Until recently, high costs and complexity have confined RTK and multi-band GNSS technologies to low-volume niche markets, such as surveying or construction. However, emerging high volume markets, such as precision agriculture, robotic guidance systems, or autonomous driving require high-precision performance that is both affordable and optimized for low power consumption. In the beginning of 2016, several industry players, including u-blox, introduced L1-based GNSS receivers with integrated RTK technology. u-blox’ products, the NEO-M8P rover and reference modules, offer an end-to-end GPS/GLONASS (or GPS/BeiDou) L1 RTK solution that delivers high-precision performance while remaining both low-cost and energy-efficient. Such products are therefore well suited for mass-market applications. However, single-band RTK intrinsically suffers from well-known limitations such as slow convergence, difficulty to detect and correct cycle slips, or necessity to operate either in close vicinity of a reference station or in a dense reference station network. A new generation of receivers using multi-band technology is now introduced to address these limitations. This paper focuses on the ZED-F9P, a multi-band RTK module that can concurrently support all major GNSS constellations. It discusses the trade-offs associated with designing a very precise multi-GNSS multi-band RTK module solution while keeping cost, size, and power consumption as low as possible. It presents the results of several test campaigns. These are used both to highlight the benefits of dual-band and increased satellite availability in challenging environments, and to benchmark the performance of the ZED-F9P modules against existing high-end products. The integration of a second band alongside the legacy L1 has the potential to double the number of available measurements and significantly improve the availability, reliability, and accuracy of the navigation solution. However, despite the modernization and expansion efforts undertaken by all major GNSS programs, civilian signal availability remains limited outside the L1 band. While new and modernized civil signals in the L5 frequency offer attractive characteristics, especially in terms of multipath mitigation, there are currently more civil signals available in the L2 band. The ZED-F9P module first aims to maximize the availability of signals from the second and therefore supports signals from the L2 band in preference to the L5 band, that is, GPS L2C, GLONASS L2OF, Galileo E5b, and BeiDou B2I. With the exception of Galileo E5b, these signals are narrowband signals that are computationally less expensive to process than their broadband L5 counterparts. Their use therefore makes the solution more energy-efficient. The paper will go into more details about why such a combination of signals was found to be superior to the alternative of using signals from the L1 and L5 bands. To better distribute the computational load and increase processing efficiency, the u-blox core RTK technology relies on a dual-filter implementation (patents pending) that decouples the ambiguity estimation from the main navigation filter. In this way, the computationally expensive ambiguity estimation algorithms can run at a lower rate than the main navigation process. Considering that the computational cost of the ambiguity estimation algorithms grow rapidly with the number of ambiguity and that, in a multi-GNSS dual-band receiver like the ZED-F9P, the number of ambiguities can easily reach 40 or more, this dual-filter implementation can lead to significant computational savings, especially when operating at high navigation rate. As will be detailed in the paper, the reliability of this dual-filter implementation depends on carefully managed inter-filter communication and cycle-slip monitoring. To spread the computational load further and to simplify the receiver frequency plan, the ZED-F9P module combines two separate chips that are respectively tuned to the L1 and the L2 bands. This approach offers both flexibility and scalability. Indeed, the second chip could easily be modified to support L5 rather than L2 signals. Similarly, the system was designed such that a third chip could be added to enable triple-band solutions. The paper will describe how careful calibration of the inter-chip synchronization and rigorous monitoring of the inter-chip communication are implemented to guarantee that the measurements produced by the receiver meets the requirements of high-precision applications. A large amount of static, automotive and pedestrian data sets has been collected to assess the performance of the ZED-F9P modules and benchmark it against existing products. The overall position and velocity accuracy is evaluated using scenarios that cover a wide range of dynamics. The availability and reliability of the fixed RTK solution is measured using scenarios that include signal degradations such as obstructions caused by trees, bridges, or low-rise buildings. To confirm the suitability of the ZED-F9P modules for a wide range of land-based mass-market applications, most data sets were collected using low-cost patch antennas. Finally, since the BeiDou constellation has yet to reach global operational status, the benefits brought by this constellation are highlighted using data sets collected in Asia.

Zero Velocity Update (ZUPT) is an important update to aid an autonomous inertial pedestrian navigation. The objectives of this paper are to briefly revisit the concept of ZUPT and its importance, testing it on real walking pedestrian and comparing its performance when used with either conventional ‘Dead Reckoning approach (DR)’ or with ‘Kalman Filter approach (KF)’ as either one of these approaches is commonly used in literature. Performances were analyzed further with the inclusion of two correction modes (Linearly Weighted Interpolation and Residual Velocity). Experiments were performed using a low cost Inerital Measurement Unit (IMU) from MicroStrain (3DM-GX1). It was shown that the KF approach outperformed DRonly approach, but comparable performance with KF was noticed when DR is combined with correction mode. Finally, a combination of RV correction mode with forward KF solution was shown to improve the position output.

Indoor pedestrian navigation suffers from the unavailability of useful GNSS signals for navigation.Often a low-cost non-GNSS inertial sensor is used to navigate indoors.However, using only a low-cost inertial sensor for the system degrades its performance due to the low observability of errors affecting such low-cost sensors.Of particular concern is the heading drift error, caused primarily by the unobservability of z-axis gyro bias errors, which results in a huge positioning error when navigating for more than a few seconds.In this paper, the observability of this error is increased by proposing a method of rotating the inertial sensor on its y-axis.The results from a field trial for the proposed innovative method are presented.The method was performed by rotating the sensor mechanically-mounted on a shoe-on a single axis.The method was shown to increase the observability of z-axis gyro bias errors of a low-cost sensor.This is very significant because no other integrated measurements from other sensors are required to increase error observability.This should potentially be very useful for autonomous low-cost inertial pedestrian navigation systems that require a long period of navigation time.

Pedestrian navigation especially indoors suffers from the unavailability of useful GNSS signals for positioning. Alternatively, a low-cost Inertial Measurement Unit (IMU) positioning system that does not depend on the GNSS signal can be used for indoor navigation. However its performance is still compromised because of the fast-accumulating heading drift error affecting such a low-cost IMU sensor. This results in a huge positioning error when navigating more than a few seconds using only the low-cost sensor. In this study, real field trials results are presented when a foot-mounted IMU is rotated on a single axis. Two promising results have been obtained. First, it mitigates the heading drift error significantly and second, it increases the observability of IMU z-axis gyro bias error. This has resulted in a greatly reduced error in position for the low-cost pedestrian navigation system.

The Precise Point Positioning (PPP) concept enables centimetre-level positioning accuracy by employing one Global Navigation Satellite System (GNSS) receiver. The main advantage of PPP over conventional Real Time Kinematic (cRTK) methods is that a local reference network infrastructure is not required. Only a global reference network with approximately 50 stations is needed because reference GNSS data is required for generating precise error correction products for PPP. However, the current implementation of PPP is not suitable for some applications due to the long time period (i.e. convergence time of up to 60 minutes) required to obtain an accurate position solution. This paper presents a new method to reduce the time required for initial integer ambiguity resolution and to improve position accuracy. It is based on combining GPS and GLONASS measurements to calculate the float ambiguity positioning solution initially, followed by the resolution of GPS integer ambiguities.The results show that using the GPS/GLONASS float solution can, on average, reduce the time to initial GPS ambiguity resolution by approximately 5% compared to using the GPS float solution alone. In addition, average vertical and horizontal positioning errors at the initial ambiguity resolution epoch can be reduced by approximately 17% and 4%, respectively.

This paper analyses the impact of using Numerical Weather Modeling (NWM) based tropospheric corrections on float and fixed ambiguity Precise Point Positioning (PPP). Positioning performance when tropospheric delay is mitigated using the NWM based corrections is compared to the conventional method, where the delay is modeled locally using the University of New Brunswick’s UNB3m tropospheric zenith model, the Global Mapping Function (GMF) and Chen’s tropospheric gradient mapping function. The method was tested using datasets captured at three IGS and OS stations in the United Kingdom. From the results, using NWM based tropospheric corrections can reduce float position solution convergence time, improve the positioning accuracy at the initial ambiguity resolution epoch and decrease the probability of incorrect ambiguity resolution. In terms of the time required to obtain an initial ambiguity resolution, there is no difference between the methods.

Satellite positioning accuracy cannot meet the required needs due to lack of GPS signals inside buildings. Wi-Fi fingerprinting has become a popular method of overcoming problems in indoor positioning and navigation. Yet the accuracy of fingerprinting is rather limited and the system is prone to the changes of the building structure and Wi-Fi networks. However, if mobile users can share their signal as well as ranging and positioning information collaboratively to form a local network, the information could be used to correct failures in the fingerprinting process and provide more signal and information to derive robust positioning results. This paper implements collaborative positioning using Particle Filters which give the potential of utilizing additional positioning information whenever possible. The filter takes into account the uncertainty of indoor positioning results. Therefore, it provides a series of possible solutions and outputs the most likely result. Simulation tests are carried out to evaluate the performance of the proposed algorithm. Results are analysed and improvement in accuracy could be seen in the results.

Traditional positioning methods, such as conventional Real Time Kinematic (cRTK) rely upon local reference networks to enable users to achieve high-accuracy positioning. The need for such relatively dense networks has significant cost implications. Precise Point Positioning (PPP) on the other hand is a positioning method capable of centimeter-level positioning without the need for such local networks, hence providing significant cost benefits especially in remote areas. This paper presents the state-of-the-art PPP method using both GPS and GLONASS measurements to estimate the float position solution before attempting to resolve GPS integer ambiguities. Integrity monitoring is carried out using the Imperial College Carrier-phase Receiver Autonomous Integrity Monitoring method. A new method to detect and exclude GPS base-satellite failures is developed. A base-satellite is a satellite whose measurements are differenced from other satellite's measurements when using between-satellite-differenced measurements to estimate position. The failure detection and exclusion methods are tested using static GNSS data recorded by International GNSS Service stations both in static and dynamic processing modes. The results show that failure detection can be achieved in all cases tested and failure exclusion can be achieved for static cases. In the kinematic processing cases, failure exclusion is more difficult because the higher noise in the measurement residuals increases the difficulty to distinguish between failures associated with the base-satellite and other satellites.

Indoor positioning technologies have the potential to be a revolutionary technology in the same way that mass market access to GPS revolutionised outdoor navigation. No single indoor positioning technology currently exists to provide a solution for every indoor navigation use case, however many candidate technologies have been proposed. Future indoor navigation systems are likely to need to call upon the strengths of many technologies to provide a hybrid position solution. We review a selection of state of the art indoor positioning technologies and discuss the results of a Context of Use analysis of potential user groups in this domain. Based on the result of this analysis we suggest challenges that need to be tackled by future research in this area.

The resolution of GPS carrier-phase ambiguities when carrying out Precise Point Positioning (PPP) has been a major research challenge in recent years. There are two main fixed-ambiguity PPP methods: Fractional Cycle Bias (FCB) estimation and Integer-Recovery Clock (IRC). Both methods should in theory provide a similar level of performance.The main challenge when using these existing fixed-ambiguity PPP methods is the long time period (up to 60 minutes) required to obtain the ambiguity fixed PPP solution. For a wide range of applications e. g. land surveying, this long convergence period is not acceptable.It has been shown that using GLONASS with GPS could facilitate accurate float PPP solution with a lower convergence time compared to GPS alone. Therefore, it is interesting to explore if using GPS/GLONASS float solution could improve fixed-ambiguity PPP. To date, not much work has been done related to the effect of using both GPS and GLONASS when performing fixed-ambiguity PPP. In this paper, GPS ambiguities only are attempted to be fixed to integers and GLONASS ambiguities are kept as float values (i.e. the paper is investigating the impact of using GLONASS float solutions on ambiguity fixed GPS PPP).Imperial College's minimum constellation method which is based on testing ambiguity fixing for all possible combinations of satellites, is used in this paper when attempting to fix ambiguities. The main aim is to reduce the time required to the initial ambiguity resolution and make ambiguity resolution possible in a larger number of scenarios.Ambiguity validation is carried out by using the ratio test and variable threshold computation driven by the required confidence level. Furthermore, integrity monitoring of the solution is carried out using the state of the art Carrier-phase Autonomous Integrity Monitoring (CRAIM).The results show that inclusion of GLONASS can reduce the time required to obtain an initial GPS ambiguity resolution, by at least, 10% compared to employing GPS alone. In addition, using both GPS and GLONASS can reduce the 3D and vertical position error at the initial ambiguity resolution epoch.

Shoe mounted Inertial Measurement Units (IMU) are often used for indoor pedestrian navigation systems. The presence of a zero velocity condition during the stance phase enables Zero Velocity Updates (ZUPT) to be applied regularly every time the user takes a step. Most of the velocity and attitude errors can be estimated using ZUPTs. However, good heading estimation for such a system remains a challenge. This is due to the poor observability of heading error for a low cost Micro-Electro-Mechanical (MEMS) IMU, even with the use of ZUPTs in a Kalman filter. In this paper, the same approach is adopted where a MEMS IMU is mounted on a shoe, but with additional constraints applied. The three constraints proposed herein are used to generate measurement updates for a Kalman filter, known as ‘Heading Update’, ‘Zero Integrated Heading Rate Update’ and ‘Height Update’.The first constraint involves restricting heading drift in a typical building where the user is walking. Due to the fact that typical buildings are rectangular in shape, an assumption is made that most walking in this environment is constrained to only follow one of the four main headings of the building. A second constraint is further used to restrict heading drift during a non-walking situation. This is carried out because the first constraint cannot be applied when the user is stationary. Finally, the third constraint is applied to limit the error growth in height. An assumption is made that the height changes in indoor buildings are only caused when the user walks up and down a staircase. Several trials were shown to demonstrate the effectiveness of integrating these constraints for indoor pedestrian navigation. The results show that an average return position error of 4·62 meters is obtained for an average distance of 1557 meters using only a low cost MEMS IMU.

An integrated positioning solution termed 'collaborative positioning' employs multiple location sensors with different accuracy on different platforms for sharing of their absolute and relative localizations. Typical application scenarios are dismounted soldiers, swarms of UAV's, team of robots, emergency crews and first responders. The stakeholders of the solution (i.e., mobile sensors, users, fixed stations and external databases) are involved in an iterative algorithm to estimate or improve the accuracy of each node's position based on statistical models. This paper studies the challenges to realize a public and low-cost solution, based on mass users of multiple-sensor platforms. For the investigation field experiments revolved around the concept of collaborative navigation, and partially indoor navigation. For this purpose different sensor platforms have been fitted with similar type of sensors, such as geodetic and low-cost high-sensitivity GNSS receivers, tactical grade IMU's, MEMS-based IMU's, miscellaneous sensors, including magnetometers, barometric pressure and step sensors, as well as image sensors, such as digital cameras and Flash LiDAR, and ultra-wide band (UWB) receivers. The employed platforms in the tests include a train on a building roof, mobile mapping vans, a personal navigator and a foot tracker unit. In terms of the tests, the data from the different platforms are recorded simultaneously. Several field experiments conducted in a week at the University of Nottingham are described and investigated in the paper. The personal navigator and a foot tracker unit moved on the building roof, then trough the building down to where it logged data simultaneously with the vans, all of them moving together and relative to each other. The platforms then logged data simultaneously covering various accelerations, dynamics, etc. over longer trajectories. Promising preliminary results of the field experiments showed that a positioning accuracy on the few meter level can be achieved for the navigation of the different platforms.

The effect of unmitigated tropospheric delay remains as one of the major error sources in the high accuracy positioning techniques, such as Precise Point Positioning (PPP). While the hydrostatic tropospheric delay can be well estimated to an accuracy of better than 5 mm (if surface pressure measurements are provided), the non-hydrostatic (or wet) delay is hard to model accurately due to the rapid spatial and temporal variation of the water vapour. In the traditional Slant Tropospheric Delay (STD) estimation methods, Zenith Tropospheric Delay (ZTD) is estimated first, then it is mapped on to individual satellite to receiver path. However both ZTD estimation and the mapping function have limited accuracy, especially when real time atmospheric profile information is not available Besides, the mapping of the single zenith delay implies that the local atmosphere profile is symmetric around the receiver site, which does not valid in the event of weather front passing. In some severe cases, the azimuth dependent residue left on each STD will significantly deteriorate the PPP performance, in terms of both convergence time and height accuracy.In this study, a new approach to estimate the STD is proposed, which is based on two external resources: Numerical Weather Modelling (NWM) and observations from the local Continuously Operating Reference Stations (CORS) network. NWM could provide the STD estimation with real time information on the actual atmosphere, and the CORS observations could further enhance the temporal resolution of the NWM based STD estimation Benefiting from these two extra inputs, applying the proposed tropospheric delay estimation could improve PPP positioning accuracy and efficiency, especially its convergence time. As both NWM and CORS observation can be available in real time, this study is aiming to propose an operational STD estimation solution. The algorithm of estimating STD using the integration of NWM and CORS observations is described, and preliminary test results are shown.

Foot mounted inertial navigation is an effective method for obtaining high quality pedestrian navigation solutions from MEMS sensors. Zero-Velocity information from stationary periods in the step-cycle can be used to regularly correct position drift and update estimates of the inertial sensor biases, hence dramatically improving the navigation solution. However the causes of heading error remain poorly observable and so foot mounted inertial navigation suffers from considerable drift over time. To address this problem the authors previously developed Cardinal Heading Aided Inertial Navigation (CHAIN). CHAIN makes use of the fact that when in a building, obstacles such as corridors and furniture constrain pedestrians to move in one of four directions parallel to the outside walls of the building. This knowledge is then appropriately weighted and used in an Extended Kalman Filter to improve error estimation. Although the CHAIN method is very effective at improving the quality of the heading estimates, position errors still accumulate with time, and threshold tests are required to cope with periods of motion away from the cardinal headings. In this work we investigate the use of a building floor plan to further aid navigation. This is achieved using a particle filter approach whereby particles which cross walls are removed and those which navigate in open spaces are allowed to continue. Previously the particle filter approach has been computationally intensive process requiring many particles to effectively model the navigation errors. In our work we recognise that heading is the primary source of navigation error and therefore incorporate heuristic heading information into the particle filter design. By weighting particles according to their heading we reduce the number of particles required to maintain a small failure rate and improve system performance in more open areas where there are few mapped walls to aid navigation. This paper will describe the design of our particle filter and the heuristic heading approach. Results from a number of representative test walks using a MEMS IMU will be used to demonstrate the system performance. The use of CHAIN is shown to be capable of significantly reducing the filter failure rate from 44% to 14% when a small number of particles is used in the filter (250) and the initial position is poorly known.

This work aims to optimise the use of GNSS for intialising a foot mounted pedestrian INS. The INS error model used in many navigation filters requires the input of an accurate, precise initial position and heading for stable filter performance. However such information is often not available at the start of a navigation session. Often a rough initialisation is performed and position updates are used to refine the INS state vector error estimates over time. This approach is often ineffective in the early portion of a navigation session and can lead to poor filter performance.In this work two innovative methods for using High Sensitivity GPS (HSGPS) observations are developed. In the first, position estimates from the receiver are used to determine rotation and translation parameters for an INS trajectory. This allows the INS to be initialised with an approximate initial position and arbitrary but precise heading. The INS then uses established methods for a foot mounted INS to minimise the relative position drift for a short period of navigation. When sufficient HSGPS positions have been obtained a weighted least squares approach is used to determine the parameters required to rotate and translate the INS trajectory. Once this has been successfully achieved, the INS may change to a traditional loosely coupled GNSS / INS format for longer term navigation.The second method investigates the use of GNSS to provide 'course over ground' measurements to the INS. These estimates can be used by the filter to directly address the problem of heading drift and can therefore be more effective than position updates in a foot mounted pedestrian INS. In this work we test the ability of course over ground measurements to control drift over time. However it is suggested that they can be directly used to determine rotation parameters for initialisation in the style proposed in the first method.To obtain course over ground observations we use the GNSS Kinematic Time Differencing method to difference GPS code pseudorange and carrier phase observations between measurement epochs. We develop an innovative approach to strengthen these estimates by utilising distance traveled information from the INS. It is suggested that in future, the INS information may be used to reduce the number of satellites required to obtain a course over ground estimate.Testing reveals that the course over ground measurement is an effective aiding measurement for a foot mounted INS. It is shown to be superior to HSGPS position aiding when sparse but precise course over ground estimates are available.

Precise Point Positioning (PPP) has been one of the major research interests in the Global Navigation Satellite System (GNSS) research field in recent years. PPP is a promising method because it can provide centimeter level positioning accuracy by using only one GNSS receiver, without using local reference networks. It is clear that this can provide cost savings compared to the traditional Real Time Kinematic (RTK) method, particularly if high accuracy positioning is required in remote areas.

Low cost MEMS sensors have been shown to provide high accuracy positioning when mounted on a user's foot through the use of zero velocity updates (ZUPT) every time the user takes a step. Although position drift is greatly reduced using ZUPTs, position errors will still accumulate over time, particularly due to heading errors which are weakly observable through zero velocity updates. Measurements from GNSS can be used to restrict position drift; however, for good GNSS reception, the GNSS antenna is usually placed on the user's back which results in a non-constant separation between the GNSS antenna and IMU. This reduces the effectiveness of GNSS measurements since the lever arm is unknown. Instead, it is much better for IMU integration if the IMU and GNSS antenna are collocated. This is because the GNSS receiver and IMU experience the same dynamics, and the GNSS measurements can be used directly to correct the IMU. This should improve INS error observability, however, the antenna position results in a significantly compromised view of the sky. This paper examines the use of GPS and GLONASS measurements from a foot mounted antenna and explores two different methods for integration with the foot mounted IMU. The first method uses GNSS derived course over ground measurements to reduce INS drift, and the second tightly integrates carrier phase measurements and attempts to resolve ambiguities. It is shown that the combination of a MEMS IMU with GNSS carrier phase measurements can be used to maintain a centimeter level accuracy trajectory even though the sky view is compromised.

In environments where GNSS is unavailable or not useful for positioning, the use of low cost MEMS-based inertial sensors has paved a way to a more cost effective solution. Of particular interest is a foot mounted pedestrian navigation system, where zero velocity updates (ZUPT) are used with the standard strapdown navigation algorithm in a Kalman filter to restrict the error growth of the low cost inertial sensors. However heading drift still remains despite using ZUPT measurements since the heading error is unobservable. External sensors such as magnetometers are normally used to mitigate this problem, but the reliability of such an approach is questionable because of the existence of magnetic disturbances that are often very difficult to predict. Hence there is a need to eliminate the heading drift problem for such a low cost system without relying on external sensors to give a possible stand-alone low cost inertial navigation system. In this paper, a novel and effective algorithm for generating heading measurements from basic knowledge of the orientation of the building in which the pedestrian is walking is proposed to overcome this problem. The effectiveness of this approach is demonstrated through three field trials using only a forward Kalman filter that can work in real-time without any external sensors. This resulted in position accuracy better than 5 m during a 40 minutes walk, about 0·1% in position error of the total distance. Due to its simplistic algorithm, this simple yet very effective solution is appealing for a promising future autonomous low cost inertial navigation system.

This paper describes the loose integration of GPS positions with a foot-mounted inertial navigation system (INS).Pedestrian navigation using foot mounted inertial measurement units (IMUs) has been the subject of intensive research in recent years. By mounting the IMU on a persons foot the positioning algorithm can make use of frequent zero-velocity updates (ZUPTs). Such a technique makes it possible to use poor quality, mass market MEMs IMUs for accurate pedestrian navigation.Foot mounted INS still suffer from position drift over time and require the initialisation of position and heading from independent sensors. In previous papers the authors have described a method for reducing position drift through the use of 'building heading aiding' which makes simple assumptions about the restrictions on a pedestrians movement when inside a building.In this work we describe the use of two forms of GPS position estimates in the navigation algorithm. Use of these positions when they are available both reduces position drift and allows initialisation of the systems position. When the pedestrian is moving, position aiding will also aid the estimation of heading and z-axis gyro bias.For comparison two forms of GPS position are used. Real Time Kinematic (RTK) carrier phase position is used to provide highly precise position estimates at the expense of solution availability and affordability. It is anticipated that the integrated RTKGPS / INS approach will provide a high accuracy reference trajectory, against which other approaches may be compared. We describe the use of high precision position estimates in a pedestrian INS, accounting for the varying GPS antenna to IMU leverarm.Secondly we integrate position solutions from a high sensitivity GPS receiver (HSGPS). These receivers are mass market products already available in many mobile computing devices. They provide position solutions in most environments, even indoors and in urban canyons. However the positions provided are of low precision and suffer from biases caused by signal blockage, attenuation and reflection.Through a test walk encompassing many environments, from open sky to indoors, we show that the HSGPS based approach can give a trajectory which closely matches that obtained using the RTKGPS approach, despite a system cost difference greater than $US20k. Over a walk 25 minutes long the mean position difference between the two approaches was 4.14m, with a maximum of 12m during initialisation. We find that the high solution availability from the HSGPS receiver combined with an appropriate weighting model compensates for the lack of precision when compared to the RTKGPS method.Finally the paper discusses possible approaches for the integration of GPS with a pedestrian INS in the future.