LOCALIZATION ALGORITHM BASED ON MINIMUM CONDITION NUMBER FOR WIRELESS SENSOR NETWORKS

During range-based self-localization of Wireless Sensor Network (WSN) nodes, the number and placement methods of beacon nodes have a great influence on the accuracy of localization. This paper proves a theorem which describes the relationship between the placement of beacon nodes and whether the node can be located in 3D indoor environment. In fact, as the highest locating accuracy can be acquired when the beacon nodes form one or more equilateral triangles in 2D plane, we generalizes this conclusion to 3D space, and proposes a beacon nodes selection algorithm based on the minimum condition number to get the higher locating accuracy, which can minimize the influence of distance measurement error. Simulation results show that the algorithm is effective and feasible.

Experimental study on location accuracy of a 3D VHF lightning-radiation-source locating network

This paper reports the investigation of the location accuracy of a three-dimensional （3D） lightning-radiation-source locating system using sounding balloon measurements. By comparing the information from the balloon-borne VHF transmitter flight path and locations using simple geometric models, the location uncertainties of sources both over and outside the network were estimated. For radiation sources inside the network and below an altitude of 7 km, the horizontal uncertainty was 12-48 m and the total mean value was 21 m （rms）, and the vertical uncertainty was 20-78 m and the total mean value was 49 m （rms）. Outside the network, the location uncertainties increased with distance. The geometric model showed that range and altitude errors increased as a function of the range squared whereas the range errors increased parabolically with distance, and that was confirmed by the covariance calculation results. The standard deviation was used inside the network and covariance was used outside the network. The results indicated that location errors from a simple geometric model exhibited good agreement with standard experimental data. The geometry of the network, set of measurements, and calculation method were verified as suitable. The chi-square values of the least squares goodness of fit algorithm were verified and the timing error （A/rms） Of the fitting formula was estimated. The distribution of the chi-square values was less than 5, corresponding to a timing error of 50-66 ns （rms）.

Real-time drogue recognition and 3D locating for UAV autonomous aerial refueling based on monocular machine vision

Drogue recognition and 3D locating is a key problem during the docking phase of the autonomous aerial refueling （AAR）. To solve this problem, a novel and effective method based on monocular vision is presented in this paper. Firstly, by employing computer vision with red-ring-shape feature, a drogue detection and recognition algorithm is proposed to guarantee safety and ensure the robustness to the drogue diversity and the changes in environmental condi- tions, without using a set of infrared light emitting diodes （LEDs） on the parachute part of the dro- gue. Secondly, considering camera lens distortion, a monocular vision measurement algorithm for drogue 3D locating is designed to ensure the accuracy and real-time performance of the system, with the drogue attitude provided. Finally, experiments are conducted to demonstrate the effective- ness of the proposed method. Experimental results show the performances of the entire system in contrast with other methods, which validates that the proposed method can recognize and locate the drogue three dimensionally, rapidly and precisely.

Understanding the dynamical-microphysical-electrical processes associated with severe thunderstorms over the Beijing metropolitan region

The Dynamical-microphysical-electrical Processes in Severe Thunderstorms and Lightning Hazards(STORM973)project conducted coordinated comprehensive field observations of thunderstorms in the Beijing metropolitan region(BMR)during the warm season from 2014 to 2018.The aim of the project was to understand how dynamical,microphysical and electrical processes interact in severe thunderstorms in the BMR,and how to assimilate lightning data in numerical weather prediction models to improve severe thunderstorm forecasts.The platforms used in the field campaign included the Beijing Lightning Network(BLNET,consisting of 16 stations),2 X-band dual linear polarimetric Doppler radars,and 4 laser raindrop spectrometers.The collaboration also made use of the China Meteorological Administration’s mesoscale meteorological observation network in the Beijing-Tianjin-Hebei region.Although diverse thunderstorm types were documented,it was found that squall lines and multicell storms were the two major categories of severe thunderstorms with frequent lightning activity and extreme rainfall or unexpected local short-duration heavy rainfall resulting in inundations in the central urban area,influenced by the terrain and environmental conditions.The flash density maximums were found in eastern Changping District,central and eastern Shunyi District,and the central urban area of Beijing,suggesting that the urban heat island effect has a crucial role in the intensification of thunderstorms over Beijing.In addition,the flash rate associated with super thunderstorms can reach hundreds of flashes per minute in the central city regions.The super(5%of the total),strong(35%),and weak(60%)thunderstorms contributed about 37%,56%,and 7%to the total flashes in the BMR,respectively.Owing to the close connection between lightning activity and the thermodynamic and microphysical characteristics of the thunderstorms,the lightning flash rate can be used as an indicator of severe weather events,such as hail and short-duration heavy rainfall.Lightning data can also be assimilated into numerical weather prediction models to help improve the forecasting of severe convection and precipitation at the cloud-resolved scale,through adjusting or correcting the thermodynamic and microphysical parameters of the model.