Multi-sensor Navigation System Research Articles

Analysis of errors of the artificial horizon sensor based on vision system

The article discusses the features of the practical implementation of the artificial horizon sensor based on the onboard vision system. Proposed, developed on the basis of well-known applications, a variant of the algorithm for the operation of an unmanned aerial vehicle orientation video system. The problems of automatic detection and determination of the position of the horizon line on changing digital images that make up the video stream from the onboard digital camera are shown. The analysis of the factors influencing the accuracy of estimation of carrier orientation angles using the proposed system is carried out. The results of a practical study are presented, characterizing the degree of influence of the considered factors on the total error. A discrete stochastic mathematical model of an unmanned aerial vehicle orientation system based on an onboard vision system has been developed. The possibility of providing an acceptable level of accuracy of the orientation video system due to certain technical and algorithmic solutions is shown. The conclusion is made about the expediency of using this system in autonomous multisensor navigation systems for unmanned aerial vehicles.

High-Accuracy Absolute-Position-Aided Code Phase Tracking Based on RTK/INS Deep Integration in Challenging Static Scenarios

Many multi-sensor navigation systems urgently demand accurate positioning initialization from global navigation satellite systems (GNSSs) in challenging static scenarios. However, ground blockages against line-of-sight (LOS) signal reception make it difficult for GNSS users. Steering local codes in GNSS basebands is a desirable way to correct instantaneous signal phase misalignment, efficiently gathering useful signal power and increasing positioning accuracy. Inertial navigation systems (INSs) have been used as effective complementary dead reckoning (DR) sensors for GNSS receivers in kinematic scenarios, resisting various forms of interference. However, little work has focused on whether INSs can improve GNSS receivers in static scenarios. Thus, this paper proposes an enhanced navigation system deeply integrated with low-cost INS solutions and GNSS high-accuracy carrier-based positioning. First, an absolute code phase is predicted from base station information and integrated solutions of the INS DR and real-time kinematic (RTK) results through an extended Kalman filter (EKF). Then, a numerically controlled oscillator (NCO) leverages the predicted code phase to improve the alignment between instantaneous local code phases and received ones. The proposed algorithm is realized in a vector-tracking GNSS software-defined radio (SDR). Results of the time-of-arrival (TOA) and positioning based on real-world experiments demonstrated the proposed SDR.

Bio-Inspired Multisensor Navigation System Based on the Skylight Compass and Visual Place Recognition for Unmanned Aerial Vehicles

In the challenging environment, a reliable and autonomous navigation is the key performance factor for robotics. This paper presents a bio-inspired multi-sensor navigation system with the skylight compass and visual place recognition for the UAVs in the GNSS-denied environment. The system can integrate the position and heading constraints outputs from a skylight polarized sensor, a micro-inertia sensor and a monocular camera. An optimal orientation algorithm is proposed with the skylight polarized patterns for obtaining the heading constraint. We also propose a two-dimensional visual place recognition for providing the position constraint in the aerial environment and the metric encoding pattern and place recognition are presented in detail. Based on the position and heading constraints, a biologically multi-sensor integrating system with Kalman Filter has been developed to accurately estimate the motion information for UAVs. Unlike previous approaches having a fragile navigation scheme and path-error accumulation, our system can build a bionic and robust association between the motion metric information and the sensor inputs, and use reliable position and heading constraints for improving the navigation performance. The flying experiments have demonstrated that the proposed bio-inspired navigation system can outperform other vision-based navigation algorithms and the position errors are convergent and do not grow with the flying distance, in which the best performance of position precision is within 5m. Finally, we present interesting insights gained with respect to further work in robotics.

Detecting Slowly Accumulating Faults Using a Bank of Cumulative Innovations Monitors in Kalman Filters

<h3>Summary</h3> Extended Kalman filters (EKFs) that monitor innovations over time have been demonstrated to be effective at detecting slowly accumulating measurement faults (Quartararo &amp; Langel, 2020; Tanil et al., 2018). This paper first demonstrates that a single cumulative monitor becomes increasingly sensitive to measurement error model uncertainty as the accumulation interval increases, leading to false alarm and detection rates that can differ significantly from predefined design parameters. In response, a bank of finite-length cumulative innovations monitors is explored for fault detection in multisensor navigation systems. A novel extension to traditional covariance analysis (<i>Covariance Analysis Including Expected Values</i> or CAIEV) is developed to accommodate measurement faults and is used in addition to Monte Carlo simulations to present detection results for a variety of GPS fault profiles and inertial measurement unit (IMU) grades. Data for time-to-detect is presented alongside the position-domain bias induced by the fault at the time of detection. We show that the monitor bank can reliably detect the presence of faulty measurements after the position-domain bias has reached only tens of meters using tactical and aviation-grade IMUs for the cases considered, an improvement over other innovations-based techniques.

An analysis of PPP-GPS-based decentralized train multi-sensor navigation system

GPS precise point positioning (PPP) is increasingly being used in many precise positioning applications to achieve sub-meter level accuracy using a stand-alone user receiver. To achieve the full-scale navigation parameters of position, velocity and attitude, GPS is often combined with the inertial navigation system (INS) to deliver navigation solutions in high update rate. However, GPS signals cannot always be tracked in difficult areas, which leads to an integration performance degradation because of INS sensor errors. Therefore, the authors propose a PPP-GPS/INS/odometer/map-matching integrated navigation system. The positioning performance based on the different International GNSS Service (IGS) products for train kinematic positioning applications is investigated. A field test was conducted on a specific low-density line to evaluate the proposed system. The test results confirm that the proposed multi-sensor navigation system can provide seamless navigation in both GPS available and unavailable situations with the accuracy of decimeter. Different IGS satellite orbits and clock offset products were used to generate the PPP results, and it was concluded that the IGS final products provide the best performance, with a distance root mean square (DRMS) of 0.138 m, and the IGS ultra-rapid product can also generate acceptable positioning solutions with a DRMS of 0.222 m.

Multisensor Navigation Systems Multicore Architecture

Multisensor navigation systems with an inertial navigation system (INS), employ GPS and other aiding sensors to correct INS errors and improve navigation accuracy. Kalman filter (KF) is used to fuse the aiding sensor measurements and estimate errors. Both the time update, in which the state vector and covariance matrix are propagated, and the measurement update, in which all the aiding sensor measurements are fused with the INS-derived variables, have high computational costs. Two forms of the KF, the original covariance KF and the newer information KF, are examined. The most efficient parts of each are combined into a hybrid covariance/information filter. The state and measurement spaces of the hybrid filter are partitioned by sensors leading to blocked matrix formulas for both time update and measurement update. The blocked equations lead directly to threaded architecture implementations. The threaded architecture permits either serial operation with a single processor or parallel operation with multiple processors. Formulas are derived and evaluated for the floating-point operation counts for a general unblocked-serial hybrid filter and its blocked-serial and blocked-parallel implementations for an INS aided by GPS and other sensors. The blocked-parallel hybrid filter shows a significant improvement compared to the blocked-serial and unblocked-serial implementations.

Improved decentralized multi-sensor navigation system for airborne applications

The integration of Global Navigation Satellite System (GNSS) and Inertial Navigation System (INS) technologies is a very useful navigation option for high-accuracy positioning in many applications. However, its performance is still limited by GNSS satellite availability and satellite geometry. To address such limitations, a non-GNSS-based positioning technology known as “Locata” is used to augment a standard GNSS/INS system. The conventional methods for multi-sensor integration can be classified as being either in the form of centralized Kalman filtering (CKF), or decentralized Kalman filtering. However, these two filtering architectures are not always ideal for real-world applications. To satisfy both accuracy and reliability requirements, these three integration algorithms—CKF, federated Kalman filtering (FKF) and an improved decentralized filtering, known as global optimal filtering (GOF)—are investigated. In principle, the GOF is derived from more information resources than the CKF and FKF algorithms. These three algorithms are implemented in a GPS/Locata/INS integrated navigation system and evaluated using data obtained from a flight test. The experimental results show that the position, velocity and attitude solution derived from the GOF-based system indicate improvements of 30, 18.4 and 20.8% over the CKF- and FKF-based systems, respectively.

Integrated Polarized Skylight Sensor and MIMU With a Metric Map for Urban Ground Navigation

This paper presents a novel multi-sensor navigation system for the urban ground vehicle. The integration system can combine the measurements from a polarized skylight sensor, an inertial sensor, and a monocular camera. Utilizing the polarized skylight sensor, we propose a robust orientation algorithm with the total least squares to provide the orientation constraint for the integrating system. In our algorithm, the ambiguity problem of polarized orientation is solved without any other sensor. In order to enhance the algorithm's robustness in the urban environment, we also propose a real-time method that uses the gradient of the degree of the polarization to remove the obstacles. With a monocular camera, we build a metric map and recognize places in the map to provide the position constraint for the integrating system. We develop the Kalman filter to integrate these constraints with the inertial navigation results to estimate the orientation and position for the ground vehicle. The results demonstrate that our proposed system outperforms other vision-based navigation algorithms-the RMSE of the position error is 2.04 m (0.01% of the travelled distance) and the RMSE of the orientation error is 0.84°. Finally, we present interesting insights gained with respect to the further work in sensors and robotics.

Acoustic Sensors for Air and Surface Navigation Applications.

This paper presents the state-of-the-art and reviews the state-of-research of acoustic sensors used for a variety of navigation and guidance applications on air and surface vehicles. In particular, this paper focuses on echolocation, which is widely utilized in nature by certain mammals (e.g., cetaceans and bats). Although acoustic sensors have been extensively adopted in various engineering applications, their use in navigation and guidance systems is yet to be fully exploited. This technology has clear potential for applications in air and surface navigation/guidance for intelligent transport systems (ITS), especially considering air and surface operations indoors and in other environments where satellite positioning is not available. Propagation of sound in the atmosphere is discussed in detail, with all potential attenuation sources taken into account. The errors introduced in echolocation measurements due to Doppler, multipath and atmospheric effects are discussed, and an uncertainty analysis method is presented for ranging error budget prediction in acoustic navigation applications. Considering the design challenges associated with monostatic and multi-static sensor implementations and looking at the performance predictions for different possible configurations, acoustic sensors show clear promises in navigation, proximity sensing, as well as obstacle detection and tracking. The integration of acoustic sensors in multi-sensor navigation systems is also considered towards the end of the paper and a low Size, Weight and Power, and Cost (SWaP-C) sensor integration architecture is presented for possible introduction in air and surface navigation systems.

Camera auto-calibration in GPS/INS/stereo camera integrated kinematic positioning and navigation system

This paper presents a novel two-step camera calibration method in a GPS/INS/Stereo Camera multi-sensor kinematic positioning and navigation system. A camera auto-calibration is first performed to obtain for lens distortion parameters, up-to-scale baseline length and the relative orientation between the stereo cameras. Then, the system calibration is introduced to recover the camera lever-arms, and the bore-sight angles with respect to the IMU, and the absolute scale of the camera using the GPS/INS solution. The auto-calibration algorithm employs the three-view scale-restraint equations (SRE). In comparison with the collinearity equations (COL), it is free from landmark parameters and ground control points (GCPs). Therefore, the proposed method is computationally more efficient. The results and the comparison between the SRE and COL methods are presented using the simulated and road test data. The results show that the proposed SRE method requires less computation resources and is able to achieve the same or better accuracy level than the traditional COL.

Chi-square and SPRT combined fault detection for multisensor navigation

A fault-detection algorithm for a redundant multisensor navigation system for hypersonic cruise vehicles (HCVs) is proposed. The algorithm comprehensively diagnoses failures according to the failure level monitored by the sequential probability ratio test (SPRT) and chi-square test as well as the failure trend monitored by the SPRT. A test statistics feedback-reset loop is also added to shorten the recovery time after failure ceases. Simulations indicate improvements in both failure detection and recovery speed, contributing to improved accuracy and stability in HCV fault-tolerant navigation.

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.

Optimal multisensor integrated navigation through information space approach

Although the centralized Kalman filtering (CKF) solution is widely accepted as providing the globally optimal parameter estimation for multisensor navigation systems, it has inherent defects such as heavy communication and computational load and poor fault tolerance. To address these problems decentralized Kalman filtering (DKF) methods have been proposed. The DKF is configured as a bank of filters instead of the central filter, and aims to achieve the same level of accuracy as the CKF. This CKF-based approach however is found to be too rigorous to limit the further development of DKF algorithms. This paper proposes an alternative framework for resolving the optimal state estimation problem of multisensor integration. The data fusion algorithm is implemented through a series of transformations of vectors from one space into another. In this way, the vectors in the source information spaces are transformed into the estimate information space, where the globally optimal solution is obtained simply by a sum of these transformed vectors. The paper demonstrates how easy it is to derive the conventional DKF algorithms, such as the federated Kalman filter that has been widely applied in the multisensor navigation community. A new global optimal fusion algorithm is derived from the proposed approach. Simulation results demonstrate that the algorithm has higher accuracy than the CKF.

GPS-Challenged Environments: Can Collaborative Navigation Help?

This paper discusses the importance of the availability and continuity of position, navigation and timing (PNT) information, whose application scope and significance have exploded in the past 10-15 years. The Global Navigation Satellite System (GNSS) navigation gap has to be, therefore, filled in, to assure continuous, accurate and reliable PNT even in GNSS-challenged environments. As a viable solution, this paper describes the concept of collaborative navigation, where a group of users, such as emergency crews, dismounted soldiers, or a swarm of robots or unmanned aerial vehicles (UAVs), navigate together in environments where GNSS may be compromised. Urban, indoor and transition environments will be considered in the experimental part of the research performed at the Satellite Positioning and Inertial Navigation (SPIN) Lab at OSU. The two primary systems that we have developed and used in a number of demonstration applications are: the multi-sensor personal navigation (PN) system and the land-based platform, GPSVan; our ongoing research is focused on UAV navigation and application to geospatial data acquisition. Our ultimate research goal is to enable multi-sensory, robust cooperative navigation, including seamless transition between different types of navigation platforms that navigate together, with a special focus on transitional environments. The research trends and experimental results are based predominantly on the OSU experience only, while references to the findings of others are provided.

The Complexity Problem in Future Multisensor Navigation and Positioning Systems: A Modular Solution

Navigation and positioning system users are demanding greater accuracy and reliability in ever more challenging environments. This is driving a wave of rapid innovation, with the result that multisensor integrated navigation systems will become much more complex. This introduces a number of problems, including how to find the necessary expertise to integrate a diverse range of technologies, how to combine technologies from different organisations that wish to protect their intellectual property, and how to incorporate new navigation technologies and methods without having to redesign the whole system. It also makes it desirable to share development effort over a range of different applications. To address this, the feasibility of a modular approach to the design and development of multisensor integrated navigation and positioning systems is analysed. Assessments of the requirements of different user communities and the adaptability of the different navigation and positioning technologies to different contexts and requirements are presented. Based on this, the adoption of an open interface standard for modular integration is recommended and the issues to be resolved in developing that standard are outlined.

A Reliable Multi-Sensor Navigation System for an Unmanned Aerial Vehicle Realized through Integration of SINS with GPS, CNS and Doppler Radar

Contemporary importance of the unmanned aerial vehicle (UAV) both for military and civilian applications has prompted vigorous research related with guidance, navigation and control of these vehicles. The potential civilian uses for small low-cost UAVs are various like reconnaissance, surveillance, rescue and search, remote sensing, traffic monitoring, destruction appraisal of natural disasters, etc. One of the most crucial parts of UAVs missions is accurate navigation of the vehicle, i.e. the real time determination of its position, velocity and attitude. Generally highly accurate Strap down Inertial Navigation Systems (SINS) are too heavy to be flown on UAVs. Moreover highly accurate SINS are also highly expensive. Therefore the low-cost and low weight MEMS based SINS with a compromised precision are the viable option for navigation of UAVs. The errors in position, velocity, and attitude solutions provided by the MEMS based SINS grow unboundedly with the passage of time. To contain these growing errors, integrated navigation is the resolution. Complementary characteristics SINS and external non-inertial navigation aids like Global Positioning System (GPS), Celestial Navigation System (CNS) and Doppler radar make the integrated navigation system an appealing and cost effective solution. The non-inertial sensors providing navigation fixes must have low weight and volume to be suitable for UAV application. In this research work GPS, CNS and Doppler radar are used as external navigation aids for SINS. The navigation solutions of all contributing systems are fused using Federated Kalman Filter (FKF). Three local filters are employed for SINS/GPS, SINS/CNS and SINS/Doppler integration and subsequently information from all three local filters is fused to acquire a global solution. Moreover adaptive and fault tolerant filtering scheme has also been implemented in each local filter to isolate or accommodate any undesirable error or noise. Simulation for the presented architecture has validated the effectiveness of the scheme, by showing a substantial precision improvement in the solutions of position, velocity and attitude.

EGNOS-BASED MULTI-SENSOR ACCURATE AND RELIABLE NAVIGATION IN SEARCH-AND-RESCUE MISSIONS WITH UAVS

Abstract. This paper will introduce and describe the goals, concept and overall approach of the European 7th Framework Programme's project named CLOSE-SEARCH, which stands for 'Accurate and safe EGNOS-SoL Navigation for UAV-based low-cost SAR operations'. The goal of CLOSE-SEARCH is to integrate in a helicopter-type unmanned aerial vehicle, a thermal imaging sensor and a multi-sensor navigation system (based on the use of a Barometric Altimeter (BA), a Magnetometer (MAGN), a Redundant Inertial Navigation System (RINS) and an EGNOS-enabled GNSS receiver) with an Autonomous Integrity Monitoring (AIM) capability, to support the search component of Search-And-Rescue operations in remote, difficult-to-access areas and/or in time critical situations. The proposed integration will result in a hardware and software prototype that will demonstrate an end-to-end functionality, that is to fly in patterns over a region of interest (possibly inaccessible) during day or night and also under adverse weather conditions and locate there disaster survivors or lost people through the detection of the body heat. This paper will identify the technical challenges of the proposed approach, from navigating with a BA/MAGN/RINS/GNSS-EGNOSbased integrated system to the interpretation of thermal images for person identification. Moreover, the AIM approach will be described together with the proposed integrity requirements. Finally, this paper will show some results obtained in the project during the first test campaign performed on November 2010. On that day, a prototype was flown in three different missions to assess its high-level performance and to observe some fundamental mission parameters as the optimal flying height and flying speed to enable body recognition. The second test campaign is scheduled for the end of 2011.

SEARCHING LOST PEOPLE WITH UAVS: THE SYSTEM AND RESULTS OF THE CLOSE-SEARCH PROJECT

Abstract. This paper will introduce the goals, concept and results of the project named CLOSE-SEARCH, which stands for ’Accurate and safe EGNOS-SoL Navigation for UAV-based low-cost Search-And-Rescue (SAR) operations’. The main goal is to integrate a medium-size, helicopter-type Unmanned Aerial Vehicle (UAV), a thermal imaging sensor and an EGNOS-based multi-sensor navigation system, including an Autonomous Integrity Monitoring (AIM) capability, to support search operations in difficult-to-access areas and/or night operations. The focus of the paper is three-fold. Firstly, the operational and technical challenges of the proposed approach are discussed, such as ultra-safe multi-sensor navigation system, the use of combined thermal and optical vision (infrared plus visible) for person recognition and Beyond-Line-Of-Sight communications among others. Secondly, the implementation of the integrity concept for UAV platforms is discussed herein through the AIM approach. Based on the potential of the geodetic quality analysis and on the use of the European EGNOS system as a navigation performance starting point, AIM approaches integrity from the precision standpoint; that is, the derivation of Horizontal and Vertical Protection Levels (HPLs, VPLs) from a realistic precision estimation of the position parameters is performed and compared to predefined Alert Limits (ALs). Finally, some results from the project test campaigns are described to report on particular project achievements. Together with actual Search-and-Rescue teams, the system was operated in realistic, user-chosen test scenarios. In this context, and specially focusing on the EGNOS-based UAV navigation, the AIM capability and also the RGB/thermal imaging subsystem, a summary of the results is presented.

Multi-sensor indoor pedestrian navigation system with vision aiding

In many indoor security and rescue missions, it is essential for the deployed personnel to have accurate positioning information available. If the scene is not explored yet, a map has to be created during the mission and exchanged between different teams, so that subsequent team members benefit from the knowledge about previously visited locations. In this paper, the multi-sensor navigation system called IndoorGuide is presented which accomplishes these tasks. It comprises two lightweight micro-electro-mechanical inertial measurement units for tracking the motion of the user. A monocular camera is integrated in the navigation system which provides attitude and position aiding. The attitude information is extracted by tracking vanishing points in the images of the camera. Position aiding is realized by mapping and recognizing visited locations. For the data fusion, a Kalman Filter and a robust optimization technique based on graphs are compared. Results from experimental test runs are presented and discussed.

Research on FKF Method Based on an Improved Genetic Algorithm for Multi-sensor Integrated Navigation System

The fusion of multi-sensor data can provide more accurate and reliable navigation performance than single-sensor methods. However, the general Federated Kalman Filter (FKF) is not suitable for large changes of complex nonlinear systems parameters and is not optimized for effective information sharing coefficients to estimate navigation preferences. This study concerns research on the FKF method for a nonlinear adaptive model based on an improved Genetic Algorithm (GA) for the Strapdown Inertial Navigation System (SINS) / Celestial Navigation System (CNS) / Global Positioning System (GPS) integrated multi-sensor navigation system. An improved fitness function avoids the premature convergence problem of a general GA and decimal coding improves its performance. The improved GA is used to build the adaptive FKF model and to select the optimized information sharing coefficients of the FKF. An Unscented Kalman Filter (UKF) is used to deal with the nonlinearity of integrated navigation system. Finally, a solution and implementation of the system is proposed and verified experimentally.