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Radar system environment is complex, and excessively high range sidelobes will cause weak targets to be lost and false targets. To solve this problem, a novel orthogonal frequency-division multiplexing(OFDM) radar low sidelobe waveform design method based on genetic algorithm(GA) is proposed. First, the OFDM waveform based on cyclic prefix(CP) is constructed. All sub carriers are modulated with m-sequence codes. Then, the main lobe and sidelobe ratio after pulse compression is taken as the objective function, and the phase parameter is taken as the optimization object. The objective function is transformed into an unconstrained optimization problem. Last, by using genetic algorithm to solve the optimization problem, the radar range sidelobe suppression is achieved. Theoretical analysis and simulation results show that this method can effectively suppress the range sidelobes.

Frequency agile radar has excellent low intercept and electronic countermeasures performance. In this paper, the research results of anti-main-lobes deceptive jamming of radar seeker are briefly reviewed, and the basic concepts and technical characteristics of frequency agile radar seeker are described. Then, the research results of frequency agile radar seeker at home and abroad are introduced, and the advantages and disadvantages of three frequency agile waveforms and the research progress of each signal processing technology are summarized. Finally, the development of adaptive frequency agility and multi-parameter combined agility radar seeker technology is prospected based on the future battlefield complex electromagnetic environment and seeker development trend.

This manuscript presents a novel scheme to achieve high-resolution laser-radar ranging with a small sample number under low signal-to-noise ratio (SNR) conditions. To reduce the sample number, the Restricted Isometry Property-based optimal multi-channel coprime-sampling (RIP-OMCS) strategy is established. In the RIP-OMCS strategy, the data collected across multiple channels with very low coprime-sampling rates can record accurate range information on each target. Further, the asynchronous problem caused by channel sampling-time errors is considered. The sampling-time errors are estimated using the cross-correlation function. After canceling the asynchronous problem, the data collected by multiple channels are then merged into non-uniform sampled signals. Using data combination, target-range estimation is converted into an optimization problem of sparse representation consisting of a non-uniform Fourier dictionary. This optimization problem is solved using adaptive hybrid re-weighted constraint (AHRC) l1 minimization. Two constraints are formed from statistical attributes of the targets and clutter. Moreover, as the detailed characteristics of the target, clutter, and noise are unknown before the solution, the two constraints can be adaptively modified, which guarantees that l1 minimization obtains the high-resolution range profile and accurate distance of all targets under a low SNR. Our experiments confirmed the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Dedicated to synchronously acquiring large-area, high-precision, and multi-scale ocean wind and wave information, a novel concept of a spaceborne ocean microwave dual-function integrated sensor is proposed in this paper. It integrates the functions of a scatterometer and SAR by sharing a single phased-array antenna. An overview of the scientific requirements and motivations for the sensor are outlined firstly. In order to fulfill the observation requirements of both the functions, the constraints on the system parameters such as frequency, antenna size, and incidence angle are analyzed. Then, the selection principles of these parameters are discussed within the limitations of antenna area, bandwidth, available time, and cost. Additionally, the constraints on the time sequence of transmitting and receiving pulses are derived to ensure that there is no conflict when the two functions operate simultaneously. Subsequently, a method for jointly designing the pulse repetition frequency (PRF) of both the functions is introduced, along with zebra maps to verify its effectiveness. At the end of the paper, the system and performance parameters of the sensor are given for further insight into it. [ABSTRACT FROM AUTHOR]

Target detection technology of synthetic aperture radar (SAR) imageis widely used in the field of military reconnaissance and surveillance. The traditional SAR image target detection methods need to be provided a lot of empirical knowledge because the characteristics of SAR images in different configurations (attitude, pitch angle, imaging parameters, etc.) will change greatly,resulting in high generalization error. Currently, deep learning method has achieved great success in the field of image processing. Research shows that deep learning can achieve a more intrinsic description of the data, while the model has a stronger ability of modeling and generalization. In order to solve the problem of insufficient data in SAR data sets, an experimental system for acquiring SAR image data in real scenes was built. Then the transfer learning method and the improved convolution neural network algorithm (PCA+Faster R-CNN) are applied to improve the target detection precision. Finally, experimental results demonstrate the significant effectiveness of the proposed method.

The special imaging mechanism of the Synthetic Aperture Radar (SAR) causes the sidelobe effect on SAR images. In target detection, the sidelobe effect changes the shapes of strong reflective targets, which results in the problems of localization difficulty and localization error. To solve this problem, this paper proposes a ship detection algorithm based on Spatially Variant Apodization (SVA) and Order Statistic-Constant False Alarm Rate (OS-CFAR). First, the global-CFAR algorithm is used to prescreen the potential target points, which reduces the computational burden of the following steps. Second, the SVA algorithm is modified to improve the speed of sidelobe suppression and applied to the raw complex image data. Then, the nonlinear method OS-CFAR is used to detect the targets on the processed image, and the morphological dilation processing is used to make up for the wrong suppressed points caused by the SVA algorithm. Finally, the GF-3 SAR images are used to test the algorithm and the comparison of the image contrast and detected numbers in the results with SVA and without SVA verifies the effectiveness of the proposed algorithm.

A low pulse repletion frequency is required and an ambiguous Doppler spectrum should be considered to obtain a high-resolution and wide-swath Synthetic Aperture Radar-Ground Moving Target Indication (SAR-GMTI) system in the azimuth direction. In this study, we have proposed a novel clutter suppression approach, where the Doppler spectrum of the single channel echo is ambiguous, with respect to the space-borne multiple channels in an azimuth high-resolution and wide-swath SAR-GMTI system. Initially, the azimuth deramping operation is utilized to compress the ambiguous Doppler spectrum, where the signal of clutter or the moving target is focused toward only some azimuth Doppler-frequency bins. Further, a covariance matrix corresponding to the clutter is estimated in the azimuth deramping and range compression domain. Subsequently, the matrix eigenvalue decomposition technique is employed to obtain an eigenvector corresponding to the minimum eigenvalue. Herein, we intend to achieve redundant channel freedom to ensure the suppression of clutter. The obtained eigenvector can be considered to be the orthogonality vector of the clutter space, which denotes the orthogonality with respect to the signal space vector. Hence, we adopt the obtained eigenvector to appropriately suppress the clutter. Simultaneously, the signal of the moving target can be appropriately preserved. Finally, some experiments are conducted to validate the proposed clutter suppression approach.

When the direction-of-arrival estimation is made by a single platform, the localization accuracy decreases with increases in the distance from the radiation source, which means the localization accuracy of the azimuth of the radiation source at long distances must be improved. To address this problem, we propose a radiation-source localization algorithm based on a long synthetic aperture. This algorithm introduces the use of synthetic aperture imaging to the passive localization of radiation sources. A long virtual aperture is obtained by movement of the platform, and received data is processed in the form of synthetic aperture radar to obtain high azimuth localization accuracy. For a single-frequency radiation source, the received signal is equivalent to a linear frequency-modulation signal in the azimuth domain, and its chirp rate is inversely proportional to the distance from the radiation source. A range search and azimuth-focusing method are used to perform signal focusing in the range–azimuth domain, and the localization of the radiation source is obtained directly by two-dimensional imaging. Moreover, with respect to the characteristics of wide-beam reconnaissance, we analyzed the range–azimuth coupling relationship of the chirp rate at a low sampling frequency. On this basis, methods for direction-of-arrival estimation and two-dimensional imaging localization are proposed. Our theoretical derivation and experimental data processing results verify the validity of this algorithm.

The multi-platform-borne Synthetic Aperture Radar (SAR) has become one of the most explored research directions in the domain of SAR. This study discusses the imaging algorithms in multi-platform-borne SARs such as airborne SAR, missile-borne SAR, and spaceborne SAR. First, the establishment of the radar echo model is briefly introduced, including two main points: slant range-model and imaging mode. Subsequently, the imaging algorithms of the aforementioned multi-platform-borne SARs developed and used in recent years are summarized. In addition, the inherent characteristics and challenges are described. Finally, the future development trends of the research are discussed.

Water segmentation of real SAR images is of great significance in military and civilian applications such as ship target detection and disaster monitoring. To solve the issues of poor robustness and inaccurate segmentation of traditional water segmentation algorithms, this paper first establishes a SAR water segmentation dataset based on the GF3 satellite and then presents a segmentation network architecture based on depthwise separable convolution. The network takes real SAR images as inputs, extracts high-dimensional features through depthwise separable and dilated convolutions, constructs an up-sampling and decoding module based on bilinear interpolation, and then outputs the corresponding segmentation results. The segmentation results of a water segmentation dataset show that the proposed segmentation method remarkably improves the segmentation accuracy, the segmentation robustness and running speed than traditional method. Therefore, the findings demonstrate the excellent practical engineering value of the proposed algorithm.

Microwave photonic radars can transmit large bandwidth and high carrier frequency signals, which makes two-dimensional high-resolution Inverse Synthetic Aperture Radar (ISAR) imaging possible. It is important to study the corresponding real-time imaging algorithms. However, the high range resolution and high carrier frequency of the signal make the space curvature of the distance bend non-negligible. This is the reason for the poor imaging performance of the traditional Doppler real-time imaging algorithm. In addition, the computationally intensive beam domain imaging algorithm is not suitable for microwave photonic radar signals of large data volume. Therefore, a high-efficiency microwave photonic ISAR high-resolution real-time imaging algorithm is proposed in this paper. Firstly, this algorithm uses the Generalized Keystone Transform (GKT) to extract the phase of the special display point. Next, it inverts the target lateral velocity from phase modulation frequency. Finally, the result of velocity estimation and Frequency Scaling (FS) are used to correct the distance space-bending and conduct matched filtering imaging in azimuth. The simulation results and the measured data have been shown to verify the effectiveness of the proposed algorithm.

An ultrahigh-resolution microwave photonic-based Synthetic Aperture Radar (SAR) imaging method based on space-variant motion error analysis is proposed to solve the influence of space-variant motion error on microwave photonic-based SAR imaging. First, the judgment rules of the influence of space-variant motion error are proposed to analyze the residual space-variant motion error. Second, on the basis of the different judgment results of microwave photonic-based SAR system conditions, the corresponding imaging process is proposed. Finally, the proposed judgment rules and imaging method are verified by point simulation, and the measured data of the 10 GHz microwave photonic-based ultrahigh-resolution SAR are analyzed and imaged. The experimental results show the effectiveness of the proposed method.

The wings of an airplane vibrate when it is nonstationary. When an airplane is observed using a microwave photonics-based ultrahigh-resolution radar, and this vibration will cause defocusing of the wings. To address this problem, we propose a vibration-parameter estimation method for airplane wings based on microwave-photonics ultrahigh-resolution radar. In this method, we first coarsely separate images of the wings and body of the airplane, and estimate the Light-Of-Sight (LOS) of the radar from a focused and scaled image of the airplane body. Next, we apply sub-aperture imaging to the wings and extract the range and Doppler curves from a sequence of sub-aperture images. By combining the range and Doppler curves with the LOS, we can obtain a preliminary estimation of the vibration parameters. Finally, by Modifying the Polar Format Algorithm (MPFA) and constructing an optimization function that minimizes image entropy, we can obtain accurate vibration parameters. This novel modified polar format algorithm can be applied to complex motion targets, such as an airplane with vibrating wings, swinging ships to effectively decouple range and cross-range dimensional coupling. Experimental results using both simulated and measured data confirm the validity and practicality of the proposed algorithm.