scholarly journals AUV-Based Plume Tracking: A Simulation Study

2016 ◽  
Vol 2016 ◽  
pp. 1-15 ◽  
Author(s):  
Awantha Jayasiri ◽  
Raymond G. Gosine ◽  
George K. I. Mann ◽  
Peter McGuire
Keyword(s):  
Simulation Study ◽  
Navigation System ◽  
Autonomous Underwater Vehicle ◽  
Unscented Kalman Filter ◽  
High Rate ◽  
Sensor Data ◽  
Boundary Tracking ◽  
Plume Tracking ◽  
Gradient Based ◽  
Rate Sensor

This paper presents a simulation study of an autonomous underwater vehicle (AUV) navigation system operating in a GPS-denied environment. The AUV navigation method makes use of underwater transponder positioning and requires only one transponder. A multirate unscented Kalman filter is used to determine the AUV orientation and position by fusing high-rate sensor data and low-rate information. The paper also proposes a gradient-based, efficient, and adaptive novel algorithm for plume boundary tracking missions. The algorithm follows a centralized approach and it includes path optimization features based on gradient information. The proposed algorithm is implemented in simulation on the AUV-based navigation system and successful boundary tracking results are obtained.

Developing data fusion and recursive estimation methods for online identification of dive plane dynamics of an autonomous underwater vehicle

2019 ◽  
Vol 234 (2) ◽  
pp. 520-533
Author(s):  
Seid Farhad Abtahi ◽  
Mohammad Mehdi Alishahi ◽  
Ehsan Azadi Yazdi
Keyword(s):  
Autonomous Underwater Vehicle ◽  
Unscented Kalman Filter ◽  
Underwater Vehicle ◽  
Recursive Estimation ◽  
High Rate ◽  
Pole Placement ◽  
Estimation Methods ◽  
Recursive Identification ◽  
Identification Algorithm ◽  
Hydrodynamic Coefficients

The purpose of this article is to develop an online method to identify the hydrodynamic coefficients of pitch plane of an autonomous underwater vehicle. To obtain necessary data for the identification, the dive plane dynamics should be excited through diving maneuvers. Hence, a controller is needed whose performance and stability are appropriate. To design such a controller, first, hydrodynamic coefficients are approximated using semi-empirical methods. Based on these approximated coefficients, a classic controller is designed at the next step. Since the estimation of these coefficients is uncertain, µ-analysis is employed to verify the robustness of stability and performance of the controller. Using the verified robust controller, some oscillating maneuvers are carried out that excite the dive plane dynamics. Using sensor fusion and unscented Kalman filter, smooth and high-rate data of depth is provided for the depth controller. A recursive identification algorithm is developed to identify the hydrodynamic coefficients of heave and pitch motions. It turns out that some inputs required by the identification are not measured directly by the sensors. But the devised fusion algorithm is able to provide the necessary data for identification. Finally, using the identified coefficients and employing pole placement method, a new controller with better performance is synthesized online. To evaluate the performance of the identification and fusion algorithms, a 6-degree-of-freedom simulation of an autonomous underwater vehicle is carried out.

scholarly journals Real time localization solution for land vehicle application using low-cost integrated sensors with GPS

2020 ◽  
Vol 18 (4) ◽  
pp. 214-228
Author(s):  
Abdalla Eldesoky ◽  
Ahmed M. Kamel ◽  
M. Elhabiby ◽  
Hadia Elhennawy
Keyword(s):  
Data Fusion ◽  
Real Time ◽  
Navigation System ◽  
Unscented Kalman Filter ◽  
Low Cost ◽  
Sensor Data ◽  
Observation Angle ◽  
Integrated Sensors ◽  
Land Vehicle ◽  
Position Errors

The technique proposed in this research demonstrates a real time nonlinear data fusion solution based on extremely low-cost and grade inertial sensors for land vehicle navigation. Here, the utilized nonlinear multi-sensor data fusion (MSDF) is based on the combination between extremely low-cost micro electrical mechanical systems (MEMS) inertial, heading, pressure, and speed sensors in addition to satellite-based navigation system. The integrated navigation system fuses position and velocity states from the Global Positioning System (GPS), the velocity measurements from an odometer, heading angle observation from a magnetometer and navigation states from an inertial navigation system (INS). The implemented system performance is assessed through the post-processing of collected raw measurements and real time experimental work. The system that runs the real-time experiments is established on three connected platforms, two of them are based on a 32-bit ARMTM core and the third one is based 16-bit AVR ATMEL microcontroller. This microcontroller is connected to an on-board diagnostics (OBD) shield to collect the vehicle speed measurements. The raw data obtained from the integrated sensors is saved and post processed in MATLAB®. In normal conditions, the estimated position errors are reduced through the usage of INS/GPS integration with heading observation angle from a magnetometer. In GPS-denied environments, the integrated system uses the observations from INS, magnetometer in addition to the velocity from odometer to ensure a continuous and accurate navigation solution. A complementary filter (CF) is implemented to estimate and improve the pitch and roll angles calculations. In addition to that, an unscented Kalman filter (UKF) is used cascaded with the designed CF to complete the designed sensors fusion algorithm. Experimental results show that the designed MSDF can achieve a good level of accuracy and a continuous localization solution of a land vehicle in different GPS availability cases and can be implemented on the available in the market processors to be run in real time.

scholarly journals Research on FKF Method Based on an Improved Genetic Algorithm for Multi-sensor Integrated Navigation System

2012 ◽  
Vol 65 (3) ◽  
pp. 495-511 ◽  
Author(s):  
Quan Wei ◽  
Fang Jiancheng
Keyword(s):  
Genetic Algorithm ◽  
Kalman Filter ◽  
Information Sharing ◽  
Navigation System ◽  
Unscented Kalman Filter ◽  
Fitness Function ◽  
Sensor Data ◽  
Integrated Navigation ◽  
Improved Genetic Algorithm ◽  
Integrated Navigation System

The fusion of multi-sensor data can provide more accurate and reliable navigation performance than single-sensor methods. However, the general Federated Kalman Filter (FKF) is not suitable for large changes of complex nonlinear systems parameters and is not optimized for effective information sharing coefficients to estimate navigation preferences. This study concerns research on the FKF method for a nonlinear adaptive model based on an improved Genetic Algorithm (GA) for the Strapdown Inertial Navigation System (SINS) / Celestial Navigation System (CNS) / Global Positioning System (GPS) integrated multi-sensor navigation system. An improved fitness function avoids the premature convergence problem of a general GA and decimal coding improves its performance. The improved GA is used to build the adaptive FKF model and to select the optimized information sharing coefficients of the FKF. An Unscented Kalman Filter (UKF) is used to deal with the nonlinearity of integrated navigation system. Finally, a solution and implementation of the system is proposed and verified experimentally.

scholarly journals Complete Coverage Path Planning in an Unknown Underwater Environment Based on D-S Data Fusion Real-Time Map Building

2012 ◽  
Vol 8 (10) ◽  
pp. 567959 ◽  
Author(s):  
Mingzhong Yan ◽  
Daqi Zhu ◽  
Simon X. Yang
Keyword(s):  
Path Planning ◽  
Real Time ◽  
Autonomous Underwater Vehicle ◽  
Evidence Theory ◽  
Sensor Data ◽  
Map Building ◽  
Complete Coverage ◽  
Coverage Path Planning ◽  
Underwater Environment ◽  
Time Map

A real-time map-building system is proposed for an autonomous underwater vehicle (AUV) to build a map of an unknown underwater environment. The system, using the AUV's onboard sensor information, includes a neurodynamics model proposed for complete coverage path planning and an evidence theoretic method proposed for map building. The complete coverage of the environment guarantees that the AUV can acquire adequate environment information. The evidence theory is used to handle the noise and uncertainty of the sensor data. The AUV dynamically plans its path with obstacle avoidance through the landscape of neural activity. Concurrently, real-time sensor data are “fused” into a two-dimensional (2D) occupancy grid map of the environment using evidence inference rule based on the Dempster-Shafer theory. Simulation results show a good quality of map-building capabilities and path-planning behaviors of the AUV.

Head acceleration measurement techniques: Reliability of angular rate sensor data in helmeted impact testing

2017 ◽  
Vol 232 (2) ◽  
pp. 176-181 ◽  
Author(s):  
Bryan R Cobb ◽  
Abigail M Tyson ◽  
Steven Rowson
Keyword(s):  
Root Mean Square ◽  
Angular Rate ◽  
Angular Acceleration ◽  
Impact Testing ◽  
Sensor Data ◽  
Mean Square ◽  
Head Acceleration ◽  
Angular Rate Sensor ◽  
Impact Tests ◽  
Rate Sensor

This study sought to evaluate the suitability of angular rate sensors for quantifying angular acceleration in helmeted headform impacts. A helmeted Hybrid III headform, instrumented with a 3-2-2-2 nine accelerometer array and angular rate sensors, was impacted (n = 90) at six locations and three velocities (3.1, 4.9, and 6.4 m/s). Data were low-pass filtered using Butterworth four-pole phaseless digital filters which conform to the specifications described in the Society of Automotive Engineers J211 standard on instrumentation for impact tests. Nine accelerometer array data were filtered using channel frequency class 180, which corresponds to a −3 db cutoff frequency of 300 Hz. Angular rate sensor data were filtered using channel frequency class values ranging from 5 to 1000 Hz in increments of 5 Hz, which correspond to −3 db cutoff frequencies of 8 to 1650 Hz. Root-mean-square differences in peak angular acceleration between the two instrumentation schemes were assessed for each channel frequency class value. Filtering angular rate sensor data with channel frequency class values between 120 and 205 all produced mean differences within ±5%. The minimum root-mean-square difference of 297 rad/s2 was found when the angular rate sensor data were filtered using channel frequency class 175. This filter specification resulted in a mean difference of 28 ± 297 rad/s2 (1.8% ± 8.6%). Condition-specific differences (α=0.05) were observed for 11 of 18 test conditions. A total of 4 of those 11 conditions were within ±5%, and 10 were within ±10%. Furthermore, the nine accelerometer array and angular rate sensor methods demonstrated similar levels of repeatability. These data suggest that angular rate sensor may be an appropriate alternative to the nine accelerometer array for measuring angular head acceleration in helmeted head impact tests with impactor velocities of 3.1–6.4 m/s and impact durations of approximately 10 ms.

Model aided airborne integrated navigation system based on an improved square-root unscented H∞ filter

2018 ◽  
Vol 41 (5) ◽  
pp. 1290-1300
Author(s):  
Jieliang Shen ◽  
Yan Su ◽  
Qing Liang ◽  
Xinhua Zhu
Keyword(s):  
Kalman Filter ◽  
Navigation System ◽  
Unscented Kalman Filter ◽  
Statistical Characteristics ◽  
Small Scale ◽  
Strong Nonlinearity ◽  
Square Root ◽  
Integrated Navigation ◽  
Fusion Algorithm ◽  
Integrated Navigation System

An inertial navigation system (INS) aided with an aircraft dynamic model (ADM) is developed as a novel airborne integrated navigation system, coping with the absence of a global navigation satellite system. To overcome the shortcomings of the conventional linear integration of INS/ADM based on an extended Kalman filter, a nonlinear integration method is proposed. Fast-update ADM makes it possible to utilize a direct filtering method, which employs nonlinear INS mechanics as system equations and a nonlinear ADM as observation equations, substituting the indirect filtering based on linear error equations. The strong nonlinearity generally calls for an unscented Kalman filter to accomplish the fusion process. Dealing with the model uncertainty, the inaccurate statistical characteristics of the noise and the potential nonpositive definiteness of the covariance matrix, an improved square-root unscented H∞ filter (ISRUHF) is derived in the paper, in which the robust factor [Formula: see text] is further expanded into a diagonal matrix [Formula: see text], to improve the accuracy and robustness of the integrated navigation system. Corresponding simulations as well as real flight tests based on a small-scale fixed-wing aircraft are operated and ISRUHF shows superiority compared with the commonly used fusion algorithm.

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