Feifei Gao

Joint direction of arrival (DOA) and direction of departure (DOD) estimation algorithms based on tensor decomposition can enhance the angle estimation performance of bistatic multiple-input multiple-output (MIMO) radar by exploiting the multi-dimensional information of received signal data. However, conventional tensor decomposition techniques fail to exploit the Vandermonde structure information in the received signal model. Therefore, this paper proposes a joint DOA and DOD estimation algorithm based on structured tensor decomposition for bistatic MIMOradar. First, a structured fourth-order tensor is constructed by exploiting the non-centro-Hermitian property of the received third-order tensor signal and the exponential Vandermonde structure of its factor matrices. Then, combined with the Hankel transformation technique, rank-1 constraints are introduced for alternating iterative optimization to achieve structured tensor de composition and obtain estimations of the factor matrices. Finally, the least squares approach is applied to derive the estimations of DOD and DOA. The effectiveness and superiority of the proposed algorithm are validated by the simulation results.

The monostatic frequency diverse array multiple-input multiple-output (FDA-MIMO) radar with unfolded coprime framework has an enlarged array aperture and extended signal bandwidth, which can significantly improve the resolution of range and angle parameter estimation for FDA-MIMO radar. Furthermore, tensor analysis techniques can effectively exploit the multidimensional structural characteristics inherent in signals to achieve superior estimation performance. Therefore, this paper mainly employs tensor analysis to improve the parameter estimation performance of FDA-MIMO radar, and proposes a joint angle and range estimation algorithm based on coupled canonical polyadic decomposition (C-CPD). The proposed algorithm first models the received signal as a third-order tensor. Then, based on the structure of the Vandermonde-like matrix, a C-CPD model is designed. Subsequently, the alternating least squares (ALS) technique is utilized to obtain steering matrix estimates. Finally, the coprime characteristic is exploited to resolve phase ambiguity of the array framework, and least squares fitting is employed to obtain angle and range estimates. Simulation experiments demonstrate the superiority and effectiveness of the proposed algorithm.

This paper investigates joint channel and symbol estimation in a multiple user uplink multiple-input multiple-output (MIMO) system assisted by several reconfigurable intelligent surfaces (RISs). With Khatri–Rao space–time (KRST) coding at the users, A PARATUCK2-based tensor framework is developed. To tackle the challenges of multi-RIS deployment, an adaptive accelerated fitting for PARATUCK2-based decomposition (AAF-PDE) algorithm is proposed. The algorithm employs an accelerated adaptive fitting (AAF) mechanism with dynamic step-size optimization, jointly estimating the RIS-base station (BS) channels, the composite user-RIS channels and the user symbols. Based on the accurately estimated channel matrices, geometric parameters can be further extracted via a two-dimensional ESPRIT procedure without dedicated radar signal. Simulation results verify that AAF-PDE outperforms benchmark algorithms in terms of channel estimation, symbol detection and convergence efficiency. Moreover, the root mean square error (RMSE) performance demonstrates that the geometric parameters estimation is enabled with high accuracy. Theoretical analysis on identifiability, complexity, and the Cramér–Rao bound (CRB) demonstrates the soundness of the proposed algorithm.

In this paper, we propose a novel integrated sensing and communications (ISAC) scheme to perform bridge micro-deformation monitoring (BMDM) in complex environments. We first provide an excitation-bridge coupling model to represent the micro-deformation process of the bridge. Next, we design a novel frame structure for BMDM applications, and construct the OFDM echo channel model for basic scene of BMDM, including micro-deformation, dynamic objects, and static environment. Then, we develop a phasor statistical analysis method based on average cancellation algorithm to suppress the interference of dynamic objects, as well as a circle fitting method based on least squares algorithm to remove the interference of static environment near the monitoring area. Furthermore, we extract the micro-deformation feature vector from the OFDM echo signals after inverse discrete fourier transform (IDFT), and derive vertical micro-deformation value with the time-frequency phase resources. Simulation results demonstrate the effectiveness of the proposed BMDM scheme and its robustness against both dynamic interferences and static interferences.

The reconfigurable intelligent surface (RIS) has been widely studied in far-field communications (FFC), and further extended to near-field communications (NFC). With the distinct electromagnetic properties in FFC and NFC, it remains debatable whether the RIS deployment strategies established for FFC are still applicable to NFC. To bridge this gap, we examine RIS-aided cooperative relaying in NFC through two representative configurations, single-RIS and multi-RIS relaying, in which a decode-and-forward relay forwards data from the source to the user with the assistance of RIS For the single RIS deployment, we show that the achievable rates with RIS deployed near the base station (BS) or near relay are completely different. It is revealed that, with a single-antenna relay, the multi-RIS is significantly preferable over the single RIS in NFC without the requirement of massive RIS reflection elements, which is completely different from that of FFC. Furthermore, we derive closed-form expressions for the achievable rates under both single-antenna and multi-antenna relay configurations to explicitly determine the distance threshold. The presented analytical results demonstrate that locating RIS near the relay yields substantial performance gains when the transmission distance exceeds a certain threshold. This conclusion is validated by numerical simulations, which systematically illustrates the distinct impact of RIS deployment strategies in near-field versus far-field regions.

In this paper, we propose an asynchronous unmanned aerial vehicle (UAV) trajectory monitoring scheme with multi-base station (BS) feature fusion in a cellular integrated sensing and communications (ISAC) system. Different from distributed radar systems that rely on wideband radar waveforms and synchronous joint processing, the proposed scheme considers practical cellular ISAC settings such as narrowband orthogonal frequency division multiplexing (OFDM) signaling and transceiver discrepancies-induced offsets. We develop a single-BS signal pre-processing method that estimates target motion parameters and effectively compensates for time offsets (TOs) and carrier frequency offsets (CFOs) caused by transceiver discrepancies. Next, we design a multi-BS feature fusion method that aligns spatial features across BSs and accurately estimates the positions and velocities of targets based on time delay and Doppler frequency features. By operating at the feature level, the fusion process circumvents the need for coherent signal-level processing as well as the extensive data-level fusion commonly required in distributed radar systems. Furthermore, we propose a cooperative trajectory tracking method that associates asynchronous trajectory observations into consistent local and global trajectories, thereby enabling reliable cross-BS trajectory fusion. Simulation results demonstrate that the proposed cooperative scheme significantly enhances the accuracy of UAV trajectory monitoring compared to traditional algorithms.

With the development of wireless sensing technology, WiFi-based identity recognition has gained widespread attention in fields such as human-computer interaction and home security. Although WiFi-based sensing has seen initial research, existing identity recognition methods primarily rely on fixed user categories and lack adaptability to dynamic identity changes. Furthermore, the limited number of samples for new identity classes poses a significant challenge. In this paper, we propose a WiFi-based identity recognition method based on few-shot incremental learning, which enables effective adaptation to scenarios with small sample sizes and evolving user identities. We introduce a new few-shot training strategy and a continuously evolving classifier during the user training phase to avoid severe overfitting and catastrophic forgetting. Moreover, we conduct comprehensive comparative experiments on our method and other systems in scenarios with fixed user identity categories. The experimental results show that the proposed method effectively mitigates performance degradation across incremental sessions in terms of accuracy, precision, recall, and F1-Score. In scenarios with an increasing number of identity categories, the proposed method maintains an accuracy of 84% despite limited sample availability, consistently outperforming baseline methods.

In this paper, we establish a comprehensive framework and real-world prototype based on Xilinx RFSoC for millimeter wave (mmWave) simultaneous localization and mapping (SLAM) using integrated sensing and communication (ISAC-SLAM). The ISAC-SLAM framework employs a time-division protocol where users alternate between communication and sensing. During communication period, users and base stations (BS) conduct beam management, from which the line-of-sight (LOS) and the first-order non-line-of-sight (1st NLOS) paths are extracted for rough user localization and map reconstruction respectively. We further design an algorithm that fuses the user’s rough location with inertial measurement unit (IMU) and wheel encoder measurements for accurate localization. During sensing period, we propose an elaborate signal processing baseband, allowing users to operate as mono-static orthogonal frequency division multiplexing (OFDM) radar and to refine the map. Finally, we design a map stitching method that enables the collaboration of multiple users and BSs to generate large-scale maps. Unlike most traditional works, we propose radar cross section based (RCS-based) grid map to represent both contour and material of the environment, and we implement ISAC-SLAM prototype on RFSoC with high robustness and real-time practicality. Experiments demonstrate that ISAC-SLAM achieves an average absolute error of 0.0347 m for localization and 0.2610 m for mapping, while maintaining real-time data transmission at over 1 Gbps. Moreover, the ISAC-SLAM map exhibits a contour similar to that of a 2D LiDAR map on a large scale, while encompassing material information of the environment.

In the upcoming next-generation wireless communication systems, high-mobility support serves as an essential part. OTFS (Orthogonal Time Frequency Space) modulation is regarded as a candidate waveform for 6G systems for its robustness in high mobility scenarios. However, its performance degrades when Doppler spreads beyond the subcarrier spacing with ultra-high speed, a condition referred to as the out-of-range Doppler (OD) channel. We analyze the input-output relationship under OD channels and reveal that the Delay-Doppler (DD) domain received signal is a superposition of the transmitted signal with an additional phase term. Based on this insight, we design a cross-frame OTFS framework for channel estimation, utilizing coprime subcarrier configurations to observe varying responses across subframes, and the out-of-range parameters are resolved using the Chinese Remainder Theorem (CRT). Furthermore, an effective channel estimation method is developed to handle multipath scenarios by decomposing subframes and matching response taps using fractional and amplitude information, which transforms the OD channel estimation problem into multiple single-domain problems by processing DD domain signals in layers. Simulation results demonstrate the effectiveness of the proposed method in estimating OD channels, outperforming existing approaches that fail under such conditions.