Tashfeen B. Karamat

Performance Enhancement of MEMS-Based INS/GPS Integration for Low-Cost Navigation Applications

The relatively high cost of inertial navigation systems (INSs) has been preventing their integration with global positioning systems (GPSs) for land-vehicle applications. Inertial sensors based on microelectromechanical system (MEMS) technology have recently become commercially available at lower costs. These relatively lower cost inertial sensors have the potential to allow the development of an affordable GPS-aided INS (INS/GPS) vehicular navigation system. While MEMS-based INS is inherently immune to signal jamming, spoofing, and blockage vulnerabilities (as opposed to GPS), the performance of MEMS-based gyroscopes and accelerometers is significantly affected by complex error characteristics that are stochastic in nature. To improve the overall performance of MEMS-based INS/GPS, this paper proposes the following two-tier approach at different levels: (1) improving the stochastic modeling of MEMS-based inertial sensor errors using autoregressive processes at the raw measurement level and (2) enhancing the positioning accuracy during GPS outages by nonlinear modeling of INS position errors at the information fusion level using neuro-fuzzy (NF) modules, which are augmented in the Kalman filtering INS/GPS integration. Experimental road tests involving a MEMS-based INS were performed, which validated the efficacy of the proposed methods on several trajectories.

Integrated Indoor Navigation System for Ground Vehicles With Automatic 3-D Alignment and Position Initialization

This paper introduces an autonomous integrated indoor navigation system for ground vehicles that fuses inertial sensors, light detection and ranging (LiDAR) sensors, received signal strength (RSS) observations in wireless local area networks (WLANs), odometry, and predefined occupancy floor maps. This paper proposes a solution for the problem of automatic self-alignment and position initialization indoors under the absence of an absolute navigation system such as Global Navigation Satellite Systems (GNSS). The initial tilt angles (roll and pitch) are estimated by an extended Kalman filter (EKF) that uses two horizontal accelerometers as measurements. The initial position and heading estimation is performed using a subimage matching algorithm based on normalized cross-correlation between projected 2-D LiDAR scans and an occupancy floor map of the environment. The ambiguities in position/heading initialization are resolved using RSS. The proposed position/heading estimation module is also utilized in navigation mode as a source of absolute position/heading updates to EKF for enhanced observability. The state predictor is an enhanced 3-D inertial navigation system that utilizes low-cost microelectromechanical system (MEMS)-based reduced inertial sensor set aided by vehicle odometry. In navigation mode, LiDAR scans are used to estimate the vehicles relative motions using an inertial-aided iterative closest point algorithm. To fuse all available measurements, a multirate multimode EKF design is proposed to correct navigation states and estimate sensor biases. The developed system was tested under a real indoor office environment covered by an IEEE 802.11 WLAN on a mobile robot platform equipped with MEMS inertial sensors, a WLAN interface, a 2-D LiDAR scanner, and a quadrature encoder. Results demonstrated the capabilities of the self-alignment and initialization module and showed average submeter-level positioning accuracy.

Experimental Results on an Integrated GPS and Multisensor System for Land Vehicle Positioning

Global position system (GPS) is being widely used in land vehicles to provide positioning information. However, in urban canyons, rural tree canopies, and tunnels, the GPS satellite signal is usually blocked and there is an interruption in the positioning information. To obtain positioning solution during GPS outages, GPS can be augmented with an inertial navigation system (INS). However, the utilization of full inertial measurement unit (IMU) in land vehicles could be quite expensive despite the use of the microelectromechanical system (MEMS)-based sensors. Contemporary research is focused on reducing the number of inertial sensors inside an IMU. This paper explores a multisensor system (MSS) involving single-axis gyroscope and an odometer to provide full 2D positioning solution in denied GPS environments. Furthermore, a Kalman filter (KF) model is utilized to predict and compensate the position errors of the proposed MSS. The performance of the proposed method is examined by conducting several road tests trajectories using both MEMS and tactical grade inertial sensors. It was found that by using proposed MSS algorithm, the positional inaccuracies caused by GPS signal blockages are adequately compensated and resulting positional information can be used to steer the land vehicles during GPS outages with relatively small position errors.

Augmented Kalman Filter and Map Matching for 3D RISS/GPS Integration for Land Vehicles

Owing to their complimentary characteristics, global positioning system (GPS) and inertial navigation system (INS) are integrated, traditionally through Kalman filter (KF), to obtain improved navigational solution. To reduce the overall cost of the system, microelectromechanical system- (MEMS-) based INS is utilized. One of the approaches is to reduce the number of low-cost inertial sensors, decreasing their error contribution which leads to a reduced inertial sensor system (RISS). This paper uses KF to integrate GPS and 3D RISS in a loosely coupled fashion to enhance navigational solution while further improvement is achieved by augmenting it with map matching (MM). The 3D RISS consists of only one gyroscope and two accelerometers along with the vehicle’s built-in odometer. MM limits the error growth during GPS outages by restricting the predicted positions to the road networks. The performance of proposed method is compared with KF-only 3D RISS/GPS integration to demonstrate the efficacy of the proposed technique.

An Enhanced 3D Multi-Sensor Integrated Navigation System for Land-Vehicles

In urban areas, Global Positioning System (GPS) accuracy deteriorates due to signal degradation and multipath effects. To provide accurate and robust navigation in such GPS-denied environments, multi-sensor integrated navigation systems are developed. This paper introduces a 3D multi-sensor navigation system that integrates inertial sensors, odometry and GPS for land-vehicle navigation. A new error model is developed and an efficient loosely coupled closed-loop Kalman Filter (Extended KF or EKF) integration scheme is proposed. In this EKF-based integration scheme, the inertial/odometry navigation output is continuously corrected by EKF-estimated errors, which keeps the errors within acceptable linearization ranges which improves the prediction accuracy of the linearized dynamic error model. Consequently, the overall performance of the integrated system is improved. Real road experiments and comparison with earlier works have demonstrated a more reliable performance during GPS signal degradation and accurate estimation of inertial sensor errors (biases) have led to a more sustainable performance reliability during long GPS complete outages.

Accuracy Enhancement of Inertial Sensors Utilizing High Resolution Spectral Analysis

In both military and civilian applications, the inertial navigation system (INS) and the global positioning system (GPS) are two complementary technologies that can be integrated to provide reliable positioning and navigation information for land vehicles. The accuracy enhancement of INS sensors and the integration of INS with GPS are the subjects of widespread research. Wavelet de-noising of INS sensors has had limited success in removing the long-term (low-frequency) inertial sensor errors. The primary objective of this research is to develop a novel inertial sensor accuracy enhancement technique that can remove both short-term and long-term error components from inertial sensor measurements prior to INS mechanization and INS/GPS integration. A high resolution spectral analysis technique called the fast orthogonal search (FOS) algorithm is used to accurately model the low frequency range of the spectrum, which includes the vehicle motion dynamics and inertial sensor errors. FOS models the spectral components with the most energy first and uses an adaptive threshold to stop adding frequency terms when fitting a term does not reduce the mean squared error more than fitting white noise. The proposed method was developed, tested and validated through road test experiments involving both low-end tactical grade and low cost MEMS-based inertial systems. The results demonstrate that in most cases the position accuracy during GPS outages using FOS de-noised data is superior to the position accuracy using wavelet de-noising.

A LiDAR-Aided Indoor Navigation System for UGVs

Autonomous Unmanned Ground Vehicles (UGVs) require a reliable navigation system that works in all environments. However, indoor navigation remains a challenge because the existing satellite-based navigation systems such as the Global Positioning System (GPS) are mostly unavailable indoors. In this paper, a tightly-coupled integrated navigation system that integrates two dimensional (2D) Light Detection and Ranging (LiDAR), Inertial Navigation System (INS), and odometry is introduced. An efficient LiDAR-based line features detection/tracking algorithm is proposed to estimate the relative changes in orientation and displacement of the vehicle. Furthermore, an error model of INS/odometry system is derived. LiDAR-estimated orientation/position changes are fused by an Extended Kalman Filter (EKF) with those predicted by INS/odometry using the developed error model. Errors estimated by EKF are used to correct the position and orientation of the vehicle and to compensate for sensor errors. The proposed system is verified through simulation and real experiment on an UGV equipped with LiDAR, MEMS-based IMU, and encoder. Both simulation and experimental results showed that sensor errors are accurately estimated and the drifts of INS are significantly reduced leading to navigation performance of sub-metre accuracy.

Performance Analysis of Code-Phase-Based Relative GPS Positioning and Its Integration With Land Vehicle’s Motion Sensors

Relative global positioning system (GPS) positioning is used to cancel common-mode errors such as satellite/receiver clock biases and atmospheric effects. The common approach is to use differential GPS (DGPS) carrier-phase measurements to provide centimeter-meter level accuracy. However, carrier-phase-based DGPS positioning requires resolution of integer ambiguities (IA) and is sensitive to cycle-slip, which are too frequent for land-vehicle navigation. This paper investigates the feasibility of using DGPS code-phase measurements integrated with land-vehicles motion sensors to provide highly accurate navigation system without the overhead of IA resolution or cycle-slip detection and correction, which are complex and time consuming processes. To reduce the effect of noise associated with differential code-phase measurements, a reduced set of vehicles sensors are used in an extended Kalman filter (EKF) employing tightly coupled integration scheme (termed as EKF-DD). Owing to bias estimation of motion sensors, the proposed system flywheels through GPS outages and mitigates multipath. The performance of the proposed system was compared, using carrier-phase based reference, with two similar integration schemes employing undifferenced GPS measurements, where atmospheric effects are mitigated using either Klobuchar model (called EKF-BC) or dual frequency receivers (designated as EKF-IF). Based on three real road tests performed in challenging GPS environments and forced GPS outages, it was found that in 2-D positioning, the proposed system performed 46% superior than EKF-BC and 21% better than EKF-IF. In altitude, EKF-DD showed 66% improvement over EKF-BC and 14% over EKF-IF. In GPS outages, the overall performance of the proposed system was 21% and 10% better than EKF-BC and EKF-IF, respectively.

An Enhanced Error Model for EKF-Based Tightly-Coupled Integration of GPS and Land Vehicle’s Motion Sensors

Reduced inertial sensor systems (RISS) have been introduced by many researchers as a low-cost, low-complexity sensor assembly that can be integrated with GPS to provide a robust integrated navigation system for land vehicles. In earlier works, the developed error models were simplified based on the assumption that the vehicle is mostly moving on a flat horizontal plane. Another limitation is the simplified estimation of the horizontal tilt angles, which is based on simple averaging of the accelerometers’ measurements without modelling their errors or tilt angle errors. In this paper, a new error model is developed for RISS that accounts for the effect of tilt angle errors and the accelerometer’s errors. Additionally, it also includes important terms in the system dynamic error model, which were ignored during the linearization process in earlier works. An augmented extended Kalman filter (EKF) is designed to incorporate tilt angle errors and transversal accelerometer errors. The new error model and the augmented EKF design are developed in a tightly-coupled RISS/GPS integrated navigation system. The proposed system was tested on real trajectories’ data under degraded GPS environments, and the results were compared to earlier works on RISS/GPS systems. The findings demonstrated that the proposed enhanced system introduced significant improvements in navigational performance.

GPS Cycle Slip Detection and Correction at Measurement Level

Carrier phase measurements are much more precise than pseudorange measurements and can be used to achieve very accurate positioning solutions. However, carrier phase measurements require resolution of integer ambiguities before precise positioning can be achieved. The GPS receiver can keep track of the integer number of cycles as long as the receiver maintains lock to the satellite signal. However, in reality, the GPS signal could be interrupted momentary by some disturbing factors leading to a discontinuity of an integer number of cycles in the measured carrier phase. This interruption in the counting of cycles in the carrier phase measurements is known as a cycle slip. When a cycle slip occurs, the Doppler counter would restart causing a jump in the instantaneous accumulated phase by an integer number of cycles. Thus, the integer counter is reinitialized meaning that ambiguities are unknown again. In this event, either the ambiguities need to be resolved again or cycle slips need to be corrected to resume the precise positioning/navigation process. These cycle slips can, to some extent, be detected and fixed to avoid delay and computation complexity attributed to integer ambiguity resolution. Researchers have been addressing the problem of cycle slip detection and correction for the last two decades. This paper provides a detailed survey for available techniques to tackle the problem showing their pros and cons.