Automatic Air Navigation

1945 ◽  
Vol 17 (3) ◽  
pp. 64-69
Author(s):  
F.H. Scrimshaw ◽  
J.A. Wells
Keyword(s):  
Dead Reckoning ◽  
Basic Method ◽  
Wind Vector ◽  
True Airspeed ◽  
The Mean

THE basic method of air navigation is deduced reckoning or simply dead reckoning. The method comprises the maintenance of an air pilot, which is made by calculating true airspeed and hence air distance run and then plotting this along the aircraft's heading from some initial ground fix. Subsequent ground positions may then be deduced by laying off the wind vector from the air position. As an example (Fig. 1) suppose an aircraft flics for one hour on a true heading of 060 deg. starting from an initial ground position A. If the true airspeed is 180 knots the air position will be at B, and if the mean wind over the flight is 45 knots from 340 deg. true then the ground position (by D.R.) corresponding to an air position at B would be at C. Now if the aircraft flics for the next hour on a true heading of 085 deg. and the mean wind over this hour is 30 knots from 310 deg. true, the air position with respect to A would be at D and the ground position at F. If a new air plot had been started at C then the air position, at the end of the second hour, would be at E and the ground position (by D.R.) again at F.

The Use of Large Scale Parameters in Small Scale Diffusion Studies

1957 ◽  
Vol 38 (1.1) ◽  
pp. 6-12 ◽  
Author(s):  
William G. Tank
Keyword(s):  
Large Scale ◽  
Meteorological Parameters ◽  
Surface Wind ◽  
Small Scale ◽  
Relative Importance ◽  
Wind Vector ◽  
Scale Parameters ◽  
Diffusion Studies ◽  
The Mean ◽  
Low Levels

A method is set forth whereby gaseous diffusion in the low levels of the atmosphere can be calculated by Roberts' diffusion equation (modified to consider instantaneous volume sources) using only large scale synoptic parameters that are readily obtainable from the surface analysis and pibal reports. The three pertinent meteorological parameters utilized are: (1) the mean surface wind, (2) the angle between the surface wind vector and the surface isobars, and (3) the height of the gradient level. Theoretical and observed dosage values are compared by means of dosage isopleth diagrams. Results show that the method yields quite satisfactory results, with regard to both dosage magnitude and distribution. The assumptions necessary for the application of the method and its limitations are mentioned and their relative importance discussed.

scholarly journals A Real-Time BLE/PDR Integrated System by Using an Improved Robust Filter for Indoor Position

2021 ◽  
Vol 11 (17) ◽  
pp. 8170
Author(s):  
Shenglei Xu ◽  
Yunjia Wang ◽  
Meng Sun ◽  
Minghao Si ◽  
Hongji Cao
Keyword(s):  
Real Time ◽  
High Precision ◽  
Dead Reckoning ◽  
Integrated System ◽  
Gross Error ◽  
True Position ◽  
Time Position ◽  
Heading Angle ◽  
The Mean ◽  
Pedestrian Dead Reckoning

Indoor position technologies have attracted the attention of many researchers. To provide a real-time indoor position system with high precision and stability is necessary under many circumstances. In a real-time position scenario, gross errors of the Bluetooth low energy (BLE) fingerprint method are more easily occurring and the heading angle of the pedestrian will drift without acceleration and magnetic field compensation. A real-time BLE/pedestrian dead-reckoning (PDR) integrated system by using an improved robust filter has been proposed. In the PDR method, the improved Mahony complementary filter based on the pedestrian motion states is adopted to estimate the heading angle reducing the drift error. Then, an improved robust filter is utilized to detect and restrain the gross error of the BLE fingerprint method. The robust filter detected the gross error at different granularity by constructing a robust vector changing the observation covariance matrix of the extended Kalman filter (EKF) adaptively when the application is running. Several experiments are conducted in the true position scenario. The mean position accuracy obtained by the proposed method in the experiment is 0.844 m and RMSE is 0.74 m. Compared with the classic EKF, these two values are increased by 38% and 18%, respectively. The results show that the improved filter can avoid the gross error in the BLE method and provide high precision and scalability in indoor position service.

scholarly journals Relationship between horizontal wind velocity and normalized surface cross section using data from the HIWRAP dual-frequency airborne radar

2020 ◽  
Author(s):  
R. Meneghini ◽  
L. Liao ◽  
G.M. Heymsfield
Keyword(s):  
Cross Section ◽  
Transfer Functions ◽  
Correlation Coefficients ◽  
Horizontal Wind ◽  
Airborne Radar ◽  
Wind Vector ◽  
Dual Frequency ◽  
Near Surface ◽  
The Mean ◽  
Ku Band

AbstractThe HIWRAP dual-frequency conically-scanning airborne radar provides estimates of the range-profiled mean Doppler and backscattered power from the precipitation and surface. A VAD (velocity azimuth display) analysis yields near-surface estimates of the mean horizontal wind vector, vh, in cases where precipitation is present throughout the scan. From the surface return, the normalized radar cross section (NRCS) is obtained which, by a method previously described, can be corrected for path attenuation.Comparisons between vh and the attenuation-corrected NRCS are used to derive transfer functions that provide estimates of the wind vector from the NRCS data under both rain and rain-free conditions. A reasonably robust transfer function is found by using the mean NRCS, 〈NRCS〉, over the scan along with a filtering of the data based on a Fourier series analysis of vh and the NRCS.The approach gives good correlation coefficients between vh and 〈NRCS〉 at Ku-band at incidence angles of 300 and 400. The correlation degrades if the Ka-band data are used rather that the Ku-band.

scholarly journals PEDESTRIAN DEAD RECKONING USING SMARTPHONES SENSORS: AN EFFICIENT INDOOR POSITIONING SYSTEM IN COMPLEX BUILDINGS OF SMART CITIES

2019 ◽  
Vol XLII-4/W18 ◽  
pp. 905-912
Author(s):  
E. Saadatzadeh ◽  
A. Chehreghan ◽  
R. Ali Abbaspour
Keyword(s):  
Decision Tree ◽  
Smart Cities ◽  
Dead Reckoning ◽  
Principal Component ◽  
Indoor Positioning ◽  
Sensor Data ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
The Mean ◽  
Pedestrian Dead Reckoning

Abstract. This paper proposes an indoor positioning method using Pedestrian Dead Reckoning (PDR) based on the detection of the mode of the user’s smartphone. In the first step, to determine the mode of carrying the smartphone (Holding, Calling, Swinging) by suitably formed feature vectors based on sensor data, three classification algorithms (Decision Tree (DT), Support Vector Machine (SVM), and K-Nearest Neighbor (KNN)) are evaluated. From the classification algorithm perspective, the decision tree algorithm had the best performance in terms of processing time and classification. Secondly, to determine the user position, the step detection is performed by defining the upper threshold and time threshold for Acceleration norm values. The orientation component is obtained by combining accelerometer, magnetometer, and gyroscope data using Complementary Filtering and Principal Component Analysis based on Global Acceleration (PCA-GA) methods. The mean standard deviation along the direct path for the three modes of carrying (Holding, Calling, and Swinging) were obtained 6.22, 6.82, and 14.68 degrees, respectively. Localization experiments were performed on 3 modes of carrying a smartphone in a rectangular geometry path. The mean final error of positioning from ordinary walking for the three modes of holding (Calling, Holding, Swinging) were obtained 2.11, 2.34, and 4.5 m, respectively.

scholarly journals Integrating Moving Platforms in a SLAM Agorithm for Pedestrian Navigation

Sensors ◽  
2018 ◽  
Vol 18 (12) ◽  
pp. 4367
Author(s):  
Susanna Kaiser ◽  
Christopher Lang
Keyword(s):  
Weighting Function ◽  
Dead Reckoning ◽  
Position Estimation ◽  
Indoor Navigation ◽  
Floor Level ◽  
Moving Platforms ◽  
Localization And Mapping ◽  
Measurement Units ◽  
The Mean ◽  
Slam Algorithm

In 3D pedestrian indoor navigation applications, position estimation based on inertial measurement units (IMUss) fails when moving platforms (MPs), such as escalators and elevators, are not properly implemented. In this work, we integrate the MPs in an upper 3D-simultaneous localization and mapping (SLAM) algorithm which is cascaded to the pedestrian dead-reckoning (PDR) technique. The step and heading measurements resulting from the PDR are fed to the SLAM that additionally estimates a map of the environment during the walk in order to reduce the remaining drift. For integrating MPs, we present a new proposal function for the particle filter implementation of the SLAM to account for the presence of MPs. In addition, a new weighting function for features such as escalators and elevators is developed and the features are learned and stored in the learned map. With this, locations of MPs are favored when revisiting the MPs again. The results show that the mean height error is about 0.1 m and the mean position error is less than 1 m for walks with long distances along the floors, even when using multiple floor level changes with different numbers of floors in a multistory environment. For walks with short walking distances and many floor level changes, the mean height error can be higher (about 0.5 m). The final floor number is in all cases except one correctly estimated.

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