 27TH EUROPEAN SIGNAL PROCESSING CONFERENCE (EUSIPCO)
Field
DocType
ISSN
Applied mathematics,Heteroscedasticity,Bayesian inference,Signal-to-noise ratio,Mean squared error,Least absolute deviations,Gaussian noise,Mathematics,Independence (probability theory),Estimator
Conference
2076-1465
Citations 
PageRank 
References 
0
0.34
0
Authors
2
Name
Order
Citations
PageRank
Christoph F. Mecklenbräuker161.66
Peter Gerstoft28622.34