In this paper, we propose an unmanned aerial vehicle (UAV) and bird recognition scheme with signal processing and deep learning for integrated sensing and communications (ISAC) system. We first provide the basic scene of low-altitude targets monitoring, and formulate the motion equations and echo signals for UAVs and birds. Next, we extract the centralized micro-Doppler (cmD) spectrum and the high resolution range profile (HRRP) of the low-altitude target from the echo signals. Then we design a dual feature fusion enabled low-altitude target recognition network with convolutional neural network (CNN), which employs both the images of cmD spectrum and HRRP as inputs to jointly distinguish between UAV and bird. Meanwhile, we generate 237600 cmD and HRRP image samples to train, validate, and evaluate the designed low-altitude target recognition network. The proposed scheme is termed asAirGuard, whose effectiveness has been demonstrated by simulation results.

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.

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.

In this paper, we present a framework to enable unmanned aerial vehicle (UAV) trajectory monitoring for an integrated sensing and communications (ISAC) system. Specifically, the base station (BS) first performs beam-scanning to acquire the echo signals from dynamic targets. Static environmental clutter is subsequently filtered out to enable real-time target detection. Next, we propose a phase-rotated discrete Fourier transform (PRDFT) algorithm to estimate the targets’ motion parameters, including distance, horizontal angle, pitch angle, radial velocity, horizontal angular velocity, and pitch angular velocity. We then convert the estimated parameters into a common Cartesian coordinate system to extract the targets’ positional and velocity features. To associate the targets with their corresponding trajectories, we propose a position wave gate and velocity differences nearest neighbor (WGVDNN) algorithm that matches targets based on similar position and velocity features relative to the trajectories. Afterward, we apply the interactive multiple model unscented Kalman filter (IMMUKF) algorithm to identify the targets’ motion model and predict their positions in the next time slot, thereby directing the beam to track the discovered ones. Simulation results demonstrate that the proposed framework effectively enables the real-time discovery of new targets and the continuous tracking of the discovered targets, thereby monitoring the complete trajectories of all targets.

In this paper, we propose a novel scheme to estimate the six-dimensional (6D) motion parameters of the dynamic target for monostatic integrated sensing and communications (ISAC) system. We first provide a generic ISAC framework for dynamic target sensing based on massive multiple input and multiple output (MIMO) array. Next, we derive the relationship between the sensing channel of ISAC base station (BS) and the 6D motion parameters of the dynamic target. Then, we employ the array signal processing methods to estimate the horizontal angle, pitch angle, distance, and virtual velocity of the dynamic target. Since the virtual velocities observed by different antennas are different, we adopt plane fitting to estimate the dynamic target’s radial velocity, horizontal angular velocity, and pitch angular velocity from these virtual velocities. Simulation results demonstrate the effectiveness of the proposed 6D motion parameters estimation scheme, which also confirms a new finding that one single BS with a massive MIMO array is capable of estimating the horizontal angular velocity and pitch angular velocity of the dynamic target.

In low-altitude economy (LAE), the widespread use of various types of unmanned aerial vehicles (UAVs) could provide convenience and enhance efficiency. However, the existence of unauthorized or illegal UAVs would pose significant challenges to urban privacy and security. In this paper, we propose a networked integrated sensing and communications (ISAC) based UAV tracking and handover scheme towards LAE. We define avirtual sensing cell (VSC)where oneprimary base station (PBS)transmits sensing signals, while both the PBS and twosecondary base stations (SBS)receive echoes. Since the echoes contain the clutter of static environment, each base station (BS) would first filter out the clutter and then estimate the UAV’s horizontal angle, elevation angle, distance, and radial velocity with the multiple signal classification (MUSIC) algorithm. Next, we employ the centralized extended Kalman filter (EKF) to fuse the estimations from the three BSs and leverage the one-step prediction results of the EKF to distinguish and track multiple UAVs. When the UAV flies within the coverage of a VSC, we design aPBS handoverstrategy to select the optimal BS from three BSs as the new PBS in real-time. Moreover, we propose aVSC handoverstrategy to track the UAV continuously when it flies from one VSC to another. Simulation results demonstrate the effectiveness of the proposed scheme and provide valuable reference for UAV tracking and handover in LAE.

In this paper, we propose to utilize the beam squint effect to realize fast non-cooperative dynamic target sensing in massive multiple input and multiple output (MIMO) based integrated sensing and communications (ISAC) systems. Specifically, we design a beamforming strategy that controls the range of beam squint by adjusting the values of phase shifters and true time delay lines. With this design, beams at different subcarriers can be aligned along different directions in a planned way. Then the received echo signals at different subcarriers will carry targets information in different directions, based on which the targets' angles can be estimated through sophisticatedly designed algorithm. Moreover, we propose a supporting method based on extended array signal estimation, which utilizes the phase changes of different frequency subcarriers within different OFDM symbols to estimate the distance and velocity of dynamic targets. Interestingly, the proposed sensing scheme only needs to transmit and receive the signals once, which can be termed as You Only Listen Once (YOLO). Compared with the traditional ISAC method that requires time consuming beam sweeping, the proposed one greatly reduces the sensing overhead. Simulation results confirm the effectiveness of the proposed scheme.

In this paper, we consider the moving target sensing problem for integrated sensing and communication (ISAC) sys-tems in clutter environment. Scatterers produce strong clutter, deteriorating the performance of ISAC systems in practice. Given that scatterers are typically stationary and the targets of interest are usually moving, we here focus on sensing the moving targets. Specifically, we adopt a scanning beam to search for moving target candidates. For the received signal in each scan, we employ high-pass filtering in the Doppler domain to suppress the clutter within the echo, thereby identifying candidate moving targets according to the power of filtered signal. Then, we adopt root-MUSIC-based algorithms to estimate the angle, range, and radial velocity of these candidate moving targets. Subsequently, we propose a target detection algorithm to reject false targets. Simulation results validate the effectiveness of these proposed methods.

In this paper, we propose a practical integrated sensing and communications (ISAC) framework to sense dynamic targets from clutter environment while ensuring users communications quality. We design multiple communications beams that can communicate with users while one rotating sensing beam can scan entire space, and then we propose the supporting beam-forming design and power allocation strategies for such design. Unlike most existing ISAC studies that ignore the interference of static environmental clutter on target sensing, we construct a mixed sensing channel that includes both static environment and dynamic targets. When base station receives echo signals, we first provide a practical clutter filtering method to filter out static environmental clutter. Then dynamic target detection and angle estimation are realized through angle-Doppler spectrum estimation (ADSE) and joint detection over multiple subcarriers (MSJD), while distance and velocity estimation are realized through the extended subspace algorithm. Simulation results are provided to demonstrate the effectiveness of the proposed scheme.

Using communications signals for dynamic target sensing is an important component of integrated sensing and communications (ISAC). In this paper, we propose to utilize the beam squint effect to realize fast non-cooperative dynamic target sensing in massive multiple input and multiple output (MIMO) Terahertz band communications systems. Specifically, we construct a wideband channel model of the echo signals, and design a beamforming strategy that controls the range of beam squint by adjusting the values of phase shifters and true time delay lines. With this design, beams at different subcarriers can be aligned along different directions in a planned way. Then the received echo signals at different subcarriers will carry target information in different directions, based on which the targets’ angles can be estimated through sophisticatedly designed algorithm. Moreover, we propose a supporting method based on extended array signal estimation, which utilizes the phase changes of different frequency subcarriers within different orthogonal frequency division multiplexing (OFDM) symbols to estimate the distances and velocities of dynamic targets. Interestingly, the proposed sensing scheme only needs to transmit and receive the signals once, which can be termed asYou Only Listen Once(YOLO). Compared with the traditional ISAC methods that require time consuming beam sweeping, the proposed one greatly reduces the sensing overhead. Simulation results are provided to demonstrate the effectiveness of the proposed schemes.

Integrated sensing and communication (ISAC) has been regarded as a key technology for 6G wireless communications, in which large-scale multiple input and multiple output (MIMO) array with higher and wider frequency bands will be adopted. However, recent studies show that the beam squint phenomenon can not be ignored in wideband MIMO system, which generally deteriorates the communications performance. In this paper, we find that with the aid of true-time-delay lines (TTDs), the range and trajectory of the beam squint in near-field communications systems can be freely controlled, and hence it is possible to reversely utilize the beam squint for user localization. We derive the trajectory equation fornear-field beam squint pointsand design a way to control such trajectory. With the proposed design, beamforming from different subcarriers would purposely point to different angles and different distances, such that users from different positions would receive the maximum power at different subcarriers. Hence, one can simply localize multiple users from the beam squint effect in frequency domain, and thus reduce the beam sweeping overhead as compared to the conventional time domain beam search based approach. Furthermore, we utilize the phase difference of the maximum power subcarriers received by the user at different frequencies in several times beam sweeping to obtain a more accurate distance estimation result, ultimately realizing high accuracy and low beam sweeping overhead user localization. Simulation results demonstrate the effectiveness of the proposed schemes.

In this paper, we propose a practical integrated sensing and communications (ISAC) framework to sense dynamic targets from clutter environment while ensuring users communications quality. To implement communications function and sensing function simultaneously, we design multiple communications beams that can communicate with the users as well as one sensing beam that can rotate and scan the entire space. To minimize the interference of sensing beam on existing communications systems, we divide the service area intosensing beam for sensing (S4S) sectorandcommunications beam for sensing (C4S) sector, and provide beamforming design and power allocation optimization strategies for each type sector. Unlike most existing ISAC studies that ignore the interference of static environmental clutter on target sensing, we construct a mixed sensing channel model that includes both static environment and dynamic targets. When base station receives the echo signals, it first filters out the interference from static environmental clutter and extracts the effective dynamic target echoes. Then a complete and practical dynamic target sensing scheme is designed to detect the presence of dynamic targets and to estimate their angles, distances, and velocities. In particular, dynamic target detection and angle estimation are realized through angle-Doppler spectrum estimation (ADSE) and joint detection over multiple subcarriers (MSJD), while distance and velocity estimation are realized through the extended subspace algorithm. Simulation results demonstrate the effectiveness of the proposed scheme and its superiority over the existing methods that ignore environmental clutter.

Integrated sensing and communication (ISAC) has been regarded as a key technology of 6G wireless communications, in which large-scale multiple input and multiple output (MIMO) with higher and wider frequency bands will be adopted. However, recent studies show that the beam squint phenomenon can not be ignored in the wideband MIMO system, which generally deteriorates the communications performance. In this paper, we find that with the aid of the true-time-delay lines (TTDs), the range and trajectory of the beam squint in the near-field communications systems can be freely controlled, and hence it is possible to reversely utilize the beam squint for user localization. We derive the trajectory equation for near-field beam squint points and design a way to control such trajectory. With the proposed design, beamforming from different subcarriers would purposely point to different angles and different distances, such that users from different positions would receive the maximum power at different subcarriers. Hence, one can simply localize multiple users from the beam squint effect in frequency domain, and thus reduce the timing overhead as compared to the conventional beam sweeping approach. Simulation results demonstrate the effectiveness of the proposed scheme.

Activity recognition plays an important role in human-computer interactions. Recently, Channel State Information (CSI), known as a fine-grained information capturing the properties of WiFi signal propagation, has been widely used for activity recognition in a device-free pattern. Since CSI is much sensitive to ambient changes, CSI can be used as fingerprints as human activities. However, existing approaches require tremendous overhead in the model training and suffer from failures due to environmental interferences. In this paper, we propose HAR, a CSI based human activity recognition system. HAR investigates the CSI intra-correlation structure (termed as topics) of different human activities. We leverage an unsupervised machine learning method, namely topic model, to extract action characters. Compared to prior works, HAR only requests minor manual intervention, significantly reducing manpower costs in the model training. We implement HAR using commodity WiFi devices to evaluate its performance under different environment settings. The results show that the extracted features are stable to different devices and volunteers, facilitating HAR to achieving an average matching accuracy, i.e., > 90%.