How to deal with low performance IMUs in an integrated navigation system: Step by step

Bento, Maria de; Eissfeller, Bernd; Machado, Fátima

doi:10.1109/navitec.2010.5708042

Cited by 10 publications

(7 citation statements)

References 5 publications

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“…One such method is assessing an IMU's error statistics and then determining how best to select an IMU for a particular application [13]. Other methods consist of an Extended Kalman Filter (EKF) to estimate attitude in a quarternion representation with depth measurements for improving accuracy [34].…”

Section: Navigation With Inertial Measurement Units (Imu)mentioning

confidence: 99%

MEMS IMU navigation with model based dead-reckoning and one-way-travel-time acoustic range measurements for autonomous underwater vehicles

Kepper¹

2017

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Recent advances in acoustic navigation methodologies are enabling the way for AUVs to extend their submerged mission time and maintain a bounded XY position error. Additionally, advances in inertial sensor technology have drastically lowered the size, power consumption, and cost of these sensors. Nonetheless, these sensors are still noisy and accrue error over time. This thesis builds on the research and recent developments in single beacon one-waytravel-time (OWTT) acoustic navigation and investigates the degree of bounding position error for small AUVs with a minimal navigation strap-down sensor suite, relying mostly on a consumer grade microelectromechanical system (MEMS) inertial measurement unit (IMU) and a vehicle's dynamic model velocity. An implementation of an Extended Kalman Filter (EKF) that includes IMU bias estimation and coupled with a range filter, is obtained in the field on two OceanServer Technology, Inc. Iver2 AUVs and one Bluefin Robotics SandShark AUV. Results from these field trials on Ashumet Pond of Falmouth, Massachusetts, the Charles River of Cambridge, Massachusetts, and Monterey Bay near Santa Cruz, California show a navigation solution accuracy comparable to current standard navigation techniques.

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Section: Navigation With Inertial Measurement Units (Imu)mentioning

confidence: 99%

MEMS IMU navigation with model based dead-reckoning and one-way-travel-time acoustic range measurements for autonomous underwater vehicles

Kepper¹

2017

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“…where -z a is the measured acceleration in output to the sensor -z is the true values of the acceleration (at the considered point) -g is the gravity acceleration a is the bias -S a is the scale factor -v is the noise The a and S a coefficient are supposed constant over long period. For the scale factor estimation, the six-position face test has been used (De Bento et al (2010); De Agostino et al (2010); Titterton and Weston (2004); Hung et al (1989)). We used the InvenSense MPU 6050 ( Fig.…”

Section: The Uwb Ubisense Real-time Localization Systemmentioning

confidence: 99%

A Biased Extended Kalman Filter for Indoor Localization of a Mobile Agent using Low-Cost IMU and UWB Wireless Sensor Network

Benini

Mancini

Marinelli

et al. 2012

IFAC Proceedings Volumes

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“…These random processes can be identified and quantified with log-log plot of the square root of Allan variance [5]. In MEMS accelerometers, Allan variance method can estimate these basic noise terms (random processes): velocity random walk, acceleration random walk and bias instability [2].…”

Section: A Allan Variance Analysismentioning

confidence: 99%

“…This part of the graph should have the slope about -10 dB per decade and it corresponds to wide band noise. The middle region with zero slope represents bias instability caused by null shift and noise floor, and the most right region (long cluster times) depicts low acceleration random walk where long time varying bias is dominant [2]. Each performance parameter for all devices under test has been evaluated from the graph and recorded for comparison in Tab.…”

Section: Measurement Setupmentioning

confidence: 99%

“…specific improvements inside the MEMS structure. There were many separate analysis of inertial measurement units (IMUs) and inertial navigation systems (INS) published in last decade with respect to noise performance and stability [2], [6], [7], [8]. But no direct comparison of noise and stability properties of MEMS devices with major principles on the market has been carried out yet.…”

Section: Introductionmentioning

confidence: 99%

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Allan variance analysis on MEMS tilt sensors with different principles of operation

Havránek

Klusáček

Beneš

et al. 2011

2011 IEEE SENSORS Proceedings

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Tilt sensing is an important part in absolute position and orientation measurement, e.g. inertial navigation units and tilt detectors. In this paper, the direct comparison of three MEMS tilt sensors with different principle of operation has been carried out and evaluated using Allan variance (AV) method. Selected samples represent major principles used in MEMS accelerometers on the market -capacitive Analog Devices ADXL203, thermal MEMSIC MXA6500M and piezoresistive Panasonic AGS61231. All devices have similar measurement range and acceleration sensitivity. The results of AV analysis show similar behavior and curve shape in capacitive principle, with the lowest acceleration random walk observed, and in thermal principle, with six times higher bias instability in compare with capacitive principle. The piezoresistive principle has lower velocity random walk, but it is very sensitive to temperature variations, seen in long cluster times.

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How to deal with low performance IMUs in an integrated navigation system: Step by step

Cited by 10 publications

References 5 publications

MEMS IMU navigation with model based dead-reckoning and one-way-travel-time acoustic range measurements for autonomous underwater vehicles

MEMS IMU navigation with model based dead-reckoning and one-way-travel-time acoustic range measurements for autonomous underwater vehicles

A Biased Extended Kalman Filter for Indoor Localization of a Mobile Agent using Low-Cost IMU and UWB Wireless Sensor Network

Allan variance analysis on MEMS tilt sensors with different principles of operation

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