Analysis of unmanned aerial vehicle navigation and height control system based on GPS

According to the characteristic of global positioning system（GPS） reflection signals,a GPS delay mapping receiver system scheme is put forward,which not only satisfies the unmanned aerial vehicle（UAV） guidance localization but also realizes height measurement.A code delay algorithm is put forward,which processes the direct and land reflected signal and outputs the navigation data and specular point.The GPS terrain reflected echo signal mathematical equation is inferred.The reflecting signal area,when the GPS signal passes the land,is analyzed.The height survey model reflected land surface characteristic is established.A simulation system which carries guidance localization of the UAV and the height measuring control through the GPS direct signal and the land reflected signal is designed,taken the GPS satellite as the illumination source,the receiver is put on the UAV.Then the UAV guidance signal,the GPS reflection signal and receiver＇s parallel processing are realized.The parallel processing reduces UAV＇s payload and raises system＇s operating efficiency.The simulation results confirms the validity of the model and also provides the basis for the UAV＇s optimization design.

Retrieval of sea surface winds under hurricane conditions from GNSS-R observations

Reflected signals from global navigation satellite systems（GNSSs） have been widely acknowledged as an important remote sensing tool for retrieving sea surface wind speeds.The power of GNSS reflectometry（GNSS-R）signals can be mapped in delay chips and Doppler frequency space to generate delay Doppler power maps（DDMs）,whose characteristics are related to sea surface roughness and can be used to retrieve wind speeds.However,the bistatic radar cross section（BRCS）,which is strongly related to the sea surface roughness,is extensively used in radar.Therefore,a bistatic radar cross section（BRCS） map with a modified BRCS equation in a GNSS-R application is introduced.On the BRCS map,three observables are proposed to represent the sea surface roughness to establish a relationship with the sea surface wind speed.Airborne Hurricane Dennis（2005） GNSS-R data are then used.More than 16 000 BRCS maps are generated to establish GMFs of the three observables.Finally,the proposed model and classic one-dimensional delay waveform（DW） matching methods are compared,and the proposed model demonstrates a better performance for the high wind speed retrievals.

Crowdsourcing RTK:a new GNSS positioning framework for building spatial high-resolution atmospheric maps based on massive vehicle GNSS data

High-quality spatial atmospheric delay correction information is essential for achieving fast integer ambiguity resolution(AR)in precise positioning.However,traditional real-time precise positioning frameworks(i.e.,NRTK and PPP-RTK)depend on spatial low-resolution atmospheric delay correction through the expensive and sparsely distributed CORS network.This results in limited public appeal.With the mass production of autonomous driving vehicles,more cost-effective and widespread data sources can be explored to create spatial high-resolution atmospheric maps.In this study,we propose a new GNSS positioning framework that relies on dual base stations,massive vehicle GNSS data,and crowdsourced atmospheric delay correction maps(CAM).The map is easily produced and updated by vehicles equipped with GNSS receivers in a crowd-sourced way.Specifically,the map consists of between-station single-differenced ionospheric and tropospheric delays.We introduce the whole framework of CAM initialization for individual vehicles,on-cloud CAM maintenance,and CAM-augmented user-end positioning.The map data are collected and preprocessed in vehicles.Then,the crowdsourced data are uploaded to a cloud server.The massive data from multiple vehicles are merged in the cloud to update the CAM in time.Finally,the CAM will augment the user positioning performance.This framework forms a beneficial cycle where the CAM’s spatial resolution and the user positioning performance mutually improve each other.We validate the performance of the proposed framework in real-world experiments and the applied potency at different spatial scales.We highlight that this framework is a reliable and practical positioning solution that meets the requirements of ubiquitous high-precision positioning.