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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.

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.

Image smear, produced by the shutter-less operation of full-frame charge-coupled device (CCD) sensors, greatly affects the performance of target detection, the centering accuracy, and visual magnitude estimation. We study the operation principle of full-frame CCDs, analyze the cause and properties of smear effect, and propose a smear removal algorithm for star images of full-frame CCDs. The proposed method locates the smears and extracts the rough profiles of the smeared stars by finding the conditional extrema. Then Gaussian fitting is applied to accurately extract the stars, in order to maintain the integrity of star images while minimizing the smear effect. The extraction of smears and stars requires parameters such as the size of the CCD, the integration time and the readout time, as well as the estimation of background noise. We assess the performance of our scheme with real observed data. The experimental results show that the proposed scheme improves the average signal-to-noise ratio of the images by about 22%, presenting better smear removal performance compared with several published methods. The limitation of the proposed algorithm includes the difficulty of distinguishing between two very close stars displaying the gray level of a single peak and overestimation of the background noise may also influence the performance of the algorithm.

An inverse synthetic aperture radar (ISAR) high-precision compensation method is proposed based on coherent processing of intermediate frequency direct sampling data. First, the compensation of high-speed movement is performed by a modified linear frequency modulation matched filter during the pulse compression. The motion trajectory in the down-range direction is then reconstructed by compensation of window sampling difference of each pulse. Modified envelope correlation is applied to calculate the range profile shift between each pulse and the first one. Polynomial fitting is adopted to accurately estimate the motion characteristics. Subsequently, coherent processing is applied by combining range alignment and initial phase compensation. The migration through range cells correction can be then realized by keystone transform to the highly coherent data. Consequently, ISAR images with high quality are achieved. Experimental results on simulated and real data have demonstrated the validity of the proposed method.

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.

A direction of arrival (DOA) estimation algorithm for coherent signals in the presence of unknown mutual coupling is proposed. A group of auxiliary sensors in a uniform linear array are applied to eliminate the effects on the orthogonality of subspaces brought by mutual coupling. Then, a Toeplitz matrix, whose rank is independent of the coherency between impinging signals, is reconstructed to eliminate the rank loss of the spatial covariance matrix. Therefore, the signal and noise subspaces can be estimated properly. This method can estimate the DOAs of coherent signals under unknown mutual coupling accurately without any iteration and calibration sources. It has a low computational burden and high accuracy. Simulation results demonstrate the effectiveness of the algorithm.

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.

The polarimetric active radar calibrator (PARC) is extensively used as an external test target for system distortion compensation and polarimetric calibration for the high-resolution polarimetric radar. However, the signal undergoes distortion in the PARC, affecting the effec- tiveness of the compensation and the calibration. The system distortion compensation resulting from the distortion of the amplitude and phase in the PARC was analyzed based on the "method of paired echoes." Then the correction method was proposed, which separated the ideal signals from the distorted signals. Experiments were carried on real radar data, and the experimental results were in good agreement with the theoretical analysis. After the correction, the PARC can be better used as an external test target for the system distortion compensation. © 2015 Society of Photo-Optical Instrumentation Engineers (SPIE) (DOI: 10.1117/1.JRS.9.096009)

To solve the problem of detecting the space-borne radar linear frequency modulated (LFM) pulse signal contaminated by spurious clutter and interference under a low signal-to-noise ratio (SNR), a detection and parameter estimation algorithm based on the time-frequency image enhancement and Hough transform (HT) is proposed. First, short-time Fourier transform (STFT) is carried out on the space-borne radar LFM pulse signal with the Gaussian window to acquire the time-frequency spectrum; the spectrum is then converted into the time-frequency image. Second, in order to observe the weak signal’s details from the image, contrast stretching is implemented on the time-frequency image and the trailing induced by the spurious clutter is eliminated to strengthen the chirp line of the LFM signal. Third, the line is detected through HT on the enhanced time-frequency image, and the coordinates and gray values of the pixels passed through by the line are extracted in the time-frequency image; then these gray values are filtered by the median filter before being binarized with the gray threshold; the anti-pulse-splitting mechanism is adopted to determine the start and the end of the chirp line segment. Measured data experiments show that the method can effectively detect the space-borne radar LFM pulse signal under a low SNR environment, determine the pulse’s arrival time and end time, estimate its parameters, and is superior to direct STFT-Radon transform and direct STFT-HT.

The typical probability based point pattern matching method is coherent point drift (CPD) algorithm, which treats one point set as centroids of a Gaussian mixture model, and then fits it to the other. It uses the expectation maximization framework, where the point correspondences and transformation parameters are updated alternately. However, the anti-outlier performance of CPD is not robust enough as outliers have always been involved in the operation until the CPD converges. Hence, an automatic outlier suppression (AOS) mechanism is proposed. First, outliers are judged by a matching probability matrix. Then, transformation parameters are fitted using accurate matching point sets. Finally, the Gaussian centroids are forced to move coherently by this transformation model. AOS-CPD can efficiently improve the anti-outlier performance of rigid CPD. Furthermore, CPD is applied to image matching. A new local changing information descriptor-relative phase histogram (RPH) is designed and RPH-AOS-CPD is proposed to embed RPH measurement into AOS-CPD as a constraint condition. RPH-AOS-CPD makes full use of grayscale information besides having an excellent anti-outlier performance. The experimental results based on both synthetic and real data indicate that compared with other algorithms, AOS-CPD is more robust to outliers and RPH-AOS-CPD offers a good practicability and accuracy in image matching applications.

We propose a new image registration method based on joint respective window sampling (RWS) and modified motion compensation (MMC) in an interferometric inverse synthetic aperture radar (InISAR) imaging system using two antennas. The causation and quantitative analysis of the offset between two ISAR images of different antennas along the baseline are analyzed. In the proposed method, the RWS method, according to the measured distance between the target and different antennas, compensates the offset in the range direction. The MMC method is adopted to eliminate the offset in the Doppler direction. Simulation results demonstrate that the offset between the two ISAR images can be compensated effectively, consequently achieving a high-quality three-dimensional InISAR image.