scholarly journals Specialized Algorithm for Navigation of a Micro Hopping Air Vehicle Using Only Inertial Sensors

2012 ◽  
Vol 135 (2) ◽  
Author(s):  
Edward Scheuermann ◽  
Mark Costello
Keyword(s):  
Data Stream ◽  
Inertial Sensors ◽  
Micro Air Vehicles ◽  
Positional Information ◽  
Estimation Accuracy ◽  
Measurement Unit ◽  
Navigation Systems ◽  
Navigation Solution ◽  
Air Vehicle ◽  
Vehicle Position

The need for accurate and reliable navigation techniques for micro air vehicles plays an important part in enabling autonomous operation. Traditional navigation systems typically rely on periodic global positioning system updates and provide little benefit when operating indoors or in other similarly shielded environments. Moreover, direct integration of the onboard inertial measurement unit data stream often results in substantial drift errors yielding virtually unusable positional information. This paper presents a new strategy for obtaining an accurate navigation solution for the special case of a micro hopping air vehicle, beginning from some known location and heading, using only one triaxial accelerometer and one triaxial gyroscope. Utilizing the unique dynamics of the hopping vehicle, a piece-wise navigation solution is constructed by selectively integrating the inertial data stream for only those short periods of time while the vehicle is airborne. Interhop data post processing and sensor bias recalibration are also used to further improve estimation accuracy. To assess the performance of the proposed algorithm, a series of tests were conducted in which the estimated vehicle position following a sequence of 10 consecutive hops was compared with measurements from an optical motion-capture system. On average, the final estimated vehicle position was within 0.70 m or just over 6% from its actual location based on a total traveled distance of approximately 11 m.

scholarly journals Feasibility Study of Multi Inertial Measurement Unit

Proceedings ◽  
2019 ◽  
Vol 42 (1) ◽  
pp. 74 ◽  
Author(s):  
Ariel Larey ◽  
Eliel Aknin ◽  
Itzik Klein
Keyword(s):  
Inertial Measurement Unit ◽  
Inertial Sensors ◽  
Low Cost ◽  
Software Tool ◽  
Experimental Results ◽  
Measurement Unit ◽  
Outlier Rejection ◽  
Inertial Measurement ◽  
Navigation Solution ◽  
Sensory Data

An inertial measurement unit (IMU) typically has three accelerometers and three gyroscopes. The output of those inertial sensors is used by an inertial navigation system to calculate the navigation solution–position, velocity and attitude. Since the sensor measurements contain noise, the navigation solution drifts over time. When considering low cost sensors, multiple IMUs can be used to improve the performance of a single unit. In this paper, we describe our designed 32 multi-IMU (MIMU) architecture and present experimental results using this system. To analyze the sensory data, a dedicated software tool, capable of addressing MIMUs inputs, was developed. Using the MIMU hardware and software tool we examined and evaluated the MIMUs for: (1) navigation solution accuracy (2) sensor outlier rejection (3) stationary calibration performance (4) coarse alignment accuracy and (5) the effect of different MIMUs locations in the architecture. Our experimental results show that 32 IMUs obtained better performance than a single IMU for all testcases examined. In addition, we show that performance was improved gradually as the number of IMUs was increased in the architecture.

An Intelligent Navigation Solution for Land Mobile Location-Based Services

2002 ◽  
Vol 55 (2) ◽  
pp. 225-240 ◽  
Author(s):  
Stephen Scott-Young ◽  
Allison Kealy
Keyword(s):  
Firm Growth ◽  
Spatial Information ◽  
Inertial Sensors ◽  
Low Cost ◽  
Dead Reckoning ◽  
Location Based Services ◽  
Geographical Information ◽  
Navigation Systems ◽  
Navigation Solution ◽  
Lower Accuracy

The increasing availability of small, low-cost GPS receivers has established a firm growth in the production of Location-Based Services (LBS). LBS, such as in-car navigation systems, are not necessarily reliant on high accuracy but a continuous positioning service. When available, the accuracy provided by the standard positioning service (SPS) of 30 metres, 95% of the time is often acceptable. The reality is, however, that GPS does not work in all situations, and it is therefore common to integrate GPS with additional sensors. The use of low-cost inertial sensors alone during GPS signal outage is severely restricted due to the accumulation of errors that is inherent with such dead reckoning (DR) systems. Through the integration of spatial information with real-time positioning sensors, intelligence can be added to the land mobile navigation solution. The information contained within a Geographical Information System (GIS) provides additional observations that can be used to improve the navigation result. With this approach, the solution is not dependent on the performance capabilities of the navigation sensors alone. This enables the use of lower accuracy navigation devices, allowing low-cost systems to provide a sustained, viable navigation solution despite long-term GPS outages. Practical results are presented comparing solutions obtained from a hand-held GPS receiver to a gyroscope and odometer.

scholarly journals MLCA—A Machine Learning Framework for INS Coarse Alignment

Sensors ◽  
2020 ◽  
Vol 20 (23) ◽  
pp. 6959
Author(s):  
Idan Zak ◽  
Reuven Katz ◽  
Itzik Klein
Keyword(s):  
Machine Learning ◽  
Field Experiments ◽  
Inertial Sensors ◽  
Initial Conditions ◽  
Low Cost ◽  
Navigation Systems ◽  
Learning Approaches ◽  
Alignment Procedure ◽  
Navigation Solution ◽  
Learning Framework

Inertial navigation systems provides the platform’s position, velocity, and attitude during its operation. As a dead-reckoning system, it requires initial conditions to calculate the navigation solution. While initial position and velocity vectors are provided by external means, the initial attitude can be determined using the system’s inertial sensors in a process known as coarse alignment. When considering low-cost inertial sensors, only the initial roll and pitch angles can be determined using the accelerometers measurements. The accuracy, as well as time required for the for the coarse alignment process are critical for the navigation solution accuracy, particularly for pure-inertial scenarios, because of the navigation solution drift. In this paper, a machine learning framework for the stationary coarse alignment stage is proposed. To that end, classical machine learning approaches are used in a two-stage approach to regress the roll and pitch angles. Alignment results obtained both in simulations and field experiments, using a smartphone, shows the benefits of using the proposed approach instead of the commonly used analytical coarse alignment procedure.

ERROR ANALYSIS OF THE INERTIAL MEASUREMENT UNIT / INERCINIO MATAVIMŲ BLOKO KLAIDŲ ANALIZĖ

Aviation ◽  
2011 ◽  
Vol 15 (1) ◽  
pp. 5-10
Author(s):  
František Adamcík
Keyword(s):  
Signal Processing ◽  
Error Analysis ◽  
Kalman Filtering ◽  
Inertial Measurement Unit ◽  
Inertial Sensors ◽  
Allan Variance ◽  
Measurement Unit ◽  
Inertial Measurement ◽  
Navigation Solution ◽  
Sensor Errors

The paper describes the results of error analysis of the new inertial measurement unit ADIS16364 produced by Analog Devices. This error analysis concerns stochastic sensor errors identified by the Allan variance method. In order to improve the performance of the inertial sensors, the users are keen to know more details about the noise components in each axis for a better modelling of the stochastic parts to improve the navigation solution. The main contribution of this paper is to present data necessary for further inertial sensors signal processing by means of Kalman filtering. Santrauka Straipsnyje pateikiami naujojo inercinio matavimų bloko ADIS16364 klaidų analizės rezultatai. Aprašyta klaidų analizė yra susijusi su stochastinio jutiklio klaidomis, nustatytomis Allano variacijos metodu. Siekiant pagerinti inercinių jutiklių našumą, naudotojai yra linkę daugiau sužinoti apie kiekvienoje ašyje esančius komponentus, kad būtų pagerintas stochastinių dalių modeliavimas bei rasti pažangesni navigacijos sprendimai. Šiuo darbu siekiama pristatyti duomenis, kurie yra reikalingi tolimesniam inercinių jutiklių signalų apdorojimui panaudojant Kalmano filtravimą.

A Standard Testing and Calibration Procedure for Low Cost MEMS Inertial Sensors and Units

2008 ◽  
Vol 61 (2) ◽  
pp. 323-336 ◽  
Author(s):  
P. Aggarwal ◽  
Z. Syed ◽  
X. Niu ◽  
N. El-Sheimy
Keyword(s):  
Moving Objects ◽  
Inertial Sensors ◽  
Low Cost ◽  
Calibration Method ◽  
Calibration Procedure ◽  
Measurement Unit ◽  
Navigation Systems ◽  
Integrated Navigation ◽  
Mems Sensors ◽  
Calibration Methods

Navigation involves the integration of methodologies and systems for estimating the time varying position and attitude of moving objects. Inertial Navigation Systems (INS) and the Global Positioning System (GPS) are among the most widely used navigation systems. The use of cost effective MEMS based inertial sensors has made GPS/INS integrated navigation systems more affordable. However MEMS sensors suffer from various errors that have to be calibrated and compensated to get acceptable navigation results. Moreover the performance characteristics of these sensors are highly dependent on the environmental conditions such as temperature variations. Hence there is a need for the development of accurate, reliable and efficient thermal models to reduce the effect of these errors that can potentially degrade the system performance. In this paper, the Allan variance method is used to characterize the noise in the MEMS sensors. A six-position calibration method is applied to estimate the deterministic sensor errors such as bias, scale factor, and non-orthogonality. An efficient thermal variation model is proposed and the effectiveness of the proposed calibration methods is investigated through a kinematic van test using integrated GPS and MEMS-based inertial measurement unit (IMU).

scholarly journals Deformation Measuring Methods Based on Inertial Sensors for Airborne Distributed POS

2017 ◽  
Vol 2017 ◽  
pp. 1-12 ◽  
Author(s):  
Xiaolin Gong ◽  
Haojie Liu ◽  
Xing-Gang Yan
Keyword(s):  
Inertial Measurement Unit ◽  
Inertial Sensors ◽  
Estimation Accuracy ◽  
Measurement Unit ◽  
Practical Application ◽  
Key Factors ◽  
Inertial Measurement ◽  
Measuring Methods ◽  
Simulation Based ◽  
Orientation System

This paper is focused on deformation measuring methods based on inertial sensors, which are used to achieve high accuracy motion parameters and the spatial distribution optimization of multiple slave systems in the airborne distributed Position and Orientation System or other purposes. In practical application, the installation difficulty, cost, and accuracy of measuring equipment are the key factors that need to be considered synthetically. Motivated by these, deformation measuring methods based on gyros and accelerometers are proposed, respectively, and compared with the traditional method based on the inertial measurement unit (IMU). The mathematical models of these proposed methods are built, and the detailed derivations of them are given. Based on the Kalman filtering estimation, simulation and semiphysical simulation based on vehicle experiment show that the method based on gyros can obtain a similar estimation accuracy to the method based on IMU, and the method based on accelerometers has an advantage in y-axis deformation estimation.

scholarly journals A Distributed Radio Beacon/IMU/Altimeter Integrated Localization Scheme with Uncertain Initial Beacon Locations for Lunar Pinpoint Landing

Sensors ◽  
2020 ◽  
Vol 20 (19) ◽  
pp. 5643
Author(s):  
Rongjun Mu ◽  
Yuntian Li ◽  
Rubin Luo ◽  
Bingzhi Su ◽  
Yongzhi Shan
Keyword(s):  
Estimation Accuracy ◽  
Measurement Unit ◽  
Iteration Algorithm ◽  
Navigation Systems ◽  
Potential Candidate ◽  
Hybrid Filter ◽  
Hybrid Form ◽  
Localization Scheme ◽  
Information Filter ◽  
Radio Beacon

As a growing number of exploration missions have successfully landed on the Moon in recent decades, ground infrastructures, such as radio beacons, have attracted a great deal of attention in the design of navigation systems. None of the available studies regarding integrating beacon measurements for pinpoint landing have considered uncertain initial beacon locations, which are quite common in practice. In this paper, we propose a radio beacon/inertial measurement unit (IMU)/altimeter localization scheme that is sufficiently robust regarding uncertain initial beacon locations. This scheme was designed based on the sparse extended information filter (SEIF) to locate the lander and update the beacon configuration at the same time. Then, an adaptive iterated sparse extended hybrid filter (AISEHF) was devised by modifying the prediction and update stage of SEIF with a hybrid-form propagation and a damping iteration algorithm, respectively. The simulation results indicated that the proposed method effectively reduced the error in the position estimations caused by uncertain beacon locations and made an effective trade-off between the estimation accuracy and the computational efficiency. Thus, this method is a potential candidate for future lunar exploration activities.

scholarly journals Fusing Stereo Camera and Low-Cost Inertial Measurement Unit for Autonomous Navigation in a Tightly-Coupled Approach

2014 ◽  
Vol 68 (3) ◽  
pp. 434-452 ◽  
Author(s):  
Zhiwen Xian ◽  
Xiaoping Hu ◽  
Junxiang Lian
Keyword(s):  
Autonomous Navigation ◽  
Inertial Sensors ◽  
Inertial Sensor ◽  
Low Cost ◽  
Three Dimensional ◽  
Fast Response ◽  
Location Estimation ◽  
Measurement Unit ◽  
Navigation Systems ◽  
Stereo Camera

Exact motion estimation is a major task in autonomous navigation. The integration of Inertial Navigation Systems (INS) and the Global Positioning System (GPS) can provide accurate location estimation, but cannot be used in a GPS denied environment. In this paper, we present a tight approach to integrate a stereo camera and low-cost inertial sensor. This approach takes advantage of the inertial sensor's fast response and visual sensor's slow drift. In contrast to previous approaches, features both near and far from the camera are simultaneously taken into consideration in the visual-inertial approach. The near features are parameterised in three dimensional (3D) Cartesian points which provide range and heading information, whereas the far features are initialised in Inverse Depth (ID) points which provide bearing information. In addition, the inertial sensor biases and a stationary alignment are taken into account. The algorithm employs an Iterative Extended Kalman Filter (IEKF) to estimate the motion of the system, the biases of the inertial sensors and the tracked features over time. An outdoor experiment is presented to validate the proposed algorithm and its accuracy.

scholarly journals A Feasibility Study of Machine Learning Based Coarse Alignment

Proceedings ◽  
2018 ◽  
Vol 4 (1) ◽  
pp. 50 ◽  
Author(s):  
Idan Zak ◽  
Itzik Klein ◽  
Reuven Katz
Keyword(s):  
Machine Learning ◽  
Feasibility Study ◽  
Inertial Sensors ◽  
Low Cost ◽  
Navigation Systems ◽  
Alignment Procedure ◽  
Navigation Solution ◽  
Alignment Process ◽  
Boosting Method ◽  
Time Required

Inertial navigation systems (INSs) require an initial attitude before its operation. To that end, the coarse alignment process is applied using inertial sensors readings. For low-cost inertial sensors, only the accelerometers readings are processed to yield the initial roll and pitch angles. The accuracy of the coarse alignment procedure is vitally important for the navigation solution accuracy due to the navigation solution drift accumulating over time. In this paper, we propose using machine learning (ML) approaches, instead of traditional approaches, to conduct the coarse alignment procedure. To that end, a new methodology for the alignment process is proposed, based on state-of-the-art ML algorithms such as random forest (RF) and the more advanced boosting method of gradient tree XGBoost. Results from a simulated alignment of stationary INS scenarios are presented accompanied by a feasibility study. ML results are compared with the traditional coarse alignment methods in terms of time to convergence and accuracy performance. When using the proposed approach, with the examined scenarios, results show a significant improvement of the accuracy and time required for the alignment process.

Optimized Processing in IMU-Aided GPS Signal Tracking

2012 ◽  
Vol 157-158 ◽  
pp. 62-65
Author(s):  
Lin Zhao ◽  
Yong Hao ◽  
Li Shu Guo
Keyword(s):  
Inertial Sensors ◽  
Low Cost ◽  
Moving Average ◽  
Dynamic Environment ◽  
Doppler Frequency ◽  
Search Space ◽  
Estimation Accuracy ◽  
Measurement Unit ◽  
Signal Tracking ◽  
Local Code

A GPS signal tracking method utilizing optimized processing in inertial measurement unit (IMU) aided GPS receiver is studied. In order to enhance the sensitivity of baseband processing, low-cost inertial sensors are applied in the GPS and strapdown inertial navigation system (SINS) integration commonly, where the estimation accuracy of Doppler frequency would influence the whole performance of the tracking loops. Stochastic errors of carrier Doppler estimation caused by inertial sensors and local oscillators are corrected utilizing auto regressive moving average (ARMA) method in this paper. And then the accuracy Doppler information is used to correct the local code phase and local carrier frequency to further determine the search space of frequency domain which is unlike to the design of traditional aided loop. Simulation results indicate that the bandwidth of carrier loop and code loop could be decreased significantly in high dynamic environment.

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