Unscented Kalman Filter

Modern Remotely Piloted Aircraft Systems (RPAS) employ a variety of sensors and multi-sensor data fusion techniques to provide advanced functionalities and trusted autonomy in a wide range of mission-essential and safety-critical tasks. In particular, Navigation and Guidance Systems (NGS) for small RPAS require a typical combination of lightweight, compact and inexpensive sensors to satisfy the Required Navigation Performance (RNP) in all flight phases. In this paper, the synergies attainable by the combination of Global Navigation Satellite System (GNSS), Micro-Electromechanical System based Inertial Measurement Unit (MEMS-IMU) and Vision-Based Navigation (VBN) sensors are explored. In case of VBN, an appearance-based navigation technique is adopted and feature extraction/optical flow methods are employed to estimate the navigation parameters during precision approach and landing phases. A key novelty of the proposed approach is the employment of Aircraft Dynamics Models (ADM) augmentation to compensate for the shortcomings of VBN and MEMS-IMU sensors in high-dynamics attitude determination tasks. To obtain the best estimates of Position, Velocity and Attitude (PVA), different sensor combinations are analysed and dynamic Boolean Decision Logics (BDL) are implemented for data selection before the centralised data fusion is accomplished. Various alternatives for data fusion are investigated including a traditional Extended Kalman Filter (EKF) and a more advanced Unscented Kalman Filter (UKF). A novel hybrid controller employing fuzzy logic and Proportional-Integral-Derivative (PID) techniques is implemented to provide effective stabilization and control of pitch and roll angles. After introducing the key mathematical models describing the three NGS architectures: EKF based VBN-IMU-GNSS (VIG) and VBN-IMU-GNSS-ADM (VIGA) and UKF based Enhanced VIGA (EVIGA), the system performances are compared in a small RPAS integration scheme (i.e., AEROSONDE RPAS platform) exploring a representative cross-section of the aircraft operational flight envelope. A dedicated ADM processor (i.e., a local pre-filter) is adopted in the EVIGA architecture to account for the RPAS maneuvering envelope in different flight phases (assisted by a maneuver identification algorithm), in order to extend the ADM validity time across all segments of the RPAS trajectory. Simulation results show that the VIG, VIGA and EVIGA systems are compliant with ICAO requirements for precision approach down to CAT-II. In all other flight phases, the VIGA system shows improvement in PVA data output with respect to the VIG system. The EVIGA system shows the best performance in terms of attitude data accuracy and a significant extension of the ADM validity time is achieved in this configuration.

The use of tethered Unmanned Aircraft Systems (UAS) in aerial robotic applications is a relatively unexplored research field. In this work a numerically efficient implementation of a sigma-point Kalman filter is applied to the attitude and relative position estimation of a small-size tethered unmanned helicopter. For that purpose, the state prediction is performed using a kinematic process model driven by measurements of the inertial sensors (accelerometer and gyroscope) onboard the helicopter and the subsequent correction is done using information from additional sensors like magnetometer, radar altimeter and magnetic encoders measuring the tether orientation relative to the helicopter. Assuming the tether is kept taut by an actuated device on ground during the system operation, this approach avoids the need of a global positioning system (GPS) as the position is estimated relative to the anchor point. The filter performance is evaluated in simulations

In this paper a new Adaptive Unscented Kalman Filter (AUKF) is proposed and applied for the state estimation of a LEO (Low earth Orbit) satellite planar model. The Unscented Kalman Filter (UKF) is preferred here because of its derivative free calculation process and superior performance in highly non linear systems. Further the choice of adaptive filter gives the opportunity to estimate the states without any prior knowledge about the noise covariances. The measurement noise covariance (R) is changed here adaptively. Previously R adaptation techniques have been employed for linear and Extended Kalman Filters (EKF) but the use of R adaptation for sigma point filter is relatively newer trend. Two versions of R-adaptive UKF are presented here depending on the residual sequences and the innovation sequences. Positive definiteness of R is ensured in the proposed algorithm. The R matrix here has been constrained to have a diagonal structure as the measurement noise sources are normally independent of each other. Proposed scheme has also the provision to modulate the speed of adaptation. The simulation results show the superiority of the AUKF compared to the non-adaptive filters like EKF, UKF and CDF (Central Difference Filter). It is also found that the steady state performance of the residual based adaptive filter is robust to the initial value of R.

This paper deals with distance or level measurements based on ultrasonic time-of-fight estimation. Moving from a past experience concerning the proposal of a method based on discrete extended Kalman filter (DEKF) to overcome some limitations of already available ultrasonic-based techniques, a new digital signal processing method capable of granting further improvements is presented. The method is based on the unscented Kalman filter (UKF), which is a new extension of the Kalman filter theory mandated to face some DEKF problems, mainly due to its inherent linearization approach. To this aim, UKF is applied to the acquired ultrasonic signal in order to estimate the returned echo envelope as well as to locate its onset more accurately.

This paper presents a new method to determine the mass of an inactive space object from the fusion of photometric and astrometric data. Typically, the effect of solar radiation pressure is used to determine area-to-mass ratio for space objects from angles observations. The area-to-mass ratio of a space object can greatly affect its orbital dynamics. As a consequence, angles data are sensitive to this quantity. On the other hand, photometric data is not sensitive to mass but is a strong function of the albedo-area and the rotational dynamics of the space object. The albedo-area can be used to determine the amount of energy reflected from solar radiation. Since these two data types are sensitive to albedo-area and area-to-mass, then through fusion of photometric data with angles data it is possible to determine the area and mass of a space object. This work employs an unscented Kalman filter to estimate rotational and translational states, area and mass of an inactive space object. Mass is not observerable with only angles data or only photometric data alone, but it is shown in this work that with the two combined data types mass can be recovered. Recovery of space object characteristics and attitude and orbit trajectories with sufficient accuracy is demonstrated in this paper via simulation.

This paper proposes an Adaptive Unscented Kalman Filter (AUKF) for nonlinear systems having non-additive measurement noise with unknown noise statistics. The proposed filter algorithm is able to estimate the nonlinear states along with the unknown measurement noise covariance (R) online with guaranteed positive definiteness. By this formulation of adaptive sigma point filter for non-additive measurement noise, the need of approximating non-additive noise as additive one (as is done in many cases) may be waived. The effectiveness of the proposed algorithm has been demonstrated by simulation studies on a nonlinear two dimensional bearing-only tracking (BOT) problem with non-additive measurement noise. Estimation performance of the proposed filter algorithm has been compared with (i) non adaptive UKF, (ii) an AUKF with additive measurement noise approximation and (iii) an Adaptive Divided Difference Filter (ADDF) applicable for non-additive noise. It has been found from 10000 Monte Carlo runs that the proposed AUKF algorithm provides (i) enhanced estimation performance in terms of RMS errors (RMSE) and convergence speed, (ii) almost 3-7 times less failure rate when prior measurement noise covariance is not accurate and (iii) relatively more robust performance with respect to the initial choice of R when compared with the other nonlinear filters involved herein.

It is presented a vision system based on a standard RGB digital camera to track an unmanned aerial vehicle (UAV) during the landing process aboard a ship. The developed vision system is located on the ship's deck and is used to track the UAV during all the landing process. A Ground-based vision system makes it possible to use a UAV with small size and weight since the UAV will have less computer requirements. The proposed method uses a particle filter (PF) for pose estimation and an unscented Kalman filter (UKF) approach for filtering. This combination provides an adapted filtering framework for tracking. The implemented particle filter is inspired in the evolution strategies present in genetic algorithms with modifications in the mutation and crossover operators to avoid sample impoverishment. Results show that position and angular estimates precision is compatible with the automatic landing system requirements.