Abstract The BDS-3 B1C signal features advanced subcarrier modulation and multiplexing techniques, making it one of the most sophisticated satellite signals to date. While offering high-precision ranging capabilities, its structure challenges conventional one-dimensional (1D) and two-dimensional (2D) tracking techniques, particularly in exploiting the high-order subcarrier component. This article proposes an all-component joint tracking method based on 2D loops. The high-power data and pilot BOC(1,1) components are jointly tracked to provide code and carrier estimates, which assist in subcarrier-only tracking of the low-power yet high-precision BOC(6,1) component. A robust ambiguity-fixing strategy is then used to generate unambiguous BOC(6,1) subcarrier observations for standard point positioning (SPP). Experiments using real BDS-3 data show that, compared with conventional 1D tracking, the proposed method improves SPP accuracy by 47.7% in the open-sky case, and achieves positioning accuracy improvements of 58.54%–80.31%, 36.53%–78.53%, and 47.40%–83.24% in the East, North, and Up directions, respectively, across three complex-environment experiments. These results confirm the effectiveness of the proposed method in enhancing both measurement quality and standalone positioning precision of the B1C signal.

The global navigation satellite system (GNSS) real-time kinematic precise point positioning (PPP-RTK) technique can be significantly impaired by severe signal obstruction conditions in urban areas. The fisheye camera can detect structures present along the satellite-receiver line-of-sight (LOS) path, and all satellites blocked by structures are generally discarded or down-weighted, resulting in a limited number of satellites and/or poor satellite geometry. However, the seemingly blocked satellites might imply distinct signal reception situations, such as non-LOS (NLOS) with only reflected path (i.e., blocked by the main body of structures) and diffraction with a slightly bending LOS path (i.e., close to the boundaries of structures). GNSS diffracted signals have significantly smaller ranging errors compared to NLOS signals, which have not been adequately addressed in open literatures leveraging fisheye cameras for satellite visibility detection. In this article, to reasonably exploit such diffracted signals propagating close to structure boundaries, we develop a fisheye camera-aided PPP-RTK/inertial navigation system (INS) tight integration system. With those seemingly (i.e., fully or partially) blocked satellites identified by the fisheye camera, a finer classification approach is introduced to further identify the diffracted signals which could be beneficial for improving the positioning performance under GNSS-challenging scenarios. Then, a dedicated weighting model to such urban GNSS measurements, which combine conventional weighting model and robust estimation with the finer classification results, is established. Experimental results from both unmanned and manned vehicle-based kinematic tests in typical complex urban environments demonstrate that, compared to conventional fisheye camera-aided approaches that totally discard or down-weight signals, the proposed method improves 3-D positioning accuracy by more than 22% for PPP-RTK and more than 47% for PPP-RTK/INS. Notably, only the proposed method achieves decimeter-level positioning precision in the vehicle-based test, while others remain at the meter level.

Positioning with fifth-generation (5G) new radio (NR) signals is promising, particularly in areas where the global navigation satellite system (GNSS) is blocked. However, the design of the receiver for 5G NR signals is challenging by the presence of multipath in indoor and urban environments, especially for time-of-arrival (TOA) tracking. In this paper, we propose a signal parameter estimation method for 5G NR that utilizes the estimation of signal parameters via rotational invariance techniques (ESPRIT) and the state space model to achieve signal parameter estimation. First, the ESPRIT is employed as the delay and phase discriminator, and a path selection criterion based on amplitude is proposed to avoid outliers in ESPRIT estimation caused by overestimating the channel order based on the minimum description length (MDL) criterion. Then, the state space model is used for carrier phase and Doppler frequency estimation, and the measurement matrix based on the phase error of the ESPRIT discriminator is derived. Finally, the performance of the proposed method is tested in an indoor environment containing a commercial small cell base station (BS). The results demonstrate that the proposed method outperforms the DLL and ESPRIT, with a pseudorange estimation root mean square error (RMSE) of 0.63m. Therefore, the proposed method in this article has essential reference significance for promoting and commercializing 5G indoor positioning.

Environmental sensing is a pivotal technology that utilizes sensor data to enable vehicles, robots, and drones to accurately perceive their surroundings. The emergence of 5G New Radio (NR) networks as specialized sensors for environmental sensing has become a promising research direction, particularly within the context of integrated sensing and communication (ISAC) systems. One category of ISAC research in 5G NR primarily focus on radar signal modeling to create range-Doppler imaging maps. However, the generation of range-Doppler maps demands a significant amount of continuous data in the time-frequency domain, which is usually unavailable in localization tasks that rely on time domain data. This paper focuses on utilizing the downlink pilots in 5G NR without modifying the existing communication functions, aiming to propose an implementation algorithm for sensing from the perspective of the channel model. Firstly, we investigate the performance of an inverse discrete Fourier transform (IDFT)-based channel impulse response (CIR) ranging scheme. Secondly, a CIR-based back projection (BP) algorithm for sensing is proposed, which exploits the downlink pilots of 5G NR. In this algorithm, the transceiver pair consisting of a gNB and user equipment (UE) is treated as a subaperture, and the subapertures from all gNBs are combined to emulate the effect of a synthetic aperture. Finally, the performance of the CIR-based BP sensing algorithm is demonstrated through simulations. Notably, the advantage of the proposed method is that it does not require redesigning 5G NR signals, does not interfere with communication functions, and only requires the pilot from a single orthogonal frequency division multiplexing (OFDM) symbol for sensing, thereby offering an innovative approach to environmental sensing and localization.

3D building maps have been urgently demanded in numerous applications such as urban planning and disaster monitoring. Conventional LiDAR and visual-based 3D building mapping techniques face challenges like high financial cost and human resource consumption for large-scale 3D map construction and update. Recently, global navigation satellite system (GNSS)-based urban mapping has received much attention, owing to the inherent advantages like wide coverage area, high update efficiency, and low cost. However, most of the existing GNSS mapping approaches merely exploit the single feature of signal power, and cannot provide satisfactory mapping accuracy. In this article, an urban building height estimation method based on smartphone GNSS data is developed, resorting to the machine learning-based GNSS line-of-sight (LOS)/non-line-of-sight (NLOS) classifier. The supervised support vector machine (SVM) uses multiple GNSS signal features extracted from raw data to achieve the LOS/NLOS signal classification. The building height, around which the signal LOS/NLOS condition changes drastically, is estimated by applying a logistic function fitting to the signal LOS probability along the altitude direction. The critical building height estimations in conjunction with a priori database of the 2D building boundaries produce the 3D building map. Experimental campaigns under four different building scenarios are performed to evaluate the performance of the proposed method. Experimental results reveal that the proposed smartphone GNSS LOS/NLOS classification-based approach yields an absolute building height estimation error lower than 2.3 m, as against 21.5 m provided by the existing GNSS building mapping algorithm.

The estimation accuracy of conventional parameter estimation methods, including the maximum likelihood (ML) estimator and subspace-based estimation methods, diverges from the Cramer-Rao lower bound (CRLB) under low signal-to-noise (SNR) ratio conditions. Conventional stochastic resonance (SR) technique has shown appealing weak signal improvement advantages under low SNR, but it still needs a priori information such as the probability density functions (pdfs) of weak signal and channel noise. In this study, to address the channel parameter estimation for weak signal conditions, a novel channel parameter estimation algorithm based on dynamic stochastic resonance networks (SRN) is introduced. Since the signal statistical properties are altered by the SRN processing, the CRLB of the wireless channel parameter estimation employing the SRN-enhanced signal is derived, and then the corresponding ML estimator is presented. Theoretical analyses show that the CRLB is lower than those from the original signal and stochastic-resonance-enhanced signal. Computer simulations are performed to verify the effectiveness of the theoretical CRLB expressions. Both simulation and real experimental results indicate that the proposed SRN processing approach outperforms the conventional SR processing through achieving the CRLB improvement, the ML estimation performance enhancement under low SNR conditions, and the hardware complexity reduction.

This article studies the intercell interference (ICI) effects in cellular networks for navigation applications. This is achieved through the derivation of an analytical expression for a cellular navigation receiver postcorrelation signal-to-noise power ratio (SNR) in the presence of multiple asynchronous cells. It reveals that in addition to the channel power, the cell loading rate and data modulation order for the interfering cells also play important roles in affecting the received signal quality of the desired cell. Furthermore, the time-of-arrival (TOA) estimation and positioning accuracy degradation due to the ICI is characterized by the Cramer–Rao and Ziv–Zakai lower bounds based on the derived postcorrelation SNR. Simulations are performed to verify the theoretical expressions and the results indicate that the ICI term can be treated as an additional Gaussian disturbance for characterizing the TOA estimation accuracy in cellular navigation receivers.

GNSS signals are easily blocked or degraded because of the dense presence of high-rise buildings in urban areas, and positioning errors arising from reflected signals amount to as much as hundreds of meters. Various conventional GNSS techniques have been utilized to resolve this problem, but applying them to urban environments has been difficult owing to the complexity of the reflected signals and their unpredictable and nonlinear variation to the signal receiving environments. In this study, multipath maps were generated for dynamic users at multiple positions on a road and a residual-based map selection algorithm was implemented to solve the problem of user position uncertainty in deep urban environments. GNSS data collected over a period of 327 min were used to train the multipath maps corresponding to 247 points near the 2.5 km stretch of the Teheran-ro road in Seoul, South Korea. The proposed system performed efficiently—it was verified to be capable of constructing a multipath map with a radius of 25 m using only 4 min of data. Moreover, it improved positioning accuracy by 45 % horizontally and by 80 % vertically, enabling the determination of the positional information of an urban vehicle with a horizontal accuracy of 18 m during 99% of the duration of a one-hour-long dynamic test. Due to the nonreliance of the proposed method on prior information or implementation of additional sensors, it is expected to be widely used for constructing map-based multipath mitigation models as part of intelligent transportation infrastructure in all cities in future.

Tall buildings that block, reflect, or diffract signals challenge GPS positioning in urban environments, resulting in positioning errors. This paper explores the use of communication signals, such as long-term evolution (LTE) signals, as signals of opportunity (SOP) leveraged to aid navigation performance. Data collected during a driving experiment in the urban center of Denver, Colorado is processed using a carrier phase tracking architecture, which provides accumulated Doppler ranging (ADR) measurements. This data is applied to the problem of characterizing LTE small cell transmitter clock and position states, a critical first step in use for navigation. Batch LLS phase profile comparisons and shadow matching techniques are used, and their combined application matches LTE transmitter locations to candidates in a known database.

Precise positioning is critical in autonomous driving, indoor navigation, and intelligent logistics. With the development of mobile communication technology, indoor base stations (BSs) have been utilized and installed to match indoor signal coverage better. Thus for indoor positioning, cellular signals have emerged as a new option. Ranging through the time of arrival (TOA) is achieved by capturing cellular signals as a signal of opportunity for localization. This approach can only estimate the user equipment (UE) to BS distance when there is just one BS and cannot obtain the two-dimensional (2D) coordinates. In this paper, we combine the fingerprint positioning approach with the signal parameters acquired via cellular signal of opportunity. TOA and one-reflection path are employed as fingerprint features, and 2D localization with a single BS is achieved. A complete signal processing flow is constructed, and the ray tracing method is performed to generate the channel parameters to evaluate the proposed positioning algorithm's performance and match the practical application as closely as feasible. Using the $100\ GHz$ carrier frequency that conforms to the sixth generation (6G) communication for simulation, the mean positioning error is $0.312 m$ when the fingerprint interval is $0.5 m$ . This method provides a feasible solution for 6G indoor positioning scenarios.

Cellular-based localization has emerged as an important means to provide independent positioning and navigation solutions or be integrated with other navigation sensors for enhancing urban and indoor positioning capability. Taking into account the low heights of both the cellular signal transmitters and receivers as well as the complex environments in urban and indoor areas, severe multipath propagation effect originating from the specular reflection and distributed diffuse scattering mostly occurs. This increases the difficulty in accurately extracting the delay and angle measurements for the received cellular multipath signals, with the aim of either combating or benefiting from the multipath effects. In general, path number estimation needs to be first performed, and followed by the multipath parameter estimation algorithms in the cellular positioning receivers. Information theoretic criteria (ITC), such as the classical minimum description length and Akaike information criterion, are commonly used as the path number estimators. However, due to the contribution of dense multipath component (DMC) from distributed diffuse scattering, the noise condition in the channel measurements might violate from the white noise characteristics and degrade the performance of ITC-based path number estimators. This paper first presents the tailored procedures of two kinds of ITC, originally developed for source enumeration in white and colored noise, for path number estimation in cellular positioning receivers. The performance of these ITC under multipath conditions in the absence or presence of DMC is comprehensively assessed by numerical simulations in terms of the probabilities of correct estimation, overestimation, and underestimation. The impact of the key design parameter in these ITCs and the DMC intensity on the path number estimation performance is also discussed. The statistical performance results clearly indicate their performance behavior under different multipath channel conditions, and motivate the necessity of designing more suitable path number estimators for cellular localization applications.

Positioning with cellular signals has been gaining attention in urban and indoor environments, where global navigation satellite system signals have limited availability due to interference, blockage, or multipath. However, accurate and reliable tracking of cellular signals under highly dynamic urban channel conditions remains a challenging task. This article presents a cellular long-term evolution (LTE) signal tracking algorithm implemented by an adaptive multipath estimating delay lock loop (AMEDLL) to achieve carrier phase synchronization and time-of-arrival (TOA) tracking under severe multipath propagation conditions. The analytical expression of the coherently integrated correlation result over multiple slots with the LTE cell-specific reference signal is derived. A multipath estimator along with a simple yet efficient multipath estimation monitoring approach is developed to estimate the parameters of all detected multipath signals. Several heuristic monitoring criteria based on historical multipath parameter estimations are established to enable adaptive adjustment of the estimated path number. Real LTE signals are collected in an urban environment for the signal tracking performance evaluation. This article presents two case studies with varying levels of multipath effects and signal power to illustrate the effectiveness of the developed signal tracking algorithm. Instead of a TOA truth reference, open-loop carrier phase estimations are used to analyze the TOA tracking error. Our analyses demonstrate that the AMEDLL-based tracking algorithm provides improved TOA estimation accuracy over the existing super-resolution-algorithm-based and delay-lock-loop-based tracking schemes.

This paper introduces a three-dimensional building model in conjunction with visibility predictions to simulate the conditions of conducting an experiment in a dense urban environment and evaluates the ability to use long term evolution (LTE) signals for positioning and navigation. In particular, this paper focuses on the direct line-of-sight (DLOS) visibility between LTE small cells and an equipped vehicle traveling along the city streets of Denver, CO. Using the DLOS visibility predictions, an interacting multiple model (IMM) filter that uses extended Kalman filters (EKFs) similar to the one developed in [1] was implemented and executed in a simulation of the urban environment while incorporating the DLOS visibility predictions to determine the accuracy of locating the LTE small cell locations.

Radio frequency interference (RFI) localization is important for ensuring the accuracy and integrity of global navigation satellite system receivers in numerous applications. In this letter, an efficient RFI localization technique is presented based on differential received signal strength (DRSS) measurements from crowdsourcing devices. A DRSS weighting scheme allocates weights to the position solutions of the receivers that detect the RFI. The weighted centroid of the receiver positions generates the RFI location. The method presented in this letter can also provide an initial estimation for the conventional iterative localization approach to improve localization accuracy and convergence speed. Simulation results are presented to demonstrate the effectiveness of the proposed method being used independently or integrated with the iterative method.

Long-term evolution (LTE) signals are potential signals-of-opportunity for position and navigation, especially in challenging urban and indoor environments. A major challenge is that the LTE signal time-of-arrival (TOA) estimations are susceptible to the multipath propagation effects. In this paper, the multipath estimating delay lock loop (MEDLL), which is originally designed for global positioning system receivers, is applied to LTE signal TOA estimation in multipath environments. We derive the analytical expression of the correlation function for LTE signals and present the procedure for estimating parameters of the detected multipath components. Two initialization methods without and with super-resolution algorithm (SRA) are developed for the MEDLL. Our analyses show that the MEDLL with SRA-based initialization can achieve better multipath resolution, while the one without SRA has less complexity. Extensive simulations involving static multipath scenarios are conducted to examine the statistical TOA estimation performance of the proposed MEDLL with LTE cell-specific reference signal. The simulation results and computational complexity analysis indicate that the proposed MEDLL outperforms the conventional delay lock loop and SRA in term of multipath mitigation performance and computational complexity. Experimental results using real collected LTE signals in urban environments are also provided to demonstrate the effectiveness of the proposed technique for realistic scenarios.

Alternative positioning techniques, such as those using ambient signals-of-opportunity, have been increasingly developed for global navigation satellite system (GNSS)-challenging urban and indoor environments. For instance, cellular long-term evolution (LTE) network supports a variety of positioning methods based on signal measurements such as received signal strength or time-of-arrival (TOA). Carrier phase measurement extracted from LTE signals has also received substantial attention due to its potential high ranging accuracy. This paper presents a closed-loop (CL) carrier phase tracking architecture for positioning in LTE networks with robustness to the channel fading effects. The pilot cell-specific reference signal (CRS) is exploited for generating the channel estimation results, which are inputs for the carrier phase discriminator to measure the carrier phase error. The undesired bias corrupting the phase discriminator output for multipath channels is analytically derived. Then, the state-space model of LTE carrier signal tracking in light of the specific CRS time-frequency domain pattern is established and the state estimator gain matrix is derived based on the proportional integral filter deign. Simulation assessment of the proposed carrier phase tracking architecture is performed in a single path Rician fading channel under different Rician factors and signal powers. The impact of loop parameters, e.g., loop bandwidth and updating interval, is investigated. Real collected LTE data in a multipath fading channel is also used to comparatively analyze the performances of the existing open-loop and proposed CL carrier tracking methods combined with three LTE signal TOA estimators. The proposed algorithm in conjunction with the multipath estimating delay lock loop is shown to provide accurate and robust carrier phase measurements against multipath fading effects compared to other considered tracking schemes.

In this paper, we provide an analysis of radio frequency interference for global navigation satellite system reflectometry (GNSS-R) using multi-frequency global positioning system (GPS) data collected on Haleakala, Hawaii during a mountain-top radio occultation and reflection experiment conducted on May 6, 2017. The results show that the interferences on GPS L1 and L2 are continuous wave signals and occur at a 2 Hz rate. The interference on GPS L5 is pulsed signals originating from two aeronautical navigation systems. Moreover, the analysis reveals that the interference on GPS L1 can overwhelm the GNSS-R delay-Doppler map (DDM) if 1 ms coherent integration time is applied. Interference mitigation techniques are suggested for different types of interference on the three GPS frequency bands.

Positioning using the time-of-arrival (TOA) of cellular signals has emerged as a promising solution in global navigation satellite system (GNSS) challenged environments, such as in urban canyons and indoors. However, harsh multipath fading propagation condition remains as one of the key factors causing the detriment of the TOA estimation accuracy. This paper presents an improved TOA estimation algorithm for cellular signals in multipath fading channels. The proposed algorithm takes advantage of the super-resolution algorithm and multipath estimating delay lock loop for estimating the multipath parameter vector. The estimation results are constantly monitored to handle the multipath fading conditions. At the beginning of each update epoch, the proposed method performs a consistency check using the previous estimated parameter vector to determine whether to activate a reinitialization process or not. While at the end of each update epoch, the method examines the validity of the detected first arriving path to remove the estimation outliers. Simulation results considering a five-path fading channel are provided to demonstrate the effectiveness of the proposed algorithm and compare its performance to those of other existing approaches.

Long term evolution (LTE) signals have the potential for use in positioning, especially in challenging environments. The time-of-arrival (TOA)-based technique supported by LTE is attractive due to its high positioning accuracy. However, it is vulnerable to multipath propagation effects in typical LTE channels. This paper will summarize several existing advanced TOA estimators for LTE signals, i.e., first peak detection, information theoretic criteria, super-resolution algorithm, and delay-lock loop (DLL). Later, the paper will evaluate the TOA estimation performances of these techniques with multipath propagation effects and varying signal conditions using simulations. For the DLL, the multipath error envelope metric is assessed for different signal bandwidths. The root mean square errors of the TOA estimations are compared to evaluate suitable TOA estimators under various conditions. Finally, some other performance characteristics of these techniques are also discussed.