Transactions of the Institute of Measurement and Control

This paper presents a simple, yet very effective filtering method to suppress short-term broadband noise in micro-electro-mechanical system (MEMS) gyros for yaw angle measurement with minimized drift. This method is suitable for autonomous indoor mobile robots that use two low-cost sensors: an inertial one, which uses a gyroscope, and an odometric one, which uses encoders mounted on the vehicle’s wheels. The main idea of the proposed approach is consists of two phases: (1) a threshold filter for translational motions, and (2) a moving average filter for rotational motions to reject the broadband noise component that affects short-term performance. Experimental results with the proposed phased method applied to an Epson XV3500 MEMS gyro demonstrate that it effectively suppresses short-term broadband noise and yields accurate yaw angle with minimized drift.

We present in this paper an integration scheme of a low-cost inertial attitude and position reference system for a mini unmanned helicopter by utilizing the robust and H_∞ filtering technique. The result has been successfully implemented and tested on our mini-scale unmanned helicopter. Simulation and flight experiment results show that the proposed technique is very effective in real-time and suitable for control, stabilization and navigation for mini-scale unmanned air vehicles.

In this paper we investigate a general multi-level quantized filter of linear stochastic systems. For a given multi-level quantization and under the Gaussian assumption on the predicted density, a quantized innovations filter that achieves the minimum mean square error is derived. The filter is given in terms of quantization thresholds and a simple modified Riccati difference equation. By optimizing the filtering error covariance with respect to quantization thresholds, the associated optimal thresholds and the corresponding filter are obtained. Furthermore, the convergence of the filter to the standard Kalman filter is established. We also discuss the design of a robust minimax quantized filter when the innovation covariance is not exactly known. Simulation and experimental results illustrate the effectiveness and advantages of the proposed quantized filter.

In this paper we propose a simple non-linear observer for attitude estimation based only on output from a typical inertial measurement unit (IMU) and dynamic pressure sensor embarked on a low-cost unmanned aerial vehicle. In particular, we aim to provide a good quality attitude estimate in the absence of global positioning system (GPS) ground truth and with potential low-frequency bias and high-frequency noise in the IMU sensor measurements. In addition, the case where the IMU only provides gyrometer and accelerometer outputs is considered; that is, there is no magnetometer output or it cannot be used due to local magnetic disturbances such as are common on a vehicle with electric motors. The proposed observer uses a simple centripetal force model (based on gyrometer and dynamic pressure measurements), augmented by a first-order dynamic model for angle of attack, to estimate non-inertial components of the acceleration. This estimate is used to correct the accelerometer output to provide a low-frequency estimate of the gravitational direction. This inertial direction, along with the gyrometer output, is then used to drive a fully non-linear attitude observer posed on the orthogonal group of rotation matrices SO(3). The observer is augmented with an integral state that ensures compensation of gyrometer bias. The resulting observer is simple to implement and fully non-linear. Experimental results are provided on a real-world data set and the performance of the filter is evaluated against the output from a full GPS/inertial navigation system (INS) that was available for the data set.

Although guidance of all aircraft is affected by wind disturbances, micro-unmanned aerial vehicles are especially susceptible. To estimate unknown wind disturbance, we consider two illustrative scenarios for planar flight. In the first scenario, we assume that measurements of the heading angle are available, while, in the second scenario, we assume that measurements of the heading angle are not available. Since the disturbance estimation problem is non-linear, we develop an extension of the unscented Kalman filter that provides an estimate of the unknown wind disturbance. Furthermore, we show through simulations that, when the heading angle is not measured, a kinematic ambiguity is introduced. However, when the initial heading angle is known and the subsequent heading angle is not measured, this kinematic ambiguity is resolved and accurate estimates of the wind velocity are obtained.

The main contribution of this paper arises from developing a modified adaptive horizon recognizing algorithm. The existing projection algorithm can only deal with those horizons whose projections are the global extrema. Based on the analysis of the projection peaks, new information was found to determine the horizon and three important indicators were extracted. An adaptive algorithm was proposed to recognize the horizon by utilizing these indicators properly. With the new criteria, more than 95% of the original algorithm failures can be overcome, and they are more robust when dealing with noise-polluted images. A speeded-up method was also derived. Verified with real MAV videos, this global searching algorithm satisfies real-time requirements, and the horizon recognizing correctness ratio is more than 99.9%.

Attitude information is essential in the control of unmanned aerial vehicles (UAVs). One way of defining attitude is through Euler angles. These angles can be determined based on the measurements of the projections of the gravity and earth magnetic fields on the three body axes of the vehicle. Twenty-five methods have been developed to compute the Euler angles and each of these methods employ a subset of the six measurements. The capability of computing the Euler angles in multiple ways provides a diversified redundancy required for fault tolerance. The proposed approach can identify the sensor failures and even separate the reference fields from the disturbances. A bank-to-turn manoeuvre of the NASA GTM UAV is used to show how to determine the correct Euler angles despite interferences by inertial acceleration.