Integrated sensing and communication (ISAC) utilizes existing communication devices for sensing and is emerging as a key technology in wireless networks, particularly for mobile applications such as vehicular networks. Most systems rely on path parameters, such as direction of arrival (DoA), for accurate sensing. However, commercial communication devices often adopt wider antenna spacings to enhance communication performance, which can lead to grating lobes and reduce DoA accuracy in mobile environments. To address this issue, we investigate the variation of grating lobes across OFDM subcarrier frequencies and propose a differential frequency array (DFA) model to suppress grating lobes through subcarrier cooperation. Furthermore, we develop an off-grid DoA estimation algorithm based on sparse Bayesian learning, tailored to the DFA structure. Simulation results show that the proposed method effectively suppresses grating lobes and significantly improves DoA estimation accuracy. Prototype experiments based on 5G picocells further confirm its feasibility in practical mobile ISAC scenarios.

With the proposal and development of integrated sensing and communication (ISAC), utilizing existing communications devices to realize the function of sensing is the current hot research. In existing research, the system is usually implemented based on path parameters, i.e., parameters such as direction of arrival (DoA). Obtaining accurate angle information requires the assumption that antennas are equally spaced and at a standard half wavelength. However, the antenna spacing of commercial-off-the-shelf (COTS) communication devices is often arranged in communication in a manner that is wider than half-wavelength, which may produce grating lobes that result in incorrect angle estimates. Therefore, to resolve this challenge, this paper first deeply analyzes the problem of incorrect angle estimates due to grating lobes. Then, we propose a phase projection-based ambiguity eliminate model and a power-based parameter estimate algorithm. Finally, simulation experiments are conducted to verify the proposed algorithm performance, and a system prototype is built for field trial. The experimental results show that using a 4 -antenna array, the proposed algorithm achieved median errors of$6.12^{\circ}, 4.18^{\circ}$, and 2.53° for antenna spacing of$0.6,0.8$, and 1 times the wavelength, respectively. In addition, we use localization as a case study to demonstrate the promising work done in this paper for ISAC.

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.

Recent research has increasingly focused on utilizing radio frequency (RF) signals for indoor sensing. Traditionally, this involves employing antenna arrays configured across multiple RF links to capture key channel parameters, such as Direction of Arrival (DoA). However, this architecture requires each sensor to have an independent RF link, which increases complexity and cost. Additionally, deploying sensing systems necessitates pre-calibration of the RF links, further laboring the deployment. To address these challenges, we design a switched antenna array (SAA) that can time-division activate each antenna within the coherence time on a single RF link, thus simulating a multi-RF links platform for accurate DoA estimation. Subsequently, we develop a switching strategy and introduce a random forest-based matching algorithm to tackle the issue that signals from different antennas exported from the single RF link cannot be differentiated due to the non-cooperative mode between the SAA and receiver. Additionally, we compensate for the carrier frequency offset caused by asynchrony between transceivers, which affects DoA estimation in the SAA system. However, residuals still remain and can be considered as an enhancement to the noise. We introduce a DoA estimation algorithm based on sparse Bayesian learning that treats noise as its hyperparameter, enhancing the robustness against noise and improving the accuracy of DoA estimation. We build prototype systems and conduct field trials in real-world environments. The experimental results show that our system achieves 4.32° angle of arrival and 4.48° elevation of arrival median estimation errors by utilizing only a single RF link.

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.