Research On Blind Detection Method Of Weak Signal Based On Angle Of Arrival

With the advent of the 5G information age,the electromagnetic environment during wireless communication has become more complicated.Due to the transmission environment of wireless communication,the influence of noise in the channel and the fading of the signal itself are more serious,so the signal is easily annihilated in the noise and is difficult to detect.In addition,due to the requirements of actual communication and military electronic warfare,blind detection of signal parameters such as the existence,number,and carrier frequency of weak signals is very necessary,which brings more severe difficulties and challenges to weak signal detection.Therefore,this paper studies the blind detection of the presence of weak signals and the number of signals when the presence of weak signals is detected.First of all,in view of the limited use of eigenvalue information in the traditional MMEbased blind detection method for weak signals,the detection performance is greatly affected by noise and the stability is poor.Therefore,this paper constructs new detection statistics by introducing the mean value of eigenvalues,proposes an improved MME detection method,and theoretically derives the detection probability,false alarm probability and detection threshold of this method.Using this method to perform detection simulation experiments under different signal-to-noise ratios,and compare the performance with the traditional MME method and ED method.The experimental results show that the improved MME detection method has better detection performance.Secondly,in view of the weak detection ability of traditional weak signal blind detection methods under low signal-to-noise ratio conditions,the concept of spatial spectrum in AOA estimation is introduced,and a weak signal detection method based on spatial spectrum-LSTM neural network is proposed.The method uses the difference between the signal space spectrum and the noise to judge whether there is a weak signal,and uses the LSTM neural network for feature learning to classify different samples,avoiding the impact of the detection threshold on the system’s detection performance.Simulation results show that this method has better detection effects than methods based on LSTM neural network,RBF neural network,and traditional MME and ED methods.In order to further improve the detection performance of the weak signal blind detection method under the condition of low signal-to-noise ratio,the WMUSIC-based spatial spectrum transformation is introduced,and the array antenna element can not be effectively used for it,resulting in when the number of weak signals is equal to the number of elements,the detection ability is lost,so a blind detection method for weak signals based on WMUSIC-like-LSTM is proposed.This method increases the number of effective array elements by introducing a fourth-order cumulant matrix when performing spatial spectrum transformation,and can effectively suppress noise energy.The simulation results show that the detection performance of this method is better than that based on LSTM neural network and based on capon-LSTM.Finally,based on the basic theory of optimization matrix reconstruction,this paper proposes a multi-objective weak signal number detection method based on the covariance matrix fitting criterion.This method can avoid the influence of signal coherence on signal detection,and reduces the requirements for the angular arrival interval between signals.The simulation experiment research shows that this method has the best performance compared with the spatial smoothing algorithm.