Precise distance measurement using wireless signals is essential for applications such as structural health monitoring, autonomous navigation, and asset tracking. Traditional high-accuracy ranging techniques typically rely on large signal bandwidths, often several hundred megahertz, to resolve carrier-phase ambiguities and achieve centimeter-level precision. However, this results in increased system complexity and high spectral resource consumption. To address these challenges, this paper proposes an optimized dual-differential carrier phase measurement method that achieves centimeter-level accuracy using only a few of discrete frequency bands, each occupying a bandwidth of approximately 20 MHz. Carrier-phase measurements at the selected frequencies are first obtained and wrapped within the (−π,+π) range. Dual-differential operations are then applied between adjacent positions to eliminate systematic phase biases and improve estimation robustness. A weighted least-squares estimator provides initial float ambiguity estimates, which are subsequently resolved using the Modified Least-squares Ambiguity decorrelation adjustment (MLAMBDA) algorithm. Compared with conventional ranging methods, the proposed approach significantly reduces bandwidth requirements while maintaining sub-5 cm relative positioning accuracy. It is particularly well-suited for deployment in bandwidth-constrained Integrated Sensing and Communication (ISAC) applications.

Sensing-assisted communication plays a critical role in integrated sensing and communication (ISAC) systems, where environmental awareness can significantly enhance communication performance. In this paper, we propose a novel indoor environment reconstruction method for Sub-6GHz systems. The approach first leverages multiband channel state information (CSI) to enable accurate localization of user equipment (UE). Based on the estimated UE positions and multiband CSI, we further estimate the distances of multipath components (MPCs). These distances, combined with UE positions, are used to infer the positions of virtual-access points (VAPs), which serve as key intermediaries for identifying environmental reflectors. By aggregating information across multiple UEs, we compute a reflector point cloud that supports high-fidelity reconstruction of the indoor environment. Our proposed algorithm is validated through simulations, achieving a corner point localization error of 0.03 meters and an intersection over union (IoU) of 0.98. Experimental results demonstrate the effectiveness of the method in reconstructing typical rectangular indoor layouts using only Sub-6GHz CSI.

Integrated Sensing and Communication (ISAC) is expected to be one of the key technologies for next-generation communication systems, with extensive application prospects in fields such as low-altitude economy, security surveillance, and smart cities. As a significant application of ISAC, Inverse Synthetic Aperture Radar (ISAR) technology offers notable advantages in detecting and imaging moving objects. However, current ISAR techniques, which primarily rely on processing along dimensions such as time-Doppler, face challenges such as limited detection accuracy and insufficient extensibility. Inspired by ISAR imaging principles, based on the ISAC echo signal model, a far-field wavenumber domain ISAR imaging method based on minimum image entropy is proposed, which realizes multiframe imaging of non-cooperative moving targets. Moreover, an ISAC system is implemented based on the 5 G communication millimeter-wave platform, and experimental results are presented to validate the performance and feasibility of the proposed system.

Owing to the proliferation of Wi-Fi devices, the employment of Wi-Fi for indoor localization has emerged as the predominant trend within the realm of indoor localization technologies. In this paper, we harness multipath assistance for the purpose of localizing indoor terminals and generating maps. Precisely, Uniform Circular Array (UCA) is deployed at both the Access Point (AP) and the terminal to augment the power of reflection paths. By constructing a triangular geometric configuration with the Angle of Arrival (AoA), Angle of Departure (AoD), and Time of Flight (ToF) of the reflection path as well as the direct path, the localization of the terminal can be accomplished. Subsequently, the localization results are optimized through Extended Kalman Filtering (EKF) tracking. Moreover, by integrating the optimized positional information, the locations of reflectors can be estimated. The positions of these scattering points mirror diverse components of the indoor scene. As the terminal device undergoes movement, more scene information within the indoor environment is acquired. Ultimately, by obtaining a sufficient number of portions of the locations of all scenes and interconnecting them, the generation of an indoor map is achieved. We simulated the indoor environment using the Wireless Insite software and extracted simulation data to verify the proposed system. The outcomes demonstrate that the system has attained a situation where 90% of the localization errors are within 1 meter and the accuracy of the generated map has reached 94.6%.

The Global Navigation Satellite System (GNSS) offers advantages such as all-weather operability and extensive spatial coverage. Utilizing GNSS-reflected signals for ground synthetic aperture radar (SAR) imaging presents a cost-effective and widely applicable technical solution. However, the small bandwidth of GNSS signals results in inadequate resolution, posing challenges for practical applications. To address this issue, an SAR fusion imaging system model is established, consisting of multiple satellites and a single ground-fixed GNSS receiver. The relationship between the ambiguity function of GNSS signals and Fisher information is investigated, allowing for the derivation of the Cramer-Rao lower bound (CRLB) for the system, which is primarily influenced by the geometrical configuration of the bistatic setup. Subsequently, the CRLB expression is employed to identify the optimal resolution direction of the satellites for ground targets, and a dual-satellite SAR imaging fusion method based on optimal matching is proposed. The effectiveness of this approach is validated through simulations and real experimental data, demonstrating that the theoretically optimal resolution direction predicted by the CRLB aligns with the actual imaging results. Furthermore, the proposed method achieves higher resolution compared to traditional techniques, with the fused imaging results demonstrating a clear correspondence with the satellite imagery of the scene map.

Ranging and localization are crucial elements in the field of sensing applications, with range-based localization techniques being a prevalent approach. However, traditional range-based localization methods are impacted by ranging accuracy, furthermore, ranging accuracy is related to bandwidth. In this paper, we achieve high resolution by splicing the channel state information (CSI) of multiple non-adjacent frequency bands. However, the multiband CSI of GHz-level sub-band spacing leads to ambiguous delay estimation. To address this issue, we first explain the reason for the delay ambiguity caused by multiband CSI. Then, a set of candidate delays is constructed using the multiband CSI estimation delay. We propose a lo-calization algorithm that utilizes the set of candidate delays to achieve accurate localization. In turn, we use the estimated accurate location for accurate ranging. Finally, we validate our proposed localization and ranging algorithm through simulation experiments, achieving a localization error of 0.02m in 90% of cases, and a ranging accuracy of 0.01m in 94% of cases. Experimental results demonstrate that the algorithm proposed in this paper effectively exploits the advantages of multiband CSI for enhanced ranging and localization.

When new unknown samples are captured continually in the open-world environment, the concept diversity accumulation of existing classes and the identification/creation of new concept classes should be considered simultaneously. Since the initial training set of existent classes may be under-prepared, adhering to immediate decisions will inevitably lead to reduced open set recognition performance and higher costs of labeling/updating. Inspired by quantitative indicators in predictive reliability assessment and semi-supervised/active learning, the inclusive-consistency-based quantitative decision-making framework (ICQdm) is proposed for incremental automatic target recognition (ATR) to evaluate the identifiability and typicality of new unknown samples, which could give the decision-making guide of recognition and updating. For recognition decision-making, the first consistency indicator calculates the reliability of the unknown sample being included by one specific training class. The test samples with low reliability should be the wrongly classified samples and new unknown classes’ samples, which are difficult to be labeled by recognition models themselves. For updating decision-making, the second consistency indicator is designed to be the sample distribution density under the inclusive constraint, which could highlight the dense sample distributions of new unknown samples outside the known training distribution. Experiments verify that the proposed ICQdm outperforms other comparison methods on the open set recognition reliability evaluation and labeling/updating efficiency.

In recent years, the research on indoor 3D localization has attracted a lot of attention in the applications of smart home, smart factory and other fields. However, as it is difficult for the hardware to meet the Nyquist sampling rate of half wavelength in the 5G/6G cases, using conventional subspace methods will lead serious pseudo peaks in parameter estimation, resulting in a sharp decline in estimation accuracy. In order to solve this problem, we propose a sparse parameter estimation and 3D localization method based on orthogonal matching pursuit algorithm (OMP). Firstly, we design an L-shaped sparse antenna to construct a sparse array manifold. Based on 2D angle of arrival (AoA) and time of flight (ToF), we construct a 3D parameter estimation model and a 3D localization method based on direct path. Then we convert the 3D parameter coupling estimation into two 2D parameter coupling estimation. Finally, we verify the feasibility of the proposed method through Wireless Insite simulation platform.

Synthetic aperture radar (SAR) human-imaging technology has been widely used in the fields of security screening and activity recognition. However, most of the existing methods aim at stationary human targets, resulting in severe restrictions on their potential applications. In this article, an SAR imaging method for walking human is proposed with short aperture terahertz (THz) radar. This method is based on a modified wavenumber domain approximate algorithm ($\text{m}{\omega }$ka), whose interpolation mapping is modified according to the approximation of region of interest (RoI). Because the nonrigid motion phase error caused by the walking human would lead to severe deformation and blur in imaging results, a compensation processing is essential. To figure it out, the fractional Fourier transform combined with the maximum and minimum generalized likelihood ratio test (FrFT-mmGLRT) is devised in this article. Within a short aperture time, the nonrigid motion of walking human can be approximately assumed as a superposition of the rigid movements of different human body parts. Particularly, by taking advantage of the superposition property of FrFT, the motion phase errors of different human body parts are distinguished and estimated by jointly searching for the peaks of FrFT energy spectrum. Furthermore, the mmGLRT-based composite imaging technique is utilized to obtain a whole body result from the compensated results of different body parts. Finally, numerical simulation and real experiments are conducted to verify the feasibility and capability of the proposed method for walking human SAR imaging.

We propose a fast algorithm to circular synthetic aperture radar (CSAR) imaging for arbitrary region of interest (ROI). This algorithm is an extension of traditional CSAR polar format algorithm (PFA). Firstly, to avoid severe defocus and distortion of off-centered ROI image caused by traditional CSAR PFA plane-wave approximation error, a novel PFA matched filter kernel is derived with ROI characteristic reference function. Then, according to the wavenumber support domain approximation of ROI, a new interpolation mapping relation is defined to obtain uniform rectilinear wavenumber domain data from polar format echo data. With the rectilinear data, the imaging result of ROI can be obtained by two dimensional inverse fast Fourier transform.

Due to the merit of low cost and flexible flight, small drones are showing tremendous application prospect in both military and civil fields. Aiming at public area monitoring, a synthetic aperture radar (SAR) imaging model using drone fleet borne radars is proposed. In this model, the radars illuminate the imaging area while the drones fly forward straightly. To make the implementation easier, we assume that each radar transmits signal and receives echo independently. Furthermore, the frequency-division (FD) signals are adopted for different radars to avoid interference. With broadband signal, synthetic aperture via the motion of the drones and linear array formed by the multiple radars, three-dimensional (3D) imaging can be realized. Numerical simulations are conducted to verify the effectiveness of the proposed model and the 3D image formation method.