Modeling of Inertial Rate Sensor Errors Using Autoregressive and Moving Average (ARMA) Models

In this chapter, a low-cost micro electro mechanical systems (MEMS) gyroscope drift is modeled by time series model, namely, autoregressive-moving-average (ARMA). The optimality of ARMA (2, 1) model is identified by using minimum values of the Akaike information criteria (AIC). In addition, the ARMA model based Sage-Husa adaptive fading Kalman filter algorithm (SHAFKF) is proposed for minimizing the drift and random noise of MEMS gyroscope signal. The suggested algorithm is explained in two stages: (i) an adaptive transitive factor (a1) is introduced into a predicted state error covariance for adaption. (ii) The measurement noise covariance matrix is updated by another transitive factor (a2). The proposed algorithm is applied to MEMS gyroscope signals for reducing the drift and random noise in a static condition at room temperature. The Allan variance (AV) analysis is used to identify and quantify the random noise sources of MEMS gyro signal. The performance of the suggested algorithm is analyzed using AV for static signal. The experimental results demonstrate that the proposed algorithm performs better than CKF and a single transitive factor based adaptive SHFKF algorithm for reducing the drift and random noise in the static condition

Signal Processing of MEMS Gyroscope Arrays to Improve Accuracy Using a 1st Order Markov for Rate Signal Modeling

This paper presents a signal processing technique to improve angular rate accuracy of the gyroscope by combining the outputs of an array of MEMS gyroscope. A mathematical model for the accuracy improvement was described and a Kalman filter (KF) was designed to obtain optimal rate estimates. Especially, the rate signal was modeled by a first-order Markov process instead of a random walk to improve overall performance. The accuracy of the combined rate signal and affecting factors were analyzed using a steady-state covariance. A system comprising a six-gyroscope array was developed to test the presented KF. Experimental tests proved that the presented model was effective at improving the gyroscope accuracy. The experimental results indicated that six identical gyroscopes with an ARW noise of 6.2 °/√h and a bias drift of 54.14 °/h could be combined into a rate signal with an ARW noise of 1.8 °/√h and a bias drift of 16.3 °/h, while the estimated rate signal by the random walk model has an ARW noise of 2.4 °/√h and a bias drift of 20.6 °/h. It revealed that both models could improve the angular rate accuracy and have a similar performance in static condition. In dynamic condition, the test results showed that the first-order Markov process model could reduce the dynamic errors 20% more than the random walk model

Data-Driven Denoising of Stationary Accelerometer Signals

Modern navigation solutions are largely dependent on the performances of the standalone inertial sensors, especially at times when no external sources are available. During these outages, the inertial navigation solution is likely to degrade over time due to instrumental noises sources, particularly when using consumer low-cost inertial sensors. Conventionally, model-based estimation algorithms are employed to reduce noise levels and enhance meaningful information, thus improving the navigation solution directly. However, guaranteeing their optimality often proves to be challenging as sensors performance differ in manufacturing quality, process noise modeling, and calibration precision. In the literature, most inertial denoising models are model-based when recently several data-driven approaches were suggested primarily for gyroscope measurements denoising. Data-driven approaches for accelerometer denoising task are more challenging due to the unknown gravity projection on the accelerometer axes. To fill this gap, we propose several learning-based approaches and compare their performances with prominent denoising algorithms, in terms of pure noise removal, followed by stationary coarse alignment procedure. Based on the benchmarking results, obtained in field experiments, we show that: (i) learning-based models perform better than traditional signal processing filtering; (ii) non-parametric kNN algorithm outperforms all state of the art deep learning models examined in this study; (iii) denoising can be fruitful for pure inertial signal reconstruction, but moreover for navigation-related tasks, as both errors are shown to be reduced up to one order of magnitude.Comment: 10 pages, 15 figures, 8 table

Validation and Experimental Testing of Observers for Robust GNSS-Aided Inertial Navigation

This chapter is the study of state estimators for robust navigation. Navigation of vehicles is a vast field with multiple decades of research. The main aim is to estimate position, linear velocity, and attitude (PVA) under all dynamics, motions, and conditions via data fusion. The state estimation problem will be considered from two different perspectives using the same kinematic model. First, the extended Kalman filter (EKF) will be reviewed, as an example of a stochastic approach; second, a recent nonlinear observer will be considered as a deterministic case. A comparative study of strapdown inertial navigation methods for estimating PVA of aerial vehicles fusing inertial sensors with global navigation satellite system (GNSS)-based positioning will be presented. The focus will be on the loosely coupled integration methods and performance analysis to compare these methods in terms of their stability, robustness to vibrations, and disturbances in measurements

Energy-aware Adaptive Attitude Estimation Under External Acceleration for Pedestrian Navigation

International audienceIn this paper, we consider the problem of rigid bodyattitudeestimationunderexternalaccelerationusingasmallinertial/magneticsensors module containing a triad of gyroscope, accelerometer,andmagnetometer.Thepaperisfocusedontwomainchallenges. The first challenge concerns the attitude estimationduring dynamic case, in which external acceleration occurs. Thislatter leads to lose performance in attitude estimation methods. Aquaternion-based adaptive Kalman filter (q-AKF) compensatingexternal acceleration from the residual in the accelerometer isdesigned. At each step, the covariance matrix of the externalacceleration is estimated to tune the filter gain adaptively. Thesecond challenge is related to the energy consumption issue ofgyroscope. In order to ensure a longer battery life for the InertialMeasurement Units (IMUs), we study the way to reduce the gyromeasurements acquisition by switching on/off the sensor whilemaintaining an acceptable attitude estimation. A smart detectionapproach isproposed to decide whether the body is indynamic orstatic case. The efficiency of the q-AKF is demonstrated throughnumerical simulations and experimental tests

Danae++: A smart approach for denoising underwater attitude estimation

One of the main issues for the navigation of underwater robots consists in accurate vehicle positioning, which heavily depends on the orientation estimation phase. The systems employed to this end are affected by different noise typologies, mainly related to the sensors and to the irregular noise of the underwater environment. Filtering algorithms can reduce their effect if opportunely con-figured, but this process usually requires fine techniques and time. This paper presents DANAE++, an improved denoising autoencoder based on DANAE (deep Denoising AutoeNcoder for Attitude Estimation), which is able to recover Kalman Filter (KF) IMU/AHRS orientation estimations from any kind of noise, independently of its nature. This deep learning-based architecture already proved to be robust and reliable, but in its enhanced implementation significant improvements are obtained in terms of both results and performance. In fact, DANAE++ is able to denoise the three angles describing the attitude at the same time, and that is verified also using the estimations provided by an extended KF. Further tests could make this method suitable for real-time applications in navigation tasks

Development of a Standalone Pedestrian Navigation System Utilizing Sensor Fusion Strategies

Pedestrian inertial navigation systems yield the foundational information required for many possible indoor navigation and positioning services and applications, but current systems have difficulty providing accurate locational information due to system instability. Through the implementation of a low-cost ultrasonic ranging device added to a foot-mounted inertial navigation system, the ability to detect surrounding obstacles, such as walls, is granted. Using these detected walls as a basis of correction, an intuitive algorithm that can be added to already established systems was developed that allows for the demonstrable reduction of final location errors. After a 160 m walk, final location errors were reduced from 8.9 m to 0.53 m, a reduction of 5.5% of the total distance walked. Furthermore, during a 400 m walk the peak error was reduced from 10.3 m to 1.43 m. With long term system accuracy and stability being largely dependent on the ability of gyroscopes to accurately estimate changes in yaw angle, the purposed system helps correct these inaccuracies, providing strong plausible implementation in obstacle rich environments such as those found indoors

CMOS systems and circuits for sub-degree per hour MEMS gyroscopes

The objective of our research is to develop system architectures and CMOS circuits that interface with high-Q silicon microgyroscopes to implement navigation-grade angular rate sensors. The MEMS sensor used in this work is an in-plane bulk-micromachined mode-matched tuning fork gyroscope (M² – TFG ), fabricated on silicon-on-insulator substrate. The use of CMOS transimpedance amplifiers (TIA) as front-ends in high-Q MEMS resonant sensors is explored. A T-network TIA is proposed as the front-end for resonant capacitive detection. The T-TIA provides on-chip transimpedance gains of 25MΩ, has a measured capacitive resolution of 0.02aF /√Hz at 15kHz, a dynamic range of 104dB in a bandwidth of 10Hz and consumes 400μW of power. A second contribution is the development of an automated scheme to adaptively bias the mechanical structure, such that the sensor is operated in the mode-matched condition. Mode-matching leverages the inherently high quality factors of the microgyroscope, resulting in significant improvement in the Brownian noise floor, electronic noise, sensitivity and bias drift of the microsensor. We developed a novel architecture that utilizes the often ignored residual quadrature error in a gyroscope to achieve and maintain perfect mode-matching (i.e.0Hz split between the drive and sense mode frequencies), as well as electronically control the sensor bandwidth. A CMOS implementation is developed that allows mode-matching of the drive and sense frequencies of a gyroscope at a fraction of the time taken by current state of-the-art techniques. Further, this mode-matching technique allows for maintaining a controlled separation between the drive and sense resonant frequencies, providing a means of increasing sensor bandwidth and dynamic range. The mode-matching CMOS IC, implemented in a 0.5μm 2P3M process, and control algorithm have been interfaced with a 60μm thick M2−TFG to implement an angular rate sensor with bias drift as low as 0.1°/hr ℃ the lowest recorded to date for a silicon MEMS gyro.Ph.D.Committee Chair: Farrokh Ayazi; Committee Member: Jennifer Michaels; Committee Member: Levent Degertekin; Committee Member: Paul Hasler; Committee Member: W. Marshall Leac

Robust Multi-sensor Data Fusion for Practical Unmanned Surface Vehicles (USVs) Navigation

The development of practical Unmanned Surface Vehicles (USVs) are attracting increasing attention driven by their assorted military and commercial application potential. However, addressing the uncertainties presented in practical navigational sensor measurements of an USV in maritime environment remain the main challenge of the development. This research aims to develop a multi-sensor data fusion system to autonomously provide an USV reliable navigational information on its own positions and headings as well as to detect dynamic target ships in the surrounding environment in a holistic fashion. A multi-sensor data fusion algorithm based on Unscented Kalman Filter (UKF) has been developed to generate more accurate estimations of USV’s navigational data considering practical environmental disturbances. A novel covariance matching adaptive estimation algorithm has been proposed to deal with the issues caused by unknown and varying sensor noise in practice to improve system robustness. Certain measures have been designed to determine the system reliability numerically, to recover USV trajectory during short term sensor signal loss, and to autonomously detect and discard permanently malfunctioned sensors, and thereby enabling potential sensor faults tolerance. The performance of the algorithms have been assessed by carrying out theoretical simulations as well as using experimental data collected from a real-world USV projected collaborated with Plymouth University. To increase the degree of autonomy of USVs in perceiving surrounding environments, target detection and prediction algorithms using an Automatic Identification System (AIS) in conjunction with a marine radar have been proposed to provide full detections of multiple dynamic targets in a wider coverage range, remedying the narrow detection range and sensor uncertainties of the AIS. The detection algorithms have been validated in simulations using practical environments with water current effects. The performance of developed multi-senor data fusion system in providing reliable navigational data and perceiving surrounding environment for USV navigation have been comprehensively demonstrated