The emergence of 6G space-air-ground integrated networks imposes stringent requirements on resource utilization and spectral efficiency, positioning integrated communication and navigation (ICAN) as a promising enabling technology. However, existing ICAN architectures lack a unified theoretical foundation. Communication is typically characterized by the Shannon capacity, whereas navigation is evaluated via the Cramér-Rao lower bound. As a result, the two functions are still treated independently, leading to suboptimal system design and resource allocation. To bridge this gap, this paper proposes a unified framework that quantifies positioning performance through a positioning information rate (PIR) derived from differential entropy and the Fisher information matrix. This formulation enables ICAN performance to be assessed in a common information-theoretic domain. Under this paradigm, we conduct a comparative study of representative ICAN schemes and develop a corresponding resource allocation optimization framework. Simulation results demonstrate that the proposed PIR metric is consistent with conventional metrics while accurately capturing system performance under a unified one-dimensional metric, thereby facilitating direct and simultaneous optimization of communication and navigation functions without separate performance evaluations.

In the context of high-dynamic satellite scenarios, the orthogonal time frequency space (OTFS) modulation demonstrates superior performance compared to conventional waveforms. Furthermore, the channel estimation results of OTFS can be directly utilized for navigation. Therefore, OTFS is considered as a promising waveform for blue the 6G satellite integrated communication and navigation (ICAN) internet. Traditional OTFS is limited to integer channel estimation, and conventional methods incur computational overhead to improve estimation accuracy. However, the estimation accuracy is gradually approaching its theoretical limits, making further breakthroughs difficult. To address these challenges, a high-resolution channel estimation method based on Artificial Perturbation (AP)-OTFS is proposed, which treats the receiver DD-grid detector as a rounding quantiser whose resolution is enhanced by zero-mean dithering and achieves estimation performance close to Cram & eacute;r-Rao lower bound. Based on this, we derive the analytical solution for the constraints of unbiased estimation and the blue minimum number of accumulation pilots, providing guidance for ICAN system. In addition, we conduct detailed analysis and experiments of various AP distributions. Results indicate that uniform AP-OTFS achieves the highest accumulation efficiency, which enhances delay resolution by 2-3 orders and improves pilot efficiency by approximately 5-10 dB compared to the no-perturbation case.

Integrated sensing and communications (ISAC) has emerged as one of the key technologies for 6G communication networks. However, achieving high-precision sensing is hindered by multipath reflections in urban environments. Traditional methods require iterative estimation and separation of multipath signals, leading to high computational cost and poor scalability as the number of reflectors increases. To address this issue, we propose a rapid perturbation-based multipath error cancellation (RP-MEC) method. The core idea is to superimpose a rapid perturbation at the receiver, inducing high-frequency oscillations in the multipath error for self-cancellation. Furthermore, this article provides analytical expressions for the multipath error of orthogonal frequency-division multiplexing signals both before and after uniform perturbation, based on which the minimum required perturbation amplitude is determined. Experimental results validate the theoretical analysis and show that millimeter-to-centimeter-scale perturbations, whose physical scale depends on waveform parameters and path geometry, can reduce the multipath error envelope by 98.7% while maintaining subsecond update rates, thereby meeting the positioning requirements of mobile ISAC users. Compared with conventional iterative techniques, the proposed RP-MEC method not only significantly reduces computational complexity but also substantially enhances environmental robustness, making it a viable solution for high-accuracy sensing and localization in future 6G ISAC systems.

The eddy current displacement sensors (ECDSs) are widely used in precision industrial applications, but they are susceptible to temperature drift under varying temperature conditions, which limits their measurement accuracy. This study proposes a novel temperature compensation method aimed at improving the performance of (ECDS) across a broad temperature range. This method utilizes phase characteristics and dual ac bridge technology to effectively separate the temperature drift of the probe coil from the target displacement changes. In this way, the temperature stability of large-range ECDS is significantly enhanced, and drift caused by temperature changes in the probe coil is further eliminated through temperature calibration. Laboratory tests have shown that this method effectively reduces temperature-related displacement drift from 2093 to 132 ppm/degrees C within a temperature range of 20 degrees C-100 degrees C. The results indicate that the proposed method significantly improves the measurement accuracy and reliability of ECDS in large temperature difference environments, providing important technical support for the precision measurement field.

Due to the lack of a precise mathematical model for the parasitic capacitance of air-core coils, it has been challenging to develop an accurate overall model for eddy current displacement sensors, thereby hindering comprehensive optimization of sensor dimensions, sensitivity, and anti-interference capabilities. To address this problem, this paper first derives and establishes an accurate mathematical model of the parasitic capacitance in air-core coils, and then integrates it with coil inductance and target coupling characteristics to form a complete modeling framework for eddy current sensors. Experimental results show that the proposed model achieves prediction errors of only 0.90 % and 3.50 % for inductance and capacitance, respectively, closely matching measured values. Guided by this model, a multi-objective optimization algorithm is employed to effectively balance structural dimensions, sensitivity, and anti-interference performance. The optimized sensor achieves a detection signal linearity of 0.05 %, demonstrating significantly enhanced performance. This research provides a solid theoretical basis and methodological support for improving eddy current sensor performance in complex application environments.

Aiming to address errors in the estimation of the position and attitude of an unmanned vessel, especially during vibration, where the rapid loss of feature point information hinders continuous attitude estimation and global trajectory mapping, this paper improves the monocular ORB-SLAM framework based on the characteristics of the marine environment. In general, we extract the location area of the artificial sea target in the video, build a virtual feature set for it, and filter the background features. When shaking occurs, GNSS information is combined and the target feature set is used to complete the map reconstruction task. Specifically, firstly, the sea target area of interest is detected by YOLOv5, and the feature extraction and matching method is optimized in the front-end tracking stage to adapt to the sea environment. In the key frame selection and local map optimization stage, the characteristics of the feature set are improved to further improve the positioning accuracy, to provide more accurate position and attitude information about the unmanned platform. We use GNSS information to provide the scale and world coordinates for the map. Finally, the target distance is measured by the beam ranging method. In this paper, marine unmanned platform data, GNSS, and AIS position data are autonomously collected, and experiments are carried out using the proposed marine ranging system. Experimental results show that the maximum measurement error of this method is 9.2%, and the average error is 4.7%.

In this study, several 3-dimensional (3-D) parameter estimation and localization algorithms for wireless near-field (NF) sources are proposed employing the uniform circular array (UCA) structure. In the single-base-station case, the algebraic relation is demonstrated between the azimuth angle under the far-field (FF) assumption and the actual NF source firstly. Secondly, two groups of antenna pairs are selected with distances less than half the wavelength, which are called short baselines in the interferometer method. The foregoing short-baseline method is qualified to localize an NF source. In addition, a long-baseline method is also proposed with further research. Two groups of antenna pairs with distances greater than half the wavelength are selected as two long baselines. In the multiple-base-stations case, another two novel algorithms are also proposed. The first one is the centroid algorithm, which is based on the centroid calculation of three estimated source locations. And the second one is the perpendicular foot algorithm, which takes the perpendicular foot within three estimated source locations as the final positioning location. Simulation results illustrate that the proposed algorithms can achieve higher localization accuracy than the conventional 3-D Root MUSIC method. Moreover, the long-baseline method performs better than the short-baseline method. And it is also shown that the proposed perpendicular foot algorithm shows better performance than the proposed centroid algorithm.

Applications of precise point positioning (PPP) are limited by PPP’s long convergence time. One effective way to shorten the convergence time is to apply ionospheric constraints because of the external ionospheric information. The conventional way to do this is to apply high precision but biased ionospheric corrections. The limitations of the method are that all ionospheric constraints must be derived from the same set of reference stations to have the same data. An approach based on single differences between satellite ionospheric constraints (SDBS-IONO) is developed to address the data issue due to having no common satellite visibility. The proposed method is more flexible and scalable in terms of adding ionospheric constraints. Based on a network of about 130 stations, we validated the proposed SDBS-ION method and compared it to the conventional method. Our results confirm that the ionospheric constraints enhance the PPP convergence time significantly depending on the accuracy of ionospheric constraints. Finally, we discuss crucial factors regarding how long and accurate the effectiveness of ionospheric constraints are in reducing PPP convergence time.

In this paper, we present an Adaptive Feature Optimization Strategy as a novel frontend feature selection strategy which can be applied in the most direct method-based Simultaneous Localization And Mapping (SLAM) system for high accuracy, efficiency and robustness compared to the original SLAM system. It chooses adaptively the ORB points or Direct Sparse Odometry (DSO)-based points for tracking depending on in which scenario the cameras situate. Our evaluation on public datasets presents that the SLAM system integrated with our strategy outperforms the state-of-the-art which significantly reduces the processing time for each frame while retains the tracking accuracy.

The minimum variance distortionless response (MVDR) beamforming technique and space-time adaptive processing (STAP) have been playing important roles in interference suppression of globe navigation satellite system (GNSS) receiver. However, the demand for conformal arrays is increasing these days and its characteristics will vitiate the traditional MVDR method. And on the other side, traditional MVDR based on STAP will inevitably distort the Beidou signal, which is unacceptable in high precision GNSS applications. To address the above issues, first, a conformal array signal processing model is proposed; and based on that, a distortionless MVDR method is proposed in this study. The simulation results show that the proposed method can not only suppress the interference better than the traditional MVDR, but also guarantee the Beidou signal to be undistorted.

Black flights of unauthorized unmanned aerial vehicle (UAV) have occurred frequently in recent years, threatening public safety. UAV mainly uses GNSS (Global Navigation Satellite System)/INS (Inertial Navigation System) integrated navigation for positioning. Due to the weakness of GNSS signal, spoofing UAV is possible. Some scholars have conducted researches on the spoofing of targets with integrated navigation, but these studies all use radar to obtain the target’s position, speed and acceleration in real time. This method has high technical complexity and expensive cost. To solve this problem, this research proposes a trap spoofing algorithm for target using GNSS/INS-integrated navigation system based on prior error compensation. By setting a spoofing trap in a certain area of the target path, when the target moves within the trap range, it will be trapped in designated areas by spoofing signals. Finally, this paper verifies the feasibility of the trap spoofing algorithm through simulation experiments.

In order to solve the problems of low monitoring accuracy, high false alarm rate, untimely pre-warning and high equipment failure rate of the transmission line tower rod oblique posture monitoring system, a transmission line tower rod oblique monitoring system based on reverse network RTK was developed. The system integrated the Beidou multi-source sensor, network communication and prediction and forewarning, used the height and millimeter level position information of the transmission line tower to measure and calculate oblique posture information of the tower pole, and made risk assessments, so as to realize the control of these information. The actual tests results showed that the monitoring system could accurately obtain the oblique posture information of the transmission line tower pole, and the pre-alarming monitoring results met the requirements of intelligent, accurate and digital management of transmission line tower pole.

全球卫星导航系统(GNSS)多径信号广泛存在于城市峡谷等复杂导航定位场景中.多径信号在干扰GNSS接收机并造成系统定位精度下降的同时,也为接收机提供了周边反射面环境信息.在码相位延迟幅度联合跟踪算法(CADLL)实现GNSS多径信号感知和特征参数提取的基础上,设计实现了基于粒子滤波的反射面参数估计算法.该算法可以在GNSS多径环境中增强接收机的环境感知能力,相关环境信息可应用于场景感知、避障、路径规划和定位增强等领域.静态环境下进行GNSS多径信号采集和算法测试,实验结果表明该算法能够有效估计反射面位置参数,反射面方位角均方根误差(RMSE)小于10°,反射面俯仰角RMSE小于5°,反射面距离RMSE小于10 m.

In urban canyon environments, the reception of GNSS multipath signals will cause the degradation of positioning precision of a GNSS receiver, and sometimes will lead to hundreds of meters positioning error. However, multipath signals, which are generated by reflections of the satellite line-of-sight signal, contain information about positions and velocities of the reflection objects. Therefore, they give means for a receiver to sense surrounding objects and environments. In this paper, an algorithm for he reflection object sensing and localization with GNSS multipath signals is proposed. The results of experiments show that this algorithm is able to estimate the distance, azimuth, and elevation of the reflection object with respect to the receiver with a root mean squared error of 10 m, 10°, and 5°, respectively. This method enables a GNSS receiver with the capability of environment sensing, avoidance of obstacles, and even positioning augmentation apart from its original positioning and navigation ability.

The autonomous navigation system based on multi-sensor fusion has many challenges in the measurement such as the sensor configuration, the platform construction and calibration, ground truth value acquisition, test scenarios reproducing, and the control of environmental variables such as weather, illumination. Traditional GNSS(Global Navigation Satellite Systems) simulations usually use simulators. Digital simulation is usually used to simulate the integration of GNSS and other sensors, which is difficult to reflect the real scenarios. Therefore, a high realistic simulation method for multi-sensor fusion positioning based on GNSS hardware-in-loop is proposed. Based on Unreal Engine, the high realistic camera simulation model and inertial sensor model are designed. The real scene intermediate frequency signal acquisition and playback system is designed and the GNSS hardware-in-loop simulation method is constructed. The experimental results demonstrate that the geometric consistency error of the virtual camera reaches the sub-pixel level when the sensor is simulated by the proposed method, and the consistency between GNSS hardware-in-the-loop simulation and the actual measurements is more than 90%. The simulation tests under various operating conditions show that the proposed method is fast and reproducible in the verification of navigation and positioning algorithm.

In the equatorial region, deep amplitude fading in global positioning system (GPS) signals frequently occurs during the strong ionospheric scintillation, it can lead to the loss of lock in GPS carrier tracking loops, and result in increased positioning error and even navigation interruption. The relationships between amplitude scintillation indices and detrended carrier frequency are investigated, based on GPS L1 C/A signals during the last peak of the solar cycle at the low latitude site of São José dos Campos, Brazil (23.2S, 45.9W) from 2013 to 2015. Corresponding mathematic model of the probability distribution function is built for the first time to provide statistical analysis on the above relationships. The results show that the standard carrier frequencies reveal an almost linear relation with the amplitude scintillation indices. Moreover, the frequency widths of de-trended frequency are proportional to levels of amplitude scintillation when the value of the peak probability is lower than the corresponding boundary. A conclusion can be drawn that different levels of amplitude scintillation will influence the fluctuation of the carrier frequency. The analysis will provide useful guidance to set the receiver's bandwidth with respect to the different scintillation levels and design the advanced tracking algorithms to improve the robustness and precision of the GPS receiver.

GNSS multipath channel modeling is important for signal simulation and error mitigation. Unfortunately, there have not been sufficient studies on channel statistical models for land vehicles in Eastern Asian cities. Therefore, the authors in this paper conducted an extensive on-field data collection in Shanghai-Lujiazui area to construct the channel models for GPS L1CA and BDS B1I signals. In addition, a systematic method is designed to solve the challenge of correctly estimating multipath echo parameters in a dynamic channel environment. The models of the delay, power, Doppler fading frequency, and lifetime for NLOS signals are established in this paper. Furthermore, factors that impact channel models such as satellite elevation and vehicle velocity are analyzed. These NLOS statistical models are developed for the first time to the authors' knowledge. They will be useful to simulate highly fidelity urban channels for advanced navigation algorithms

Based on the joint phase interferometer and multiple signal classification (MUSIC) algorithm, two high-performance 3-dimensional (3-D) parameters estimation and localization algorithms for near field sources (NFS) are proposed in this study employing the uniform circular array (UCA). Firstly, the algebraic relation is demonstrated between the azimuth angle under the far-field assumption and the actual near-field source. Using the relation, we estimate the far-field azimuth angle using the 2-dimensional (2-D) MUSIC method and then obtain the estimation result of the azimuth angle for NFS. Secondly, we select two groups of antenna pairs whose distances are less than half of the wavelength, which are called short baselines in the interferometer method. Using a phase detector, the phase differences of the two baselines are measured, which have no phase ambiguity. Then it can estimate the elevation angle and range depending on the algebraic relations between the elevation angle, range and phase differences. The foregoing short-baseline method is qualified to localize a NF source. In addition, in order to get better performance, we propose a long-baseline method with further research. Two groups of antenna pairs whose distances are greater than half of the wavelength are selected as two long baselines. By solving the phase ambiguity, the elevation angle and range can finally be estimated with high precision. The simulation results illustrate that the proposed algorithms can achieve higher localization accuracy than the conventional 3-D MUSIC method. Moreover, the long-baseline method performs better than the short-baseline method. And it is also presented that the computational complexity of the proposed methods can be reduced owing to the fact that the 3-D spectrum peak searching process in the conventional method is not required any more.

With the development of the Internet of Things, a large number of connection requirements for sensing and control are generated. However, in wireless positioning, Narrowband Internet of Things (NB-IoT) has poor positioning accuracy which takes the cell-ID positioning method. The further integration of 5G and NB-IoT networks is expected to effectively improve the positioning accuracy of NB-IoT networks. Therefore, the high-precision positioning algorithm for researching converged networks has broad application prospects and academic significance. In order to improve the positioning accuracy of NB-IoT, based on the 5G and NB-IoT heterogeneous positioning framework, we propose to introduce a number of cluster nodes, which have the function of communicating with 5G and NB-IoT networks simultaneously. The signal bandwidth in NB-IoT network is narrow and clock synchronization is difficult to accomplish, so only DOA (Direction of Arrival) and RSSI principles can be considered. In this paper, we firstly use 5G to perform high-precision positioning of cluster nodes according to the principles of TDOA (Time Difference of Arrival). Based on the solution space (x ± εx, y ± εy), the NB-IoT terminal is located by the cluster nodes according to the DOA and RSSI fusion method. This method helps reduce the matching time and improve the accuracy of single DOA/RSSI positioning method. Meanwhile, in the case of allowing cluster node errors, higher precision NB-IoT network positioning results can be obtained. Compared to a single NB-IoT network positioning, the final positioning accuracy of NB-IoT terminal can be improved by 80–90