Signal conditioning algorithms on accelerometers in an Inertial Navigation System (INS)
01 Oct 2016-Vol. 2016, pp 1-4
TL;DR: Observations from the analysis of the various signal processing algorithms used to extract position and velocity from an accelerometer are displayed to enhance reliability and accuracy and overcome drawbacks of accelerometers in an inertial navigation system.
Abstract: Localization of an unmanned vehicle, locating it in a three-dimensional space and identifying its acceleration, velocity, position and orientation accurately is a very challenging and demanding task. One of the most common systems used for localization is an Inertial Navigation System, which uses accelerometers and gyroscopes to determine position and orientation of the body in three-dimensional space. Accelerometers used in an Inertial Motion Unit (IMU) are very sensitive and susceptible to noise due to vibrations and shocks. This paper displays observations from the analysis of the various signal processing algorithms used to extract position and velocity from an accelerometer. This paper aims to enhance reliability and accuracy and overcome drawbacks of accelerometers in an inertial navigation system.
References
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TL;DR: A new zero detection algorithm is proposed in the paper, where only one Gyroscope value is used and a Markov model is constructed using segmentation of gyroscope outputs instead of using gyro scope outputs directly, which makes the zero velocity detection more reliable.
Abstract: In pedestrian navigation systems, the position of a pedestrian is computed using an inertial navigation algorithm. In the algorithm, the zero velocity updating plays an important role, where zero velocity intervals are detected and the velocity error is reset. To use the zero velocity updating, it is necessary to detect zero velocity intervals reliably. A new zero detection algorithm is proposed in the paper, where only one gyroscope value is used. A Markov model is constructed using segmentation of gyroscope outputs instead of using gyroscope outputs directly, which makes the zero velocity detection more reliable.
161 citations
01 Dec 2009
TL;DR: An enhanced low-cost 3-D navigation system using a Kalman filter that integrates odometry from wheel encoders, low cost MEMS-based inertial sensors, and GPS is described.
Abstract: An unprecedented surge of developments in mobile robot outdoor navigation was witnessed after the US government removed selective availability of the global positioning system (GPS). However, in certain situations GPS becomes unreliable or unavailable due to obstructions such as buildings and trees. During GPS outages, a positioning solution with a minimum cost is preferred for small wheeled robots. A low-cost inertial measurement unit (IMU) is a good choice to provide such a solution; however, low-cost MEMS-based inertial sensors suffer from several errors that are stochastic in nature. These errors accumulate and cause a rapid deterioration in the quality of position estimate. The purpose of this paper is to describe an enhanced low-cost 3-D navigation system using a Kalman filter (KF) that integrates odometry from wheel encoders, low cost MEMS-based inertial sensors, and GPS. The proposed technique uses reduced inertial sensor system (RISS). The RISS used here includes three accelerometers and one gyroscope aligned with the vertical axis of the body frame of the robot. The benefits of eliminating the two other gyroscopes normally used are decreasing the cost further, and improving the performance by having less inertial sensors and thus less contribution of these sensors errors towards positional errors. These two eliminated gyroscopes were used to calculate pitch and roll which are now calculated using the two horizontal accelerometers. The experimental results show that, during GPS outages, this KF with velocity update derived from the forward speed from wheel encoders is a good technique for greatly reducing localization errors. Real localization data from one trajectory is presented. This data is post-processed and some simulated GPS outages are introduced to assess the effectiveness of the proposed technique.
38 citations
"Signal conditioning algorithms on a..." refers background in this paper
...Another drawback of an accelerometer is that it is very sensitive to minute vibrations which lead to stochastic errors....
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21 Aug 2000
TL;DR: This paper mainly studies the problem of the noise suppression for images corrupted by noise, and presents a weighted average filter based on fuzzy membership that is not only better than the average filter and median filter, but also in the field of the suppression of impulse and mixture noise.
Abstract: This paper mainly studies the problem of the noise suppression for images corrupted by noise. A weighted average filter based on fuzzy membership is presented. The algorithm optimizes the weights of the filter by the fuzzy membership functions. The filter is not only better than the average filter and median filter in the field of suppression of Gaussian noise, but also in the field of the suppression of impulse and mixture noise.
26 citations
"Signal conditioning algorithms on a..." refers background in this paper
...The error present in the readings is 0.5-1% of the entire range and has a non-zero mean....
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10 Oct 2012
13 citations
Additional excerpts
...Thus, it is vital to use signal conditioning algorithms which enhance the sensor’s data and help to provide a reliable and accurate description of the system’s position and orientation [3]....
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02 Jun 2014
TL;DR: The usage of the most recent algorithms developed in the last decade, especially based on Cubature rule, called Cubature Kalman Filter (CKF) is proposed as the integration algorithm for the inertial measurements, which improves the mean and covariance propagation consequently comparing with EKF and previous SPKF (UKF, CDKF).
Abstract: The Extended Kalman Filter (EKF) has been the state of the art in integrated navigation systems and especially in Pedestrian Dead-Reckoning PDR for foot-mounted Inertial Measurements Units. However in most related work with PDR, indirect filtering approach is used based on linear error Kalman Filter (KF). In this work, it is proposed to outperform this approach by the use of Direct filtering approach, which involves the non-linearity in the propagation of the orientation, velocity and in some models, in position coordinates, where the EKF can not achieve optimal estimation. We propose then, the usage of the most recent algorithms developed in the last decade; Sigma Point Kalman Filters (SPKF), especially based on Cubature rule, called Cubature Kalman Filter (CKF) as the integration algorithm for the inertial measurements. The CKF improves the mean and covariance propagation consequently comparing with EKF and previous SPKF (UKF, CDKF). Although the CKF provides a better estimate of the orientation, velocity and position with Zero velocity UPdaTes (ZUPT) measurements and Zero Angular Rate UpdaTes (ZARUT), additional sensors are necessary to measure other states such as yaw angle and to estimate properly the gyroscope bias. We studied then the possibility to integrate electronic compass as additional measure and also Map street data base or Map building data base depending on the type of navigation "Indoor", "Outdoor". In order to get much better estimation based on the Cubature rule, it is proposed to synthesize CKF in order to get robust estimation against nonlinearity of the process and the multiple measurement sensors.
9 citations