In this work, a parametric-based approach is proposed to perform joint range alignment and phase adjustment based on the intention of fully exploiting the energy of all the scatterers in the moving target and the two-dimensional coherent accumulation gain of both range and azimuth compressions. To that end, first, translational motion is modeled as a polynomial signal, and inspired by the fact that all the scatterers in the moving target experience the same translational range history, the phase difference operation and keystone transform (KT) are utilized to transform the energy of all the scatterers into one range cell. Second, by the virtue of the fractional Fourier transform (FrFT), the energy of all the scatterers is coherently accumulated into a peak point, and from which the polynomial coefficients can be obtained accurately. With the estimated polynomial coefficients, the dechirp operation and KT are applied jointly to compensate range misalignment and phase error. The analysis of the proposed method shows that it is of low computational complexity due to avoiding multidimensional search and improves the output SNR providing satisfactory low SNR performance. The experimental results are provided to demonstrate the performance of the proposed method compared with the state-of-the-art algorithms.

Since only the spatial feature information of ship target is utilized, the current deep learning-based synthetic aperture radar (SAR) ship detection approaches cannot achieve a satisfactory performance, especially in the case of multiscale or rotations, and the complex background. To overcome these issues, a novel multidimensional domain deep learning network for SAR ship detection is developed in this work to exploit the spatial and frequency-domain complementary features. The proposed method consists of the following main three steps. First, to learn hierarchical spatial features, the feature pyramid network (FPN) is adopted to produce ship target spatial multiscale characteristics with a top-down structure. Second, with a polar Fourier transform, the rotation-invariant features of SAR ship targets are obtained in the frequency domain. After that, a novel spatial-frequency characteristics fusion network is then presented, which seeks to learn more compact feature representations across different domains by updating the parameters of sub-networks interactively. The detection results are obtained due to utilizing the multidimensional domain information, and we evaluate the effectiveness of the proposed method using the existing SAR ship detection data set (SSDD). The results of the proposed method outperform other convolutional neural network (CNN)-based algorithms, especially for multiscale and rotation ship targets under complex backgrounds.

This letter presents a conceptually simple and effective dual-granularity triplet loss for visible-thermal person re-identification (VT-ReID). Generally, ReID models are always trained with the sample-based triplet loss and identification loss from the fine granularity level. Further, center-based loss could be introduced to encourage the intra-class compactness and inter-class discrimination from the coarse granularity level. Our proposed dual-granularity triplet loss well organizes the sample-based triplet loss and center-based triplet loss in a hierarchical fine to coarse granularity manner, just with some simple configurations of typical operations, such as pooling and batch normalization. Experiments on RegDB and SYSU-MM01 datasets show that with only the global features our dual-granularity triplet loss can improve the VT-ReID performance by a significant margin. It can be a strong VT-ReID baseline to boost future research with high quality.

The inverse synthetic aperture radar (ISAR) imaging for maneuvering targets has always been a challenging task due to the time-varying Doppler parameter, especially in the low signal-to-noise ratio (SNR) environment. In this paper, a novel ISAR imaging algorithm for maneuvering targets in the low SNR environment based on the parameter estimation approach is presented. First, the received signals of the ISAR in a range bin are modeled as a multicomponent quadratic frequency-modulated (QFM) signal after the migration compensation. Then, to estimate the parameters of the QFM signal, two cubic phase functions (CPFs), namely coherently integrated generalized CPF (CIGCPF) and coherently integrated CPF (CICPF), are developed. The CIGCPF and CICPF are simple and only require the fast Fourier transform (FFT) and the nonuniform FFT (NUFFT). Due to the usage of the NUFFT, the computational cost is reduced, and the searching procedure is unnecessary for the nonuniformly spaced signal. Thanks to the coherent integrations and NUFFT, the CIGCPF and CICPF, which demonstrate the excellent noise-tolerant performance and reduce the error propagation effect, are efficient and suitable for the multicomponent QFM signals in the low SNR environment. Finally, several simulation examples, analyses of the noise-tolerant performance of the proposed method, and ISAR imaging results of the simulated data demonstrate the effectiveness of the proposed method.

The detection and parameters estimation of linear frequency-modulated (LFM) signal are important for modern radar applications, but they are also challenged by the fact that echo signal is often of low signal-to-noise ratio (SNR) due to reasons of long imaging distance and/or limited transmitted power, and the target of small size and/or hidden characteristics. To enhance the SNR, in our previous work, a novel coherently integrated cubic phase function (CICPF) was recently developed for the parameters estimation of the multicomponent LFM signal. In the CICPF, the auto-terms are coherently integrated to enhance the performance in the case of low SNR and also to suppress the cross-terms and spurious peaks. In this paper, as an extension of our previous work, the theoretical performance analyses including several important properties and the fast implementation are provided. Furthermore, the asymptotic mean squared error of a CICPF-based estimator as well as the output SNR of a CICPF-based detector are theoretically derived in closed-forms. From the performance point of view, the proposed CICPF attains the Cramer-Rao bound at low input SNR. The complexity analysis also indicates that the CICPF with the nonuniform fast Fourier transform is computationally efficient without needing the interpolation operation and parameter search. Numerical studies of the CICPF confirm the theoretical analysis and demonstrate superior performance of the proposed approach compared with other state-of-the-art approaches, especially under the low-SNR condition. Finally, the proposed CICPF is applied for the ground moving target imaging in synthetic aperture radar. Results using simulated and experimental data demonstrate that it provides an effective means to obtain well-focused image for ground moving targets.

This paper addresses the problem of recovering a synthetic aperture radar (SAR) signal that is corrupted by both radio frequency interference (RFI) and wideband interference (WBI). The time-frequency domain is utilized for both the SAR signal and interference in the form of sparse representations. By doing so, a unified framework that allows one to suppress both the RFI and WBI while recovering the SAR signal can be developed. The resulting framework is an optimization problem that is efficiently solved using a customized alternating direction method of multipliers approach. Finally, simulation results are provided to demonstrate that the performance of the joint estimation algorithm is superior to the performances of other methods in terms of both subjective and objective evaluation standards.