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. 2024 Nov 20;24(22):7390.
doi: 10.3390/s24227390.

Enhancing Direction-of-Arrival Estimation with Multi-Task Learning

Simone Bianco  1 , Luigi Celona  1 , Paolo Crotti  1 , Paolo Napoletano  1 , Giovanni Petraglia  2 , Pietro Vinetti  2
Affiliations

Enhancing Direction-of-Arrival Estimation with Multi-Task Learning

Simone Bianco et al. Sensors (Basel). .

Abstract

There are numerous methods in the literature for Direction-of-Arrival (DOA) estimation, including both classical and machine learning-based approaches that jointly estimate the Number of Sources (NOS) and DOA. However, most of these methods do not fully leverage the potential synergies between these two tasks, which could yield valuable shared information. To address this limitation, in this article, we present a multi-task Convolutional Neural Network (CNN) capable of simultaneously estimating both the NOS and the DOA of the signal. Through experiments on simulated data, we demonstrate that our proposed model surpasses the performance of state-of-the-art methods, especially in challenging environments characterized by high noise levels and dynamic conditions.

Keywords: convolutional neural networks; direction-of-arrival (DOA) estimation; multi-task learning; ordinal regression.

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Conflict of interest statement

The authors declare no conflicts of interest.

Figures

Figure 1
Figure 1
Uniform Linear Array (ULA); d is the distance between the sensors; θi is the angle of arrival of the impinging signal, and M is the number of sensor array antennas.
Figure 2
Figure 2
Architecture of the proposed multi-task CNN for DOA estimation. The network processes the signal covariance matrix through the backbone. The resulting feature vector is passed to two branches: the Number-of-Source estimator predicts the Number of Sources b (i.e., a binarized version of the logits s); the Direction-of-Arrival estimator provides multiple angles of arrival, denoted as d, corresponding to the number of angles b predicted by the other branch. A compound loss L is used to optimize the model based on the two task-specific losses.
Figure 3
Figure 3
Ball chart reporting the RMSE versus accuracy. The size of each ball corresponds to the number of model parameters.
Figure 4
Figure 4
The DOA estimation performance for the T1 test set at varying SNRs divided by (a) one signal only, (b) two signals, and (c) three signals.
Figure 5
Figure 5
Boxplots showing CRLB index distributions for (a) test sets T1, T3, and T4, (b) various snapshots within T2, and (c) various snapshots within T5.
Figure 6
Figure 6
Scenario-independent total performance comparison.
See this image and copyright information in PMC

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