Accuracy of Flight Altitude Measured with Low-Cost GNSS, Radar and Barometer Sensors: Implications for Airborne Radiometric Surveys

Development of a Robust Sensor Calibration for a Commercially Available Rising Platemeter to Estimate Herbage Mass on Temperate Seminatural Pastures

Rising platemeters are commonly used in Ireland and New Zealand for managing intensive pastures. To assess the applicability of a commercial rising platemeter operating with a microsonic sensor to estimate herbage mass with its own equation, the objectives were (i) to validate the original equation; (ii) to identify possible factors hampering its accuracy and precision; and (iii) to develop a new equation for heterogeneous swards. A comprehensive dataset (n = 1511) was compiled on the pastures of dairy farms. Compressed sward heights were measured by the rising platemeter. Herbage mass was harvested to determine reference herbage availability. The adequacy of estimating herbage mass was assessed using root mean squared error (RMSE) and mean bias. As the adequacy of the original equation was low, a new equation was developed using multiple regression models. The mean bias and the RMSE for the new equation were overall low with 201 kg dry matter/ha and 34.6%, but it tended to overestimate herbage availability at herbage mass < 500 kg dry matter/ha and underestimate it at >2500 kg dry matter/ha. Still, the newly developed equation for the microsonic sensor-based rising platemeter allows for accurate and precise estimation of available herbage mass on pastures.

Airspeed-Aided State Estimation Algorithm of Small Fixed-Wing UAVs in GNSS-Denied Environments

Aimed at improving the navigation accuracy of the fixed-wing UAVs in GNSS-denied environments, this paper proposes an algorithm of nongravitational acceleration estimation based on airspeed and IMU sensors, which use a differential tracker (TD) model to further supplement the effect of linear acceleration for UAVs under dynamic flight. We further establish the mapping relationship between vehicle nongravitational acceleration and the vehicle attitude misalignment angle and transform it into the attitude angle rate deviation through the nonlinear complementary filtering model for real-time compensation. It can improve attitude estimation precision significantly for vehicles in dynamic conditions. Furthermore, a lightweight complementary filter is used to improve the accuracy of vehicle velocity estimation based on airspeed, and a barometer is fused on the height channel to achieve the accurate tracking of height and the lift rate. The algorithm is actually deployed on low-cost fixed-wing UAVs and is compared with ACF, EKF, and NCF by using real flight data. The position error within 30 s (about 600 m flying) in the horizontal channel flight is less than 30 m, the error within 90 s (about 1800 m flying) is less than 50 m, and the average error of the height channel is 0.5 m. The simulation and experimental tests show that this algorithm can provide UAVs with good attitude, speed, and position calculation accuracy under UAV maneuvering environments.

A Method of Vision Aided GNSS Positioning Using Semantic Information in Complex Urban Environment

High-precision localization through multi-sensor fusion has become a popular research direction in unmanned driving. However, most previous studies have performed optimally only in open-sky conditions; therefore, high-precision localization in complex urban environments required an urgent solution. The complex urban environments employed in this study include dynamic environments, which result in limited visual localization performance, and highly occluded environments, which yield limited global navigation satellite system (GNSS) performance. In order to provide high-precision localization in these environments, we propose a vision-aided GNSS positioning method using semantic information by integrating stereo cameras and GNSS into a loosely coupled navigation system. To suppress the effect of dynamic objects on visual positioning accuracy, we propose a dynamic-simultaneous localization and mapping (Dynamic-SLAM) algorithm to extract semantic information from images using a deep learning framework. For the GPS-challenged environment, we propose a semantic-based dynamic adaptive Kalman filtering fusion (S-AKF) algorithm to develop vision aided GNSS and achieve stable and high-precision positioning. Experiments were carried out in GNSS-challenged environments using the open-source KITTI dataset to evaluate the performance of the proposed algorithm. The results indicate that the dynamic-SLAM algorithm improved the performance of the visual localization algorithm and effectively suppressed the error spread of the visual localization algorithm. Additionally, after vision was integrated, the loosely-coupled navigation system achieved continuous high-accuracy positioning in GNSS-challenged environments.

TDMA Datalink Cooperative Navigation Algorithm Based on INS/JTIDS/BA

Position information is very important tactical information in large-scale joint military operations. Positioning with datalink time of arrival (TOA) measurements is a primary choice when a global navigation satellite system (GNSS) is not available, datalink members are randomly distributed, only estimates with measurements between navigation sources and positioning users may lead to a unsatisfactory accuracy, and positioning geometry of altitude is poor. A time division multiple address (TDMA) datalink cooperative navigation algorithm based on INS/JTIDS/BA is presented in this paper. The proposed algorithm is used to revise the errors of the inertial navigation system (INS), clock bias is calibrated via round-trip timing (RTT), and altitude is located with height filter. The TDMA datalink cooperative navigation algorithm estimate errors are stated with general navigation measurements, cooperative navigation measurements, and predicted states. Weighted horizontal geometric dilution of precision (WHDOP) of the proposed algorithm and the effect of the cooperative measurements on positioning accuracy is analyzed in theory. We simulate a joint tactical information distribution system (JTIDS) network with multiple members to evaluate the performance of the proposed algorithm. The simulation results show that compared to an extended Kalman filter (EKF) that processes TOA measurements sequentially and a TDMA datalink navigation algorithm without cooperative measurements, the TDMA datalink cooperative navigation algorithm performs better.

Advances in detection algorithms for radiation monitoring

This paper presents a review of up-to-date advancements in detection algorithms employed in radiation monitoring for generating radiation maps of ground contamination and tracking radioactive release into the atmosphere. Detection algorithms for true count processing, spectroscopy processing, and plume tracking are discussed in chronological order of development. Process steps of detection include height correction, solid-angle correction, background radioactivity correction, Compton continuum elimination, de-noising of gamma-radiation spectra, and recording of plume passage events.

Monitoring Aircraft Position Using EGNOS Data for the SBAS APV Approach to the Landing Procedure

The aim of this paper is to present the problem of the implementation of the EGNOS (European Geostationary Navigation Overlay Service) data for the processing of aircraft position determination. The main aim of the research is to develop a new computational strategy which might improve the performance of the EGNOS system in aviation, based on navigation solutions of an aircraft position, using several GNSS (Global Navigation Satellite System) onboard receivers. The results of an experimental test conducted by the Cessna 172 at EPDE (European Poland Deblin) (ICAO (International Civil Aviation Organization) code, N51°33.07’/E21°53.52’) aerodrome in Dęblin are presented and discussed in this paper. Two GNSS navigation receivers with the EGNOS positioning function for monitoring changes in the parameters of the aircraft position in real time during the landing phase were installed onboard a Cessna 172. Based on obtained research findings, it was discovered that the positioning accuracy was not higher than 2.1 m, and the integrity of positioning did not exceed 19 m. Moreover, the availability parameter was found to equal 1 (or 100%); also, no intervals in the continuity of the operation of the EGNOS system were recorded. In the paper, the results of the air test from Dęblin were compared with the parameters of positioning quality from the air test conducted in Chełm (ICAO code: EPCD, N51°04’57.8” E23°26’15”). In the air test in Chełm, the obtained parameters of EGNOS quality positioning were: better than 4.9 m for accuracy, less than 35.5 m for integrity, 100% for availability, and no breaks in continuity. Based on the results of the air tests in Dęblin and Chełm, it was concluded that the parameters of the EGNOS positioning quality in aviation for the SBAS (Satellite Based Augmentation System) APV (Approach to Vertical guidance) procedure were satisfied in accordance with the ICAO (International Civil Aviation Organization) requirements. The presented research method can be utilized in the SBAS APV landing procedure in Polish aviation. In this paper, the results of PDOP (Position Dilution of Precision) are presented and compared to the two air tests in Dęblin and Chełm. The maximum results of PDOP amounted to 1.4 in the air test in Dęblin, whereas they equaled 4.0 in the air test in Chełm. The paper also shows how the EGNOS system improved the aircraft position in relation to the only GPS solution. In this context, the EGNOS system improved the aircraft position from about 78% to 95% for each ellipsoidal coordinate axis.

Application of GNSS/INS and an Optical Sensor for Determining Airplane Takeoff and Landing Performance on a Grassy Airfield

The performance of a PZL 104 Wilga 35A airplane was determined and analyzed in this work. Takeoff and landing distances were determined by means of two different methods: one which utilized a Global Navigation Satellite System/Inertial Navigation System (GNSS/INS) sensor and another in which airplane ground speed was measured with the use of an optical non-contact sensor. Based on the airfield measurements, takeoff and landing distances as well as rolling resistance coefficients were determined for the used airplane on a grassy runway at the Radawiec airfield, located near Lublin, southeast Poland. The study was part of the "GARFIELD" project that is expected to deliver an online information system on grassy airfield conditions. It was concluded that both sensors were suitable for the aimed research. The results obtained in this study showed the effects of high grass upon the takeoff and landing performances of the test airplane. Also, the two methods were compared against each other, and the final results were compared to calculations of ground distances by means of the chosen analytical models.

Low-Cost Real-Time PPP/INS Integration for Automated Land Vehicles

The last decade has witnessed a growing demand for precise positioning in many applications including car navigation. Navigating automated land vehicles requires at least sub-meter level positioning accuracy with the lowest possible cost. The Global Navigation Satellite System (GNSS) Single-Frequency Precise Point Positioning (SF-PPP) is capable of achieving sub-meter level accuracy in benign GNSS conditions using low-cost GNSS receivers. However, SF-PPP alone cannot be employed for land vehicles due to frequent signal degradation and blockage. In this paper, real-time SF-PPP is integrated with a low-cost consumer-grade Inertial Navigation System (INS) to provide a continuous and precise navigation solution. The PPP accuracy and the applied estimation algorithm contributed to reducing the effects of INS errors. The system was evaluated through two road tests which included open-sky, suburban, momentary outages, and complete GNSS outage conditions. The results showed that the developed PPP/INS system maintained horizontal sub-meter Root Mean Square (RMS) accuracy in open-sky and suburban environments. Moreover, the PPP/INS system could provide a continuous real-time positioning solution within the lane the vehicle is moving in. This lane-level accuracy was preserved even when passing under bridges and overpasses on the road. The developed PPP/INS system is expected to benefit low-cost precise land vehicle navigation applications including level 2 of vehicle automation which comprises services such as lane departure warning and lane-keeping assistance.

Smartphone-Based 3D Indoor Pedestrian Positioning through Multi-Modal Data Fusion

Combining research areas of biomechanics and pedestrian dead reckoning (PDR) provides a very promising way for pedestrian positioning in environments where Global Positioning System (GPS) signals are degraded or unavailable. In recent years, the PDR systems based on a smartphone’s built-in inertial sensors have attracted much attention in such environments. However, smartphone-based PDR systems are facing various challenges, especially the heading drift, which leads to the phenomenon of estimated walking path passing through walls. In this paper, the 2D PDR system is implemented by using a pocket-worn smartphone, and then enhanced by introducing a map-matching algorithm that employs a particle filter to prevent the wall-crossing problem. In addition, to extend the PDR system for 3D applications, the smartphone’s built-in barometer is used to measure the pressure variation associated to the pedestrian’s vertical displacement. Experimental results show that the map-matching algorithm based on a particle filter can effectively solve the wall-crossing problem and improve the accuracy of indoor PDR. By fusing the barometer readings, the vertical displacement can be calculated to derive the floor transition information. Despite the inherent sensor noises and complex pedestrian movements, smartphone-based 3D pedestrian positioning systems have considerable potential for indoor location-based services (LBS).

Feasibility Analysis of LTE-Based UAS Navigation in Deep Urban Areas and DSRC Augmentation

The current autonomous navigation of unmanned aircraft systems (UAS) heavily depends on Global Navigation Satellite Systems (GNSS). However, in challenging environments, such as deep urban areas, GNSS signals can be easily interrupted, so that UAS may lose navigation capability at any instant. For urban positioning and navigation, Long Term Evolution (LTE) has been considered a promising signal of opportunity due to its dense network in urban areas, and there has recently been great advancement in LTE positioning technology. However, the current LTE positioning accuracy is found to be insufficient for safe UAS navigation in deep urban areas. This paper evaluates the positioning performance of the current network of LTE base stations in a selected deep urban area and investigates the effectiveness of LTE augmentations using dedicated short range communication (DSRC) transceivers through the optimization of the ground LTE/DSRC network and cooperative positioning among UAS. The analysis results based on simulation using an urban canyon model and signal line of sight propagations show that the addition of four or five DSRC transceivers to the existing LTE base station network could provide better than 4–6 m horizontal positioning accuracy (95%) in the selected urban canyon at a position of 150 ft above the ground, while a dense LTE network alone may result in a 15–20 m horizontal positioning error. Additionally, the simulation results of cooperative positioning with inter-UAS ranging measurements in the DSRC augmented LTE network were shown to provide horizontal positioning accuracy better than 1 m in most flight space, assuming negligible time-synchronization errors in inter-UAS ranging measurements.

Corrections of BDS Code Multipath Error in Geostationary Orbit Satellite and Their Application in Precise Data Processing

Multipath error is a main error source in Global Navigation Satellite System (GNSS) data processing, which cannot be removed by a differential technique because of the strong relationship with the environment around the station. The multipath effect of the code observables is more complex than that of the carrier-phase observables, especially for BeiDou Navigation Satellite System (BDS) geostationary orbit (GEO) satellites. In this contribution, we deeply analyzed the characteristic and effect on the precise data processing of GEO satellite multipath errors based on a large number of permanent GNSS stations. A linear combination of code and carrier-phase observables was used to analyze the characteristics of repeatability for BDS GEO’s multipath. Then, a correction method was proposed to eliminate the multipath error of the GEO code observables, based on wavelet transform. The experiment data were collected at 83 globally distributed stations, from multi-GNSS experiments and national BDS augmentation systems, from days 32 to 66 in 2017. The results show that the systematic multipath variation component of the GEO code observables can be obtained with wavelet transform, which can significantly contribute to correcting the multipath error of GEO satellites. The average root mean square error (RMSE) of the multipath series is decreased by approximately 19.5%, 20.2%, and 7.5% for B1, B2, and B3, respectively. In addition, some experiments, including ionospheric delay extraction and satellite clock estimation, were conducted in simulated real-time mode in order to validate the effect of the correction methods. For the ionospheric delay estimation, the average RMSE of the slant ionospheric delay is reduced by approximately 15.5%. Moreover, the multipath correction can contribute greatly to shortening the convergence time of the satellite clock estimation of the BDS GEO satellites.

Research on Time-Correlated Errors Using Allan Variance in a Kalman Filter Applicable to Vector-Tracking-Based GNSS Software-Defined Receiver for Autonomous Ground Vehicle Navigation

The global navigation satellite system (GNSS) has been applied to many areas, e.g.,the autonomous ground vehicle, unmanned aerial vehicle (UAV), precision agriculture, smart city,and the GNSS-reflectometry (GNSS-R), being of considerable significance over the past few decades.Unfortunately, the GNSS signal performance has the high risk of being reduced by the environmentalinterference. The vector tracking (VT) technique is promising to enhance the robustness in highdynamics as well as improve the sensitivity against the weak environment of the GNSS receiver.However, the time-correlated error coupled in the receiver clock estimations in terms of the VT loopcan decrease the accuracy of the navigation solution. There are few works present dealing with thisissue. In this work, the Allan variance is accordingly exploited to specify a model which is expectedto account for this type of error based on the 1st-order Gauss-Markov (GM) process. Then, it is usedfor proposing an enhanced Kalman filter (KF) by which this error can be suppressed. Furthermore,the proposed system model makes use of the innovation sequence so that the process covariancematrix can be adaptively adjusted and updated. The field tests demonstrate the performance of theproposed adaptive vector-tracking time-correlated error suppressed Kalman filter (A-VTTCES-KF).When compared with the results produced by the ordinary adaptive KF algorithm in terms of the VTloop, the real-time kinematic (RTK) positioning and code-based differential global positioning system(DGPS) positioning accuracies have been improved by 14.17% and 9.73%, respectively. On the otherhand, the RTK positioning performance has been increased by maximum 21.40% when comparedwith the results obtained from the commercial low-cost U-Blox receiver.

Tight Fusion of a Monocular Camera, MEMS-IMU, and Single-Frequency Multi-GNSS RTK for Precise Navigation in GNSS-Challenged Environments

Precise position, velocity, and attitude is essential for self-driving cars and unmanned aerial vehicles (UAVs). The integration of global navigation satellite system (GNSS) real-time kinematics (RTK) and inertial measurement units (IMUs) is able to provide high-accuracy navigation solutions in open-sky conditions, but the accuracy will be degraded severely in GNSS-challenged environments, especially integrated with the low-cost microelectromechanical system (MEMS) IMUs. In order to navigate in GNSS-denied environments, the visual–inertial system has been widely adopted due to its complementary characteristics, but it suffers from error accumulation. In this contribution, we tightly integrate the raw measurements from the single-frequency multi-GNSS RTK, MEMS-IMU, and monocular camera through the extended Kalman filter (EKF) to enhance the navigation performance in terms of accuracy, continuity, and availability. The visual measurement model from the well-known multistate constraint Kalman filter (MSCKF) is combined with the double-differenced GNSS measurement model to update the integration filter. A field vehicular experiment was carried out in GNSS-challenged environments to evaluate the performance of the proposed algorithm. Results indicate that both multi-GNSS and vision contribute significantly to the centimeter-level positioning availability in GNSS-challenged environments. Meanwhile, the velocity and attitude accuracy can be greatly improved by using the tightly-coupled multi-GNSS RTK/INS/Vision integration, especially for the yaw angle.

HiMeter: Telling You the Height Rather than the Altitude

The altitude of a moving user is important context information for mobile technologies and applications. However, with the increasing pervasiveness of smartphones and abundant mobile applications, developers and users have gradually discovered that the height is more useful than altitude in many situations. The height is often a relative value, which is the vertical distance to the ground rather than the vertical distance to sea level, and we believe that it is useful in many applications, such as localization/navigation, sport/health and tourism/travel. In this paper, we first carried out a nation-wide online survey to confirm the desirability for the height information in mobile applications, and the result is positive. Then, we proposed HiMeter, an effective and accurate approach to calculating the height of the smartphone. HiMeter makes use of a low-power barometer on the smartphone and does not require GPS or back-server support. We concentrate on the vertical moving pattern of the user and designed several novel techniques, resulting in HiMeter not needing any reference points, and the complex process of calculating the absolute altitude can be avoided. The field studies show that HiMeter can achieve an accuracy of within 5 m in 90% of cases indoors and an accuracy of 10 m in 83% of cases outdoors. Compared to the existing works, HiMeter is more accurate and practical and is more suitable for usage in many mobile applications.