This paper investigates the problem of unmanned aerial vehicle (UAV) recognition in the presence of WiFi interference using passive radio frequency (RF) detection. The proposed method relies on machine learning based RF recognition and considers the scenario in the bandwidth of the video signal (VS) and WiFi are identical. Our machine learning strategy involves extracting 31 features from the WiFi signal and the UAV VS, which are then input to the classifier. Among the 31 features, 30 are statistical in the time and frequency domain, while the remaining one involves the effective subcarrier feature. We evaluate four different machine learning (ML) classifier variants and demonstrate through simulation and experiments that the proposed method can accurately recognize UAV VS in the presence of WiFi interference. We also improve the feature-vector compactness and reduce the 31-feature vector to a 6-feature vector composed of the most significant features and demonstrate that the recognition performance of random forest method (RandF) classifier is not compromised. The RandF obtains the best result, which has a recognition rate of 100% in the indoor experiment. While in the 2 km outdoor experiment, the recognition rate of the four ML classifiers is larger than 95.52%, which is better than other UAV detection methods such as radar, acoustic and video.INDEX TERMS UAV video signal, recognition, WiFi interference, machine learning.Ming Zuo Author received Bachelor's degree in Communication Engineering from Jiangxi Normal University, Nanchang, China, in 2014, and he Master's degree in Electronic and Communication Engineering from Nanchang University, Nanchang, China, in 2016. He is currently pursuing the PHD degree with the School of Electronic and Information Engineering, Beihang University. His research interests focus on radar signal processing, electronic countermeasure, passive positioning, anti-unmanned aerial vehicles.
In this paper, a weighted l1-norm is proposed in a l1-norm-based singular value decomposition (L1-SVD) algorithm, which can suppress spurious peaks and improve accuracy of direction of arrival (DOA) estimation for the low signal-to-noise (SNR) scenarios. The weighted matrix is determined by optimizing the orthogonality of subspace, and the weighted l1-norm is used as the minimum objective function to increase the signal sparsity. Thereby, the weighted matrix makes the l1-norm approximate the original l0-norm. Simulated results of orthogonal frequency division multiplexing (OFDM) signal demonstrate that the proposed algorithm has s narrower main lobe and lower side lobe with the characteristics of fewer snapshots and low sensitivity of misestimated signals, which can improve the resolution and accuracy of DOA estimation. Specifically, the proposed method exhibits a better performance than other works for the low SNR scenarios. Outdoor experimental results of OFDM signals show that the proposed algorithm is superior to other methods with a narrower main lobe and lower side lobe, which can be used for DOA estimation of UAV and pseudo base station.
Strong interference will affect direction of arrival (DOA) estimation of weak desired signal and even cause DOA estimation failure. This paper investigates the weak signal DOA estimation for an antenna array under strong interference signals, and proposed a novel DOA estimation method for strong interference source suppression and weighted l1-norm sparse representation. A parallel adaptive beamforming algorithm based on power inversion is used to suppress strong interference and form new array data. To reduce spurious peaks in the spectrum under strong interference, a weighted matrix is determined by the optimized subspace algorithm for the subspace projection. Then, the DOA estimation, which is calculated by weighted l1-norm sparse representation, is formed by the weighted matrix and new array data. In this paper, the superiority of the proposed algorithm is illustrated by an example of unmanned aerial vehicle (UAV) video signal DOA estimation under strong interference signals. The simulated results of an orthogonal frequency division multiplexing signal indicate that the proposed algorithm shows merits of fewer snapshots, a sharper main lobe, a lower average noise spectrum value, higher DOA estimation accuracy and success rate. For validation, an outdoor experiment was conducted which demonstrated that the proposed algorithm is superior to other algorithms and can be used for DOA estimation of UAV video signals under strong WiFi interference. Both the simulations and experiments verify that the proposed algorithm can effectively suppress strong interference and achieve better DOA estimation performance for weak signals.
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