Search results

Simultaneous localization and mapping (SLAM) is the problem of determining the pose (position and orientation) of an autonomous robot moving through an unknown environment. The classical FastSLAM is a well-known solution to SLAM. In FastSLAM, a particle filter is used for the robot pose estimation, and the Kalman filter (KF) is used for the feature location’s estimation. However, the performance of the conventional FastSLAM is inconsistent. To tackle this problem, this study aims to propose a mutated FastSLAM (MFastSLAM) using soft computing.

The proposed method uses soft computing. In this approach, particle swarm optimization (PSO) estimator is used for the robot’s pose estimation and an adaptive neuro-fuzzy unscented Kalman filter (ANFUKF) is used for the feature location’s estimation. In ANFUKF, a neuro-fuzzy inference system (ANFIS) supervises the performance of the unscented Kalman filter (UKF) with the aim of reducing the mismatch between the theoretical and actual covariance of the residual sequences to get better consistency.

The simulation and experimental results indicate that the consistency and estimated accuracy of the proposed algorithm are superior FastSLAM.

The main contribution of this paper is the introduction of MFastSLAM to solve the problems of FastSLAM.

This paper aims to propose a novel adaptive Kalman filter (APRKF) designed to accommodate the operational environment of maglev pipeline trains, enhancing positioning accuracy by mitigating the impact of noise and sensor errors.

This approach is built upon the Kalman filtering algorithm, using a limited-memory weighted method to derive adaptive factors, while establishing judgment thresholds to constrain the range of their variation.

A comparative analysis with the traditional Kalman filter and the Sage-Husa adaptive filter reveals that APRKF offers superior speed estimation performance while effectively mitigating error accumulation caused by speed integration. The results indicate that APRKF excels in both speed and position estimation, demonstrating its effectiveness in improving estimation accuracy.

This study proposes an innovative method for the velocity and position estimation system of maglev tube-track trains.

The measurement of geomagnetic field can provide a reliable and economical basis for attitude and orbit information of low earth orbiting satellite. Because the earth's magnetic field is a function of position, and its measurement on the orbit are fully observable, orbit estimation can be obtained using extend Kalman filter (EKF) algorithm. With the assistant of angle velocity information from gyro measurement, attitude estimation can also be obtained. At the same time, gyro drift rate estimation is a part of the filter output. Although orbit and attitude determination are independent of each other, the filter can give the orbit and attitude estimation at the same time. The results of the numerical test show that a signal EKF can estimate both orbit and attitude by using magnetometer and gyro measurement only. The accuracy, usually is sufficient for low earth orbiting satellites.

The purpose of this paper is to address the flaws of traditional methods and fulfil the special fault‐tolerant re‐entry navigation requirements of reusable boost vehicle (RBV).

A kind of improved estimation method based on strong tracking unscented Kalman filter (STUKF) is put forward. According to the fact that the traditional state χ²‐test‐based fault diagnosis method is incompetent to detect the signal point small jerks and slowly varying fault in the measurement, a kind of original fault diagnosis technology based on STUKF is used to check the working states of navigation sensors.

The comparisons with χ²‐test method under typical failure distributions validate the perfect state tracking and fault diagnosis performances of this improved method.

This kind of state estimation and fault diagnosis method could be used in the navigation and guidance systems for many kinds of aeronautical and astronautical vehicles.

A kind of novel strong tracking state estimation filter is used, and a kind of very effective fault diagnosis criterion is put forward for the navigation of RBV.

Despite the huge potential of social media, its functionality and impact for enhanced risk communication remain unclear. Drawing on dialogic theory by integrating both “speak from power” and “speak to power” measurements, the article aims to propose a systematic framework to address this issue.

The impact of social media on risk communication is measured by the correlation between “speak from power” and “speak to power” levels, where the former primarily spoke to two facets of the risk communication process – rapidness and attentiveness, and the latter was benchmarked against popularity and commitment. The framework was empirically validated with data relating to coronavirus disease (COVID-19) risk communication in 25,024 selected posts on 17 official provincial Weibo accounts in China.

The analysis results suggest the relationship between the “speak from power” and “speak to power” is mixed rather than causality, which confirms that neither the outcome-centric nor the process-centric method alone can render a full picture of government–public interconnectivity. Besides, the proposed interconnectivity matrix reveals that two provinces have evidenced the formation of government–public mutuality, which provides empirical evidence that dialogic relationships could exist in social media during risk communication.

The authors' study proposed a prototype framework that underlines the need that the impact of social media on risk communication should and must be assessed through a combination of process and outcome or interconnectivity. The authors further divide the impact of social media on risk communication into dialogue enabler, “speak from power” booster, “speak to power” channel and mass media alternative.

The purpose of this paper is to design a compact, light and relatively inexpensive navigation and guidance system capable of providing the required navigation performance (RNP) in all phases of flight of small unmanned aircrafts (UA), with a special focus on precision approach and landing.

Two multi-sensor architectures for navigation and guidance of small UA are proposed and compared in this paper. These architectures are based, respectively, on a standard extended Kalman filter (EKF) approach and a more advanced unscented Kalman filter (UKF) approach for data fusion of global navigation satellite systems (GNSS), micro-electro-mechanical system (MEMS)-based inertial measurement unit (IMU) and vision-based navigation (VBN) sensors.

The EKF-based VBN-IMU-GNSS-aircraft dynamics model (ADM) (VIGA) system and the UKF-based system (VIGA+) performances are compared in a small UA integration scheme (i.e. AEROSONDE UA platform) exploring a representative cross-section of this UA operational flight envelope, including high-dynamics manoeuvres and CAT-I to CAT-III precision approach tasks. The comparison shows that the position and attitude accuracy of the proposed VIGA and VIGA+ systems are compatible with the RNP specified in the various UA flight profiles, including precision approach down to CAT-II.

The novelty aspect is the augmentation by ADM in both architectures to compensate for the MEMS-IMU sensor shortcomings in high-dynamics attitude determination tasks. Additionally, the ADM measurements are pre-filtered by an UKF with the purpose of increasing the ADM attitude solution stability time in the UKF-based system.

The aim of this article is to present a PIN (pedestrian inertial navigation) solution that incorporates altitude error correction, which eliminates the altitude error accurately without using external sensors. The main problem of PIN is the accumulation of positioning errors due to the drift caused by the noise in the sensors. Experiment results show that the altitude errors are significant when navigating in multilayer buildings, which always lead to localization to incorrect floors.

The PIN proposed is implemented over an inertial navigation systems (INS) framework and a foot-mounted IMU. The altitude error correction idea is identifying the most probable floor of each horizontal walking motion. To recognize gait types, the walking motion is described with angular rate measured by IMU, and the dynamic time warping algorithm is used to cope with the different dimension samples due to the randomness of walking motion. After gait recognition, the altitude estimated with INS of each horizontal walking is checked for association with one of the existing in a database.

Experiment results show that high accuracy altitude is achieved with altitude errors below 5 centimeters for upstairs and downstairs routes in a five floors building.

The main limitations of the study is the assumption that accuracy floor altitude information is available.

Our PIN system eliminates altitude errors accurately and intelligently, which benefits from the new idea of combination of gait recognition and map-matching. In addition, only one IMU is used which is different from other approach that use external sensors.

This paper aims to investigate the effect of unknown noise parameters of Kalman filter on velocity and displacement and to enhance the measured accuracy using adaptive Kalman filter with particle swarm optimization algorithm.

A novel method based on adaptive Kalman filter is proposed. Combined with the displacement measurement model, the standard Kalman filtering algorithm is established. The particle swarm optimization algorithm fused with Kalman is used to obtain the optimal noise parameter estimation using different fitness function.

The simulations and experimental results show that the adaptive Kalman filter algorithm fused with particle swarm optimization can improve the accuracy of the velocity and displacement.

The adaptive Kalman filter algorithm fused with particle swarm optimization can serve as a new method for optimal state estimation of moving target.

The purpose of this paper is to present the problem of implementation of the differential global navigation satellite system (DGNSS) differential technique for aircraft accuracy positioning. The paper particularly focuses on identification and an analysis of the accuracy of aircraft positioning for the DGNSS measuring technique.

The investigation uses the DGNSS method of positioning, which is based on using the model of single code differences for global navigation satellite system (GNSS) observations. In the research experiment, the authors used single-frequency code observations in the global positioning system (GPS)/global navigation satellite system (GLONASS) system from the on-board receiver Topcon HiperPro and the reference station REF1 (reference station for the airport military EPDE in Deblin in south-eastern Poland). The geodetic Topcon HiperPro receiver was installed in Cessna 172 plane in the aviation test. The paper presents the new methodology in the DGNSS solution in air navigation. The aircraft position was estimated using a “weighted mean” scheme for differential global positioning system and differential global navigation satellite system solution, respectively. The final resultant position of aircraft was compared with precise real-time kinematic – on the fly solution.

In the investigations it was specified that the average accuracy of positioning the aircraft Cessna 172 in the geocentric coordinates XYZ equals approximately: +0.03 ÷ +0.33 m along the x-axis, −0.02 ÷ +0.14 m along the y-axis and approximately +0.02 ÷ −0.15 m along the z-axis. Moreover, the root mean square errors determining the measure of the accuracy of positioning of the Cessna 172 for the DGNSS differential technique in the geocentric coordinates XYZ, are below 1.2 m.

In research, the data from GNSS onboard receiver and also GNSS reference receiver are needed. In addition, the pseudo-range corrections from the base stations were applied in the observation model of the DGNSS solution.

The presented research method can be used in a ground based augmentation system (GBAS) augmentation system, whereas the GBAS system is still not applied in Polish aviation.

The paper is destined for people who work in the area of aviation and air transport.

The study presents the DGNSS differential technique as a precise method for recovery of aircraft position in civil aviation and this method can be also used in the positioning of aircraft based on GPS and GLONASS code observations.

This paper seeks to examine three types of measurement and evaluation currently used in the special library environment and to identify the relationships that must exist between the individual measurement processes to enable holistic and strategic evaluations of special libraries to take place.

Looks at how responsible measurement and evaluation within the context of the special (corporate, government, hospital, etc.) library must reflect the ways in which the library uses its resources (budget, staff, buildings, collections, technology, equipment), how well it delivers its services and how well it satisfies its clients now and into the future.

If these measurements and evaluations are to be understood, respected, supported and valued by management, it is also necessary to take it one step further and include the ways in which the information provided through the library services supports the achievement of organisational objectives. One needs to measure how well the services are seamlessly integrated into workflows, how they are prioritised according to their importance and how closely aligned they are with the strategic goals and objectives of the organisation.

May mean a change of mindset on the part of the library managers to enable them to let go of the traditional services and resources and to approach service development and improvement with a strategic view.