Xingdong Liang

In recent years, joint wireless communication and Synthetic Aperture Radar (SAR) imaging has received extensive attention. The realization of this concept remains a significant challenge. For communication and SAR, they have conflicting requirements on waveforms. The state-of-the-art solutions are based on the Orthogonal Frequency Division Multiplexing (OFDM) techniques. However, OFDM waveforms are sensitive to the Doppler frequency. Even worse, their unavoidable Cyclic Prefix (CP) would introduce false peaks into SAR images. To be insensitive to frequency bias and free of CPs, Filter Bank Multicarrier (FBMC) waveforms with stepped frequency chirp and Offset Quadrature Amplitude Modulations (OQAM) are proposed in this paper. Particularly, stepped frequency chirps, which have been optimally weighted for SAR imaging, are interleaved into FBMC subcarriers as pilots. Meanwhile, the channel changes can be obtained from these pilots for communication equalization. Moreover, concerning the intrinsic interference of imaginary parts between pilots and OQAM symbols, compensation algorithms are introduced. With the proposed waveform schemes, the identical frequency band can be utilized for data transmission while performing high-resolution SAR imaging. Theoretical analysis and simulation results demonstrate that the proposed waveform scheme is feasible.

A skeletal pose estimation method, named RVRU-Pose, is proposed to estimate the skeletal pose of vulnerable road users based on distributed non-coherent mmWave radar. In view of the limitation that existing methods for skeletal pose estimation are only applicable to small scenes, this paper proposes a strategy that combines radar intensity heatmaps and coordinate heatmaps as input to a deep learning network. In addition, we design a multi-resolution data augmentation and training method suitable for radar to achieve target pose estimation for remote and multi-target application scenarios. Experimental results show that RVRU-Pose can achieve better than 2 cm average localization accuracy for different subjects in different scenarios, which is superior in terms of accuracy and time compared to existing state-of-the-art methods for human skeletal pose estimation with radar. As an essential performance parameter of radar, the impact of angular resolution on the estimation accuracy of a skeletal pose is quantitatively analyzed and evaluated in this paper. Finally, RVRU-Pose has also been extended to the task of estimating the skeletal pose of a cyclist, reflecting the strong scalability of the proposed method.

High-precision computation with low latency and high energy efficiency is required for AI-driven application and scientific computing. Emerging compute-in-memory (CIM) technology shows a great potential to accelerate multiplication and accumulation (MAC) operations which are frequently executed in such scenarios. Resistive RAM (RRAM) is highly suitable for CIM due to its excellent features such as nonvolatility, small cell size and MAC-friendly structure. However, the existing RRAM CIMs focus on the acceleration of fixed-point/integer operations. Several works adopt the logic-CIM structure to support high-precision Floating-point (FP) calculations, but they require lots of cycles and area to perform a FP operation. To meet the need of low latency and high energy efficiency of widely used FP calculation, we propose an accelerated FP-MAC architecture, based on 40nm RRAM CIM array. A full-parallel data input scheme and triangle weights arrangement is proposed for low latency multi-bits multiplication. A non-uniformly grouped sense amplifiers (NUGSAs) array is adopted for energy and area saving. Experiments show that the proposed FP-MAC design achieves an energy efficiency of up to 8.8 TFLOPS/W at FP8 mode and 3.3 TFLOPS/W at bFP16 mode, and the computing latency is 3.34ns.

Cross water–air interface acoustic and electromagnetic integrated communication (AEIC) technology refers to an underwater speaker that excites the water surface micro-amplitude wave (WSAW) on the water’s surface, and millimeter wave radar detects the vibrations of the WSAW to realize the transmission of information. The research on cross-media communication meets many challenges due to the large amplitude of the water surface disturbance and the small amplitude of the WSAW. In this paper, a novel sea wave interference suppression method based on improved complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and wavelet transform (WT) is presented. This method divides the phase change into different intrinsic mode functions (IMFs) and obtains a reconstructed scale of the WSAW signal through wavelet decomposition and correlation procession to separate the WSAW signal and the sea wave interference. It is proved to be better than the reference filtering method by experiment. By using this novel method, the bit error rate (BER) of the communication system can be reduced effectively.

Special attention has been devoted to joint wireless communication and radar sensing concepts over the past decades. Considerable confusion arose, however, with regard to waveform designs. In this paper, various joint wireless communication and radar sensing waveform schemes are reviewed and classified into two categories. Disadvantages and limitations of each category are clarified theoretically. It is shown that waveforms designed merely in conventional time and frequency domains cannot fulfill the conflict requirements posed by radar and wireless communication. As a solution to this fundamental challenge, the multidimensional waveform concepts, along with a generalized correlation model, are proposed for joint wireless communication and radar sensing. To showcase our current research advances in this emerging area, several concrete waveforms designed in multiple dimensions are introduced. Theoretical analysis is validated by simulations and outfield demonstration experiments. It is shown that the suggested waveforms can simultaneously achieve superior detection and communication performances with shared spectrum resources.

A joint radar-communication (JRC) system utilizes the integrated transmit waveform and a single platform to perform radar and communication functions simultaneously. Admittedly, the multibeam waveform design approach could transmit the assigned waveforms in different beams with the aid of spatial and temporal degrees of freedom. However, a high sidelobe level (SLL) in the beampattern reduces energy efficiency and expands exposure probability. In this study, we propose a novel spatial–temporal joint waveform design method based on the beamforming algorithm to form a low SLL beampattern. Waveform synthesis constraints are considered to synthesize desired radar and communication waveforms at designated directions. Furthermore, we impose the constant modulus constraint to lessen the impact of the high peak-to-average ratio (PAPR). The optimization process of the whole model can be summarized as two stages. First, the covariance matrix is created by convex optimization with respect to the minimum SLL. Second, the integrated transmit waveform is tuned through an alternating projection algorithm. Based on the simulation findings, we demonstrate that the proposed method outperforms the traditional methods in terms of low SLL and waveform synthesis. Meanwhile, we validate the effectiveness of the proposed method using semi-physical experiment results.

Cross-media communication underpins many vital applications, especially in underwater resource exploration and the biological population monitoring domains. Water surface micro-amplitude wave (WSAW) frequency detection is the key to cross-media communication, where the WSAW frequency can invert the underwater sound source frequency. However, extracting the WSAW frequency information encounters many challenges in a real environment, such as low precision and symbol synchronization, leading to inaccurately estimating the WSAW frequency. Thus, this paper proposed a WSAW frequency estimation method based on an improved RELAX algorithm, incorporating two improvements. First, adding a nonlinear filter to the RELAX kernel function compensates for the filtered gain and enhances the WSAW frequency precision. Second, the improved RELAX kernel function is combined with the generalized inner product method to obtain the time distribution of the non-stationary signals, which is convenient for decoding. Several simulations and experiments applying our method on a Ka-band frequency modulated continuous wave (FMCW) radar demonstrate that our algorithm attains a better performance than traditional methods, e.g., periodogram and the RELAX algorithm. Using the improved algorithm affords to extract the frequency information of the WSAW signal accurately with a short sampling duration, further improving the performance indicators of the communication system, such as communication rate.

Building reconstruction using high-resolution tomographic synthetic aperture radar (TomoSAR) point clouds has been very attractive in numerous applications, such as urban planning and dynamic city modeling. However, for side-looking TomoSAR, it is a challenge to reconstruct the obscured backs of buildings using traditional single-bounce scattering-based methods. It comes to our attention that the higher-order scattering points in airborne array TomoSAR point clouds may provide rich information on the backs of buildings. In this paper, the fourfold bounce (FB) scattering model of combined buildings in airborne array TomoSAR is derived, which not only explains the cause of FB scattering but also gives the distribution pattern of FB scattering points. Furthermore, a novel FB scattering-based method for the reconstruction of building backs is proposed. First, a two-step geometric constraint is used to detect the candidate FB scattering points. Subsequently, the FB scattering points are further detected by seed point selection and density estimation in the radar coordinate system. Finally, the backs of buildings can be reconstructed using the footprint inverted from the FB scattering points and the height information of the illuminated facades. To verify the FB scattering model and the effectiveness of the proposed method, the results from the simulated point clouds and the real airborne array TomoSAR point clouds are presented. Compared with the traditional roof point-based methods, the outstanding advantage of the proposed method is that it allows for the high-precision reconstruction of building backs, even in the case of poor roof points. Moreover, this paper may provide a novel perspective for the three-dimensional (3D) reconstruction of dense urban areas.

Foreign object debris (FOD) radar systems are often used to detect foreign materials that appear on the pavement that can pose as a threat to aircraft and personnel. Scan-mode millimeter-wave (MMW) radar with a fixed installation position is widely used as a leading solution. Small target detection is a puzzling challenge that requires a breakthrough for practical application. The conventional matched filter (MF) process along the range would encounter frequency leakage when strong scattering targets generate high sidelobe and mutual shield interference to object detection. The compressed sensing (CS) approach used for detection achieves efficiency by suppressing sidelobes and preventing small targets from being submerged by larger targets. The compressed imaging is introduced to retrieve the image scene with higher resolution and a small detectable object, such as 2-cm-diameter metal ball that equals −35-dBsm radar cross section (RCS). The long stripes scattering phenomenon can be reduced, which is beneficial to further detection processing. Furthermore, we demonstrate the concept of the MMW radar detection approach based on compressed imaging with the actual data. It validates the improved efficiency in at least two conditions: a small interval between multiple targets and strong scattering coverage of small targets. Nevertheless, we highlight some previously unrealized benefits of the FOD radar system application. There is no prior detection approach based on compressed imaging to enhance the performance of an FOD radar, as we present in this study.

Tomographic Synthetic Aperture Radar (TomoSAR) is a breakthrough of the traditional SAR, which has the three-dimentional (3D) observation ability of layover scenes such as buildings and high mountains. As an advanced system, the airborne array TomoSAR can effectively avoid temporal de-correlation caused by long revisit time, which has great application in high-precision mountain surveying and mapping. The 3D reconstruction using TomoSAR has mainly focused on low targets, while there are few literatures on 3D mountain reconstruction. Due to the layover phenomenon, surveying in high mountain areas remains a difficult task. Consequently, it is meaningful to carry out the research on 3D mountain reconstruction using the airborne array TomoSAR. However, the original TomoSAR mountain point cloud faces the problem of elevation ambiguity. Furthermore, for mountains with complex terrain, the points located in different elevation periods may intersect. This phenomenon increases the difficulty of solving the problem. In this paper, a novel elevation ambiguity resolution method is proposed. First, Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and Gaussian Mixture Model (GMM) are combined for point cloud segmentation. The former ensures coarse segmentation based on density, and the latter allows fine segmentation of the abnormal categories caused by intersection. Subsequently, the segmentation results are reorganized in the elevation direction to reconstruct all possible point clouds. Finally, the real point cloud can be extracted automatically under the constraints of the boundary and elevation continuity. The performance of the proposed method is demonstrated by simulations and experiments. Based on the airborne array TomoSAR experiment in Leshan City, Sichuan Province, China in 2019, the 3D model of the surveyed mountain is presented. Moreover, three kinds of external data are applied to fully verify the validity of this method.