Integrating Long-Term Evolution (LTE) signal which are readily available through smartphones and stable in indoor environments with Pedestrian Dead Reckoning (PDR) provides a low-cost and accurate solution for indoor positioning. Existing LTE-based indoor positioning methods typically rely on fingerprinting, where databases are built using offline-collected data or regression models. These approaches, however, often ignore the influence of building structure on signal propagation, which limits database accuracy and degrades positioning performance. In fact, some studies have applied propagation models to improve database efficiency and accuracy. However, they primarily focus on signals from indoor stations such as Wi-Fi, and generally use fixed model parameters, making them sensitive to environmental changes and device heterogeneity. To overcome these limitations, this paper introduces an indoor positioning method that integrates LTE signal with PDR, aided by real-time inversion of propagation model parameters. Firstly, the spatial distribution characteristics of LTE signal are analyzed, then a suitable propagation model is selected. Secondly, the model parameters are inversely estimated in real time to enhance environmental and device adaptability. The propagation model is then applied for database construction, and position observations are extracted through fingerprint matching. Meanwhile, the proximity method is introduced to maintain positioning performance as well as support parameter inversion with reliable position references. Finally, the observations extracted from LTE signal are combined with PDR to achieve positioning. Experimental results demonstrate that the proposed method enhances the adaptability of the fingerprint databases to device and environmental changes without requiring prior data, thereby improving positioning performance.

The demand for indoor location services is growing rapidly. Various indoor positioning techniques have been proposed. As a supplement, public FM radio signal based methods can provide a possible solution for infrastructure-free indoor positioning which benefits from its wide coverage. However, existing research mainly focuses on algorithm development, with a notable absence of theoretical performance analysis. To bridge this gap, this paper firstly analyzed the FM signal indoor positioning performance by deriving the Fisher information. Our findings indicate that: FM RSS theoretically has difficulties in two-dimensional (2D) indoor positioning and exhibits directional differences in one-dimensional (1D) performance. Thus, when both directions are used for localization simultaneously, the direction with blurred features will significantly affect the overall accuracy of 2D positioning. Besides, there exists a problem of finding effective FM positioning region in large-scale scenes. To address these issues: First, the method for finding positioning region within effective FM signal environmental field with PDR aided is designed. Second, focusing on the problem of 2D positioning, a new strategy is suggested that takes the 1D characteristics of FM RSS by employing the split-direction accuracy estimation and parameter adjustment. To begin with, three improved lightweight accuracy estimation indicators are adopted to assess the FM signal positioning performance. Then, the split-direction adaptive FM fingerprinting method is proposed, which integrates the FM RSS signal and PDR-constrained space metrics by using the accuracy estimation results in different directions. Practical experiments are conducted in three large-scale scenes, and the results demonstrated an improvement of 86.6% and 33.4% compared to traditional 2D fingerprinting and fusion methods, respectively.

The Position information is essential for large-scale Internet of Things (IoT) devices and services. Multipath and non-line of sight (NLOS) effects introduce additional delays in pseudorange measurements in urban areas. It is one of the main unmodeled errors in Global Navigation Satellite Systems (GNSS). To mitigate interference, various techniques have been developed, including antenna design and sensor fusion. However, traditional estimation approaches often produce biased estimates under the additional path delays. To improve estimation accuracy and robustness, we present an Observation Space Representation Refinement (OSRR) algorithm. The initial position is estimated by least squares without the additional path error. Then, the multipath projection method is used to get possible compensation in pseudorange measurements. Subsequently, the Moving Horizontal Estimation (MHE) is leveraged to get the position with corrected observation space. Field experiments demonstrate that the proposed OSRR algorithm significantly reduces the impact of interference on positioning accuracy. There is no empirical constraint to easily adapt to real-time static and kinematic GNSS pseudorange positioning with unilateral obstruction scenarios.

Autonomous pedestrian localization involving complex motion modes is important for applications, such as firefighting and counter-terrorism in global navigation satellite system denied environments. Inertial navigation based on Zero Velocity Update is a crucial method, and adaptive zero velocity detector (AZVD) is a key part to improve performance. However, current AZVD still face challenges in adaptability to different motion speeds and complex motion modes (such as crawling), as well as high-computational load. To address these issues, this article proposes an AZVD that represents the generalized likelihood ratio test statistics within a time window as a ratio relative to the minimum statistic and sets thresholds based on these ratios (relative statistics). First, we theoretically analyze the method based on relative statistics. To ensure that the time window for data segmentation includes zero-velocity phases, and the minimum statistic corresponds to the zero-velocity phase, we divide the algorithm into two steps: 1) design a gait cycle (GC) detection suitable for single support motions and segment the data according to the GC. For motions where the GC cannot be detected, a designed time window is used for data segmentation and 2) for motions where steps are detectable, we restrict the zero-velocity phase to a specific range within the GC; for motions where the GC are undetectable, no restriction is imposed. Then, the threshold is set based on the relative statistics. Finally, through experiments involving multiple individuals and various motion speeds and motion modes, the adaptability of the proposed algorithm is validated.

Among various indoor positioning methods, fingerprinting-based localization technology which utilizes ubiquitous radio signals has been widely studied due to advantages, such as low cost and high accuracy. To tackle the issues of time-consuming fingerprint database construction and enhance its applicability in unknown environments, radio signal simultaneous localization and mapping (Radio-SLAM) technology is utilized to automate the construction process of fingerprint database (also known as radio map). Nevertheless, existing Radio-SLAM methods mainly employ regression models for radio map construction, neglecting the influence of building structures on signal propagation, which leads to limited applicability in nonrepetitive path scenarios. Moreover, existing methods usually fail to generate indoor floorplans. To address these problems, this article integrates pedestrian dead reckoning (PDR) with frequency modulation (FM) radio signal from outdoor stations and Wi-Fi signal from indoor access points (APs) to achieve simultaneous localization and rough indoor floorplan mapping. First, windows, corners, and APs are selected as landmarks. Utilizing the impact of building structures on signal propagation, landmark detection is achieved through extracting the unique attenuation patterns of received signal strength indicator (RSSI). Second, the parameters of the propagation model containing landmark positions are inverted, based on which rough floorplan and large-area radio map are automatically constructed. Finally, PDR and fingerprinting localization results are fused. Experimental results demonstrate that the average positioning errors for landmarks are less than 2 m. Compared with the commonly used Gaussian process regression model, the proposed method significantly improves the accuracy of the radio map, thereby enhancing the localization performance.

Low Earth Orbit (LEO) communication constellations, as signal sources of opportunity, can be utilized to provide Positioning, Navigation, and Timing (PNT) services, serving as an important backup to the Global Navigation Satellite Systems (GNSS). Moreover, location information is a critical component in many Internet of Things (IoT) applications. The Globalstar constellation, which has launched bidirectional IoT modules, has recently broadcast a new downlink periodic burst signal composed of chirp signals and Quadrature Phase Shift Keying (QPSK)-modulated Direct-Sequence Spread Spectrum (DSSS) signals. To address the challenge of unknown signal parameters, this paper first establishes a blind parameter estimation framework to enable non-cooperative recovery of the complete signal structure, including frame structure, modulation parameters, and spreading-code sequences. Considering the weak signal strength and burst-type transmission characteristics, an Iterative Cascaded Parallel Search (ICPS) method is further proposed. ICPS consists of two stages: the Iterative Equivalent Matching Filtering (IEMF) method for coarse synchronization, and the Cascaded Parallel Search (CPS) method for precise synchronization. This framework enables fast and accurate signal detection and time-frequency synchronization, thereby facilitating Doppler extraction. Based on real-world measurement data, the proposed framework successfully extracts signal parameters and validates the effectiveness of the ICPS algorithm. Subsequently, Doppler-based positioning equations are constructed, and Doppler positioning is performed.

The demand for indoor positioning-based services grows rapidly. Fingerprinting technology has been widely applied in indoor positioning since its high accuracy and low dependence on external devices. However, the main dilemma limiting its development is the time-consuming and laborious site survey in the offline stage. In this paper, we use FM radio signal as the navigation source, and present a completely online FM indoor fingerprint database construction and calibration method based on the propagation model. With the floorplan obtained only, FM data initially collected inside the building is utilized to estimate the model parameters and build the fingerprint database. In the stage of online calibration and positioning, PDR is used to calibrate the database and constrain the fingerprint matching range, then an EKF-based fusion method is developed to improve the positioning performance and ensure the accuracy of calibration. Considering that the little data collected at the initial walking stage may cause inaccurate estimation of model parameters, different calibration strategies are designed using the least square and characteristics of FM fingerprint distribution to increase the stability and accelerate the convergence speed of database error. Field experiments are conducted in the campus office building, and the results indicate the effectiveness of the proposed method.

Among indoor positioning systems, Pedestrian dead reckoning (PDR) system has been widely used for less requirement for expensive infrastructure or laborious surveys. Heading estimation is one of the important parts of PDR. There are two main sources for heading estimation. The gyroscope-based method suffers from error accumulation problem, while the method using magnetometer is vulnerable to magnetic disturbances. Therefore, a novel heading estimation method using adaptive Unscented Kalman Filter (UKF) is proposed in this paper, which fuse accelerometer, magnetometer and gyroscope on the basis of motion mode recognition. ZARU (Zero angular rate update) is utilized to estimate gyro biases and correct the gyro output based on pedestrian still/walking classification. Straight feature is then applied on the basis of straight/turning classification. Magnetometer is finally used to further reduce heading error. In addition, the adaptive adjustment mechanism of filter parameters based on the quality evaluation of measurements is designed in this paper to improve the applicability of the method to different speeds and people. Experiments have been conducted by four experimenters at three sites using two smartphones. During the experiments, the phone is waist-mounted or handheld. The results show that the 3σ positioning error of the proposed method is reduced by more than 55% compared with the gyroscope-based method and magnetometer-based method.

The methods using the linear combination or simultaneous equations of global navigation satellite system (GNSS) carrier phase and pseudorange as the observations have become the main technology applied in GNSS dynamic differential positioning. Though the usage of pseudorange measurements can improve the efficiency of the integer ambiguity resolution, it may cause larger positioning errors than only using carrier phase. In response, we propose a new rapid integer ambiguity resolution algorithm that only using carrier phase for GNSS differential positioning. Based on weighted constrained least-squares AMBiguity Decorrelation Adjustment (WC-LAMBDA), this method utilizes the baseline resolution of GNSS pseudorange differential positioning to constrain the integer ambiguity resolution of phase-only differential positioning. This method can make full use of the pseudorange constraints to improve the efficiency of phase-only integer ambiguity resolution. Besides, it can avoid the large error in positioning results caused by errors of pseudorange measurements. The experimental results show that the proposed method can shorten the initialization time of the integer ambiguity resolution and achieve a rapid and high-precision phase-only differential positioning.

With the increasing use of high data rate navigation signals, the detection performance is severely affected by sign transition. An acquisition method based on multiple correlation strategies fusion (MCSF) has been proposed to reduce the correlation loss caused by the sign transition. The detection performances of non‐coherent (NCH) method, zero‐padding (ZP) method and MCSF in the presence of sign transition have been analysed. The theoretical and simulation results show that the proposed method can provide 1–3 dB acquisition sensitivity improvement compared with the NCH and ZP methods, and reduce the mean acquisition time by 21.3 and 8.5%, respectively. Moreover, the real data from GPS L5 and BeiDou Navigation Satellite System B2I have been used to analyse the performance.

This study proposes a double differentially (DDF) coherent pseudorandom code acquisition method for code‐division multiple‐access (CDMA) system. Nonlinear loss, false alarm probability and detection probability of this method have been analysed. Compared with real differentially coherent method, DDF method can be applied to improve detection performance in CDMA system. Furthermore, the analysis and simulations in this study illustrate that the proposed detector can provide 1–3 dB improvement compared with the complex differentially coherent detector, and 2–5 dB improvement compared with the non‐coherent detector.