Most synthetic aperture radar (SAR) automatic target recognition (ATR) methods can achieve good recognition results only under the closed-set assumption. However, in practical applications, ATR models are often exposed to open environments, the general closed-set method may misclassify unknown categories as known categories, which is not reasonable. To tackle this issue, this paper proposes an end-to-end hybrid model for SAR image open-set recognition (OSR), named GANFlow, which combines a generative adversarial network (GAN) with a flowbased module. The GANFlow achieves accurate classification of known categories and effective rejection of unknown categories. In this model, a classifiable convolution GAN is first designed to complete the training of the feature extraction module and classifier. Through adversarial training, the generated images enrich the training samples, which improves the ability of feature extraction and classification of the discriminator. Then, to find the difference in the probability density distribution of the extracted features, a flow-based module is adopted. Also, the features avoid interference from irrelevant background information in SAR images. Furthermore, by establishing an appropriate threshold, unknown categories can be efficiently rejected. Finally, the outputs of the classifier and the flow-based module are combined to complete the OSR of the SAR image target. The experimental results on the MSTAR and OpenSARShip publicmeasured datasets verify the robustness and generalization of the proposed method

While there are recent researches on hypersonic vehicle-borne multichannel synthetic aperture radar in ground moving target indication (HSV-MC-SAR/GMTI), this article, which specifically explores a robust GMTI scheme for the highly squinted HSV-MC-SAR in dive mode, is novel. First, an improved equivalent range model (IERM) for stationary targets and GMTs is explored, which enjoys a concise expression and therefore offers the potential to simplify the GMTI process. Then, based on the proposed model, a robust GMTI scheme is derived in detail, paying particular attention to Doppler ambiguity arising from the high-speed and high-resolution wide-swath. Furthermore, it retrieves the accurate two-dimensional speeds of GMTs and realizes the satisfactory performance of clutter rejection and GMT imaging, generating the matched beamforming and enhancing the GMT energy. Finally, it applies the inverse projection to revise the geometry shift induced by the vertical speed. Simulation examples are used to verify the proposed GMTI scheme.

Due to the flexible configuration and maneuvering platform, bistatic synthetic aperture radar (SAR) plays an important role in modern remote sensing applications, but the non-ideal track simultaneously introduces model mismatch and spatial-variant phase problems. This paper proposes a sub-aperture parametric polar format algorithm (PFA) for high-resolution bistatic spotlight SAR imaging. First, a bistatic parametric polar format algorithm is proposed to focus on the generally configured bistatic SAR data. A high-precision range model in the bistatic range and ellipsoid parameter angle coordinate space is established to modify the PFA interpolation kernels. To further enhance the azimuth resolution, the PFA sub-images are fused in the image domain based on the coordinate transformation between the unified Cartesian coordinates and local imaging polar coordinates. During the sub-image fusion, in order to ensure the accurate projection and non-aliasing spectrum, we also consider the geometric deformation of the coarse PFA image and analyze the wavenumber support region along with the Nyquist sampling requirement. This novel sub-aperture method, which is theoretically more efficient than the fast back-projection algorithm, alleviates the limitation of full aperture resolution on PFA’s depth of focus. Finally, our method is applied to the general bistatic spotlight SAR data, involving the level-flight airborne transmitter and the dive hypersonic vehicle-borne receiver, and the results of both points and distributed targets demonstrate its effectiveness and efficiency.

Structural deformation caused by environmental loads (solar irradiation, snow and ice, devices heat dissipation, etc.), could severely deteriorate the performance of high-frequency large-aperture active phased array antenna (HLAPAA). The existing sensor placement methods for monitoring the real-time status of HLAPAA have issues with slow convergence rate, limited global search ability, and low optimization accuracy, especially with large HLAPAA with tens thousands of degrees of freedom. In this paper, we propose a novel HLAPAA sensor placement method with an adaptive double population grey wolf optimizer (ADGWO). This method introduces adaptive sentinel wolves into the basic grey wolf optimizer (GWO), improves the population update strategy and thus improves the convergence rate plus the global search ability. First, based on four benchmark functions, the test performance of ADGWO and other popular intelligence algorithms is provided. The comparison shows that the proposed method has excellent convergence speed, higher solution quality, stronger global search ability, and better robustness, making it suitable for optimal sensor placement (OSP) of large structures. Then, the ADGWO is applied to the sensor placement of an X-band HLAPAA. Compared with other OSP methods, the proposed method has advantages in terms of sensor distribution and computational and hardware cost. Finally, verification is carried out using an experimental antenna platform equipped with the optimal fiber Bragg grating (FBG) strain sensor configuration. The measurement results show that the reconstructed shape is in good agreement with the real (deformed) shape, and the calculated radiation pattern matches the radiation pattern measured in the anechoic chamber. Therefore, the proposed method and monitoring system based on ADGWO has broad application prospects in condition monitoring of large, lightweight, flexible antenna structures working in harsh environments.

This paper investigates a robust clutter suppression and detection of ground moving target (GMT) imaging method for a multichannel synthetic aperture radar (MC-SAR) with high-squint angle mounted on hypersonic vehicle (HSV). A modified coarse-focused method with cubic chirp Fourier transform (CFT) is explored first that permits the coarsely focused imageries to be recovered, thus alleviated the impacts of GMT Doppler ambiguity and range cell migration (RCM). After that, in combination with joint-pixel model, a robust clutter suppression method which enhances the GMT integration, and improving the accuracy of radial speed (RS) recovery by modifying the matching between the beamformer center and GMT, is proposed. Due to that the first-order phase compensation and RS retrieval are predigested, the proposed algorithm has lower the algorithmic complexity. Finally, the feasibility of our proposed method are verified via experimental results based on simulated and real measured data.

In this paper, the possibility to improve target detection performance in passive bistatic radar by jointly exploiting multiple signals at different carrier frequencies emitted by the same illuminator is investigated, namely multi-frequency passive bistatic radar (MFPBR) coherent integration. Since the carrier frequency of each signal is agile, the MFPBR coherent integration suffers from the problems of range phase incoherence and Doppler broadening. In order to tackle these challenges, a multi-frequency coherent integration target detection algorithm for passive bistatic radar is proposed. Specifically, this scheme corrects the Doppler broadening effect via TSP. Then, low sidelobe filtering based on convex optimization is carried out to remove the range phase incoherence and obtain the MF of the target's energy. Meanwhile, the high-range-resolution profiles (HRRP) of target can be generated. The advantage of the proposed algorithm is that it can obtain superior coherent integration and detection performance for both the single and multiple targets scenarios compared to existing methods. Finally, a series of measured and simulation results are presented to demonstrate the effectiveness of the proposed algorithm.