A Method to Check ADS-B Message Based on Direction-Finder Antenna

2014 ◽  
Vol 926-930 ◽  
pp. 1861-1864
Author(s):  
Lin Lin ◽  
Zhi Yong Liu ◽  
Yue Fei Zhou

Automatic Dependent Surveillance Broadcast (ADS-B) is a recommended advanced surveillance technology in Future Air Navigation Systems by ICAO. ADS-B is the technology that can provide the surveillance capabilities based on GNSS and ground-air data link. Because the frequency and the coding mechanism are in public, it is easy to be disturbed and deceived. The essay analyzed some traditional methods to check ADS-B information, and introduced a new checking method based on Direction-finder Antenna that fused radio character and Kalman Filter algorithm together . It can identify the false ADS-B message efficiently.

1977 ◽  
Vol 30 (1) ◽  
pp. 135-145 ◽  
Author(s):  
M. J. Dove

Anyone concerned with navigation, be he practising navigator, design or development engineer, teacher, or amateur yachtsman, will be aware that the great majority of developments took place prior to Captain Cook's epic voyages (1768–1779) or after 1904 when the first radio time signals were transmitted. During the first period the science of astro-navigation had been developed and such aids as log, chronometer, sextant andNautical Almanacwere available. The methods developed during that period differ very little from the traditional methods which are still in use today.


2020 ◽  
pp. 1-17
Author(s):  
Haiying Liu ◽  
Jingqi Wang ◽  
Jianxin Feng ◽  
Xinyao Wang

Abstract Visual–Inertial Navigation Systems (VINS) plays an important role in many navigation applications. In order to improve the performance of VINS, a new visual/inertial integrated navigation method, named Sliding-Window Factor Graph optimised algorithm with Dynamic prior information (DSWFG), is proposed. To bound computational complexity, the algorithm limits the scale of data operations through sliding windows, and constructs the states to be optimised in the window with factor graph; at the same time, the prior information for sliding windows is set dynamically to maintain interframe constraints and ensure the accuracy of the state estimation after optimisation. First, the dynamic model of vehicle and the observation equation of VINS are introduced. Next, as a contrast, an Invariant Extended Kalman Filter (InEKF) is constructed. Then, the DSWFG algorithm is described in detail. Finally, based on the test data, the comparison experiments of Extended Kalman Filter (EKF), InEKF and DSWFG algorithms in different motion scenes are presented. The results show that the new method can achieve superior accuracy and stability in almost all motion scenes.


Energies ◽  
2021 ◽  
Vol 14 (4) ◽  
pp. 924
Author(s):  
Zhenzhen Huang ◽  
Qiang Niu ◽  
Ilsun You ◽  
Giovanni Pau

Wearable devices used for human body monitoring has broad applications in smart home, sports, security and other fields. Wearable devices provide an extremely convenient way to collect a large amount of human motion data. In this paper, the human body acceleration feature extraction method based on wearable devices is studied. Firstly, Butterworth filter is used to filter the data. Then, in order to ensure the extracted feature value more accurately, it is necessary to remove the abnormal data in the source. This paper combines Kalman filter algorithm with a genetic algorithm and use the genetic algorithm to code the parameters of the Kalman filter algorithm. We use Standard Deviation (SD), Interval of Peaks (IoP) and Difference between Adjacent Peaks and Troughs (DAPT) to analyze seven kinds of acceleration. At last, SisFall data set, which is a globally available data set for study and experiments, is used for experiments to verify the effectiveness of our method. Based on simulation results, we can conclude that our method can distinguish different activity clearly.


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