The traditional Direction of Arrival (DOA) estimation algorithms are based on model parameters, which depends on the accuracy of the array model. When the array model has errors, the matching between the model and the data will fail, which affects the estimation performance to some extent. Therefore, this paper constructs the nonlinear relationship between the received signal and its spatial spectrum through the neural network framework, and uses the data-driven of deep learning to realize the DOA estimation. The neural network consists of an autoencoder network and multiple parallel 1-D VGG networks to achieve spatial spectrum estimation of the angle region. The simulation results show that the DOA estimation method proposed in this paper has good generalization ability, and also show good robustness under array error conditions.
Bistatic radar target tracking is challenging due to the fact that the measurements are nonlinear functions of the Cartesian state. The converted measurement Kalman filter (CMKF) converts the raw measurement into Cartesian coordinates prior to tracking and is superior to the extended Kalman filter for certain problems. The challenges of CMKF are debiasing the converted measurement and approximating the converted measurement error covariance. Due to no closed form of biases, we utilize the second-order Taylor series expansion of the conventional measurement conversion to find the conversion bias in bistatic radar and propose the unbiased converted measurement (UCM). In order to decorrelate the converted measurement error covariance from the measurement noise, we evaluate the covariance using the prediction in Bayesian recursive filtering, designated as the decorrelated unbiased converted measurement (DUCM). Monte Carlo simulations show that the DUCM is unbiased and consistent, and the DUCM filter exhibits an improved performance compared with the conventional CMKF and the UCM filter in bistatic radar tracking.
Our work aims to address the problem of estimating the parameters of constant-amplitude, time-unsynchronized linear frequency-modulated (LFM) signals that have single or multiple components, which is a crucial task in electronic countermeasure techniques. A method for estimating the parameters, center frequency f0, and chirp rate μ of an LFM signal is proposed; the method is referred to as the Wigner–Ville distribution complex-valued convolutional neural network (WVD-CV-CNN). The method can be regarded as an application of neural networks for extracting parameter features from the signal spectrogram, wherein the CV-CNN is the main body of the network, which takes a complex-valued WVD matrix as the input and outputs several sets of estimated parameters. A performance analysis shows that the estimation accuracy and computational efficiency of the proposed method are significantly improved compared with those of the conventional methods. Further, the proposed method shows strong robustness to changes in modulation parameters. We apply the CV-CNN to other spectrograms and prove compatibility of the WVD and CV-CNN by comparison. We also demonstrate that the estimation accuracy of the proposed method is robust against cross interference on the WVD. Our study shows the advantages of using deep learning systems in signal parameter estimation.
In irregular pulse repetition interval (PRI) radar, successive pulses each with different PRIs are used as the transmission waveform. After analyzing the signal model of irregular PRI radar, we propose a coherent integration method based on Radon-iterative adaptive approach (Radon-IAA) to deal with the problems of irregular range cell migration (RCM) and the irregular phase fluctuations among different pulses introduced by the irregular PRI. In our method, the irregular RCM is compensated by searching through the motion parameters, and the irregular phase fluctuations can be resolved by an IAA-based spectral analysis method. The effectiveness of the proposed method is verified by simulation experiments.
KEYWORDS: Target detection, Radar, Signal detection, Doppler effect, Synthetic aperture radar, Signal to noise ratio, Fourier transforms, Numerical simulations, Monte Carlo methods, Baryon acoustic oscillations
As random stepped-frequency chirp (RSFC) signal is used in wide-band radar applications such as synthetic aperture radar (SAR) and inverse SAR. RSFC has advantages over the linear stepped-frequency chirp, including suppressing the range ambiguity, decoupling the range-Doppler coupling, and reducing the signal interference. RSFC is usually descrambled and then fed to the inverse fast Fourier transform (IFFT) to achieve a coherent integration as well as a high-resolution range. However, this method needs frequency descrambling and accurate velocity pre-estimation for moving target detection. We propose a coherent integration method based on time-dechirping for bistatic radar. This method can detect moving targets without frequency descrambling or accurate velocity pre-estimation. This paper first models the target echo mathematically and outlines the difficulties associated with the processing of IFFT for RSFC. Then the detailed principles of the proposed method are introduced and the flowchart is given. Finally, numerical simulations are conducted to verify the effectiveness of the proposed method and show its detecting ability in the presence of noise.
In order to suppress multiple mainlobe interferences and sidelobe interferences simultaneously, a mainlobe interference suppression algorithm is proposed. In this algorithm, the number of mainlobe interferences is estimated through a matrix filter at first. Then, the eigenvectors associated with mainlobe interference are determined and the eigen-projection matrix can be calculated. Next, the sidelobe-interference-plus-noise covariance matrix is reconstructed through eigenvalue replacement procedure. Finally, we can get the adaptive weight vector. Simulation results demonstrate the effectiveness of the proposed method when multiple mainlobe interferences exist.
Aiming to precisely estimate the velocity of high-speed targets for step frequency (SF) radar, a positive–positive–negative SF waveform consisting of two continuous positive SF pulse trains and a negative one is designed, and a velocity estimation method is proposed based on two-dimensional time-domain cross correlation (2-D TDCC). Making full use of the characteristics of the designed waveform, the coarse velocity estimation is achieved by 2-D TDCC of positive–positive SF pulse trains and then the Radon transform is applied to solve velocity ambiguity for high-speed targets. After velocity compensation for positive–negative SF pulse trains, the velocity residual is estimated precisely by 2-D TDCC. Simulation results show that the proposed method exhibits good performance for estimation accuracy, stability performance, computational complexity, and data rate by comparisons.
As most illuminators of opportunity are relatively narrowband and of low-frequency, passive bistatic radar (PBR) is so weak in target discrimination that it can hardly distinguish adjacent aircraft or ships. To solve this problem, we propose a matched filter-based method. This method uses the bistatic range of the target to construct the corresponding filter groups and then produces a two-dimensional image by correlating the echo signals. We finally convert the target discrimination problem to distinguish the peaks in the image. The proposed method overcomes the target discrimination problem for PBR using the narrowband and low-frequency illuminator. Simulation results indicate the effectiveness and validity of the proposed method in distinguishing adjacent targets.
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