Browsing by Author "Liu, Mingqian"

Now showing 1 - 6 of 6
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    Carrier Frequency Estimation of Time-Frequency Overlapped MASK Signals for Underlay Cognitive Radio Network
    Liu, Mingqian; Zhang, Junlin; Lin, Yun; Wu, Zhen; Shang, Bodong; Gong, Fengkui (IEEE, 2019)
    As the single-signal carrier frequency estimation method is unsuitable for the time-frequency overlapped signals in an underlay cognitive radio network (CRN), in this paper, we propose a novel carrier frequency estimation method for the time-frequency overlapped multi-level amplitude-shift keying (MASK) signals in the underlay CRN. In this method, the diagonal slice spectrum of cyclic bispectrum for the time-frequency overlapped MASK signals is first estimated, and then, the carrier frequency of component MASK signals is estimated by extracting the position information of the diagonal slice spectrum line based on the norm theory and the adaptive threshold. In addition, the Cramer-Rao bound (CRB) of the carrier frequency estimation for the time-frequency overlapped MASK signals is also derived. The simulation results show that the proposed method can estimate the carrier frequency of the time-frequency overlapped MASK signals effectively, especially in low signal-to-noise ratio (SNR) regions.
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    DOA Robust Estimation of Echo Signals Based on Deep Learning Networks With Multiple Type Illuminators of Opportunity
    Hu, Bo; Liu, Mingqian; Yi, Fei; Song, Hao; Jiang, Fan; Gong, Fengkui; Zhao, Nan (2020-01-14)
    Traditional DOA estimation algorithms have poor adaptability to antenna errors. To enhance the direction of arrival (DOA) estimation performance for moving target echo signals in the environment of multiple type illuminators of opportunity, a DOA estimation framework leveraging deep learning networks (DLN) is proposed. In the proposed framework, the DLN is divided into two main components, including linear classification networks (LCN) and convolutional neural networks (CCN). The LCN is utilized to identify the spatial subregion of received signals and divide the signals from each subregion into corresponding output modules. Then, the output of the LCN after matrix transformations will be input into multiple parallel CNNs, where DOA estimations are carried out. Extensive simulation studies are conducted, demonstrating that our proposed method has excellent estimation performance and strong universality with high estimation accuracy even under large antenna defects.
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    Electromagnetic Signal Classification Based on Deep Sparse Capsule Networks
    Liu, Mingqian; Liao, Guiyue; Yang, Zhutian; Song, Hao; Gong, Fengkui (IEEE, 2019)
    In complex electromagnetic environments, electromagnetic signal classification rates are low as long time have to be the cost to extract features. To cope with the issue, in this paper, an electromagnetic signal classification method is proposed based on deep sparse capsule networks. In the proposed method, received signals are frequency reduced and sampled processing first. Subsequently, a cross ambiguity function based on linear canonical transformation, a cross ambiguity function based on linear canonical domain, and higher-order spectrum are estimated, respectively. The maximum value of each section of the cross ambiguity function is combined with the maximum value of equally spaced cross sections of higher order amplitude spectrum to obtain the two-dimensional feature information. Finally, electromagnetic signals are classified by the deep sparse capsule networks. The simulation results show that the proposed method not only has good classification performance but also can automatically get a hierarchical feature representation by learning. Moreover, the corresponding time cost can be effectively reduced.
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    Non Data-Aided SNR Estimation for UAV OFDM Systems
    Li, Junfang; Liu, Mingqian; Tang, Ningjie; Shang, Bodong (MDPI, 2020-01-10)
    Signal-to-noise ratio (SNR) estimation is essential in the unmanned aerial vehicle (UAV) orthogonal frequency division multiplexing (OFDM) system for getting accurate channel estimation. In this paper, we propose a novel non-data-aided (NDA) SNR estimation method for UAV OFDM system to overcome the carrier interference caused by the frequency offset. First, an absolute value series is achieved which is based on the sampled received sequence, where each sampling point is validated by the data length apart. Second, by dividing absolute value series into the different series according to the total length of symbol, we obtain an output series by stacking each part. Third, the root mean squares of noise power and total power are estimated by utilizing the maximum and minimum platform in the characteristic curve of the output series after the wavelet denoising. Simulation results show that the proposed method performs better than other methods, especially in the low synchronization precision, and it has low computation complexity.
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    Passive Detection of Moving Aerial Target Based on Multiple Collaborative GPS Satellites
    Liu, Mingqian; Gao, Zhiyang; Chen, Yunfei; Song, Hao; Li, Yuting; Gong, Fengkui (MDPI, 2020-01-12)
    Passive localization is an important part of intelligent surveillance in security and emergency applications. Nowadays, Global Navigation Satellite Systems (GNSSs) have been widely deployed. As a result, the satellite signal receiver may receive multiple GPS signals simultaneously, incurring echo signal detection failure. Therefore, in this paper, a passive method leveraging signals from multiple GPS satellites is proposed for moving aerial target detection. In passive detection, the first challenge is the interference caused by multiple GPS signals transmitted upon the same spectrum resources. To address this issue, successive interference cancellation (SIC) is utilized to separate and reconstruct multiple GPS signals on the reference channel. Moreover, on the monitoring channel, direct wave and multi-path interference are eliminated by extensive cancellation algorithm (ECA). After interference from multiple GPS signals is suppressed, the cycle cross ambiguity function (CCAF) of the signal on the monitoring channel is calculated and coordinate transformation method is adopted to map multiple groups of different time delay-Doppler spectrum into the distance–velocity spectrum. The detection statistics are calculated by the superposition of multiple groups of distance-velocity spectrum. Finally, the echo signal is detected based on a properly defined adaptive detection threshold. Simulation results demonstrate the effectiveness of our proposed method. They show that the detection probability of our proposed method can reach 99%, when the echo signal signal-to-noise ratio (SNR) is only −64 dB. Moreover, our proposed method can achieve 5 dB improvement over the detection method using a single GPS satellite.
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    Using Heterogeneous Satellites for Passive Detection of Moving Aerial Target
    Liu, Mingqian; Li, Kunming; Song, Hao; Chen, Yunfei; Gao, Xiuhui; Gong, Fengkui (MDPI, 2020-04-03)
    Passive detection of a moving aerial target is critical for intelligent surveillance. Its implementation can use signals transmitted from satellites. Nowadays, various types of satellites co-exist which can be used for passive detection. As a result, a satellite signal receiver may receive signals from multiple heterogeneous satellites, causing difficult in echo signal detection. In this paper, a passive moving aerial target detection method leveraging signals from multiple heterogeneous satellites is proposed. In the proposed method, a plurality of direct wave signals is separated in a reference channel first. Then, an adaptive filter with normalized least-mean-square (NLMS) is adopted to suppress direct-path interference (DPI) and multi-path interference (MPI) in a surveillance channel. Next, the maximum values of the cross ambiguity function (CAF) and the fourth order cyclic cumulants cross ambiguity function (FOCCCAF) correspond into each separated direct wave signal and echo signal will be utilized as the detection statistic of each distributed sensor. Finally, final detection probabilities are calculated by decision fusion based on results from distributed sensors. To evaluate the performance of the proposed method, extensive simulation studies are conducted. The corresponding simulation results show that the proposed fusion detection method can significantly improve the reliability of moving aerial target detection using multiple heterogeneous satellites. Moveover, we also show that the proposed detection method is able to significantly improve the detection performance by using multiple collaborative heterogeneous satellites.