NaveGo一个开源的MATLAB GNU Octave工具箱（用于gps，惯导和组合导航学习）

# NaveGo [](https://github.com/rodralez/NaveGo/releases) [](https://zenodo.org/badge/latestdoi/12745155) NaveGo: an open-source MATLAB/GNU Octave toolbox for processing integrated navigation systems and performing inertial sensors profiling analysis. NaveGo is an open-source framework for processing INS/GPS sensors that is freely available online. It is developed under MATLAB/GNU Octave due to this programming language has become a *de facto* standard for simulation and mathematical computing. NaveGo has been verified by processing real-world data from a real trajectory and contrasting results with a commercial, closed-source software package. Difference between both solutions have shown to be negligible. NaveGo is supported at the moment by three academic research groups: GridTics at the National University of Technology (Argentina), ITIC at the National University of Cuyo (Argentina), and DIATI at the Politecnico di Torino (Italy). ## Features Main features of NaveGo are: * Processing of an inertial navigation system (INS). * Processing of a loosely-coupled integrated navigation system (INS/GPS). * Implementation of the Allan variance procedure to characterize inertial sensors' typical errors. * Simulation of inertial sensors and GPS (in a very early stage). ## How to cite this work Rodrigo Gonzalez, Carlos Catania, and Paolo Dabove (2016). NaveGo: an open-source MATLAB/GNU Octave toolbox for processing integrated navigation systems and performing inertial sensors profiling analysis. http://doi.org/10.5281/zenodo.165125. URL: https://github.com/rodralez/NaveGo/. ## Contributions We are looking for contributors for NaveGo! Since integrated navigation is a topic used in several fields (Geomatics, Geology, Mobile Mapping, Autonomous Driving, even Veterinary) we hope other communities than the navigation community compromise and contribute with this open-source project. You can contribute in many ways: * Writing code. * Writing a manual. * Reporting bugs. * Suggesting new features. If you are interested, please feel free to contact Dr. Rodrigo Gonzalez at rodralez [at] frm [dot] utn [dot] edu [dot] ar. ## Publications The underlying mathematical model of NaveGo is based on two articles which are recommended for reading: * R. Gonzalez, J.I. Giribet, and H.D. Patiño. NaveGo: a simulation framework for low-cost integrated navigation systems, Journal of Control Engineering and Applied Informatics, vol. 17, issue 2, pp. 110-120, 2015. [Link](http://ceai.srait.ro/index.php?journal=ceai&page=article&op=view&path%5B%5D=2478). * R. Gonzalez, J.I. Giribet, and H.D. Patiño. An approach to benchmarking of loosely coupled low-cost navigation systems. Mathematical and Computer Modelling of Dynamical Systems, vol. 21, issue 3, pp. 272-287, 2015. [Link](http://www.tandfonline.com/doi/abs/10.1080/13873954.2014.952642). ## Acknowledgments We would like to thank to many people that have contribute to make NaveGo a better tool: * Dr. Juan Ignacio Giribet (Universidad Nacional de Buenos Aires, Argentina) for this continuous support on theory aspects of INS/GPS systems. * Dr. Charles K. Toth (The Ohio State University, USA), Dr. Allison Kealy, and M.Sc. Azmir Hasnur-Rabiain (both from The University of Melbourne, Australia) for generously sharing IMU and GPS datasets, and in particular, for Azmir's unselfish help. * Prof. Zhu, Dr. Yang, and Mr. Bo Sun, all from the Laboratory of Precision Measuring Technology and Instruments, Tianjin University, Tianjin, China, for contributing with IMU static measurements to test Allan variance routines. * Dr. Paolo Dabove and Dr. Marco Piras (both from DIATI, Politecnico di Torino, Italy) for helping to debug NaveGo and suggesting new features. # Examples ## Allan variance example Just execute the file `navego_allan_example.m`. It process 2-hours of static measurements from an Sensonor STIM300 IMU. ## INS/GPS example The file `navego_example.m` tries to demonstrate the use of NaveGo. It compares the performances of two simulated IMUs, ADIS16405 IMU and ADIS16488 IMU, both integrated with a simulated GPS. Next, a description of this file. ### Reset section ```matlab clc close all clear matlabrc fprintf('\nStarting simulation ... \n') ``` ### Code execution parameters ```matlab % Comment any of the following parameters in order to NOT execute a particular portion of code GPS_DATA = 'ON'; % Simulate GPS data IMU1_DATA = 'ON'; % Simulate ADIS16405 IMU data IMU2_DATA = 'ON'; % Simulate ADIS16488 IMU data IMU1_INS = 'ON'; % Execute INS/GPS integration for ADIS16405 IMU IMU2_INS = 'ON'; % Execute INS/GPS integration for ADIS16488 IMU PLOT = 'ON'; % Plot results. % If a particular parameter is commented above, it is set by default to 'OFF'. if (~exist('GPS_DATA','var')), GPS_DATA = 'OFF'; end if (~exist('IMU1_DATA','var')), IMU1_DATA = 'OFF'; end if (~exist('IMU2_DATA','var')), IMU2_DATA = 'OFF'; end if (~exist('IMU1_INS','var')), IMU1_INS = 'OFF'; end if (~exist('IMU2_INS','var')), IMU2_INS = 'OFF'; end if (~exist('PLOT','var')), PLOT = 'OFF'; end ``` ### Conversion constants ```matlab G = 9.81; % Gravity constant, m/s^2 G2MSS = G; % g to m/s^2 MSS2G = (1/G); % m/s^2 to g D2R = (pi/180); % degrees to radians R2D = (180/pi); % radians to degrees KT2MS = 0.514444; % knot to m/s MS2KMH = 3.6; % m/s to km/h ``` ### Load reference data ```matlab fprintf('Loading reference dataset from a trajectory generator... \n') load ref.mat % ref.mat contains the reference data structure from which inertial % sensors and GPS wil be simulated. It must contain the following fields: % t: Nx1 time vector (seconds). % lat: Nx1 latitude (radians). % lon: Nx1 longitude (radians). % h: Nx1 altitude (m). % vel: Nx3 NED velocities (m/s). % roll: Nx1 roll angles (radians). % pitch: Nx1 pitch angles (radians). % yaw: Nx1 yaw angle vector (radians). % kn: 1x1 number of elements of time vector. % DCMnb: Nx9 Direct Cosine Matrix nav-to-body. Each row contains % the elements of one matrix ordered by columns as % [a11 a21 a31 a12 a22 a32 a13 a23 a33]. % freq: sampling frequency (Hz). ``` ### ADIS16405 IMU error profile ```matlab % IMU data structure: % t: Ix1 time vector (seconds). % fb: Ix3 accelerations vector in body frame XYZ (m/s^2). % wb: Ix3 turn rates vector in body frame XYZ (radians/s). % arw: 1x3 angle random walks (rad/s/root-Hz). % vrw: 1x3 angle velocity walks (m/s^2/root-Hz). % gstd: 1x3 gyros standard deviations (radians/s). % astd: 1x3 accrs standard deviations (m/s^2). % gb_fix: 1x3 gyros static biases or turn-on biases (radians/s). % ab_fix: 1x3 accrs static biases or turn-on biases (m/s^2). % gb_drift: 1x3 gyros dynamic biases or bias instabilities (radians/s). % ab_drift: 1x3 accrs dynamic biases or bias instabilities (m/s^2). % gb_corr: 1x3 gyros correlation times (seconds). % ab_corr: 1x3 accrs correlation times (seconds). % gpsd : 1x3 gyros dynamic biases PSD (rad/s/root-Hz). % apsd : 1x3 accrs dynamic biases PSD (m/s^2/root-Hz); % freq: 1x1 sampling frequency (Hz). % ini_align: 1x3 initial attitude at t(1). % ini_align_err: 1x3 initial attitude errors at t(1). % ref dataset will be used to simulate IMU sensors. ADIS16405.arw = 2 .* ones(1,3); % Angle random walks [X Y Z] (deg/root-hour) ADIS16405.vrw = 0.2 .* ones(1,3); % Velocity random walks [X Y Z] (m/s/root-hour) ADIS16405.gb_fix = 3 .* ones(1,3); % Gyro static biases [X Y Z] (deg/s) ADIS16405.ab_fix = 50 .* ones(1,3); % Acc static biases [X Y Z] (mg) ADIS16405.gb_drift = 0.007 .* ones(1,3); % Gyro dynamic biases [X Y Z] (deg/s) ADIS16405