scholarly journals Signal-Based Self-Organization of a Chain of UAVs for Subterranean Exploration

2021 ◽  
Vol 8 ◽  
Author(s):  
Pierre Laclau ◽  
Vladislav Tempez ◽  
Franck Ruffier ◽  
Enrico Natalizio ◽  
Jean-Baptiste Mouret
Keyword(s):  
Kalman Filter ◽  
Optic Flow ◽  
Distributed Algorithm ◽  
Base Station ◽  
Experimental Results ◽  
Self Organization ◽  
Signal Quality ◽  
Ground Speed ◽  
A Chain ◽  
Flying Robots

Miniature multi-rotors are promising robots for navigating subterranean networks, but maintaining a radio connection underground is challenging. In this paper, we introduce a distributed algorithm, called U-Chain (for Underground-chain), that coordinates a chain of flying robots between an exploration drone and an operator. Our algorithm only uses the measurement of the signal quality between two successive robots and an estimate of the ground speed based on an optic flow sensor. It leverages a distributed policy for each UAV and a Kalman filter to get reliable estimates of the signal quality. We evaluate our approach formally and in simulation, and we describe experimental results with a chain of 3 real miniature quadrotors (12 by 12 cm) and a base station.

scholarly journals Self-replication of a quantum artificial organism driven by single-photon pulses

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Daniel Valente
Keyword(s):  
Self Assembly ◽  
Artificial Life ◽  
Single Photon ◽  
Self Organization ◽  
Physical Systems ◽  
Self Organized ◽  
Living Organisms ◽  
A Chain ◽  
Thermodynamic Mechanism ◽  
Self Replication

AbstractImitating the transition from inanimate to living matter is a longstanding challenge. Artificial life has achieved computer programs that self-replicate, mutate, compete and evolve, but lacks self-organized hardwares akin to the self-assembly of the first living cells. Nonequilibrium thermodynamics has achieved lifelike self-organization in diverse physical systems, but has not yet met the open-ended evolution of living organisms. Here, I look for the emergence of an artificial-life code in a nonequilibrium physical system undergoing self-organization. I devise a toy model where the onset of self-replication of a quantum artificial organism (a chain of lambda systems) is owing to single-photon pulses added to a zero-temperature environment. I find that spontaneous mutations during self-replication are unavoidable in this model, due to rare but finite absorption of off-resonant photons. I also show that the replication probability is proportional to the absorbed work from the photon, thereby fulfilling a dissipative adaptation (a thermodynamic mechanism underlying lifelike self-organization). These results hint at self-replication as the scenario where dissipative adaptation (pointing towards convergence) coexists with open-ended evolution (pointing towards divergence).

Real-Time Identification and Tracking of Infrared Markers Based on Kalman Filter

2012 ◽  
Vol 249-250 ◽  
pp. 1147-1153
Author(s):  
Qiao Na Xing ◽  
Da Yuan Yan ◽  
Xiao Ming Hu ◽  
Jun Qin Lin ◽  
Bo Yang
Keyword(s):  
Kalman Filter ◽  
Visible Light ◽  
Real Time ◽  
Complex Terrain ◽  
Experimental Results ◽  
Identification Method ◽  
In The Wild ◽  
Real Time Identification

Automatic equipmenttransportation in the wild complex terrain circumstances is very important in rescue or military. In this paper, an accompanying system based on the identification and tracking of infrared LEDmarkers is proposed. This system avoidsthe defect that visible-light identification method has. In addition, this paper presents a Kalman filter to predict where infraredmarkers may appear in the nextframe imageto reduce the searchingarea of infrared markers, which remarkablyimproves the identificationspeed of infrared markers. The experimental results show that the algorithm proposed in this paper is effective and feasible.

scholarly journals Performance Evaluation of GPS Auto-Surveying Techniques

Sensors ◽  
2021 ◽  
Vol 21 (21) ◽  
pp. 7374
Author(s):  
João Manito ◽  
José Sanguino
Keyword(s):  
Kalman Filter ◽  
Least Squares ◽  
Unscented Kalman Filter ◽  
Weighted Least Squares ◽  
Base Station ◽  
Global Navigation Satellite Systems ◽  
Precise Position ◽  
Satellite Systems ◽  
Global Navigation Satellite ◽  
Position Estimate

With the increase in the widespread use of Global Navigation Satellite Systems (GNSS), increasing numbers of applications require precise position data. Of all the GNSS positioning methods, the most precise are those that are based in differential systems, such as Differential GNSS (DGNSS) and Real-Time Kinematics (RTK). However, for absolute positioning, the precision of these methods is tied to their reference position estimates. With the goal of quickly auto-surveying the position of a base station receiver, four positioning methods are analyzed and compared, namely Least Squares (LS), Weighted Least Squares (WLS), Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF), using only pseudorange measurements, as well as the Hatch Filter and position thresholding. The research results show that the EKF and UKF present much better mean errors than LS and WLS, with an attained precision below 1 m after about 4 h of auto-surveying. The methods that presented the best results are then tested against existing implementations, showing them to be very competitive, especially considering the differences between the used receivers. Finally, these results are used in a DGNSS test, which verifies a significant improvement in the position estimate as the base station position estimate improves.

Self-organization of the critical state in a chain of SQUIDs

JETP Letters ◽  
1998 ◽  
Vol 68 (9) ◽  
pp. 719-725 ◽  
Author(s):  
S. L. Ginzburg ◽  
E. Savitskaya
Keyword(s):  
Critical State ◽  
Self Organization ◽  
A Chain

Influence analysis of measurement errors in satellite attitude determination based on extended Kalman filter

2012 ◽  
Vol 116 (1178) ◽  
pp. 373-389
Author(s):  
Y. Jiao ◽  
J. Wang ◽  
X. Pan ◽  
H. Zhou
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Measurement Errors ◽  
Attitude Determination ◽  
Previous Analysis ◽  
Measurement Model ◽  
Experimental Results ◽  
Influence Analysis ◽  
Satellite Attitude ◽  
Structural Error

Abstract The satellite attitude determination approach based on the Extended Kalman Filter (EKF) has been widely used in many real applications. However, the accuracy of this method largely depends on the fitness of measurement model. We aim to analyse the influence of measurement errors to the accuracy of EKF based attitude determination approach in this paper. The measurement errors, which are divided into structural error and nonstructural error by their influences, are analysed in principle. In the setting of the combination of star sensors and gyros, according to the property of innovation, we employ the technique of correlation test to analyse the influences of different kinds of measurement errors. Experimental results demonstrate the effectiveness of our previous analysis.

The Design and Implementation of Parallel Distributed Algorithm FIR Filter Based on FPGA

2012 ◽  
Vol 571 ◽  
pp. 534-537
Author(s):  
Bao Feng Zhang ◽  
De Hu Man ◽  
Jun Chao Zhu
Keyword(s):  
Signal Processing ◽  
Real Time ◽  
Field Programmable Gate Array ◽  
Distributed Algorithm ◽  
Design Method ◽  
Fir Filter ◽  
Experimental Results ◽  
Linear Phase ◽  
Design And Implementation ◽  
Field Programmable

The article proposed a new method for implementing linear phase FIR filter based on FPGA. For the key to implementing the FIR filter on FPGA—multiply-add operation, a parallel distributed algorithm was presented, which is based on LUT. The designed file was described with VHDL and realized on Altera’s field programmable gate array (FPGA), giving the design method. The experimental results indicated that the system can run stably at 120MHz or more, which can meet the requirements of signal processing for real-time.

scholarly journals Robust-Extended Kalman Filter and Long Short-Term Memory Combination to Enhance the Quality of Single Point Positioning

2020 ◽  
Vol 10 (12) ◽  
pp. 4335 ◽  
Author(s):  
Truong-Ngoc Tan ◽  
Ali Khenchaf ◽  
Fabrice Comblet ◽  
Pierre Franck ◽  
Jean-Marc Champeyroux ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Short Term Memory ◽  
Single Point ◽  
Base Station ◽  
Base Stations ◽  
Short Term ◽  
Position Accuracy ◽  
Long Short Term Memory ◽  
Position Precision

In the recent years, multi-constellation and multi-frequency have improved the positioning precision in GNSS applications and significantly expanded the range of applications to new areas and services. However, the use of multiple signals presents advantages as well as disadvantages, since they may contain poor quality signals that negatively impact the position precision. The objective of this study is to improve the Single Point Positioning (SPP) accuracy using multi-GNSS data fusion. We propose the use of robust-Extended Kalman Filter (referred to as robust-EKF hereafter) to eliminate outliers. The robust-EKF used in the present work combines the Extended Kalman Filter with the Iterative ReWeighted Least Squares (IRWLS) and the Receiver Autonomous Integrity Monitoring (RAIM). The weight matrix in IRWLS is defined by the MM Estimation method which is a robust statistics approach for more efficient statistical data analysis with high breaking point. The RAIM algorithm is used to check the accuracy of the protection zone of the user. We apply the robust-EKF method along with the robust combination of GPS, Galileo and GLONASS data from ABMF base station, which significantly improves the position accuracy by about 84% compared to the non-robust data combination. ABMF station is a GNSS reception station managed by Météo-France in Guadeloupe. Thereafter, ABMF will refer to the acronym used to designate this station. Although robust-EKF demonstrates improvement in the position accuracy, its outputs might contain errors that are difficult to estimate. Therefore, an algorithm that can predetermine the error produced by robust-EKF is needed. For this purpose, the long short-term memory (LSTM) method is proposed as an adapted Deep Learning-Based approach. In this paper, LSTM is considered as a de-noising filter and the new method is proposed as a hybrid combination of robust-EKF and LSTM which is denoted rEKF-LSTM. The position precision greatly improves by about 95% compared to the non-robust combination of data from ABMF base station. In order to assess the rEKF-LSTM method, data from other base stations are tested. The position precision is enhanced by about 87%, 77% and 93% using the rEKF-LSTM compared to the non-robust combination of data from three other base stations AJAC, GRAC and LMMF in France, respectively.

Removing Blinks in Video-Oculography

2012 ◽  
Vol 239-240 ◽  
pp. 1165-1168
Author(s):  
Xue Jun Chen ◽  
Chen Hua Zhang
Keyword(s):  
Kalman Filter ◽  
Eye Movement ◽  
Detection Method ◽  
Threshold Level ◽  
Experimental Results ◽  
Video Oculography ◽  
Non Invasive ◽  
Pupil Center ◽  
Simulation And Experiment ◽  
Blink Artifact

Video-oculography (VOG) is a non-invasive detection method used for eye movement. However, during testing, if object blinks, VOG would be difficult to acquire eye movement. A removing blink method based on Kalman Filter was presented. A cubic spline was employed to patch the removed data. Then simulation and experiment were done. The experimental results show that the method well predicts the next state. Compared to a threshold level, it eliminates blink artifact and patches the removed data. The method is a viable means of predicting pupil center for blink in VOG.

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