Refocusing moving targets in synthetic aperture radar (SAR) poses inherent challenges. The difficulty is amplified when SAR raw data are missing in the azimuth direction, mainly because of the unknown motion parameters of non-cooperative targets. Estimating these parameters from SAR azimuth missing data (SAR-AMD) is notably more challenging than from complete echoes. To address this problem, we propose the MPE-CM method, a fast and robust motion parameter estimation method for SAR-AMD based on contrast maximization. Initially, following the range walk correction (RWC) by Keystone transform (KT), a coarse-focused image is derived in the range-Doppler (RD) domain by constructing a phase compensation function with varying focusing factors. Subsequently, the estimation of motion parameters is converted into the estimation of focusing factors, which is accomplished through the maximization of contrast in the coarse-focused image. Concurrently, we propose a Five-Point method to efficiently and robustly determine the focusing factor. Finally, the along-range and azimuth velocities can be retrieved from the estimated focusing factor and Doppler shift, where the Doppler shift is obtained by identifying the peak energy shift of the coarse-focused image. The proposed MPE-CM method, ensures computational efficiency through a fast and robust approach, while coherent processing improves its anti-noise performance. The experimental results demonstrate the effectiveness of the proposed MPE-CM method in SAR-AMD.

One-bit synthetic aperture radar (SAR) imaging has garnered significant interest due to its ability to lower the cost of storing enormous amounts of data during sampling and transmission, as well as the expense of analog-to-digital converters (ADCs). However, existing one-bit SAR imaging methods suffer from high computational complexity and artifacts in the resulting images. To address these problems, the sparse logic regression model (SLR) solved by iterative hard threshold (IHT) is applied to one-bit SAR imaging, and a new SLR-IHT imaging method is proposed. The SLR-IHT method models the one-bit SAR imaging problem as an SLR task and optimizes the solution using the IHT framework. By leveraging the joint sparsity of the real and imaginary components, the proposed method enhances imaging quality while effectively suppressing artifacts. To accelerate computation, the Armijo step size criterion is employed to adjust the step size and support set during the iterative procedure. Moreover, a theoretical investigation into the convergence properties of the proposed method was conducted. Extensive simulations and real data experiments are conducted to evaluate the performance of the SLR-IHT method. The results demonstrate its superiority over existing one-bit SAR imaging techniques in terms of imaging quality and computational efficiency.

In order to solve the problem of SAR imaging with azimuth missing data, a novel missing data SAR imaging algorithm is proposed in this paper. In the algorithm, the complete echo can be recovered at the two-dimensional frequency-domain by using the generalized orthogonal matching pursuit (GOMP) algorithm. The simulation result verifies the effectiveness of the proposed algorithm. Since the proposed algorithm only needs to recover the echo corresponding to sparse target-located range gates, compared with the state-of-the-art SAR imaging algorithm with azimuth missing data, it shows the advantage in the computational complexity when the targets are sparse enough in the range direction and it has a better imaging performance than the state-of-the-art azimuth missing data algorithm in noiseless and noisy settings.

Synthetic aperture radar (SAR) raw data missing occurs when the radar is interrupted for various reasons during the work. Different solutions have been proposed to this problem. In recent years, with the continuous deepening of the research on compressed sensing (CS), it has also been fully utilized in solving the problem of missing data. When using traditional greedy algorithms to recover missing data, we need to know the sparsity, but it is often unknowable in practice. This letter proposes to apply the sparsity adaptive segmented orthogonal matching pursuit (SAStOMP) algorithm to the recovery of SAR missing data. Simulation results show that the proposed method can recover missing SAR data under the condition of unknown sparsity and can adapt to a wider range of threshold parameters. It has good recovery performance for periodic and nonperiodic missing SAR raw data, thus improving SAR imaging results.

Recently, we have proposed the coprime tomographic synthetic aperture radar (TomoSAR) technique, whose baseline configuration conforms to the coprime array geometry. This technique is devoted to reducing the required number of acquisitions in the practical application where the number of flight passes is usually restricted due to cost consideration and temporal decoherence. This letter extends the tomographic reconstruction of the coprime TomoSAR to the atomic norm minimization (ANM) framework to pursue super-resolution. A compact ANM approach is proposed in this letter for the tomographic reconstruction of coprime TomoSAR in the presence of multiple looks data. Compared with the conventional ANM approach, the proposed approach compresses the dimension of the ANM model to a smaller size by two operations. One operation is that the equivalent covariance matrix is constructed to be conformed with the covariance matrix of the real acquisition data. The other operation is adopting the singular value decomposition (SVD) technique to reduce the look dimension of acquisition data. As a result, the compact approach reduces the computation complexity without performance loss. It is confirmed by simulation experiments.

Due to the small size of foreign object debris (FOD) and varied complex weather conditions, the detection of FOD on airport runways is a great challenge. Radar is an important method for detecting FOD targets. However, almost all the existing systems are based on real apertures, which have disadvantages such as low azimuth resolution and susceptibility to rain interference. Here, an innovative FOD detection radar system based on arc-scanning synthetic aperture radar (AS-SAR) technology, the AS-SAR based FOD detection system (AS-FODR), achieves omnidirectional coverage with a very high azimuth resolution and the suppression of flicker clutter, such as rain drops in severe weather. According to the radar imaging simulation of a scene under rainy conditions and the information processing analysis of field experiments, a prototype system was built, and an efficient data process flow was proposed. In short, a one centimeter FOD target was detected on the runway more than 250 m away, proving that the use of AS-FODR is feasible and effective.

As is well known, multibaseline phase unwrapping (PU) is put forward to overcome single-baseline PU in discontinuous-terrain-height estimation. This letter presents a refined algorithm based on the cluster analysis (CA)-based noise-robust efficient multibaseline PU algorithm proposed by H. Yu. The basic idea is to combine multiple interferometric synthetic aperture radar interferograms with different baseline lengths by a linear combination. The new interferograms after the linear combination increase the ambiguity heights. The number of resulting groups on the envelope of the intercept histogram is decreased and the distance between different intercept groups is widened. Compared with the conventional CA method, the significant advantage of the refined CA (RCA) algorithm is that it improves noise robustness when the intercept groups are densely distributed. The proposed RCA algorithm is validated using the simulated interferometric data. The results demonstrate that the noise robustness performance is better than that of the CA method.

The interferometric radar which is used to monitor and measure the displacement of large artificial buildings is one of the new advanced technology in recent years, but the research on interferometric radar about the comparison of radar signal system and the argument about the key parameters of radar are rarely seen. Firstly, the basic principle of interferometry was introduced in this paper, then the general constraint relation between the no fuzzy deformation measurement and the data update rate of radar was induced. After then, this general constraint relation was used in analyzing radar signal system and designing experiments, proved the importance of the constraint relation in the interferometric radar of displacement measurement.

Radar sensors have received more and more interest for unmanned ground vehicle to sense positive and negative obstacles in unstructured environments or out fields, especially on negative obstacle. In this paper, we present an approach for extracting the features of obstacles from radar images. Based on interferometric synthetic aperture radar (InSAR) images focused by the back-projection (BP) algorithm, range compensation, speckle filtering and threshold segmentation are performed. And morphological operations are used to perform some simple connectivity filtering to smooth the image and remove spurious pixels. Finally, feature fusion is applied to the amplitude and the correlation coefficient images. Both the theoretical analysis and the experimental results indicate that the proposed method is an efficient method.

Off-road positive and negative obstacle detection is a challenge problem to be solved by unmanned ground vehicle. Traditional sensors, such as: optical camera, lidar and millimeter wave radar, have limited performance in off-road environments, especially when obstacles are far away or covered by sparse grasses. We have proposed a forward-looking InSAR sensor to tackle the problem and have built a rail-based InSAR prototype. The forward-looking InSAR can provide more information of harsh off-road environments than existing unmanned ground vehicle (UGV) based radars. The forward-looking InSAR can provide a scattering image, a coherence image and a digital terrain model (DTM) of the same scene ahead the radar during each scan. Each type of image can highlight some unique features of an obstacle. In this paper, an obstacle detection method is proposed by combining the shadow feature and the edge scattering feature. The principle method is close related to the scattering property difference between positive obstacles, negative obstacles and other objects. Positive obstacle feature large amplitude followed by low coherence area; while at the same time, negative obstacles feature low coherence area followed by large amplitude. Other objects don't have the unique feature. To mitigate false alarms, shadows are segmented in coherence images, and edge scattering features are extracted in scattering image. Firstly, the coherence image is converted into a binary image by applying a threshold. Shadow areas are roughly segmented as their coherences are low. Then the binary image is filtered by morphologic opening operation to eliminate small patches. Subsequently, an edge detection operation is applied to the filtered image. The edges of positive and negative obstacle are among the detected edge image. For each position of the detected edge, a cut is performed on the same position in the scattering image to extract a slice along the range direction. The judgment is formed by calculating the energy ratio between the near half slice of the farther half slice. Finally, positive and negative obstacles can be discriminated by comparing the judgment with two thresholds in an unsupervised fashion. We have conducted a field experiment on a ground covered by sparse grasses. A pit and a mound are deliberately built in the experiment scene. Experimental results have validated the proposed method.

Inspired by recent advances in multistatic radar systems, the problem of sensor placement is investigated. Our study is motivated by the fact that it is not always clear what the placement of the radars giving the best performance for the targets of interest might be. To account for the issue, we derive the multistatic Cramér-Rao lower bounds (CRLBs) for range and velocity estimation as the fitness function. Then we use genetic algorithms (GAs) to perform the optimized sensor placement as one of global optimization. The simulation example indicates that our proposed approach is a flexible and effective tool and is capable of suggesting optimal sensor placement strategies to meet required radar performance goals.

A time domain 3D Rank Deficit Robust Capon Filter Bank (RD-RCFB) is presented. An additional preprocess and postprocess are adopted to transform the time domain model into the frequency domain model. And matrix vectorization is used to reduce the dimension. Optimal implement of the algorithm is discussed by comparing the cascading 1D RD-RCFB, cascading 2D RD-RCFB and 3D RD-RCFB. The algorithm outperforms the 3D Adaptive Sidelobe Reduction (ASR) and the 3D Amplitude and Phase Estimation of a Sinusoid(APES). Simulated planar aperture 3D image processing verified the algorithm.