A robust generalized sidelobe canceller is proposed to combat direction of arrival(DOA)mismatches.To estimate the interference-plus-noise(IPN)statistics characteristics,conventional signal of interest(SOI)extraction m...A robust generalized sidelobe canceller is proposed to combat direction of arrival(DOA)mismatches.To estimate the interference-plus-noise(IPN)statistics characteristics,conventional signal of interest(SOI)extraction methods usually collect a large number of segments where only the IPN signal is active.To avoid that collection procedure,we redesign the blocking matrix structure using an eigenanalysis method to reconstruct the IPN covariance matrix from the samples.Additionally,a modified eigenanalysis reconstruction method based on the rank-one matrix assumption is proposed to achieve a higher reconstruction accuracy.The blocking matrix is obtained by incorporating the effective reconstruction into the maximum signal-to-interferenceplus-noise ratio(MaxSINR)beamformer.It can minimize the influence of signal leakage and maximize the IPN power for further noise and interference suppression.Numerical results show that the two proposed methods achieve considerable improvements in terms of the output waveform SINR and correlation coefficients with the desired signal in the presence of a DOA mismatch and a limited number of snapshots.Compared to the first proposed method,the modified one can reduce the signal distortion even further.展开更多
Interference suppression is a challenge for radar researchers, especially when mainlobe and sidelobe interference coexist. We present a comprehensive anti-interference approach based on a cognitive bistatic airborne r...Interference suppression is a challenge for radar researchers, especially when mainlobe and sidelobe interference coexist. We present a comprehensive anti-interference approach based on a cognitive bistatic airborne radar. The risk of interception is reduced by lowering the launch energy of the radar transmitting terminal in the direction of interference;main lobe and sidelobe interferences are suppressed via cooperation between the two radars. The interference received by a single radar is extracted from the overall radar signal using multiple signal classification(MUSIC), and the interference is cross-located using two different azimuthal angles. Neural networks allowing good, non-linear nonparametric approximations are used to predict the location of interference, and this information is then used to preset the transmitting notch antenna to reduce the likelihood of interception. To simultaneously suppress mainlobe and sidelobe interferences, a blocking matrix is used to mask mainlobe interference based on azimuthal information, and an adaptive process is used to suppress sidelobe interference. Mainlobe interference is eliminated using the data received by the two radars. Simulation verifies the performance of the model.展开更多
基金Project supported by the National Natural Science Foundation of China(No.61571436)
文摘A robust generalized sidelobe canceller is proposed to combat direction of arrival(DOA)mismatches.To estimate the interference-plus-noise(IPN)statistics characteristics,conventional signal of interest(SOI)extraction methods usually collect a large number of segments where only the IPN signal is active.To avoid that collection procedure,we redesign the blocking matrix structure using an eigenanalysis method to reconstruct the IPN covariance matrix from the samples.Additionally,a modified eigenanalysis reconstruction method based on the rank-one matrix assumption is proposed to achieve a higher reconstruction accuracy.The blocking matrix is obtained by incorporating the effective reconstruction into the maximum signal-to-interferenceplus-noise ratio(MaxSINR)beamformer.It can minimize the influence of signal leakage and maximize the IPN power for further noise and interference suppression.Numerical results show that the two proposed methods achieve considerable improvements in terms of the output waveform SINR and correlation coefficients with the desired signal in the presence of a DOA mismatch and a limited number of snapshots.Compared to the first proposed method,the modified one can reduce the signal distortion even further.
文摘Interference suppression is a challenge for radar researchers, especially when mainlobe and sidelobe interference coexist. We present a comprehensive anti-interference approach based on a cognitive bistatic airborne radar. The risk of interception is reduced by lowering the launch energy of the radar transmitting terminal in the direction of interference;main lobe and sidelobe interferences are suppressed via cooperation between the two radars. The interference received by a single radar is extracted from the overall radar signal using multiple signal classification(MUSIC), and the interference is cross-located using two different azimuthal angles. Neural networks allowing good, non-linear nonparametric approximations are used to predict the location of interference, and this information is then used to preset the transmitting notch antenna to reduce the likelihood of interception. To simultaneously suppress mainlobe and sidelobe interferences, a blocking matrix is used to mask mainlobe interference based on azimuthal information, and an adaptive process is used to suppress sidelobe interference. Mainlobe interference is eliminated using the data received by the two radars. Simulation verifies the performance of the model.
