Mohamed Maher Atia

Dynamic Online-Calibrated Radio Maps for Indoor Positioning in Wireless Local Area Networks

Context-awareness and Location-Based-Services are of great importance in mobile computing environments. Although fingerprinting provides accurate indoor positioning in Wireless Local Area Networks (WLAN), difficulty of offline site surveys and the dynamic environment changes prevent it from being practically implemented and commercially adopted. This paper introduces a novel client/server-based system that dynamically estimates and continuously calibrates a fine radio map for indoor positioning without extra network hardware or prior knowledge about the area and without time-consuming offline surveys. A modified Bayesian regression algorithm is introduced to estimate a posterior signal strength probability distribution over all locations based on online observations from WLAN access points (AP) assuming Gaussian prior centered over a logarithmic pass loss mean. To continuously adapt to dynamic changes, Bayesian kernels parameters are continuously updated and optimized genetically based on recent APs observations. The radio map is further optimized by a fast features reduction algorithm to select the most informative APs. Additionally, the system provides reliable integrity monitor (accuracy measure). Two different experiments on IEEE 802.11 networks show that the dynamic radio map provides 2-3m accuracy, which is comparable to results of an up-to-date offline radio map. Also results show the consistency of estimated accuracy measure with actual positioning accuracy.

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.

INS/GPS/LiDAR Integrated Navigation System for Urban and Indoor Environments Using Hybrid Scan Matching Algorithm.

This paper takes advantage of the complementary characteristics of Global Positioning System (GPS) and Light Detection and Ranging (LiDAR) to provide periodic corrections to Inertial Navigation System (INS) alternatively in different environmental conditions. In open sky, where GPS signals are available and LiDAR measurements are sparse, GPS is integrated with INS. Meanwhile, in confined outdoor environments and indoors, where GPS is unreliable or unavailable and LiDAR measurements are rich, LiDAR replaces GPS to integrate with INS. This paper also proposes an innovative hybrid scan matching algorithm that combines the feature-based scan matching method and Iterative Closest Point (ICP) based scan matching method. The algorithm can work and transit between two modes depending on the number of matched line features over two scans, thus achieving efficiency and robustness concurrently. Two integration schemes of INS and LiDAR with hybrid scan matching algorithm are implemented and compared. Real experiments are performed on an Unmanned Ground Vehicle (UGV) for both outdoor and indoor environments. Experimental results show that the multi-sensor integrated system can remain sub-meter navigation accuracy during the whole trajectory.

A WiFi-aided reduced inertial sensors-based navigation system with fast embedded implementation of particle filtering

Global Positioning System (GPS) accuracy deteriorates significantly in dense urban areas and it is almost unavailable inside buildings. Thus, an alternative accurate navigation system for such GPS-denied systems is of great importance. In this paper, the popular IEEE 802.11 WLAN (WiFi) is utilized along with a MEMS-based reduced inertial sensors system (RISS) to provide an accurate and smooth positioning system for wheeled vehicles inside buildings based on WiFi received signal strength (RSS). The WiFi/RISS integration is performed based on a fast version of Mixture Particle Filter (PF) which is a nonlinear non-Gaussian filtering algorithm that handles well the complex MEMS inertial sensors and WiFi stochastic nature. The proposed system was physically implemented on an embedded system on an OMAP 600 MHz processor board and tested on a mobile robot. Results show that drifts of RISS are greatly removed and the scattered noisy WiFi positioning is significantly smoothed. Experiments show that the integrated system can provide smooth indoor positioning of 2m accuracy 60% of time.

Particle-filter-based WiFi-aided reduced inertial sensors navigation system for indoor and GPS-denied environments

Indoor navigation is challenging due to unavailability of satellites-based signals indoors. Inertial Navigation Systems (INSs) may be used as standalone navigation indoors. However, INS suffers from growing drifts without bounds due to error accumulation. On the other side, the IEEE 802.11 WLAN (WiFi) is widely adopted which prompted many researchers to use it to provide positioning indoors using fingerprinting. However, due to WiFi signal noise and multipath errors indoors, WiFi positioning is scattered and noisy. To benefit from both WiFi and inertial systems, in this paper, two major techniques are applied. First, a low-cost Reduced Inertial Sensors System (RISS) is integrated with WiFi to smooth the noisy scattered WiFi positioning and reduce RISS drifts. Second, a fast feature reduction technique is applied to fingerprinting to identify the WiFi access points with highest discrepancy power to be used for positioning. The RISS/WiFi system is implemented using a fast version of Mixture Particle Filter for state estimation as nonlinear non-Gaussian filtering algorithm. Real experiments showed that drifts of RISS are greatly reduced and the scattered noisy WiFi positioning is significantly smoothed. The proposed system provides smooth indoor positioning of 1 m accuracy 70% of the time outperforming each system individually.

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.

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.

A novel systems integration approach for multi-sensor integrated navigation systems

Accurate navigation systems are of great importance in intelligent transportation systems and modern connected vehicles technology. Commonly, Global Positioning System (GPS) is integrated with inertial navigation systems (INS) and other sensors to provide robust navigation solution. Currently, the dominant systems integration approach for multi-sensor integrated navigation is Kalman Filter (KF) or Particle Filter (PF). However, KF and PF enhance accuracy only when GPS updates are frequent and accurate enough. During GPS long outages, these integration approaches fail to sustain reliable performance. For these reasons, this work introduces a new systems integration approach that based on a nonlinear systems identification technique called Fast Orthogonal Search (FOS). FOS is a general purpose nonlinear systems modelling method that can model complex nonlinearities. In this work, FOS is proposed to enhance integrated navigation systems performance during long GPS outages. The proposed integration approach is applied on a low-cost 3D land-vehicle multi-sensors navigation system consists of GPS receiver, two horizontal low-cost MEMS-grade accelerometers, single vertical MEMS gyroscope, and the vehicle odometer. The validation of the proposed methodology is verified over real road data and results are be compared to a reference high-end navigation system. Results show improved performance with FOS during GPS outages.

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.