Nonlinear Modeling of Azimuth Error for 2D Car Navigation Using Parallel Cascade Identification Augmented with Kalman Filtering

Land vehicle positioning relies mostly on satellite navigation systems such as the Global Positioning System (GPS). However, GPS signals may be degraded or suffer from blockage in urban canyons and tunnels, and the positioning information provided is interrupted. One solution for such a problem is to integrate GPS with an inertial measurement unit (IMU) and the navigation solution is achieved using an estimation technique which is traditionally based on a Kalman filter (KF). In order to have a low cost navigation solution for land vehicles, MEMS-based inertial sensors are used. To further reduce the cost a reduced inertial sensor system (RISS) which consists of only one gyroscope and a speed sensor is integrated with GPS. The position and velocity errors can be estimated by a KF relying on RISS dynamic error model and GPS position and velocity updates. However, low-cost MEMS sensors suffer from complex error characteristics, which are difficult to model by the linearized KF models. The positional accuracy of the integrated system can be improved using Parallel Cascade Identification (PCI) that is cascaded with the KF. The proposed augmented KF-PCI method can handle both linear and nonlinear system errors as the linear parts of the errors are modeled inside the KF and the nonlinear residual RISS errors are modeled by PCI. The performance of this method is examined by road test trajectories in a land vehicle and compared to KF.

Navigation of land or self-driving vehicles is essential for safe and accurate travel. The global navigation satellite systems (GNSSs), such as global positioning system (GPS) are the primary sources of navigation information for such purpose. However, high-rise buildings in urban canyons block the GPS satellites signals. Alternatively, inertial navigation system (INS) is typically working as a backup. A reduced inertial sensor system (RISS) is used instead of the full INS to achieve the same purpose in land vehicles navigation with fewer sensors and computations. Unfortunately, the RISS solution drifts over time, but this can be mitigated when integrated with the GPS. However, the integration solution drifts in the case of GPS signal loss (outages). Therefore, the position errors grow especially during extended periods of GPS outages. Azimuth/heading angle is critical to keep the vehicle on the route. In this paper, an azimuth update estimated from a calibrated magnetometer is introduced to improve the accuracy of the overall system. A new approach is proposed for pre-processing the magnetometer data utilizing a discrete-cosine-transform (DCT)-based pre-filtering stage. The obtained azimuth is utilized in updating the RISS system during the whole trajectory and mainly during GPS outage periods. The proposed approach significantly decreases both the azimuth error and the position error growth rate when driving in urban canyons where the GPS signals are blocked. Finally, the proposed system was tested on a real road trajectory data for a metropolitan area. The results demonstrate that the accuracy of the whole system improved, especially during the GPS outage periods.

The Global Navigation Satellite Systems (GNSS) such as Global Positioning System (GPS) are the prime land and autonomous vehicles navigation information source. However, urban canyons high rise buildings block the GPS signal. Therefore, the Inertial Navigation System (INS) or the Reduced Inertial Sensor System (RISS) are utilized as an alternative source of the navigation information. The RISS is able to produce a full navigation solution using less number of sensors and calculations. Dramatically, the RISS solution drifts over time as the INS system. The integration between the GPS and the RISS is mitigating each system drawbacks. However, during the GPS outage periods, the navigation system is only depending on the inertial measurements. The RISS is utilizing the odometer/speedometer to measure the vehicle's forward speed. Many types of error affect the odometer measurements. These errors are either due to the vehicle's specifications as the differences in wheel diameters, and/or inefficient wheelbase or the road nature as wheel slips, uneven road surfaces, and/or skidding. In this paper, a Radar-based RISS system is introduced to take place the traditional RISS or INS systems. The radar unit is an essential part of the adaptive cruise control (ACC) system. Furthermore, it is capable of measuring the relative velocity and distance between the carrying and the in front vehicle to reduce collisions and increase the safety of driving. In addition, the radar measurements are not affected by the error sources that affect the odometer. The idea is to invest the ground reflection and derive the forward speed of the onboard vehicle as the ACC keep a safe distance between the cars. A novel method based on extracting the ground reflection features is introduced to obtain the onboard vehicle's speed. The obtained speed is utilized with two accelerometers and one vertical gyroscope to produce the Radar-based RISS system. The proposed system has been integrated with the GPS producing a Radar-based RISS/GPS integrated navigation system. The system has been tested on a real road trajectory in a downtown area and involved several GPS signal outages. The results show the significant capabilities of the proposed system in keeping the navigation solution drift to a minimum especially when the GPS signal is in outage compared with the traditional RISS/GPS system.

Autonomous and Land vehicles call for continuous and robust navigation information. Such vehicles, typically depend on one of the Global Navigation Satellite Systems (GNSS) such as the Global Positioning System (GPS) to provide the navigation information. However, GPS signal is vulnerable to many error sources starting such as atmospheric effects, multipath, or full signal blockage. Driving in urban canyons with high rising buildings causes an outage of the GPS signal. Therefore, another system is required to take its place to maintain the navigation information provided to the vehicle. The Inertial Navigation system (INS) such as Reduced Inertial Sensor System (RISS) is the typical choice to carry out this job. However, RISS system solution error grows with time. The RISS/GPS, integrated navigation system, benefits from both systems advantages and reduces the error. However, when there is an outage of GPS signal, the integrated system drifts because of low-cost Inertial Measurement Unit (IMU) that typically used. To solve this problem and improve the overall system performance especially during GPS signal outages, a magnetic-based RISS/GPS integrated system (Mag/RISS/GPS) is introduced. The magnetic system is a 3D-magnetometer providing measurement used for the azimuth angle calculation. The correct azimuth angle is critical in velocity and position calculations. Updating the conventional RISS/GPS system with azimuth from magnetometer enhanced the overall system performance. The introduced system Mag/RISS/GPS tested on real trajectory data from different IMU units with different specifications. The results show a significant enhancement when using the proposed system compared with the traditional RISS/GPS especially when GPS signal blocked.

This paper presents an adaptive learning method for data fusion in autonomous\ndriving vehicles. The localization is based on the integration of Inertial\nMeasurement Unit (IMU) with two Real-Time Kinematic (RTK) Global Positioning\nSystem (GPS) units in an adaptive Kalman filter (KF). The observability\nanalysis reveals that $i$) integration of a single GPS with IMU does not\nconstitute an observable system; $ii$) integration of two GPS units with IMU\nresults in a locally observable system provided that the line connecting two\nGPS antennas is not parallel with the vector of the measured acceleration,\ni.e., the sum of inertial and gravitational accelerations. The latter case\nmakes it possible to compensate for the error in the estimated orientation due\nto gyro drift and its bias without needing additional instruments for absolute\norientation measurements, e.g., a magnetic compass. Moreover, in order to cope\nwith the fact that GPS systems sometimes lose their signal and receive\ninaccurate position data, the self-tuning filter estimates the covariance\nmatrix associated with the GPS measurement noise. This allows the KF to\nincorporate GPS measurements in the data fusion process heavily only when the\ninformation received by GPS becomes reliably available.\n

The car navigation service has opened through the global positioning system (GPS). People's lives have changed through car navigation services, and this service is indispensable nowadays. GPS provides reliable location accuracy in open space, but its location accuracy is degraded where multipath and other error factors are present. In particular, since GPS signals must be received in order to calculate the position, the navigation system cannot calculate the position in an indoor space such as underground parking lot, subway or tunnel where GPS signals are cut off. When car navigation is performed in an indoor parking lot where GPS signals are not transmitted, the user cannot calculate the current location, so the user has to go out by relying on the traffic sign because he does not know the current location. Also, when the user leave the parking lot, the car navigation system has problems. In an urban canyon, it is difficult to calculate accurate current position due to multipath. Because the route is calculated through the current location and route, the car navigation system provides the wrong route to the user. In addition, when entering a tunnel while using car navigation, the car navigation system calculates the position using the initial tunnel entry velocity estimated by the GPS. As the speed of the user increases or decreases, the position error also increases. Then the position suddenly changes when passing through the tunnel. And it is difficult to provide a proper guidance at the branch road or exit in the tunnel because of the positioning error. People are demanding a complete car navigation service, but the current navigation service provides the wrong route and position information to the user where the GPS signal quality is poor. In order to solve the above problem, in this paper, we have studied to improve the user's position accuracy by using LTE signal and mobile phone internal sensor. In general, observed time difference of arrival (OTDOA) technique is applied for the LTE based positioning. Because this method gives tens of meters of error, it is difficult to be used for the car navigation service. In our research, we estimate the position using the LTE signal strength. The difference from conventional fingerprinting method is that instead of using only current signal strength, the signal strength pattern accumulated while moving is utilized to estimate the user position. The signal strength increases as the user approach the access point (AP), and decreases as the user move away from the AP. Therefore, when a user moves and stores signal strength with respect to a specific AP, a pattern that could occur only in the corresponding region is saved. There are many similar candidates in the database when using only the current signal strength. However, when using a pattern, similar candidates exist only around the true position. And, when using several LTE signals, the positioning accuracy could be increased. Since the user receives multiple LTE signals, the pattern becomes surface. The surface is compared with the database, and the position with the highest correlation value is estimated as the current position. We call this method surface correlation (SC). In order to verify the performance of the proposed method, experiments were conducted in the Gangnam beltway tunnel in Seoul. The testbed consisted of three tunnel and total distance is almost 11.8 Km. In the tunnel, repeater and several antennas are installed to widen the area of the LTE signal. When an LTE signal is received by the vehicle, the largest signal strength is received every time when the vehicle passes the nearest antenna. As a result, for a specific LTE signal, when the vehicle has passed through the LTE signal region, a unique pattern with multiple peaks is generated. In order to estimate the position, we first estimate the position with the SC technique. Then, the estimated position is corrected by comparing the peak of the currently received pattern with the peak of the database. Through this method, the position was estimated with an error of less than 50meter in the tunnel. Conclusion In this research, the LTE signal strength based positioning technology is presented. Especially, this technology, SC, provides the positioning performance that could be used for the car navigation in the tunnel where GPS signal is blocked. We expected that user would be use the accurate car navigation service through this technology in the GPS challenging environment.

This paper reviews the pivotal role of Global Positioning System (GPS) and Inertial Measurement Unit (IMU) technologies in the navigation and control of Unmanned Aerial Vehicles (UAVs). GPS offers precise global positioning, while IMU provides high-frequency motion and orientation data. However, GPS signal interruptions and IMU drift pose challenges, particularly in dynamic or GPS-denied environments. This review explores the integration of GPS and low cost IMU systems through advanced sensor fusion techniques, such as Kalman filtering and machine learning, to enhance navigation reliability. Future directions, including advancements in hardware, adaptive algorithms, and swarm navigation, are discussed to address operational challenges and unlock the potential of UAVs in diverse applications.

In the recent decades, the integration of a MEMS-based (Micro-Electro Mechanical Systems) Inertial Measuring Unit (IMU) with a GNSS receiver-chip has become commonly used navigation techniques by virtue of their advantages such as their complementary characteristics overcome each other's shortcomings, and small sized, light weight, with low power consumption, and have extremely low cost. However, when there is no external auxiliary information, the inertial navigation error based on low-cost MEMS IMU will quickly accumulate over time. Reducing inertial navigation error has great potential in many applications. Conventional ground vehicles are restricted by Non-Holonomic Constraint (NHC), during the outage of GPS. Although the navigation performance of the inertial system with only NHC as the motion constraint is improved, the observability of the system is still poor due to the lack of forward speed constraint. According to MEMS IMU, an innovative method is proposed to improve the observability of the inertial navigation system through continuous rotation of a single MEMS IMU around a certain axis, during the outage of GPS. In this paper, although the azimuth error is still unobservable, both theoretical analysis and the results indicate that the system observability is improved by rotating IMU. The enhanced estimability of the gyro bias in the vertical direction can effectively mitigate the azimuth error accumulation.

Focusing on the determining attitude of land vehicles which equipped with antenna platform for communication satellite, the mathematical model of the integrated navigation system of Strapdown Inertial Navigation System(SINS) and Global Positioning System(GPS) has been built at first. Then the observability of the attitude errors of the integrated navigation system has been concluded to be very weak, and the observability of the heading error is weaker than the horizontal attitude's. Due to this characteristic and the requirement of miniaturization and low-cost, a new inertial measurement unit (IMU) structure has been designed, which comprises three gyros and three accelerometers. Two MEMS gyros were installed on the horizontal plane, and a high degree of accuracy fibre optical gyro was installed in the vertical direction. During navigation period, the Kalman filter is used for calculating the navigation information. Both the theoretical analysis and simulation results show that the new unsymmetrical design of IMU can improve the precision of heading significantly, and when this IMU is integrated with GPS, the attitude precision can meet the requirement of the antenna platform control.

Automated vehicles (AVs) have gained increasing interest over the past few years. A crucial feature of these vehicles is an accurate and robust positioning system. Global navigation satellite system (GNSS) precise point positioning (PPP) can achieve decimeter-level accuracy without the need for local reference stations. Nevertheless, the solution availability is affected by GNSS signal outages, which frequently occur in AVs driving scenarios. The integration with an inertial navigation system (INS) provides a continuous positioning solution; however, high-end inertial sensors are bulky and expensive. The recent improvements to the low-cost micro-electro-mechanical (MEMS) sensors opened the way to utilize these sensors in high-precision applications. The objective of this work is to investigate the performance of integrating dual-frequency PPP with low-cost MEMS sensors for land vehicles on highways and suburban areas. Furthermore, the Reduced Inertial Sensor System (RISS) is used instead of the traditional INS system. RISS uses two horizontal accelerometers and one vertical gyroscope in addition to the vehicle odometer, eliminating two gyroscopes and one accelerometer compared to the full IMU system. The lower number of sensors contributes to reducing the error growth over time and reducing the system cost and complexity. A road test was performed that included suburban areas and highway driving with multiple overpasses. The result showed that the developed PPP/RISS system was able to achieve decimeter-level rms positioning errors and a maximum of one meter horizontal positioning error.

The subject of this paper is the fusion of measurement data from IMU (Inertial Measurement Unit), GPS (Global Positioning System) sensors and cameras used in autonomous vehicles to locate an autonomous land vehicle. The experimental part presents the design of a fusion filter, which based on the fusion of data from the IMU, GPS, and camera can determine the position, orientation and speed of the land vehicle. The design of the fusion filter was tested using several simulations in the Matlab environment, the results of which are analyzed at the end of this paper.KeywordsAutonomous vehiclesGPSIMUMVO

A trajectory reference system based on the Global Positioning System (GPS) can be used instead of conventional radars during missile flight tests. The high quality of the GPS-based trajectory reference makes it useful for evaluating the performance of the missiles' Inertial Measurement Units (IMUs). Such a system was installed and flight tested on two recently launched ballistic missiles. The GPS hardware configuration used on these flights is described. A Kalman filter approach is used to estimate individual IMU errors based on the GPS range and delta range data. The ability of the GPS-based system to identify IMU errors is compared to that of radar; GPS is found to provide superior estimates of the IMU errors.

We present a novel technique to send GPS coordinates to other mobiles through short message service (SMS) based on Global Positioning System (GPS) technology. Two algorithms, Kalman filter and velocity renovation, which can be used in conjunction with GPS are used as a basis for location tracking. This technique can be used to help people, using their mobile with or without GPS, to find the location of a friend using Google maps. The first coordinates are generated from a GPS assisted mobile on Google map, this location is then sent through SMS to another person. The latter can then see the exact location of the sender on his map with an accuracy of 0.57 m (Table 1). The advantages of this technology is by using existing equipments and free services like Google maps and GPS, we can construct a very reliable location tracking system. The basis of this program is GPS. GPS is a satellite-based navigation system made up of a network of 24 satellites placed into orbit by the United States (US) Department of Defense (DoD). GPS was originally intended for military applications, but in the 1980s, the government made the system available for civilian use. GPS can show you your exact position on the Earth in any weather conditions, anywhere in the world, 24 hours a day. There are no subscription fees or setup charges to use GPS. The improved location tracking algorithm which uses the Kalman filter with the velocity renovation process is implemented. The velocity renovation process consists of a velocity estimator and direction finder. By this process, the proposed algorithm can use accurately estimated velocity in the location estimation.

Now a day navigation system has been receiving a high demand for numerous applications. Among these systems global positioning system (GPS) is more popular which is frequently using for the aircraft for navigation and autonomous navigation purposes. However GPS suffers because of continuity and reliability problems in many applications. In this proposed model a novel architecture of kalman filter has been selected to integrate with GPS to overcome above problem. A software defined radio (SDR) is used for the GPS data extraction which is nothing but an analog to digital converter. The integrated GPS with kalman filter system can be able reduce velocity, position and attitude errors compared to the many conventional algorithms. To verify this technique a real time model is created using Simulink/Matlab. The simulated outcomes confirm the advantages of assimilated system in both urban and open areas and suitable for real time implementations.

In this paper, an Adaptive Neuro-Fuzzy Inference System (ANFIS) is proposed for low-cost navigation of ground vehicles. The ANFIS structure is proposed for fusing the measurements of the Global Positioning System (GPS) and inertial measurement unit (IMU). Low-cost GPS receivers provide the position and velocity of the vehicle at a lower rate than the IMU sampled vehicle dynamics. Additionally, the GPS measurements might be missed/delayed due to receiver's inability to lock on the signal or due to obstruction from neighboring vehicle's or infrastructures. Therefore, the variable delay between the IMU and GPS signals is selected as an additional input to the ANFIS architecture. The performance of the proposed ANFIS architecture is demonstrated through experimental results. The instrumentation system used in this experiment consists of a microcontroller system, a GPS receiver, and an IMU.