Bendehiba Dahmane, Brahim Lejdel, Eliseo Clementini, Fayssal Harrats, Sameh Nassar, Lahcene Hadj Abderrahmane Department of Technology, Faculty of Technology, El-Oued University, El-Oued, Algeria Department of Computer Sciences, Faculty of Exact Sciences, El-Oued University, El-Oued, Algeria University of L'Aquila, L'Aquila, Italy Department of Electronics, Faculty of Electrical Engineering, Science and Technology University (USTO-MB), Oran, Algeria Mobile Multi-Sensor Systems (MMSS) Research Group, Department of Geomatics Engineering, University of Calgary, Alberta, Canada Department of Space Instrumentation, Satellite Development Center (CDS), Space Agency (ASAL), Oran, Algeria

In this paper a practical method for estimating the full kinematic state of a land-vehicle, along with sensors, low-cost inertial measuring unit (IMU), and Global Positioning System (GPS). However, this INS-GPS system requires in generally a robust architecture such as an Extended Kalman Filter (EKF) approach in direct configuration, by reason of its properties of extensive evaluations of nonlinear equations. In addition, a practical approach for controlling the Degree of Observability (DoO) in GPS-INS integrated systems is used in these tests. Other than that, traditional observability analysis is inadequate for a long navigation trajectories matrix that becomes very large, such that it rises computational difficulties. Two datasets are used to verify the efficacy of the proposed approach against the existing GPS-INS integration scheme. The first set is real road data collected from a higher grade IMU at each (0.01 s) that was combined with DGPS data at each (1 s) in order to obtain the assumed true solution for the trajectory. The second one is real test data collected during land-vehicle trajectory. The implementation consists of three main algorithms that as well namely: Strapdown (Dead Reckoning DR), DoO, and EKF algorithms. The results are shown, implementation of the both approaches based on EKF and concept of DoO in GPS/INS Integrated systems are enough robust for its use along with low-cost sensors.

Experimental setup implements the concept of degree of observability (DoO) adequate a land-vehicle navigation application with noised inertial measurement unit (IMU) and global positioning system (GPS) sensors based on a loosely coupled approach. The navigation systems such as IMU-GPS require extensive evaluations of nonlinear equations as used in an extended Kalman filter (EKF). According to DoO and during our test, we have implemented a method for measuring the DoO of all states continuously. Where, the results showed that applying the fusion IMU-GPS system based on EKF be enhanced the DoO measure. The real dataset consists of outputs a high sampling rate for IMU sensor at each (0.01s) and GPS receiver at each (1s). In addition, an aloft category IMU was put together with differential GPS (DGPS) information to produce a real trajectory. GPS has acceptable long-term accuracy, it is used to update the position and velocity in IMU outputs before processing in the EKF algorithm. The implementation consists of three main algorithms: Strapdown (dead reckoning DR), DoO and EKF algorithms. The results are shown, implementation of both approaches based on EKF and the concept of DoO in GPS/INS integrated systems are sufficient robustness to use with low-cost sensors.

Pulse compression techniques are commonly used in linear frequency modulated (LFM) waveforms to improve the signal-to-noise ratios (SNRs) and range resolutions of pulsed radars, whose detection capabilities are affected by the sidelobes. In this study, a sidelobe reduction filter (SRF) was designed and implemented using software defined radio (SDR). An enhanced matched filter (EMF) that combines a matched filter (MF) and an SRF is proposed and was implemented. In contrast to the current commonly used approaches, the mathematical model of the SRF frequency response is extracted without depending on any iteration methods or adaptive techniques, which results in increased efficiency and computational speed for the developed model. The performance of the proposed EMF was verified through the measurement of four metrics, including the peak sidelobe ratio (PSLR), the impulse response width (IRW), the mainlobe loss ratio (MLR), and the receiver operational characteristics (ROCs) at different SNRs. The ambiguity function was then used to characterize the Doppler effect on the designed EMF. In addition, the detection of single and multiple targets using the proposed EMF was performed, and the results showed that it overcame the masking problem due to its effective reduction of the sidelobes. Hence, the practical application of the EMF matches the performance analysis. Moreover, when implementing the EMF proposed in this paper, it outperformed the common MF, especially when detecting targets moving at low speeds and having small radar cross-sections (RCS), even under severe masking conditions.

To enhance the inertial sensors performance, accurate characterization as well as modeling of the corresponding stochastic errors are required. In addition, accurate position and orientation information can be achieved when integrating inertial sensors with other position fix systems, such as Global Navigation Satellite Systems (GNSS). This paper investigates the Generalized Method of Wavelet Moments (GMWM), as a Wavelet Variance (WV)-based approach, for the stochastic characterization and model identification of the stochastic errors related to low-cost MEMS-based inertial sensor errors. Raw inertial sensor measurements were collected using MTi-G-710 MEMS Inertial Measurement Unit (IMU) where the GMWM was utilized for estimating the coefficients of different random processes associated with inertial sensor residuals. Results demonstrated that highly complicated error model structures are identified for the tested IMU, which contradicts the common assumption of modeling such errors as a single 1st order Gauss-Markov (GM) random process. In addition, the results showed that the GMWM is an efficient framework that could be used for estimating the parameters associated with complex structured random processes with the advantage of correlated residual characterization and identification.

In many navigation applications, the integrated system of Inertial Navigation System (INS) and a Differential Global Positioning System (DGPS) has become a standard tool. In such systems, the INS p...

The task of inertial sensor calibration has always been challenging, especially when dealing with stochastic errors that remain after the deterministic errors have been filtered out. Among others, the number of observations is becoming increasingly high since sensor measurements are taken at high frequencies over longer periods of time, thereby placing considerable limitations on the estimation of the complex models that characterize stochastic errors (without considering testing and selection procedures). Moreover, before estimating these models, there is a need for tests that determine whether the error signals are characterized by a model that remains constant over time and, if so, which model best predicts these errors. Considering these needs, this paper presents an open-source software platform that allows practitioners to carry out these procedures by making use of two recent proposals which stem from the Generalized Method of Wavelet Moments framework. These proposals make use of the growing amount of signal replicates issued during sensor calibration procedures and the proposed platform allows users to easily employ various functions that implement these methods in a user-friendly and computationally efficient manner.

The Global Navigation Satellite System (GNSS) is currently used in many fields, such as autonomous driving, robotics application, and Unmanned Aerial Vehicles (UAVs), where accurate position information is required. These applications require high positioning accuracy which, in turn, require precise analysis of the residual noise characteristics of the GNSS positioning solutions and their quantitative models. This paper investigates the Generalized Method of Wavelet Moments (GMWM) method for stochastic modelling of low-cost GNSS receiver signal. The paper also compares the results of GMWM to the Allan Variance (AV) which is currently the most common method to study the stochastic characteristics of different time series. Different datasets were collected using two low-cost GNSS receivers at different frequencies and were processed in Single Point Positioning (SPP) mode where position errors are expressed in the Local-Level Frame (LLF) of reference. Both techniques were used in identifying and characterizing the different latent stochastic process and their related coefficients for GNSS position residual signals where precise models of the latter have been built. The test results showed that for low-cost GNSS receivers, a white noise process alone is not sufficient for accurate position residual signals' modeling. The results also stressed out that the GNSS error signal models are complicated where the corresponding error model structures were represented as a sum of white noise and one or more 1st order Gauss-Markov (GM) processes which indicates the existence of short and relatively long correlation between consecutive observations, especially for observations collected at higher sampling rates. Moreover, the results showed that the GMWM approach in general outperforms the AV method in terms of correlated noise identification and characterization.

This paper aims at investigating and analyzing the behavior of Micro-Electromechanical Systems (MEMS) inertial sensors stochastic errors in both static and varying dynamic conditions using two MEMSbased Inertial Measurement Units (IMUs) of two different smartphones. The corresponding stochastic error processes were estimated using two different methods, the Allan Variance (AV) and the Generalized Method of Wavelets Moments (GMWM). The developed model parameters related to laboratory dynamic environment are compared to those obtained under static conditions. A contamination test was applied to all data sets to distinguish between clean and corrupted ones using a Confidence Interval (CI) investigation approach. A detailed analysis is presented to define the link between the error model parameters and the augmented dynamics of the tested smartphone platform. The paper proposes a new dynamically dependent integrated navigation algorithm which is capable of switching between different stochastic error parameters values according to the platform dynamics to eliminate dynamics-dependent effects. Finally, the performance of different stochastic models based on AV and GMWM were analyzed using simulated Inertial Navigation System (INS)/Global Positioning System (GPS) data with induced GPS signal outages through the new proposed dynamically dependent algorithm. The results showed that the obtained position accuracy is improved when using dynamic-dependent stochastic error models, through the adaptive integrated algorithm, instead of the commonly used static one, through the non-adaptive integrated one. The results also show that the stochastic error models from GMWM-based model structure offer better performance than those estimated from the AV-based model.

Terrestrial Laser Scanning (TLS) has been emerging as a revolutionary surveying and geomatics technology in the past decade. Currently, 3D TLS is being used as a standard tool for several applications that require high accuracy in position and 3D modeling, high resolution with dense data points and high efficiency that includes all aspects of data collection and data processing in a timely manner scenario. These applications cover the whole spectrum of engineering disciplines such as geomatics, civil, architecture, environmental, industrial, petroleum and oil processing. Several TLS systems are currently available in the market that differ in size, used laser (in terms of power, class and wavelength), range accuracy, scan acquisition rate and available sampling resolution. In this paper, the application of 3D TLS in a petroleum and oil processing project will be shown and discussed. In this project, one of the major objectives is to create a 3D model of an existing Treater located in the Oil Processing Building on SAIT (South Alberta Institute of Technology, Calgary, Alberta, Canada) campus that is to be replaced by a new Treater with a different design. Piping attached to and surrounding the existing Treater needs to be removed and redesigned to install the new Treater. Thus, building a replicate 3D model of the existing Treater is used to aid the design of the new piping. In addition, such created 3D model will function as an as-built documentation for the existing Treater. To accomplish this, the Leica ScanStation C10 3D Scanner was used and the data was processed by the Leica Cyclone software and then the created 3D model is presented in CAD using the AutoCAD Civil 3D software. In the paper, the work performed as well as the procedures followed will be presented and the obtained results will be shown. In addition, data analysis and accuracy assessment with respect to the industry requirements for the project will be presented and discussed.

Currently, the concept of multisensor system integration is implemented in land-vehicle navigation (LVN) applications. The most common LVN multisensor configuration incorporates an integrated Inertial Navigation System/Global Positioning System (INS/GPS) system based on the Kalman filter (KF). For LVN, the demand is directed toward low-cost inertial sensors such as microelectromechanical systems (MEMS). Due to the combined problem of frequent GPS signal loss during navigation in urban centers and the rapid time-growing inertial navigation errors when the INS is operated in stand-alone mode, some methodologies should be applied to improve the LVN accuracy in these cases. One of these approaches is to apply smoothing algorithms such as the Rauch-Tung-Striebel smoother (RTSS), which uses only the output of the forward KF. In this paper, the development of the two-filter smoother (TFS) algorithm and its implementation in LVN applications is introduced. Two different LVN INS/GPS data sets that include tactical-grade and MEMS inertial measuring units are utilized to validate the TFS algorithm and to compare its performance with the RTSS.

Pipelines are constructed to transport or dispose liquid and gases, commonly operated by oil, gas, sewerage and chemical industries. The pipeline system consists of three basic components namely gathering lines, trunk lines and distribution lines. Environmental, safety and economic concerns necessitate the constant monitoring of pipelines to avoid potentially hazardous failures. Currently, Pipeline Inspection Gauges (PIGs) can be sent through the pipelines to monitor the inside conditions. The Inertial Navigation System (INS) is employed to conduct the overall PIG navigation due to the unavailability of GPS signals inside the pipeline. Due to the time-dependent errors of INS only navigation, additional aiding sensors and/or auxiliary velocity and position updates need to be used to compensate for such errors. In addition, optimal smoothing methods are required since optimal smoothing is a post-mission estimator that provides the optimal estimates by using all available measurements. In this paper, two smoothers, namely Two-Filter Smoother (TFS) and the Rauch-Tung-Striebel Smoother (RTSS) will be implemented based on an Extended Kalman Filter (EKF). Using real pipeline inertial data collected with a tactical-grade Inertial Measuring Unit (IMU), the effect of the additional velocity updates and available position updates will be shown. Moreover, the performance of both optimal smoothers will be evaluated. The combined implementation of the additional filter updates and smoothing demonstrated a remarkable improvement in the navigation accuracy compared to the original KF solution.

In the last decade, the demand for accurate land-vehicle navigation (LVN) in several applications has grown rapidly. In this context, the idea of integrating multisensor navigation systems was implemented. For LVN, the most efficient multisensor configuration is the system integrating an inertial navigation system (INS) and a global positioning system (GPS), where the GPS is used for providing position and velocity and the INS for providing orientation. The optimal estimation of the system errors is performed through a Kalman filter (KF). Unfortunately, a major problem occurs in all INS/GPS LVN applications that is caused by the frequent GPS signal blockages. In these cases, navigation is provided by the INS until satellite signals are reacquired. During such periods, navigation errors increase rapidly with time due to the time-dependent INS error behavior. For accurate positioning in these cases, some approaches, known as bridging algorithms, should be used to estimate improved navigation information. In this paper, the main objective is to improve the accuracy of the obtained navigation parameters during periods of GPS signal outages using different bridging methods. As a first step, three different KF approaches will be used, including the linearized, extended, and unscented KF algorithms for the INS/GPS integration. Two land-vehicle kinematic data sets with different-quality INSs are used with several induced GPS outages, and then two bridging approaches are implemented. The first method is to apply different backward smoothing algorithms postmission that are associated with the different used KF approaches. The second bridging method is a near real-time approach based on developing an INS error model to be applied only during GPS signal blockages. After applying each bridging method, the results showed remarkable improvement of position errors regardless of the KF used.

Navigation comprises the integration of methodologies and systems for estimating the time varying position, velocity and attitude of moving objects. Navigation using integrated INS/GPS systems requires in general extensive evaluations of nonlinear equations involving double integration. Currently, integrated navigation systems are commonly implemented using Extended Kalman Filter (EKF) and most recently Unscented Kalman Filter (UKF). The EKF assumes linear process and measurement models while both the EKF and UKF approximate the noise models with Gaussian fits. This approximation is unrealistic for highly nonlinear systems, which is true for both EKF and UKF implementations. To overcome these limitations, Particle Filter (PF) was proposed lately since it is a non-parametric filter and hence it can easily deal with non-linearities and non-Gaussian noises. In this paper, an Extended Particle Filter (EPF) is developed as an alternative to the common EKF for land-vehicle navigation applications. Experimental GPS/INS datasets including dual frequency carrier phase GPS receiver data and inertial measurements from two different MEMSgrade Inertial Measuring Units (IMUs) installed on same vehicle are used to evaluate the proposed EPF technique. The performance of the developed EPF is compared to the performance of current estimation techniques such as the EKF. The comparison is the difference between the navigation errors of the two filters when GPS signals are available all the time and during GPS signal outages.

This article on surveying and mapping innovations describes a system that uses photogrammetry to bridge the gaps in a Global Positioning System/Inertial Navigation System (GPS/INS). The authors seek to investigate using photogrammetry techniques to support navigation during GPS signal outages. They propose to fuse GPS data with imagery by adjusting the image measurements and the GPS raw data in a single adjustment step. The navigation solution is based on the INS stand-alone solution when the GPS signals are blocked. The photogrammetric reconstruction during GPS signal outages is investigated using a hybrid simulation. Images are simulated based on the imaging system used. The simulated exposure stations are contaminated with errors. Results show that the photogrammetric reconstruction accuracy is distance dependent, while the INS solution drift is time dependent. The adjustment process seems stable, and the researchers plan to extend the framework to include pictorial and navigation data from airborne mobile mapping systems and include shapes rather than just point features.

ABSTRACT: In the last decade, the Land-Vehicle Navigation (LVN) market has grown rapidly. For most LVN systems, GPS is used for positioning. However, GPS has poor accuracy in urban areas due to signal blockages. Therefore, the LVN market has targeted the integration of other sensors with GPS. In this case, sensors' cost and size are major issues. Recent advances in MEMS inertial sensors made it possible to develop low-cost and compact IMUs. However, MEMS provide poor accuracy when used without updates (e.g., during GPS outages). In such periods, other updates are required for better performance. Vehicle full-stops, i.e., Zero-Velocity-Updates (ZUPTs), are usually applied for this purpose. However, this is not practical especially when GPS blockages are frequent. In this paper, 3D Auxiliary Velocity Updates (AVUs) are used, namely, non-holonomic constraints and odometer-derived velocity. Using kinematic MEMS/GPS data with several GPS signal blockages, the results showed a significant accuracy improvement after applying AVUs.

An emerging trend in mapping applications is the Mobile Mapping Systems (MMSs). It allows a task-oriented implementation of mapping concepts at the measurement level. An MMS provides the necessary data for the production of georeferenced images to create and update three dimensional geographic information system (GIS) databases quickly and economically. The accuracy depends mainly on the ability of the navigation system to accurately and continuously provides navigation solutions which in turn depends on the availability of the global positioning system signals. The technique merges photogrammetric measurements and the inertial navigation system (INS) stand-alone navigation solution in one adjustment step.

With the development of low-cost inertial sensors and GPS technology, MEMS-based INS/GPS navigation systems are beginning to meet the increasing demands of lower cost, smaller size, and seamless navigation solutions for land vehicles. But there are still two challenges for current MEMS navigation systems before they can be commercialized. The first one is to further reduce the cost of the systems, which is mainly governed by the cost of MEMS gyros (>$10/axis). The second is to improve the accuracy of the systems, especially during GPS signal outages. The Mobile Multi-Sensor Systems (MMSS) Research Group in the University of Calgary developed its prototype MEMS navigation system in 2004 and published preliminary results in 2005. This paper will report further progress of the systems that tried to fulfill the challenges of the current MEMS system. The system cost issue was addressed by introducing the Partial IMU (ParIMU) configuration that consists of only one heading gyro (Gz) and two horizontal accelerometers (Ax and Ay). The system cost can be reduced significantly since the hardware required for two gyros and one accelerometer is eliminated. A universal algorithm based on the concept of pseudo sensors was developed to process the ParIMU signals. Results have shown that the performance has obvious degradation but still can meet the requirements of some applications, especially with additional aiding, (such as non-holonomic constraint). On the other hand, a Backward Smoothing (BS) algorithm (Rauch-Tung-Strieber smoother) was introduced to improve the navigation performance of the MEMS navigation system. Results showed that the BS can reduce the navigation errors significantly; especially the position drifts during GPS signal outages. Of course, this BS can only be applied for post-processing scenarios. Studies in this paper have shown that the ParIMU and the BS are two measures that can well meet the challenges of current MEMS navigation systems to a large extent.