Using quasi ionosphere-free post-processing algorithm on the medium-range kinematic high accuracy GPS relative positioning

2003 ◽  
Vol 8 (2) ◽  
pp. 610-618
Author(s):  
Chen Horng-yue ◽  
Kuo Long-chen ◽  
Chung Wang-shung ◽  
Yu Shui-beih
Keyword(s):  
High Accuracy ◽  
Processing Algorithm ◽  
Relative Positioning ◽  
Post Processing ◽  
Medium Range

scholarly journals Noise power spectral density scaled SNR response estimation with restricted range search for sound source localisation using unmanned aerial vehicles

2020 ◽  
Vol 2020 (1) ◽  
Author(s):  
Benjamin Yen ◽  
Yusuke Hioka
Keyword(s):  
Spectral Density ◽  
Noise Reduction ◽  
Power Spectral Density ◽  
Sound Source ◽  
Processing Algorithm ◽  
Post Processing ◽  
Time Frequency ◽  
Distance Error ◽  
Rotor Noise ◽  
Power Spectral

Abstract A method to locate sound sources using an audio recording system mounted on an unmanned aerial vehicle (UAV) is proposed. The method introduces extension algorithms to apply on top of a baseline approach, which performs localisation by estimating the peak signal-to-noise ratio (SNR) response in the time-frequency and angular spectra with the time difference of arrival information. The proposed extensions include a noise reduction and a post-processing algorithm to address the challenges in a UAV setting. The noise reduction algorithm reduces influences of UAV rotor noise on localisation performance, by scaling the SNR response using power spectral density of the UAV rotor noise, estimated using a denoising autoencoder. For the source tracking problem, an angular spectral range restricted peak search and link post-processing algorithm is also proposed to filter out incorrect location estimates along the localisation path. Experimental results show the proposed extensions yielded improvements in locating the target sound source correctly, with a 0.0064–0.175 decrease in mean haversine distance error across various UAV operating scenarios. The proposed method also shows a reduction in unexpected location estimations, with a 0.0037–0.185 decrease in the 0.75 quartile haversine distance error.

Remote Sensing Image Mosaic Algorithm

2012 ◽  
Vol 500 ◽  
pp. 716-721
Author(s):  
Yi Ding Wang ◽  
Shuai Qin
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Hot Spot ◽  
Remote Sensing Image ◽  
Processing Algorithm ◽  
Geometric Features ◽  
Image Mosaic ◽  
Post Processing ◽  
Remote Sensing Images ◽  
Image Pixels

In the field of remote sensing, the acquirement of higher resolution of remote sensing images has become a hot spot issue with widely use of high resolution of remote sensing images. This paper focus on the characteristics of high resolution remote sensing images, on the basis of fully considerate of the correlation between geometric features and image pixels, bring forward a fusion of image mosaic processing algorithm. With this algorithm, the surface features can be well preserved after the processing of mosaic the remote sensing images, and the overlapping area can transit naturally, it will be better for the post-processing, analysis and application.

scholarly journals A novel post-processing algorithm for Halo Doppler lidars

2019 ◽  
Vol 12 (2) ◽  
pp. 839-852 ◽  
Author(s):  
Ville Vakkari ◽  
Antti J. Manninen ◽  
Ewan J. O'Connor ◽  
Jan H. Schween ◽  
Pieter G. van Zyl ◽  
...  
Keyword(s):  
Boundary Layer ◽  
Radial Velocity ◽  
Planetary Boundary Layer ◽  
Data Availability ◽  
Processing Algorithm ◽  
Stream Line ◽  
Limiting Factor ◽  
High Temporal Resolution ◽  
Post Processing ◽  
Uncertainty Estimates

Abstract. Commercially available Doppler lidars have now been proven to be efficient tools for studying winds and turbulence in the planetary boundary layer. However, in many cases low signal-to-noise ratio is still a limiting factor for utilising measurements by these devices. Here, we present a novel post-processing algorithm for Halo Stream Line Doppler lidars, which enables an improvement in sensitivity of a factor of 5 or more. This algorithm is based on improving the accuracy of the instrumental noise floor and it enables longer integration times or averaging of high temporal resolution data to be used to obtain signals down to −32 dB. While this algorithm does not affect the measured radial velocity, it improves the accuracy of radial velocity uncertainty estimates and consequently the accuracy of retrieved turbulent properties. Field measurements using three different Halo Doppler lidars deployed in Finland, Greece and South Africa demonstrate how the new post-processing algorithm increases data availability for turbulent retrievals in the planetary boundary layer, improves detection of high-altitude cirrus clouds and enables the observation of elevated aerosol layers.

Many-Objective Optimal Design of Sliding Mode Controls

2016 ◽  
Vol 139 (1) ◽  
Author(s):  
Yousef Sardahi ◽  
Jian-Qiao Sun
Keyword(s):  
Optimal Design ◽  
Pareto Front ◽  
Sliding Mode ◽  
Design Method ◽  
Tracking Error ◽  
Control Design ◽  
Processing Algorithm ◽  
Order System ◽  
Post Processing ◽  
Control Effort

This paper presents a many-objective optimal (MOO) control design of an adaptive and robust sliding mode control (SMC). A second-order system is used as an example to demonstrate the design method. The robustness of the closed-loop system in terms of stability and disturbance rejection are explicitly considered in the optimal design, in addition to the typical time-domain performance specifications such as the rise time, tracking error, and control effort. The genetic algorithm is used to solve for the many-objective optimization problem (MOOP). The optimal solutions known as the Pareto set and the corresponding objective functions known as the Pareto front are presented. To assist the decision-maker to choose from the solution set, we present a post-processing algorithm that operates on the Pareto front. Numerical simulations show that the proposed many-objective optimal control design and the post-processing algorithm are promising.

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