The multireceiver synthetic aperture sonar (SAS) data are usually converted to equivalent monostatic data through the displaced phase center approximation (DPCA) before the monostatic imaging. However, the DPCA error is azimuth-variant in the wide-beam case, resulting in the traditional algorithms compensating for the DPCA error in the extended Doppler domain of each receiver, which obviously increases the computational complexity. To solve the problem, this letter analyzes the space-variant characteristics of the DPCA error and discovers that the DPCA error exhibits significant receiver-variant and weak azimuth-variant characteristics. Based on this, a subaperture preprocessing is proposed to reduce computational complexity without sacrificing imaging accuracy. The proposed algorithm compensates for the DPCA error uniformly within the subaperture using the DPCA error at the center of the subaperture and then superposes all subapertures coherently to obtain equivalent monostatic data. The weak azimuth-variant characteristic ensures that the subapertures are very sparse, Additionally, the algorithm allows data recording and preprocessing to be synchronized, significantly reducing the imaging waiting time further. The simulated data and actual data experiments verify the effectiveness of the proposed algorithm.

The interpolation-free chirp z-transform (CZT) algorithm is efficient but requires approximating the accurate 2-D spectrum’s phase. However, this approximation is invalid for data from multiple-receiver synthetic aperture sonar (MRSAS) systems with wide beamwidths, large fractional bandwidths, and wide subswaths. To address the issue above, this article derives a new range history and 2-D spectrum, and proposes a new method to limit the approximation error of the 2-D spectrum’s phase. This article first represents the range history as a combination of basic functions and derives a new 2-D spectrum without any approximation. After the receiver dependence of the 2-D spectrum’s phase is partially reduced, the echo data are divided into multiple subapertures in the receiver domain to ensure that the 2-D spectrum’s phase difference of different receivers within each subaperture can be ignored. A frequency-domain filter is designed based on the 2-D spectrum of the reference receiver to compensate for all receivers’ data in the subaperture. Therefore, only one imaging process is needed in a subaperture, rather than receiver-by-receiver imaging. The 2-D spectrum’s phase is expanded up to the third range frequency and its error is limited by the subswath-subband processing rather than considering the higher order terms in Taylor expansion. The quadratic and cubic terms in each subband are linearized through subband-wise linear approximation (SLA) to facilitate the CZT algorithm. Finally, the high-resolution image of the whole swath is obtained through the subimage fusion. The superiority of the proposed range history and subswath-subband processing has been verified under wide beamwidths, large fractional bandwidths, and wide subswaths through simulation and measured data.

Two-dimensional phase unwrapping (2-D PU) is a crucial step in interferometric signal processing. The accuracy of phase unwrapping (PU) will significantly affect subsequent processing. Existing algorithms perform poorly in areas with rapid terrain changes or with low coherence. In order to solve the above problems, this letter proposes a method of using a neural network to predict residuals, followed by the minimum$L^{1}$-norm algorithm for phase reconstruction. The network for predicting residuals is called residual prediction network (RPNet). To construct more complex loss terms, the continuous relaxation strategy is applied to its training. The proposed method overcomes the effects of noise and terrain changes due to the accurate prediction of residuals. The compensated phase gradients enable the minimum$L^{1}$-norm algorithm to reconstruct phases more accurately. The experimental results performed on simulation and interferometric synthetic aperture radar (InSAR) data demonstrate the effectiveness of the proposed method.

In multireceiver synthetic aperture sonars (SASs), the traditional non-stop-hop-stop model normally ignores the intrapulse motion of sonar platform. However, such intrapulse motion brings the Doppler scale effect (DSE) to the echo signal, which will lead to the change in carrier frequency (CF) and frequency modulation (FM) rate of echo signal and may eventually result in image defocus. In this article, considering the intrapulse motion, the time-varying time delay and range history of multireceiver SAS were derived by introducing the range time into the range history, and then the Doppler scale signal model was established. After approximating the derived time-varying range history, the 2-D spectrum was derived by using the shear theorem. When compared with the traditional 2-D spectrum, an interesting phenomenon was found that the change in FM rate has no effect on the 2-D spectrum. Actually, only two additional terms, i.e., range cell migration (ARCM) and azimuth modulation (AAM), were introduced into the new 2-D spectrum due to the change in CF. Besides, the condition of neglecting the ARCM was given by further analyzing the 2-D spectrum, while the AAM is usually small and can be easily compensated together with the ARCM correction. Finally, a chirp-z transform (CZT) algorithm for the Doppler scale signal model was proposed to correct the ARCM, which can simultaneously correct the ARCM and compensate the AAM (although not required) in the 2-D frequency domain by phase multiplication. The effectiveness of the signal model and the proposed algorithm is verified by simulations and field data.

Recent deep learning (DL) detectors adopted by radar or sonar (RS) are normally trained with transfer learning, where the typical workflow is to pretrain a convolutional neural network (CNN) on external large-scale classification datasets (e.g., ImageNet) as the backbone and then finetune the entire detector on detection datasets. Though transfer learning could effectively avoid overfitting, transferred models are usually redundant and might not generalize well on RS datasets. To achieve high generalization and to eliminate the dependence on transfer learning, a self-trained target detection method is established by including Automatic Deep Learning (AutoDL) to design optimal detectors. This self-trained target detection consists of three stages. First, a derived classification dataset (DCD) consisting of image blocks of targets and backgrounds is derived from detection datasets. Then, a memory-efficient Differentiable Architecture Search algorithm with flexible search space and large inputs (FL-DARTS), which is characterized by its predefined multistride convolutions, poolings, and unique super-structure, is proposed to automatically design and self-train optimal CNNs on DCDs. Finally, self-trained AutoDL detectors are implemented with the automatic backbone designed by FL-DARTS. We evaluated three self-trained AutoDL detectors on the public SAR ship detection dataset (SSDD) and the self-made sonar common target detection dataset (SCTD). The experiments show that while the number of parameters of automatic backbones designed for SSDD and SCTD are only 11.8% and 15.2% of that of ResNet50, self-trained AutoDL detectors implemented with automatic backbones significantly outperform their transfer learning detectors and achieve state-of-the-art detection precisions and high detection speeds. Data, codes are publicly available.

Phase unwrapping is one of the key problems in reconstructing the digital elevation model of a scene from its interferometric synthetic aperture radar (InSAR) or interferometric synthetic aperture sonar (InSAS) data. In this letter, we propose a quality-guided and local minimum discontinuity based phase unwrapping algorithm, which enhances the precision of the unwrapped result by local minimum discontinuity optimization in low quality areas, and still keeps a high efficiency. The new algorithm can be divided into two steps. Firstly, the quality-guided phase unwrapping algorithm is performed on the original wrapped phase image, which helps to remove all the discontinuities caused by the wrapping operation quickly, but tends to spread the unwrapped errors in low quality areas. Secondly, the initial unwrapped result is divided into high and low quality areas according to its quality map, and the minimum discontinuity optimization process is performed in the low quality areas of the initial unwrapped result, which helps to remove the remaining improving loops defined as a loop with more positive jumps than negative ones. This prevents the unwrapped errors spreading from low quality areas to high quality areas and accelerates the optimization process by restricting the optimization place in low quality areas. Tests performed on InSAR and real InSAS data confirm the accuracy and efficiency of the proposed algorithm.

Phase unwrapping is one of the key problems in reconstructing the elevation map of a scene from interferometric synthetic aperture radar or interferometric synthetic aperture sonar (InSAS) data. In this letter, an improved quality-guided phase-unwrapping algorithm is proposed, which depends on the quality value and its surrounding quality information to guide the path of unwrapping more accurately. In order to design highly efficient quality-guided algorithm, a guided map deduced from the quality map is introduced and taken as the new quality map which the quality-guided algorithm should completely depend on. A quantized quality-guided method is designed, which adopts the quantized new quality map and the priority queue designed to optimize the process of unwrapping. The quantized quality map establishes the relation between the quality values and the indexes of array, which can save the time of inserting one pixel into the priority queue according to its integral quality value. Priority queue keeps all the pixels by their quantized quality in a nondecreasing order by maintaining the doubly linked lists in an increasing order. An optimized strategy is used to accelerate the process of finding the pixel with the highest quality value in the priority queue. Tests performed on real InSAS data and simulated interferograms confirm the accuracy and efficiency of the proposed algorithm, and the improved algorithm is suitable for our real-time processing InSAS system.

In the above letter (ibid., vol. 8, no. 2, pp. 364-368, Mar. 2011), in the Abstract, a sentence is incorrect. The correct sentence is presented here.

In this paper, an extended COvariance Matching Estimation Techniques-Extend Invariance Principle (COMET-EXIP) method is proposed to estimate interferometric synthetic aperture radar or interferometric synthetic aperture sonar (InSAS) interferometric phase in the presence of large coregistration errors, even up to one pixel. First, the extended COMET-EXIP method is presented for the application of joint-pixel-model-based interferogram estimation, through choosing a novel “unstructured model” in terms of the parameters to be estimated and decoupling the interesting parameters from the uninteresting “nuisance parameters.” Then, a fast algorithm of COMET-EXIP is proposed for the interferometric phase estimation. Finally, the ambiguity problem of the COMET-EXIP method is solved without introducing performance degradation. The simulated data and real data from the trial InSAS and X-SAR are used to verify the validity of the method. The results show that the method is robust for a wide range of signal-to-noise ratio and has a good performance on both fringe preserving and noise suppressing. In addition, the same computational speed level of the proposed method as that of the pivoting mean filtering is very attractive.