Multipath Bias Research Articles

Connected Preceding Vehicle Identification for Enabling Cooperative Automated Driving in Mixed Traffic

To enable the safe and fast formation of connected automated vehicle (CAV) platoons in real-world traffic, a preceding vehicle identification system for mixed traffic (PVIS-mixed) is proposed. PVIS-mixed utilizes the vehicle’s radar measurements and global positioning system (GPS) measurements reported by surrounding connected vehicles to find the communication identity of the preceding vehicle. The design of PVIS-mixed is based on three goals: a low probability of making a wrong identification, a low probability of missing the connected preceding vehicle, and short time consumption of the identification procedure. The proposed PVIS-mixed is evaluated in highway traffic simulated by real vehicle trajectory data from the Next Generation Simulation (NGSIM) program. Evaluation results showed that the performance of PVIS-mixed is not related to the adoption rate of connected vehicles, and 1 m is found to be the required relative positioning accuracy to make 99th percentile time consumption <10 s. It was observed that the multipath bias of GPS positioning could affect the usability of CAV platooning. The possible solutions are then discussed as future work.

Kinematic ME-MAFA for Pseudolite Carrier-Phase Ambiguity Resolution in Precise Single-Point Positioning.

Precise single-point positioning using carrier-phase measurements can be provided by the synchronized pseudolite system. The primary task of carrier phase positioning is ambiguity resolution (AR) with rapidity and reliability. As the pseudolite system is usually operated in the dense multipath environment, cycle slips may lead the conventional least-squares ambiguity decorrelation adjustment (LAMBDA) method to incorrect AR. A new AR method based on the idea of the modified ambiguity function approach (MAFA), which is insensitive to the cycle slips, is studied in this paper. To improve the model strength of the MAFA and to eliminate the influence of constant multipath biases on the time-average model in static mode, the kinematic multi-epoch MAFA (kinematic ME-MAFA) algorithm is proposed. A heuristic method for predicting the ‘float position’ corresponding to every Voronoi cell of the next epoch, making use of Doppler-based velocity information, is implemented to improve the computational efficiency. If the success rate is very close to 1, it is possible to guarantee reliable centimeter-level accuracy positioning without further ambiguity validation. Therefore, a computing method of the success rate for the kinematic ME-MAFA is proposed. Both the numerical simulations and the kinematic experiment demonstrate the feasibility of the new AR algorithm according to its accuracy and reliability. The accuracy of the horizontal positioning solution is better than 1.7 centimeters in our pseudolite system.

Multipath modeling and mitigation by using sparse estimation in global navigation satellite system-challenged urban vehicular environments

Multipath interference has been one of the most difficult problems when using global navigation satellite system-based vehicular navigation in urban environments. In this article, we develop a multipath mitigation algorithm exploiting the sparse estimation theory that improves the absolute positioning accuracy in urban environments. The navigation observation model is established by considering the multipath bias as additive positioning errors, and the assumption for the proposed method is that global navigation satellite system signals contaminated due to multipath are the minority among the received signals, which makes the unknown bias vector sparse. We investigated an improved elastic net method to estimate the sparse multipath bias vector, and the global navigation satellite system measurements can be corrected by subtracting the estimated multipath error. The positioning performance of the proposed method is verified by analytical and experimental results.

A subspace-based code tracking loop design for GPS multi-antenna receiver in multipath environment

Global Navigation Satellites Systems have been greatly developed in recent years, but receivers usually suffer the undesired measurement bias errors caused by multipath incidence in an urban area. We propose an effective subspace-based estimator cooperating with the conventional delay-locked loop (DLL) for Global Positioning System L1 C/A Signal. With the help of the multi-antenna, the incident rays could be distinguished in space. A forward and backward space spatial smooth technique is then taken to solve the coherent problem in signal subspace estimation. By using the structure of the receiving array, a subspace rotation invariance can be built, and the angles of arrival and relative delays of incident rays could be estimated jointly. After combining the estimates from DLL and subspace-based estimators, we can enhance the strength of the line-of-sight ray and achieve a modified delay tracking output without multipath bias effectively. Simulation results prove that compared with the existing methods, such as the narrow early-minus-late and the multipath estimating delay lock loop, the proposed method has the best multipath mitigation in the delay estimation.

Assessment of errors in NavIC observables for stationary receivers

Our objective in this paper is to assess all errors in NavIC's pseudorange so that they can be mitigated or accounted for appropriately in the dynamics model to achieve high-accuracy navigation. Multipath errors in the L5 and S1 pseudoranges are different; hence, oscillatory multipath is removed using sidereal repeatability for these frequencies separately for accurate iono delay estimation. Iono delay estimates using uncompensated pseudoranges are 7-m greater than the normal night time estimates due to multipath. When oscillations are removed, the difference reduces to =3 m. Dual-frequency iono delay estimates using code phase are within two-sigma of the grid-based estimates. A comparison with the NTCM-GL and Klobuchar iono delay estimates is presented for the low-latitude region in India. The ephemeris line-of-sight errors blended with the unknown multipath bias errors are estimated using a low-pass filter and are found to lie at the edges of the two-sigma signal-in-space error.

BeiDou system satellite‐induced pseudorange multipath bias mitigation based on different orbital characteristic for static applications

Satellite-induced pseudorange multipath bias exists in the BeiDou two-generation system, which affects the accuracy of positioning, navigation and the rate of convergence seriously. After analysing the characteristic of the three orbital types satellite-induced pseudorange multipath bias and deriving the orbital repetition period of the satellites, a new integrated method based on different orbital characteristic respective correction strategy, is proposed to mitigate this bias for static applications. The main idea of the new algorithm is to correct the IGSO and MEO satellites by the elevation model, which is established based on different satellites and different frequencies. And, for the GEO satellites, a new sidereal filtering strategy based on adaptive wavelet packet transform has been used to mitigate the multipath bias. In order to evaluate the performance of the proposed method, two datasets were collected from the MGEX stations. The results indicate that the proposed method can mitigate the systematic satellite-induced multipath bias for all satellites since the mean improvement rate for GEO satellites is nearly 26.63% and is about 18.53% for IGSO and MEO satellites. However, since the sidereal filtering method requires the reference day's data for extracting correction model, it can only be used for static applications.

Simulation of Multiplatform Source Localization Using Three Dimensional Time Difference of Arrival with Multipath Exploitation and Clock Bias Among Receivers

Source localization using Time Difference of Arrival has been a subject of research over the years due to its wide applications in civilian and military sectors. In electronic warfare systems, the source estimation is important for better surveillance and planning of military strategies. Among the various approaches of Time Difference of Arrival, Three Dimensional Time Difference of Arrival algorithms have drawn much attention as it does not require synchronization between emitter and the base stations and also provides better estimation performance. However, it certainly requires synchronization among the receiving base stations. In this paper an algorithm is proposed for source localization using Three Dimensional Time Difference of Arrival in presence of multipath scenario and clock bias in the receiving base stations. Most of the existing techniques require range data for obtaining the closed form Time Difference of Arrival solution. The algorithm presented in this paper is based on Ezzat’s closed form solution, which does not depend on range data. Simulations are carried out and performance is evaluated by placing receivers on ship/land and Unmanned Aerial Vehicle platforms. Standard deviation in estimated Three Dimensional Time Difference of Arrival is measured using cross correlation between the signals and a closed form solution is obtained. Simulations support the theoretical developments in multipath scenario and clock bias.

Experimental results of multipath behavior for GPS L1-L2 and Galileo E1-E5b in static and kinematic scenarios

Abstract Current trends like Autonomous Driving (AD) increase the need for a precise, reliable, and continuous position at high velocities. In both natural and man-made environments, Global Navigation Satellite System (GNSS) signals suffer challenges such as multipath, attenuation, or loss-of-lock. As Highway Assist and Highway Pilot are AD next steps, multipath knowledge is necessary for this typical user-case and kinematic situations. This paper presents a multipath performance analysis for GPS and Galileo satellites in static, slow, and high kinematic scenarios. The data is provided from car test-drives in both controlled and unrestricted, near-natural environments. The Code-Minus-Carrier (CMC) and cycle-slip implementations are validated with measurement data from consecutive days. Multipath statistical models based on satellite elevation are evaluated for the three investigated scenarios. Static models derived from the car setup measurements for GPS L1, L2 and Galileo E1 and E5b show a good agreement with a state-of-the-art model as well as the enhanced Galileo signals performance. Slow kinematic multipath results in a controlled environment showed an improvement for both navigation systems compared to the static measurements at the same place. This result is confirmed by static and slow kinematic multipath simulations with the same GNSS receiver. Post-processing analysis on highway measurements revealed a bigger multipath bias, compared to the open-sky static and slow kinematic measurement campaigns. Although less critical as urban or rural, this indicates the presence of multipath in this kind of environment as well. The impact of different parameters, including receiver architecture and Signal-to-noise ratio (SNR) are analyzed and discussed. Differential position (DGNSS) based on code is computed for each epoch and compared against GNSS/INS integrated position for all three measurement campaigns. The most significant 3D absolute error occurs where the greatest multipath envelope is found.

Improving Localization Accuracy in Connected Vehicle Networks Using Rao–Blackwellized Particle Filters: Theory, Simulations, and Experiments

A crucial function for automated vehicle technologies is accurate localization. Lane-level accuracy is not readily available from low-cost global navigation satellite system (GNSS) receivers because of factors such as multipath error and atmospheric bias. Approaches such as differential GNSS can improve localization accuracy, but usually require investment in expensive base stations. Connected vehicle technologies provide an alternative approach in improving the localization accuracy. It will be shown in this paper that localization accuracy can be enhanced using crude GNSS measurements from a group of connected vehicles, by matching their locations to a digital map. A Rao–Blackwellized particle filter is used to jointly estimate the common biases of the pseudo-ranges and the vehicle positions. Multipath biases, which introduce receiver-specific (non-common) error, are mitigated by a multi-hypothesis detection-rejection approach. The temporal correlation of the estimations is exploited through the prediction-update process. The proposed approach is compared with existing methods using both simulations and experimental results. It was found that the proposed algorithm can eliminate the common biases and reduce the localization error to below 1 m under open sky conditions.

An Improved BeiDou-2 Satellite-Induced Code Bias Estimation Method.

Different from GPS, GLONASS, GALILEO and BeiDou-3, it is confirmed that the code multipath bias (CMB), which originate from the satellite end and can be over 1 m, are commonly found in the code observations of BeiDou-2 (BDS) IGSO and MEO satellites. In order to mitigate their adverse effects on absolute precise applications which use the code measurements, we propose in this paper an improved correction model to estimate the CMB. Different from the traditional model which considering the correction values are orbit-type dependent (estimating two sets of values for IGSO and MEO, respectively) and modeling the CMB as a piecewise linear function with a elevation node separation of 10°, we estimate the corrections for each BDS IGSO + MEO satellite on one hand, and a denser elevation node separation of 5° is used to model the CMB variations on the other hand. Currently, the institutions such as IGS-MGEX operate over 120 stations which providing the daily BDS observations. These large amounts of data provide adequate support to refine the CMB estimation satellite by satellite in our improved model. One month BDS observations from MGEX are used for assessing the performance of the improved CMB model by means of precise point positioning (PPP). Experimental results show that for the satellites on the same orbit type, obvious differences can be found in the CMB at the same node and frequency. Results show that the new correction model can improve the wide-lane (WL) ambiguity usage rate for WL fractional cycle bias estimation, shorten the WL and narrow-lane (NL) time to first fix (TTFF) in PPP ambiguity resolution (AR) as well as improve the PPP positioning accuracy. With our improved correction model, the usage of WL ambiguity is increased from 94.1% to 96.0%, the WL and NL TTFF of PPP AR is shorten from 10.6 to 9.3 min, 67.9 to 63.3 min, respectively, compared with the traditional correction model. In addition, both the traditional and improved CMB model have a better performance in these aspects compared with the model which does not account for the CMB correction.

GNSS Positioning Enhancement Based on NLOS Multipath Biases Estimation Using Gaussian Mixture Noise

Global navigation satellite systems (GNSS) have been widely used in many applications where positioning plays an important role. However, the performances of these applications can be degraded in urban canyons, due to Non-Line-Of-Sight (NLOS) and Multipath interference affecting GNSS signals. In order to ensure high accuracy positioning, this article proposes to model the NLOS and Multipath biases by Gaussian Mixture noise using Expectation Maximization (EM) algorithm. In this context, an approach to estimate the Multipath and NLOS biases for real time positioning is presented and statistical tests for searching the probability distribution of NLOS and Multipath biases are illustrated. Furthermore, a hybrid approach based on PF (Particle Filter) and EM algorithm for estimating user position in hard environment is presented. Using real GPS (Global Positioning System) signal, the efficiency of the proposed approach is shown, and a significant improvement of the positioning accuracy over the simple PF estimation is obtained.

BDS satellite-induced code multipath: Mitigation and assessment in new-generation IOV satellites

This article is dedicated to the developments of the new-generation of BDS satellites and the mitigation of the satellite-induced code multipath error. Basic signal parameters of three new-generation BDS IOV satellites were introduced. A monitoring receiver connected to a wideband choke ring antenna GNSS-750 from NovAtel on the rooftop of a tall building. The used antenna has high quality and is multipath insensitive at the user's end. Code multipath errors of their triple-frequency civil signals were assessed based on data of several days in Xi’an. The means and standard deviations of MP series for IOV BDS civil signals across different elevation-intervals were calculated to assess the multipath error variations across different elevation-intervals. Thorough hypothesis-testing experiments were carried out to determine whether the code multipath errors significantly change with elevation using one-way analysis of variance. The code multipath errors of three BDS IOV satellites and three operational BDS satellites are compared under the same condition demonstrating the effective mitigation of satellite-induced code multipath errors for the new-generation BDS IOV satellites. Experimental results show that the satellite-induced code multipath bias is not obvious for the new-generation BDS IOV satellites under analysis.

Mitigating Multipath Bias Using a Dual-Polarization Antenna: Theoretical Performance, Algorithm Design, and Simulation.

It is well known that multipath effect remains a dominant error source that affects the positioning accuracy of Global Navigation Satellite System (GNSS) receivers. Significant efforts have been made by researchers and receiver manufacturers to mitigate multipath error in the past decades. Recently, a multipath mitigation technique using dual-polarization antennas has become a research hotspot for it provides another degree of freedom to distinguish the line-of-sight (LOS) signal from the LOS and multipath composite signal without extensively increasing the complexity of the receiver. Numbers of multipath mitigation techniques using dual-polarization antennas have been proposed and all of them report performance improvement over the single-polarization methods. However, due to the unpredictability of multipath, multipath mitigation techniques based on dual-polarization are not always effective while few studies discuss the condition under which the multipath mitigation using a dual-polarization antenna can outperform that using a single-polarization antenna, which is a fundamental question for dual-polarization multipath mitigation (DPMM) and the design of multipath mitigation algorithms. In this paper we analyze the characteristics of the signal received by a dual-polarization antenna and use the maximum likelihood estimation (MLE) to assess the theoretical performance of DPMM in different received signal cases. Based on the assessment we answer this fundamental question and find the dual-polarization antenna’s capability in mitigating short delay multipath—the most challenging one among all types of multipath for the majority of the multipath mitigation techniques. Considering these effective conditions, we propose a dual-polarization sequential iterative maximum likelihood estimation (DP-SIMLE) algorithm for DPMM. The simulation results verify our theory and show superior performance of the proposed DP-SIMLE algorithm over the traditional one using only an RHCP antenna.

Detecting, estimating and correcting multipath biases affecting GNSS signals using a marginalized likelihood ratio-based method

In urban canyons, non-line-of-sight (NLOS) multipath interferences affect position estimation based on global navigation satellite systems (GNSS). This paper proposes to model the effects of NLOS multipath interferences as mean value jumps contaminating the GNSS pseudo-range measurements. The marginalized likelihood ratio test (MLRT) is then investigated to detect, identify and estimate the corresponding NLOS multipath biases. However, the MLRT test statistics is difficult to compute. In this work, we consider a Monte Carlo integration technique based on bias magnitude sampling. Jensen׳s inequality allows this Monte Carlo integration to be simplified. The multiple model algorithm is also used to update the prior information for each bias magnitude sample. Some strategies are designed for estimating and correcting the NLOS multipath biases. In order to demonstrate the performance of the MLRT, experiments allowing several localization methods to be compared are performed. Finally, results from a measurement campaign conducted in an urban canyon are presented in order to evaluate the performance of the proposed algorithm in a representative environment.

A Framework for Bayesian Receiver Autonomous Integrity Monitoring in Urban Navigation

: A Bayesian framework for RAIM designed to meet the requirements of today's urban navigation is introduced. The framework is general in the sense that when contaminated observations can be modeled, integrity information can be computed. We consider the case of positioning in the presence of multipath signals. The proposed method consists of a Bayesian filter that computes the posterior distribution of the history of the presence of multipath signals and the sizes of multipath biases, along with the navigation parameters. From the posterior distribution, optimal integrity information can be theoretically derived. For practical implementation, the optimal Bayesian filter is approximated with various techniques, and suboptimal integrity information is achieved. Simulations show that the approximations of the Bayesian RAIM framework have good performance compared to the traditional least squares RAIM, the drawback being the computational complexity of the methods.

A Particle Filtering Approach for Joint Detection/Estimation of Multipath Effects on GPS Measurements

Multipath propagation causes major impairments to global positioning system (GPS) based navigation. Multipath results in biased GPS measurements, hence inaccurate position estimates. In this paper, multipath effects are considered as abrupt changes affecting the navigation system. A multiple model formulation is proposed whereby the changes are represented by a discrete valued process. The detection of the errors induced by multipath is handled by a Rao-Blackwellized particle filter (RBPF). The RBPF estimates the indicator process jointly with the navigation states and multipath biases. The interest of this approach is its ability to integrate a priori constraints about the propagation environment. The detection is improved by using information from near future GPS measurements at the particle filter (PF) sampling step. A computationally modest delayed sampling is developed, which is based on a minimal duration assumption for multipath effects. Finally, the standard PF resampling stage is modified to include an hypothesis test based decision step

Development of Multipath Error Budgets for JPALS Ground Station Receivers

This paper develops code multipath error budgets for land-based Joint Precision Approach and Landing System (JPALS) ground station receivers. The approach is similar to that used for the civil Local Area Augmentation System (LAAS), with modifications for P(Y)-code tracking and the use of beam-steering antenna arrays. The budget includes two error allocations, one for receiver noise and one for code multipath, both of which are defined as a function of satellite elevation. The noise allocations are based on an analytical model that is fit to JPALS test data, while the multipath allocations are based on test data alone. The results show that JPALS ground stations have some advantages over LAAS ground stations due to antenna differences and the more limited multipath susceptibility of the P(Y)-code. We also investigate code multipath biases by way of simulations. The results indicate that the multipath allocations developed herein capture multipath biases for the expected environments.