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Direction-of-arrival estimation of an unknown number of signals using a machine learning framework

Kanters, N.B. (2020) Direction-of-arrival estimation of an unknown number of signals using a machine learning framework.

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Abstract:Direction-of-arrival (DOA) estimation is a well-known problem in the field of array signal processing with applications in, e.g., radar, sonar and mobile communications. Many conventional DOA estimation algorithms require prior knowledge about the source number, which is often not available in practical situations. Another common feature of many DOA estimators is that they aim to derive an inverse of the mapping between the sources’ positions in space and the array output. However, in general this mapping is incomplete due to unforeseen effects such as array imperfections. This degrades the performance of the DOA estimators. In this work, a machine learning (ML) framework is proposed which estimates the DOAs of waves impinging an antenna array, without any prior knowledge about the number of sources. The inverse mapping mentioned above is made up by an ensemble of single-label classifiers, trained on labeled data by means of supervised learning. Each classifier in the ensemble analyses a number of segments of the discretized spatial domain. Their predictions are combined into a spatial spectrum, after which a peak detection algorithm is applied to estimate the DOAs. The framework is evaluated in combination with feedforward neural networks, trained on synthetically generated data. The antenna array is a uniform linear array of 8 elements with half wavelength element spacing. A framework with a grid resolution of 2�, trained on 105 observations of 100 snapshots each, achieved an accuracy of 93% regarding the source number for signal-to-noise ratios (SNRs) of at least -5 dB when 2 uncorrelated signals impinge the array. The root-mean-square error (RMSE) of the estimates of the DOAs of these observations is below 1� and equals 0.5� for SNRs of 5 dB and higher. It is shown that in the remaining 7%, the DOAs are spaced 2.4� degree on average, making the resolution of the grid too coarse for resolving these DOAs. Increasing the resolution of the grid is at the cost of an increased class imbalance, which complicates the classification procedure. Nevertheless, it is shown that a 100% probability of resolution is obtained for observations of 15 dB SNR with DOA spacings of at least 3.2� for a framework of 0.8� resolution, whereas the framework of 2� resolution achieves this for spacings larger than 5.9�. However, 4 times more training data is used to realize this. A scenario with a variable source number showed that the performance of the ML framework decreases gradually with an increasing number of sources. When a single signal with a 15 dB SNR impinges the array, this is estimated correctly in 100.0% of the observations, with an RMSE of 0.4�. However, when 7 sources exist, the performance decreases to 3.3% and 1.8� respectively. A decreased accuracy of the source number estimates was expected because of the 2� resolution that was used. However, it is shown that the performance of the neural networks in terms of their predictions decreases with an increasing source number as well. The results indicate that the resolution of the framework has a significant impact on its DOA estimates. It is observed that for the considered learning strategy, additional training data is required to actually benefit from an increased resolution. Further research is required to determine if alternative learning algorithms and advanced techniques for handling class imbalance could diminish this need for additional data. Furthermore, it should be verified if the proposed data-driven approach indeed adapts better to unforeseen effects compared to model-based algorithms by evaluating it on real-world data.
Item Type:Essay (Master)
Faculty:EEMCS: Electrical Engineering, Mathematics and Computer Science
Subject:53 electrotechnology
Programme:Electrical Engineering MSc (60353)
Link to this item:http://purl.utwente.nl/essays/80493
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