Carrier phase measurement is a ranging technique that uses the receiver to determine the phase difference between the received signal and the transmitted signal. Carrier phase ranging has a high resolution; thus, it is an important research direction for high precision positioning. It is widely used in global navigation satellite systems (GNSS) systems but is not yet commonly used inwireless orthogonal frequency division multiplex (OFDM) systems. Applying carrier phase technology to OFDM systems can significantly improve positioning accuracy. Like GNSS carrier phase positioning, using the OFDM carrier phase for positioning has the following two problems. First, multipath and non-line-of-sight (NLOS) propagation have severe effects on carrier phase measurements. Secondly, ambiguity resolution is also a primary issue in the carrier phase positioning. This paper presents a ranging scheme based on the carrier phase in a multipath environment. Moreover, an algorithm based on the extended Kalman filter (EKF) is developed for fast integer ambiguity resolution and NLOS error mitigation. The simulation results show that the EKF algorithm proposed in this paper solves the integer ambiguity quickly. Further, the high-resolution carrier phase measurements combined with the accurately estimated integer ambiguity lead to less than 30-centimeter positioning error for 90% of the terminals. In conclusion, the presented methods gain excellent performance, even when NLOS error occur.

In positioning research based on the orthogonal frequency division multiplex (OFDM) system, the observed time difference of arrival positioning method is a widely studied direction. The terminal measures the reference signal sent by the base station to calculate the time of arrival (TOA). Using TOA information, the location of the terminal can be identified. So the accuracy of the positioning is highly dependent on the results of the TOA measurements. Terminals often have difficulty measuring accurate TOA due to multipath channels and occlusion of obstacles. This study proposes two clustering‐based TOA estimation methods. The cluster‐based Max‐peak estimator is used to solve the problem that the energy of the first‐path is not the strongest in the case of multipath. Moreover, a cluster‐based adaptive threshold estimator is used to estimate TOA when the power of the direct path is low. The joint estimator of the two methods is designed to improve estimation accuracy. The proposed method is tested in the OFDM localisation environment, and the results demonstrate that the proposed method has better performance than the conventional way.

Carrier phase positioning technology is widely used in global navigation satellite systems (GNSS) but not applied to wireless orthogonal frequency division multiplex (OFDM) systems. Carrier phase technology has a high resolution, which can improve the positioning accuracy of wireless cellular networks. Applying the carrier phase to a wireless mobile positioning system has some problems, such as continuous phase tracking, accurate integer ambiguity resolution, positioning errors caused by non‐line‐of‐sight (NLOS) and so forth. This paper offers a positioning technique combining the time‐of‐arrival (TOA) and carrier phase to solve the above problems. Based on Bayesian theory, a two‐step position estimator is introduced in our scheme to solve the terminal position. Besides, an NLOS identification and suppression scheme is proposed to enhance the robustness of the algorithm. Experiments show that even in the NLOS environment, the joint positioning algorithm using TOA and carrier phase can effectively improve the positioning accuracy.

Time of Arrival (TOA) is a widely used wireless cellular network ranging technology. How to perform accurate TOA estimation in multi-path and non-line-of-sight (NLOS) environments and then accurately calculating mobile terminal locations are two critical issues in positioning research. For NLOS identification, it can be performed in the TOA measurement part and the position calculation part, respectively. In this paper, for the above two steps, two schemes for mitigating NLOS errors are proposed. First of all, a TOA ranging method based on clustering theory is proposed to solve the problem of line of sight (LOS) path estimation in multi-path channels. We model TOA ranging as a Gaussian mixture model and illustrate how LOS and NLOS can be measured and identified based on non-parametric Bayesian methods when the wireless transmission environment is unknown. Moreover, for NLOS propagation channels, this paper proposes a user location estimator based on the maximum a posteriori (MAP) criterion. Combined with the TOA estimation and user location computation scheme proposed in this paper, the terminal's positioning accuracy is improved. Experiments show that the TOA measurement and localization algorithms presented in this paper have good robustness in complex wireless environments.

Carrier Aggregation (CA) is investigated to address spectrum scarcity in 6G Integrated Sensing and Communication (ISAC) systems. However, inter-band phase offsets impose high complexity on achieving coherent gain across multiple bands. Furthermore, typical CA focuses on multi-frequency-band expansion, while multi-time-duration expansion has received limited attention. To address the above issues, this paper proposes a novel high-accuracy Multi-Band Joint Carrier Phase Sensing (MB-JCPS) framework that utilizes carrier phase information from multiple time-frequency bands to improve range and velocity sensing accuracy. A carrier phase extraction method is introduced based on oversampling Range-Doppler phase spectrum. The impact of non-ideal factors—including time, frequency, and phase offsets, as well as phase noise—on carrier phase is modelled, analyzed, and eliminated via an inter-path difference method. A Real-valued Multi-Carrier Ambiguity Resolution (RMCAR) algorithm is designed to resolve integer ambiguities in the range and velocity carrier phase models. The time-frequency selection problem is formulated as a constrained Rayleigh-quotient optimization problem and solved via successive convex approximation. The Cramér-Rao lower bound (CRLB) for the range and velocity estimation under phase offsets is derived, demonstrating the performance gain of CA in both frequency and time domains. Numerical simulations show that the proposed method achieves higher sensing accuracy with lower complexity than benchmark algorithms in 3GPP Uma-AV environments.