Augmenting Kalman Filtering with Parallel Cascade Identification for Improved 2D Land Vehicle Navigation

Present land vehicle navigation relies mostly on the Global Positioning System (GPS) that may be interrupted or deteriorated in urban areas. In order to obtain continuous positioning services in all environments, GPS can be integrated with inertial sensors and vehicle odometer using Kalman filtering (KF). For car navigation, low-cost positioning solutions based on MEMS-based inertial sensors are utilized. To further reduce the cost, a reduced inertial sensor system (RISS) consisting of only one gyroscope and speed measurement (obtained from the car odometer) is integrated with GPS. The MEMS-based gyroscope measurement deteriorates over time due to different errors like the bias drift. These errors may lead to large azimuth errors and mitigating the azimuth errors requires robust modeling of both linear and nonlinear effects. Therefore, this paper presents a solution based on Parallel Cascade Identification (PCI) module that models the azimuth errors and is augmented to 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 and residual parts of the azimuth errors are modeled by PCI. The performance of this method is examined using road test experiments in a land vehicle.

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.

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.

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.

The global positioning system (GPS) has become the most extensively used positioning and navigation tool in the world. Applications of GPS abound in surveying, mapping, transportation, agriculture, military planning, GIS, and the geosciences. However, the positional and elevation accuracy of any given GPS location is prone to error, due to a number of factors. The applications of Global Positioning System (GPS) positioning is more and more popular, especially the intelligent navigation system which relies on GPS and Dead Reckoning technology is developing quickly for future huge market in China. In this paper a practical combined positioning model of GPS/DR/MM is put forward, which integrates GPS, Gyro, Vehicle Speed Sensor (VSS) and digital navigation maps to provide accurate and real-time position for intelligent navigation system. This model is designed for automotive navigation system making use of Kalman filter to improve position and map matching veracity by means of filtering raw GPS and DR signals, and then map-matching technology is used to provide map coordinates for map displaying. In practical examples, for illustrating the validity of the model, several experiments and their results of integrated GPS/DR positioning in intelligent navigation system will be shown for the conclusion that Kalman Filter based GPS/DR integrating position approach is necessary, feasible and efficient for intelligent navigation application. Certainly, this combined positioning model, similar to other model, can not resolve all situation issues. Finally, some suggestions are given for further improving integrated GPS/DR/MM application.

Accurate, reliable, and robust global positioning system (GPS) localization is desirable for many navigation applications. Unfortunately, it is challenging for a single GPS receiver to always provide accurate positioning solutions. In urban environments, intermittent signal availability leads to degraded GPS signal tracking and position estimation of the single GPS receiver. In addition, equipment malfunction of the single GPS receiver results in inaccurate navigation solutions. This paper presents a deeply coupled multireceiver vector tracking (MRVT) architecture that improves the reliability and robustness of GPS signal tracking and position estimation. MRVT jointly tracks GPS signals received by multiple GPS receivers, mitigating GPS signal tracking disruptions, improving the reliability of GPS localization in periods of intermittent signal availability. In addition, the MRVT receiver is more robust to equipment malfunctions than the single GPS receiver. We implemented an MRVT receiver using commercial radio frequency front ends and our PyGNSS software. We experimentally validated the reliability of our MRVT receiver in periods of intermittent GPS availability experienced in downtown San Francisco. Our MRVT receiver exhibited consistent GPS signal tracking and position estimation as compared to vector tracking. In addition, we experimentally validated the robustness of our MRVT receiver to the failure of a single GPS receiver.

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.

In current state-of-the-art navigation systems combining a global positioning system (GPS) and an inertial navigation system (INS), the two subsystems are complimentary, and the combined system only works when GPS signals are available. Much research is being undertaken on methods to replace the role of the GPS in the GPS-INS systems for use in environments where the GPS signals are not available, including areas such as urban canyons, indoors, and areas where the GPS signals have been jammed. In this paper we present a method of replacing the GPS-aiding of the INS system, by using a ranging sensor such as a lidar or radar combining with an evidence grid methodology. The evidence grid is used to create three-dimensional maps based on the measurements of the ranging sensor and the inertial system. These maps are cross-correlated in time to determine how best to correct for navigation drift, with the corrections then fed back into the extended Kalman filter to aid the inertial navigation solution. Experimental results, based on lidar measurements, show that the method is capable of remarkable accuracy, able to correct errors of less than one meter over six minutes of travel.

The application of unmanned vehicles in civilian and military fields is increasingly widespread. Traditionally, unmanned vehicles primarily rely on Global Positioning Systems (GPSs) for positioning; however, GPS signals can be limited or completely lost in conditions such as building obstructions, indoor environments, or electronic interference. In addition, countries are actively developing GPS jamming and deception technologies for military applications, making precise positioning and navigation of unmanned vehicles in GPS-denied or constrained environments a critical issue that needs to be addressed. In this work, authors propose a method based on Visual–Inertial Odometry (VIO), integrating the extended Kalman filter (EKF), an Inertial Measurement Unit (IMU), optical flow, and feature matching to achieve drone localization in GPS-denied environments. The proposed method uses the heading angle and acceleration data obtained from the IMU as the state prediction for the EKF, and estimates relative displacement using optical flow. It further corrects the optical flow calculation errors through IMU rotation compensation, enhancing the robustness of visual odometry. Additionally, when re-selecting feature points for optical flow, it combines a KAZE feature matching technique for global position correction, reducing drift errors caused by long-duration flight. The authors also employ an adaptive noise adjustment strategy that dynamically adjusts the internal state and measurement noise matrices of the EKF based on the rate of change in heading angle and feature matching reliability, allowing the drone to maintain stable positioning in various flight conditions. According to the simulation results, the proposed method is able to effectively estimate the flight trajectory of drones without GPS. Compared to results that rely solely on optical flow or feature matching, it significantly reduces cumulative errors. This makes it suitable for urban environments, forest areas, and military applications where GPS signals are limited, providing a reliable solution for autonomous navigation and positioning of drones.

How to achieve continuous, reliable and accurate positioning performance using low-cost sensors is one of the main challenges for aviation navigation system. Global Positioning System (GPS) can provide the primary means of navigation in a number of aviation navigation applications (e.g., manned and unmanned aircraft vehicle, airport ground vehicle). However, GPS signal deteriorations typically occur due to aircraft itself during maneuvering, ionospheric scintillation, Doppler shift, multipath and so on. Thus, there is a need to research GPS augmentation strategies which can be used in the Communication, Navigation, Surveillance/Air Traffic Management. GPS integration with Inertial Navigation system (INS) is one of the key strategies. But once GPS signal outages, the integrated navigation system works in pure INS, and positioning accuracy deteriorates with time. When using low cost GPS/INS integration, a primary problem is the rapid performance deteriorate during GPS outages. To provide continuous, accurate and reliable positioning information in aviation, discrete grey prediction model (DGPM) aided fusion methodology is proposed. The DGPM provides pseudo-GPS position information for INS during GPS outages. The mathematical model of integrated navigation system is established, including INS error equations, Kalman filter and DGPM. The model works in the update mode when there is no GPS failure, whereas it switches to the prediction mode in case of GPS outages. To verify the feasibility and effectiveness of the proposed methodology, real road test is performed. The comparison results show that accuracy of longitude and latitude are improved by more than 80% and 70%, respectively. The DGPM can effectively provide position corrections for standalone INS during GPS outages.

: Software GPS receiver development has been undertaken. We are particularly interested in improving the GPS signal-to-noise/interference ratio using beam forming techniques. The phase relationship among the antenna array elements requires careful calibration. In this study, we will report a phase calibration technique for a 2 by 2 GPS antenna array using both simulated and real GPS signals. This technique is based on the GPS signal-processing algorithm developed for the software GPS receiver. A four-channel digital data collecting system was used in the experiment. For a simulated GPS signal, the experiment was conducted in an anechoic chamber in which a GPS simulation system was facilitated. For real GPS signals, we conducted the experiment on a rooftop to receive the signal from GPS satellites. The calibration verified the coherent nature of the signals among the elements. The results also allowed the source's direction to be determined.

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.

This paper presents an alternate recursive state estimator of moving horizon estimation as compared to the conventional state estimator of Kalman filtering. The state estimator estimates the position set, velocity set and attitude data of the dynamic aerial vehicle. In the first scenario, the available data for processing are the measurement set from accelerometers assembly, gyroscope triad, and global positioning system (GPS) from the low cost inertial measuring unit (IMU). The GPS position and GPS velocity measurements are the aiding source as well the comparison reference of the computed solution of the dynamic aerial vehicle (DAV). In the second scenario receding window discrete time state estimator, extended for non-linear vehicle navigation application is implemented using the deterministic cost function of least squares, for integrated filtering. The receding data window approach uses the past measurements samples over the horizon as the tuning parameter of the moving horizon state estimator. The moving horizon state estimator is evaluated offline in the numerical experiment including the flight testing data collected. The flight test on a small aerial vehicle with all the sensors, GPS receiver, power systems instrumented onboard is considered for this paper. Matlab platform is used to simulate the noise environment and implementing the algorithms. This paper designed algorithm results reveals that proposed moving horizon estimator is faster in convergence in the presence of large initialization errors, linearization errors and outliers as compared to the reference filter i.e. EKF tested online and offline.

In shaded areas, Global Positioning System (GPS) signals may suffer significant degradation, making the measurements to be contaminated by different kinds of errors. These errors such as outliers arising due to multipath and line-of-sight signals can degrade the accuracy of GPS positioning. Moreover, the poor geometric constellation of satellites that the receiver is tracking may cause inverse problems, resulting in unstable and unreliable GPS positioning solutions. To tackle these problems, an iterative robust regularization method (IRRM) for the GPS positioning is proposed in this article. The IRRM employs the Cholesky factorization method to preclude computing the inverse of matrices, making it numerically stable and be easily implemented in engineering. Also included in this work is the derivation of the sufficient condition for the IRRM. Performance comparisons of the proposed algorithm with the least squares, the Cholesky factorization method for iterative least squares (CM), and the robust estimation are demonstrated via two static positioning problems. The experimental results, presented herein, exhibit the improved performance of the IRRM over conventional algorithms.

A smart city’s main purpose is to provide intelligent responses to different problems of the rapid urban population growth. For instance, integrating fleet management solutions into intelligent transportation systems (ITS) can efficiently resolve transportation problems relying on each vehicle information. Usually, the position estimate is ensured by the integration of the Global Positioning System (GPS) and Inertial Navigation Systems (INS). For multisensor data fusion, the Extended Kalman Filter (EKF) is generally applied using the sensor’s measures and the GPS position as a helper. However, the INS are expensive and require more complex computing which induces restrictions on their implementation. Furthermore, the EKF performance depends on the vehicle dynamic variations and may quickly diverge because of environmental changes. In this paper, we present a robust low cost approach using EKF and Support Vector Machines (SVM) to reliably estimate the vehicle position by limiting the EKF drawbacks. The sensors used are a GPS augmented by a low cost wireless sensor network. When GPS signals are available, we train SVM on different dynamics and outage times to learn the position errors so we can correct the future EKF predictions during GPS signal outages. We obtain empirically an improvement of up to 94% over the simple EKF predictions in case of GPS failures.