Zhiqin Zhao

Microwave-induced thermoacoustic imaging (MITAI) uses long-wavelength microwave excitation to achieve deeper tissue penetration and a larger illumination field than laser-based methods. Intracavitary MITAI is particularly attractive because microwave excitation can be delivered extracorporeally while reusing conventional ultrasound probes, making the three-dimensional (3-D) intracavitary MITAI conceptually simple and highly feasible. However, 3-D MITAI based on a self-rotating transrectal linear-array probe suffers from severe artifacts caused by the limited receive aperture in both the rotational and elevational directions, which can drastically degrade image quality. To address this problem, we design a 3-D MITAI framework that employs a transrectal intracavitary linear-array probe performing 360$^\circ$ self-rotation, and we propose two 3-D image reconstruction strategies: (1) a fast method based on two-dimensional (2-D) slice post-processing and direct 3-D point-cloud mapping, in which, for each rotation angle, the multi-channel radio-frequency (RF) data are independently reconstructed using 2-D delay-and-sum (DAS) combined with band-pass filtering, depth window gating, dynamic aperture control, coherence-factor (CF) weighting, ring-buffer detection, and multi-threshold gating, after which the cleaned pixels in the $r$–$z$cross-sectional plane are geometrically mapped into 3-D voxels or point clouds to achieve efficient 3-D reconstruction; and (2) a full-wavefield method that treats the multi-angle data acquired during rotation as a virtual 2-D cylindrical aperture, explicitly computes the propagation delays from each voxel in 3-D space to all virtual elements, and performs 3-D DAS beamforming to obtain a volumetric dataset with effective suppression of limited-aperture artifacts. Preliminary experimental results demonstrate that the proposed system achieves lateral resolutions of 0.924 mm and 0.979 mm and axial resolutions of 0.737 mm and 0.966 mm for the two methods, respectively. Both strategies successfully realize 360$^\circ$ 3-D imaging, and with GPU acceleration, the full-wavefield 3-D reconstruction time is reduced from 2193 s to approximately 192 s. The proposed approach provides a practical solution for transrectal 3-D MITAI and, when combined with efficient large-area microwave illumination and potentially with conventional ultrasound readout, has strong potential for multimodal intracavitary applications such as rectum and surrounding vasculature imaging, where complementary acoustic structural and electromagnetic functional information could improve diagnostic accuracy.

Wide-beam array antenna has been widely used in satellite communication systems, such as the geostationary AsiaStar, in which even a small expansion of array mainlobe beamwidth (AMB) can significantly increase the signal converge area. In this Communication, the AMB maximization (AMBM) problem with a desired minimum mainlobe power gain (MMPG) is addressed. Current methods obtaining a wide AMB via the shaped beampattern synthesis (SBPS) cannot ensure the desired MMPG and sidelobe level (SLL). To overcome this deficiency, a new AMBM strategy, which iteratively expands the mainlobe via relaxation optimization, is proposed. The strategy first constructs a non-convex AMBM mathematical model with controllable MMPG and SLL. Then, it reformulates the non-convex AMBM problem into a series of power gain pattern synthesis (PGPS) problems. An iterative algorithm is proposed with the aid of Taylor’s theorem and a relaxation method by approximating the objective function from l₀-norm to l₁norm. The superiority of the proposed algorithm is validated in comparison with the existing SBPS-based and PGPS-based algorithms. Simulation results demonstrate that the proposed algorithm can achieve a substantially wider AMB with average improvements of 6.41° and 0.18° over the SBPS-based and PGPS-based algorithms, respectively.

Time-division broadcast positioning systems offer advantages such as low implementation complexity and support for an unlimited number of passive receivers. However, most existing joint localization and synchronization (JLAS) methods for moving terminals rely on the assumption of pre-synchronized base stations (BSs), which restricts their flexibility and scalability in ad hoc or large-scale deployments. This article proposes a fully asynchronous JLAS (FA-JLAS) framework that operates without any prior inter-BS synchronization. We derive a new set of composite observables, the symmetric asynchronous time difference of arrival (SA-TDOA) and the interframe difference observable (IFDO), from timestamp measurements shared among BSs. These observables are designed to analytically eliminate nuisance parameters, including clock offsets and static non-line-of-sight (NLOS) delays. Based on these observables, a maximum a posteriori (MAP)-based estimation framework is developed that incorporates motion priors and jointly estimates the kinematic state and network synchronization parameters of user equipment (UE), thereby improving robustness in challenging 3-D environments. Theoretical performance bounds are derived using the Cramér–Rao lower bound (CRLB), and simulation results demonstrate that the FA-JLAS framework achieves near-CRLB accuracy across various scenarios.

The electromagnetic (EM) inverse scattering problems (ISPs) are characterized by nonlinearity, presenting a challenge in achieving a satisfactory reconstruction of the relative permittivity of strong scatterers. Joint inversion has been demonstrated as an effective approach for improving the quality of EM inversion through the integration of acoustic inversion. However, when solving the ISPs associated with complex structured scatterers and multiple scatterers, the accuracy of the EM parameters reconstructed by previous joint inversion methods is inadequate. Aim to solving this problem, this paper proposes a new inversion method, which is based on framework of the subspace-based optimization method (SOM). The proposed method adopts the joint inversion architecture and incorporates the image segmentation technique. The acoustic data is processed before being employed to construct structural similarity constraints for EM inversion, rather than being directly utilized as in previous methods. And in this new approach, Pyramid Scene Parsing Network (PSPNet) is employed to extracting the boundary information of the targets from high-resolution acoustic images. Subsequently, this information is leveraged to formulate a more effective objective function or a better initial contrast for EM inversion. Compared with previous joint inversion method, this new imaging segmentation-based inversion method (ISIM) of EM and acoustic data offers advantages in inversion accuracy and computational speed by making more efficient use of acoustic information. Additionally, in scenarios involving complex structural scatterers or multiple scatterers, ISIM is capable of achieving more precise results. The effectiveness and advantages of ISIM are validated through several numerical examples.

Assessment of jamming against multi-functional radar (MFR) requires mapping radar operating states to ordinal threat levels. The use of state coding enhances the expression of single-valued state features. This paper uses Variational Auto-Encoder (VAE) for state coding to address the inefficiency problem caused by the expansion of radar state space. Besides, this paper uses mutual information to enhance the expressiveness of the hidden variables to the input states. The proposed method can effectively compress the code length and reduce the number of network parameters. The results show that the decision-making efficiency of the proposed method is better than ordinal coding, One-Hot, Gray and the Transformer encoder, etc. In addition, it is difficult to design an appropriate reward function when prior information is absent. To address this, the paper introduces a backward experience feedback mechanism. It increases the value redundancy of short-term harmful but long-term favourable (SH-LF) actions compared to standard experience replay. This approach enhances the network's tolerance to the reward function and long-term discounting while improving its robustness.

Recently, the radar high-resolution range profiles (HRRPs) have gained significant attention in the field of radar automatic target recognition due to their advantages of being easy to acquire, having a small data footprint, and providing rich target structural information. However, existing recognition methods typically focus on single-domain features, utilizing either the raw HRRP sequence or the extracted feature sequence independently. To fully exploit the multi-domain information present in HRRP sequences, this paper proposes a novel target feature fusion recognition approach. By combining a convolutional long short-term memory (ConvLSTM) network with a cascaded gated recurrent unit (GRU) structure, the proposed method leverages multi-domain and temporal information to enhance recognition performance. Furthermore, a multi-input framework based on learnable parameters is designed to improve target representation capabilities. Experimental results of 6 ship targets demonstrate that the fusion recognition method achieves superior accuracy and faster convergence compared to methods relying on single-domain sequences. It is also found that the proposed method consistently outperforms the other previous methods. And the recognition accuracy is up to 93.32% and 82.15% for full polarization under the SNRs of 20 dB and 5 dB, respectively. Therefore, the proposed method consistently outperforms the previous methods overall.

Integrated sensing and communication (ISAC) technology has garnered considerable attention in the 5G and 6G wireless communication fields. In particular, blind moving target sensing shows significant development prospects in areas such as low-altitude security, intelligent transportation, and smart ports. However, most existing sensing methods for moving targets using communication signals are based on location and tracking, limiting the accuracy of subsequent target recognition. Inverse synthetic aperture radar (ISAR) imaging technology can obtain two-dimensional imaging results of moving targets and provide more fine sensing information. Inspired by this, an ISAC-ISAR imaging system is proposed for fine sensing of moving target in the paper. Firstly, a millimeter-wave ISAC system is established using universal software radio peripheral (USRP). Then, the ISAC-ISAR echo signal model and imaging mechanism are introduced with static path suppression technology. Furthermore, the wavenumber domain algorithm is proposed for fast ISAC-ISAR imaging, while the minimum entropy criterion is adopted for fine imaging of non-cooperative moving target. Finally, the ISAC-ISAR imaging experiments are conducted with USRP within the 27.95GHz-28.05GHz frequency range to demonstrate the performance and feasibility of the proposed system.

This paper proposes a fast hybrid method model. Different from the traditional facet-based method, this model is used to quickly calculate the complex electromagnetic (EM) scattering of 3-D time-varying sea surface. The sea surface is divided by a coarse mesh of small planes, each plane is covered with small scale fluctuation waves. The modified facet-based Two-scale model (MFTSM) is used to calculate the scattered field of each small plane, and the shooting and bouncing ray (SBR) method is used to calculate the coupling scattering between sea surface elements. Finally, the validity of the model is verified by comparing with the numerical method MLFMA and the measured results. Compared with traditional sea surface EM calculations, this model offers fast and precise EM calculation for sea surfaces.

In many real-time kinematic (RTK) positioning applications, reference observations are transmitted over wireless links that can experience frequent interruptions or substantial delays. This results in large differential ages between base and rover observations, which, in turn, leads to a deterioration in positioning performance. To bridge the significant age difference, in this work, we propose a simple and effective scheme for modeling and compensating for such errors. Firstly, the overall differential age error was modeled using truncated Taylor expansion. Then, a time-differenced carrier phase (TDCP)-based observation model was established to estimate the errors with the Kalman framework. Since estimating the receiver’s clock error is unnecessary, equivalent transformation and sequential filtering technology were adopted to significantly reduce the computational complexity. Furthermore, a predictor performance monitor was introduced to mitigate the integrity risks that may occur due to model mismatches. The effectiveness of this scheme was validated by static and dynamic field experiments. The static experiment results showed that when the differential age was 60 s, the GPS and BDS satellites’ overall root mean square error (RMSE) with the asynchronous RTK (ARTK) prediction method was 2.8 and 5.5 times that of the proposed method, respectively. Moreover, when the differential age was 120 s, these values were 3.3 and 5.4 times that of the proposed method, respectively. The field experiment results showed that when the differential age was 60 s, the integer ambiguity fixed rate and false fixed rate of the ARTK method were 0.90 and 1.63 times that of the proposed method, respectively. Finally, at a 120 s differential age, these values were 0.78 and 4.78 times that of the proposed, respectively.

Thermoacoustic imaging (TAI) has the potential for detecting hemorrhagic stroke. However, in transcranial TAI, the speed of sound (SoS) between the skull and brain tissue varies significantly. Therefore, if the image reconstruction assumes a uniform SoS, accurately locating the hemorrhagic lesion becomes challenging. In this Letter, we propose a fast inhomogeneous layer back-projection (BP) method based on the basic boundary line with a statistical approach to reconstruct TA images for noninvasive and non-ionizing hemorrhage detection. To validate our proposed method, we conducted numerical simulations using real human skull data and two phantom transcranial TAI experiments. In the numerical simulation, the proposed method improves the structural similarity index measure from 0.034 879 for BP with uniform SoS to 0.624 44. The phantom experimental results demonstrate that the proposed method renders the targets in the reconstructed image more consistent with the real targets. In the case of considering a three-layer SoS distribution, the time reversal method requires 1 min and 37.391 s to reconstruct a 201 × 201 pixels TA image. Meanwhile, the proposed method accomplishes the same-sized TA image reconstruction in only 2.113 397 s. The simulation and experimental results indicate that the proposed method enhances TAI's ability for accurate and fast identification of cerebral hemorrhage.