To design configuration parameters for coherent multistatic synthetic aperture radar (C-MuSAR), a wavenumber spectra projection (WSP) approach is proposed in this paper based on the relationship between the wavenumber support regions (WSRs) and configuration parameters, including synthetic aperture time, positions and flight directions of receivers. First, the projected pattern of multiple WSRs is deduced, and the relationship between multiple WSRs and the point spread function (PSF) is analyzed. Second, the primary WSR is designed based on the relationship between the transmitter and the leading receiver. A WSP method is proposed to quickly deduce the configuration parameters of the following receivers. Finally, based on the designed configuration parameters of C-MuSAR, an adaptive WSP method is adopted to reconstruct the targets. Simulations are carried out to testify the proposed method.

In the problem of ship detection in complex scenes, in addition to the characteristics of ship targets, there is rich semantic information in the global and local background of the whole scenes, which provides more valuable inference information for ship detection. Therefore, in this paper, we propose a ship detection method in complex scenes considering both global and local information perception for SAR images. Firstly, the proposed method detects the globally stable region and the locally significant region respectively, and then designs a judgment method combining the two to eliminate false alarms, so as to ensure that the detected target has both globally stable characteristics and locally significant characteristics. The detection performance of the proposed method is verified by the spaceborne SAR images covering the coastal areas. The result shows that the proposed method can effectively detect ships in complex scenes, especially eliminating most false alarms in land areas.

Recently, multistatic airborne SAR (MuA-SAR) is becoming a research hotspot due to its flexibility. Multi-platform data fusion requires that the imaging algorithm has strong adaptability to the flight path and relative spatial configuration of the airborne platforms. Therefore, the time domain algorithm based on back projection (BP) is suitable. However, in the existing BP-based methods, data needs to be projected into each grid one by one. In fact, not all pixels are target pixels that need to be projected, and the back projection of non-target pixels leads to a lot of invalid computation. Applying these methods directly to MuA-SAR will inevitably lead to a great increase in computation. To reduce the redundant back projection operation of BP algorithm and improve the efficiency of imaging processing in MuA-SAR, a fast imaging method based on segmentation before imaging is proposed in this paper. On the basis of fast factorized back projection (FFBP) algorithm architecture, an image segmentation method based on maximally stable extremal regions (MSER) is introduced. In the process of recursive fusion at each stage, only the pixel information of the segmented suspected target area is transferred to the next stage for fusion, and then the imaging efficiency is improved. The simulation and comparative experiments verify the effectiveness of the proposed method.

Several radar systems have been proposed in the past decades, including real aperture radar (RAR) and synthetic aperture radar (SAR). Spatial resolutions of different radar systems cannot be compared together because their work modes are different. In this paper, a normalized spatial resolution analysis model is proposed to deduce the spatial resolution of different systems. First, the normalized wavenumber spectra of different radar systems are deduced. Second, the relationship between spatial resolution and the wavenumber spectra distribution is analyzed. Finally, the point spread functions (PSFs) of different radar systems are simulated.

Multistatic airborne synthetic aperture radar (MuA-SAR) systems can achieve high-resolution imaging in a short time by fusing observation data from multiple radar platforms. However, its imaging quality relies on a rigorous design of the spatial configuration (SC) of each platform, mainly including the relative spatial separation and velocity. The rigorously designed SCs make it difficult to obtain in actual flight and weaken the flexibility advantage brought by the airborne platforms. Therefore, it is meaningful and necessary to explore a new SC design method to obtain relaxed SCs under the condition of ensuring imaging quality. In this paper, to relax the limitations of SC, an optimal design method for MuA-SAR SC is proposed. First, the relationship between the spatial configuration, wavenumber spectrum (WS) distribution, and imaging performance is established, and it visually reveals the configuration limitations. Second, an optimized search space of SC is defined by the peak to sidelobe ratio (PSLR) to relax the space to compromised configurations. Finally, the SC design problem is transformed into a constrained multiple objective optimization problem (CMOP) which is solved by the multiple objective particle swarm optimization (MOPSO) algorithm. The simulation results show that the proposed method can still obtain the optimized SC beyond the strictly restricted configuration space, which expands the SC limitations of the MuA-SAR system.

Multistatic Synthetic Aperture Radar (Mu-SAR) can obtain rich target information through multi-platform collaboration, and topology configuration is one of the most important factors that affecting the imaging performance. In this paper, a Mu-SAR topology design method is proposed. First, the echo of Mu-SAR is analyzed in wavenumber domain, the relationship between wavenumber spectrum and topology configuration is deduced. Then, a topology design method based on wavenumber spectrum range extension is proposed to obtain topology configuration that can achieve high resolution imaging in range direction. Finally, through numerical simulation, the effectiveness of the proposed method is verified.

Ship target segmentation plays an important role in synthetic aperture radar (SAR) image interpretation. However, existing segmentation methods for marine SAR images have the problem of inaccurate edge segmentation, a concern for real-world applications. In this letter, we propose a clustering center shifted adaptive target segmentation (CCSATS) method. Firstly, the proposed clustering center shift method is used to update the clustering centers of each iteration, which can quickly and accurately capture ship pixels. Then, based on regional homogeneity coefficients, we define a new similarity measurement criterion with two adaptive weight factors to ensure the homogeneity of segmentation results. Finally, neighborhood patches are used to represent pixel information, which can reduce the influence of speckle noise and enhance the target edge fitting ability. Our segmentation results of measured SAR images show that the proposed method effectively ensures segmentation accuracy. Compared with other existing methods, the proposed target segmentation method achieves better edge capture performance.

Angular resolution of real aperture radar (RAR) can be improved using deconvolution methods to achieve enhanced target information based on the convolution relationship between target scatterings and an antenna pattern. However, depending on the wide scanning scope and dense sampling angular interval, the computational complexity of the deconvolution methods will drastically increase as the dimension of azimuthal data increases. In this paper, to efficiently improve the angular resolution of RAR, a generalized adaptive asymptotic minimum variance (GAAMV) estimator that relies on a normalized projection array (NPA) model is proposed. On the one hand, the traditional convolution model of RAR is transformed into an NPA model to compress the data dimension. The proposed NPA model can normalize the signal model to make it independent of the sampling parameters. On the other hand, based on the NPA model, a GAAMV estimator is proposed to efficiently reconstruct the targets by adaptively updating each grid. Moreover, the penalty parameter is extended as a generalized case to improve its adaptability to different scenes. Based on the proposed model and method, the computational complexity can be decreased, especially for high-dimensional azimuthal data. Simulations and experimental data verify the proposed model and method.

Iterative adaptive approach (IAA) is an effective super-resolution method to improve the resolution of airborne forward-looking radar imaging. Regretfully, the noise sensitivity caused by the non-full rank of matrix lead to the poor performance under low signal-to-noise ratio condition in the forward-looking imaging process. In response to this problem, a regularized IAA method (RIAA) based on singular value decomposition is proposed in this paper which utilizes singular value theory to decompose the autocorrelation matrix in the iteration which is applied to suppress the noise amplification and keep the main information of targets. Compared with conventional IAA method, the proposed method enjoys a preferable noise suppression performance without image quality degradation. Simulations are given to verify the performance gain.

In SAR image target detection, harbor detection can help the detection of harbor targets and maritime traffic planning. In this paper, we propose a harbor detection method of SAR images based on multidirectional one-dimensional scanning. Take the candidate points along the coastline and the multidirectional one-dimensional scanning is performed. Using the distribution characteristics of land, sea and dock in the one-dimensional vector, training a convolutional neural network to classify the candidate points into harbor and non-harbor feature points. Then we get the harbor feature points map reflecting the distribution of harbors. The Sentinel-1 spaceborne SAR images covering a coastal region are used to verify the proposed method. The experimental results show the effectiveness and accuracy of the proposed method.

Synthetic Aperture Radar (SAR) is widely used in environmental monitoring and disaster early warning due to its high resolution imaging performance. A distributed radar system can be established by mounting radars on multiple unmanned aerial vehicle (UAV) platforms. Distributed radar utilizes multiple transmitters distributed in different spatial positions, flying along a certain planned path and enable multiple transmitters to obtain as large an aperture as possible in a certain time. In this paper, an intelligent optimal path planning method for distributed radar short-time aperture synthesis is proposed, which can deal with terrain obstacles and line-of-sight occlusion in UAV flight path and achieve the goal of maximum aperture accumulation in a specific time. Simulation results verified the effectiveness of the UAV intelligent optimal path planning method.

SAR ship detection is essential for marine monitoring. Due to the high similarity between the harbor and the ship body on gray and texture features, the traditional methods are unable to achieve effective inshore ship detection. An improved Faster R-CNN based on MSER decision criterion for SAR ship detection in harbor is proposed in this paper. It is a ship detection method based on the combination of feature-based method and pixel-based method. Firstly, Faster R-CNN is used to generate region proposals. Then, replace the threshold decision criterion of Faster R-CNN with the maximum stability extremal region (MSER) method to reassess the generated region proposals with higher scores, aiming at improving the detection rate and reducing the false alarm rate simultaneously. Experimental results based on satellite-borne SAR data illustrate that the proposed method obtains excellent detection performance and low false alarm rate.

High resolution is always the most concerned issue of radar imaging. Traditional radar systems, which obtain echo data using single platform, can achieve limited imaging resolution in a specific view angle. Distributed radar system, which expands multi-platform in space to obtain high imaging resolution by forming a large aperture, is a novel and hot research point. Matched filter, such as inverse fast Fourier transform (IFFT), is a conventional method to deal with distributed radar imaging. However, the method relies strictly on geometric configuration. In this paper, an iterative adaptive approach (IAA) based method is proposed to solve the problem of configuration adaptability. It can maintain the performance of matrix during the iteration. Then, the distributed radar system can keep high resolution in different geometric configurations. Simulation results verified the excellent performance of the proposed IAA-based imaging method.

Distributed radar imaging utilizes expanded array elements in space to form a large aperture and obtain high imaging resolution. A great number of array elements are required in traditional distributed radar system which uses multiple platforms. The distribution of spatial spectrum is affected by the number and the signal form of array elements. In this research, to improve the utilization efficiency of platform resources, a resource allocation optimization method based on Unmanned Aerial Vehicle(UAV) is proposed. It chooses the optimized bandwidth and sampling frequency points of array elements by analyzing the relationship between spatial spectrum and imaging performance. This method can use a small number of UAVs to maintain high imaging resolution. Simulation results verified the effectiveness of the resource allocation optimization method for image quality improvement.