Performance Advantages of Deep Neural Networks for Angle of Arrival Estimation

Bialer, Oded; Garnett, Noa; Tirer, Tom

doi:10.1109/icassp.2019.8682604

Cited by 52 publications

(50 citation statements)

References 21 publications

(27 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, ( 5) is a nonconvex problem (due to its objective) that is not tractable to solve. Therefore, to obtain a convex problem that can be efficiently solved we perform convex relaxation on (5). Following the common practice [24,25], we replace rank(Z) with the nuclear norm Z * that sums the singular values of the matrix, and replace 0 -type pseudo-norms with their 1 -norm counterparts, i.e., we replace Z 0,2 with…”

Section: The Proposed Methodsmentioning

confidence: 99%

“…Several existing strategies can handle the single snapshot scenario. While the classical methods are based on minimization (often greedy) of the negative log-likelihood function [2][3][4] and more recent methods are based on training deep neural networks [5], another popular approach is based on formulating the problem as a sparse signal reconstruction task, and estimating the DOAs from the peaks of the magnitude of the recovered high-dimensional signal [6][7][8].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Direction Of Arrival Estimation For Non-Coherent Sub-Arrays Via Joint Sparse And Low-Rank Signal Recovery

Tirer

Bialer

2021

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Self Cite

View full text Add to dashboard Cite

Estimating the directions of arrival (DOAs) of multiple sources from a single snapshot obtained by a coherent antenna array is a well-known problem, which can be addressed by sparse signal reconstruction methods, where the DOAs are estimated from the peaks of the recovered high-dimensional signal. In this paper, we consider a more challenging DOA estimation task where the array is composed of non-coherent sub-arrays (i.e., sub-arrays that observe different unknown phase shifts due to using low-cost unsynchronized local oscillators). We formulate this problem as the reconstruction of a joint sparse and low-rank matrix and solve its convex relaxation. While the DOAs can be estimated from the solution of the convex problem, we further show how an improvement is obtained if, instead, one estimates from this solution only the phase shifts, creates "phase-corrected" observations and applies another final (plain, coherent) sparsity-based DOA estimation. Numerical experiments show that the proposed approach outperforms strategies that are based on non-coherent processing of the sub-arrays as well as other sparsity-based methods.

show abstract

Section: The Proposed Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Direction Of Arrival Estimation For Non-Coherent Sub-Arrays Via Joint Sparse And Low-Rank Signal Recovery

Tirer

Bialer

2021

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Self Cite

View full text Add to dashboard Cite

show abstract

“…From the early investigation into AoA estimation, Machine Learning (ML) implementations using Radial Basis Function Neural Networks (RBFNN) were proposed to be a computational feasible method with outstanding performance (El Zooghby, 1997;El Zooghby et al, 2000). Ongoing work showed the high potential of ML with outstanding precision (Adavanne et al, 2018;Bialer et al, 2019;Huang et al, 2018;Khan et al, 2019;Ravindran and Jose, 2019). Those investigations are mostly based on a specific antenna design and had been evaluated in a simulation, where noise distributions were parametrized as gaussian or white noise with fixed Signal to Noise Ratio parameters (Huang et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

Machine Learning calibration of Angle of Arrival methods based on different experimental Unified Linear and Rectified Array measurements

Merk¹,

Nasa²,

Rezai³

et al. 2021

Preprint

View full text Add to dashboard Cite

Generic Angle of Arrival methods for indoor positioning are highly affected by specific antenna and environment scenarios through design impurities or multipathcomponent propagations. Here we acquired a large dataset of four different antenna designs in three different measurement environments with >140000 snapshots obtained from Bluetooth 5.1 receiver. Using the spatial power spectral densities of the PDDA angle of arrival algorithm as feature set for a small Random Forest model, we could show that angle estimation performances for all antennas in all measured environments were significantly improved (PDDA MAE >16 vs RF MAE < 3). Based on the small model size the proposed architecture can be implemented in microcontroller applications for super resolution angle of arrival applications. <br>

show abstract

“…For instance, paper [41] applied DNN to DOA estimation and evaluated the estimation performance under a scenario where two equal-power and uncorrelated signals arriving on a uniform linear array. Authors in [42] combined classification with regression in a single DNN. Most of the signal processing algorithms have the inherent capability to solve nonlinear problems such as nonlinear classification, regression, information retrieval, and so on.…”

Section: Introductionmentioning

confidence: 99%

Deep Neural Network for Estimation of Direction of Arrival With Antenna Array

2020

View full text Add to dashboard Cite

For many object tracking systems, how to quickly and efficiently estimate the direction of arrival (DOA) of radio waves impinging on the antenna array is an urgent task. In this paper, a new efficient DOA estimation approach based on the deep neural networks (DNN) is proposed, in which a nonlinear mapping that relates the outputs of the receiving antennas with its associated DOAs is learned by using the DNN-based network. The novel network architecture is divided into two phases, the detection phase and the DOA estimation phase. Additional detection network dramatically reduces the size of the training set and the process of the training data preparation is discussed in detail. After finishing the training phase, the corresponding DOAs can be identified based on current input data during testing phase. It has been shown that the proposed method can not only achieve reasonably high DOA estimation accuracy, but also reduce the computational complexity required by traditional superresolution DOA estimation methods such as multiple signal classification (MUSIC) and estimation of signal parameters via rotation invariance (ESPRIT). The computer simulation results are performed to investigate the generalization and effectiveness of the proposed approach in different scenarios.INDEX TERMS Deep neural networks (DNN), detection network, direction of arrival (DOA) estimation network, testing process, training data preparation process.

show abstract

Performance Advantages of Deep Neural Networks for Angle of Arrival Estimation

Cited by 52 publications

References 21 publications

Direction Of Arrival Estimation For Non-Coherent Sub-Arrays Via Joint Sparse And Low-Rank Signal Recovery

Direction Of Arrival Estimation For Non-Coherent Sub-Arrays Via Joint Sparse And Low-Rank Signal Recovery

Machine Learning calibration of Angle of Arrival methods based on different experimental Unified Linear and Rectified Array measurements

Deep Neural Network for Estimation of Direction of Arrival With Antenna Array

Contact Info

Product

Resources

About