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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 study aims to design an optimized algorithm for low-cost pedestrian navigation system (PNS) to correct the heading drift and altitude error, thus achieving high-precise pedestrian location in both two-dimensional (2-D) and three-dimensional (3-D) space.

A novel heading correction algorithm based on smoothing filter at the terminal of zero velocity interval (ZVI) is proposed in the paper. This algorithm adopts the magnetic sensor to calculate all the heading angles in the ZVI and then applies a smoothing filter to obtain the optimal heading angle. Furthermore, heading correction is executed at the terminal moment of ZVI. Meanwhile, an altitude correction algorithm based on step height constraint is proposed to suppress the altitude channel divergence of strapdown inertial navigation system by using the step height as the measurement of the Kalman filter.

The verification experiments were carried out in 2-D and 3-D space to evaluate the performance of the proposed pedestrian navigation algorithm. The results show that the heading drift and altitude error were well corrected. Meanwhile, the path calculated by the novel algorithm has a higher match degree with the reference trajectory, and the positioning errors of the 2-D and 3-D trajectories are both less than 0.5 per cent.

Besides zero velocity update, another two problems, namely, heading drift and altitude error in the PNS, are solved, which ensures the high positioning precision of pedestrian in indoor and outdoor environments.

This paper aims to propose a 3D-map aided tightly coupled positioning solution for land vehicles to reduce the errors caused by non-line-of-sight (NLOS) and multipath interference in urban canyons.

First, a simple but efficient 3D-map is created by adding the building height information to the existing 2D-map. Then, through a designed effective satellite selection method, the distinct NLOS pseudo-range measurements can be excluded. Further, an enhanced extended Kalman particle filter algorithm is proposed to fuse the information from dual-constellation Global Navigation Satellite Systems and reduced inertial sensor system. The dependable degree of each selected satellite is adjusted through fuzzy logic to further mitigate the effect of misjudged LOS and multipath.

The proposed solution can improve positioning accuracy in urban canyons. The experimental results evaluate the effectiveness of the proposed solution and indicate that the proposed solution outperforms all the compared counterparts.

The effect of NLOS and multipath is addressed from both the observation level and fusion level. To the authors’ knowledge, mitigating the effect of misjudged LOS and multipath in the fusion algorithm of tightly coupled integration is seldom considered in existing literature.

THIS conference had as its theme the measurement and effect of workload on pilots and air traffic controllers and the consequences for safe air transport operations. Held at the Royal Aeronautical Society, it addressed assessment techniques for workload and their applications to situations worldwide. The delegates included scientists and psychologists from civil and military organisations as well as representatives from regulatory bodies and associations of pilots and air traffic controllers.

This paper aims to propose a hybrid method based on polynomial fitting bias self-compensation, grey forward-backward linear prediction (GFBLP) and moving average filter (MAF) for error compensation in micro-electromechanical system gyroscope signal especially under motion state.

The error compensation can be divided into two processes: bias correction and noise reduction. A polynomial drift angle fitting algorithm is used to correct bias before denoising processing. For noise reduction, operation can be taken in two stages: detection and processing. First, sample variances are used to judge motion state. According to the detection results, algorithmic system switches between grey GFBLP and MAF to ensure fast convergence rate and small steady-state error.

Experimental results show that the proposed method can correct bias effectively for practical gyroscope signal, and can eliminate noise effectively for both practical gyroscope signal and synthetic signal, which indicates the effectiveness of the proposed method.

Bias correction and noise reduction are considerations. Noise contained in practical or synthetic signal can be reduced rapidly and effectively, which benefits from the new idea of combination grey GFBLP, MAF and sample variances. And most importantly, it is applicable for signal denoising under arbitrary motion state condition, which is different from other methods where the convergence performance is seldom analyzed.

THE altimeter is one of the most important aircraft instruments, and is likely to remain so for many years to come. It is pertinent therefore to attempt to forecast what the future holds for this instrument. As the future is to some extent an extrapolation of the past, it is germane to review briefly the altimeter's development history. A study of the subject reveals four key features which are responsible for the present form of the instrument and which will determine its future development. These are:—

Laboratory calibration methods are time-consuming and require accurate devices to find the error coefficients of the low-cost microelectromechanical system (MEMS) accelerometer. Besides, low-cost MEMS sensors highly depend on temperature because of their silicon property and the effect of temperature on error coefficients should also be considered for compensation. This paper aims to present a field calibration method in which the accelerometer is placed in different positions without any accurate equipment in a few minutes and its temperature is changed by a simple device like a hairdryer.

In this paper, a non-linear cost function is defined based on this rule that the magnitude of the acceleration measured by the accelerometer in static mode is equal to the gravity plus error factors. Also, the dependency of error coefficients of the accelerometer is presented as a second-order polynomial in this cost function. By minimizing the cost function, the accelerometer error coefficients include bias, scale factor and non-orthogonality and their temperature dependency are obtained simultaneously.

Simulation results in MATLAB and empirical results of a MPU6050 accelerometer verify the good performance of the proposed calibration method.

Finding a fast and simple field calibration method to calibrate a low-cost MEMS accelerometer and compensate for the temperature dependency without using accurate laboratory equipment can help a wide range of industries that use advanced and expensive sensors or use expensive laboratory equipment to calibrate their sensors, to decrease their costs.

The purpose of this paper is to discuss and evaluate the performances of the ionospheric delays for spaceborne global positioning system (GPS) receivers with changing altitudes, and to calculate the scale factors and receiver differential code biases (DCBs). Ionospheric delay is one of the major error sources in GPS positioning.

The fractional total electron content (TEC) above the receiver was obtained from the TEC above the Earth and a scale factor. Methods to determine scale factors were implemented and further developed, based on global ionospheric maps (GIM), Klobuchar model and modified Klobuchar model. Receiver DCB values were achieved at the same time. Methods were validated using flight data from the Gravity Recovery and Climate Experiment mission.

Scale factors are influenced by the receiver altitude, TECs along the line of sight and the ionospheric correction method. In a given case, scale factors obtained using GIM are more regular, whereas those obtained using Klobuchar model and modified Klobuchar model are closely related to TECs. DCBs obtained using GIM method are larger than those obtained using Klobuchar model and modified Klobuchar model.

With scale factors and receiver DCBs, accuracy of GPS positioning solutions can be improved, which are useful for spaceborne engineering applications.

THERE have been many articles published from time to time referring to the Official Air Ministry data for the conversion of observed test bench powers and boost of supercharged engines to corrected powers and boosts at altitude.

The air data system of an aircraft includes, amongst other items, the airspeed indicator, altimeter and machmeter all of which derive their readings from measurement of air pressures. The instruments are designed on the assumption that they will be fed with pressures from the undisturbed free‐stream in which the aircraft is flying; this is not generally the case. In flight the aircraft disturbs the air mass and in doing so causes a pressure field around itself which produces the forces necessary for lift and control. The pressure sensors which detect the airstream pressures may be located within the aircraft pressure field and the pressures transmitted to the instruments may not correspond to the free‐stream pressures; if this is so then the instrument indications will be in error.