Department Publications

Multiple antenna techniques that allow energy beamforming have been looked upon as a possible candidate for increasing the transfer efficiency between the energy transmitter (ET) and the energy receiver in wireless power transfer. This paper introduces a novel scheme that facilitates energy beamforming by utilizing received signal strength indicator (RSSI) values to estimate the channel. First, in the training stage, the ET will transmit using each beamforming vector in a codebook, which is predefined using a Cramer-Rao lower bound analysis. RSSI value corresponding to each beamforming vector is fed back to the ET, and these values are used to estimate the channel through a maximum likelihood analysis. The results that are obtained are remarkably simple, requires minimal processing, and can be easily implemented. The paper also validates the analytical results numerically, as well as experimentally, and …

Many real world stochastic processes are non-stationary, which means that the probability distribution that generates data samples is time-varying. In the context of machine learning, this phenomenon is known as concept drift. It is important that machine learning models are able to adapt to concept drift in order to prevent degradation in accuracy. In this paper, we present two algorithms for drift detection and adaptation. Drift is measured by continuously tracking a difference metric between probability distributions estimated from two sample windows preceding a time point. High values for the difference metric indicates that concept drift has occurred, and the model must be adapted. Adaptation is done by training a new model for the drifted process, and adding it to an ensemble of models. Previously trained models are retained, and their weights in the ensemble are adjusted to reflect similarity with the current …

Accurate attitude estimation in high dynamic motion scenarios like unmanned aerial vehicle navigation or human motion and magnetically disturbed indoor environments requires compensation of accelerometer disturbances as well as magnetometer disturbances. This paper proposes a novel algorithm for accelerometer disturbance rejection by adaptive estimation of accelerometer measurement covariance matrix online. Magnetic disturbances are generally lasting long compared to accelerometer disturbances which are generally short term in indoor navigation environments. Novel two step Kalman filter update method is proposed to separate attitude correction from heading correction. The magnetometer is used only for heading update such that the attitude estimation is not affected by errors in magnetometer measurement. Performances of the proposed algorithms are shown by using real world sensor data.

Pose tracking for outdoor rovers is generally a complex task which is further complicated in conditions where a Global Positioning System (GPS) signal is denied such as in planetary exploration, underground mines and covered areas. In these conditions the rover's pose needs to be calculated purely based on the rover's current environment observations. However, conventional wheel odometry is not reliable on rough terrain where wheels are prone to slip and the wheels do not have a common plane of motion due to suspension systems. This paper proposes a Fast State Variable Extension (Fast-SVE) method in which 2D state variables (x, y, yaw) are extended to the full 3D state (x, y, z, roll, pitch, yaw) to achieve effective real time 3D pose tracking of the rover. A particle filter implementation incorporating the Fast-SVE method is used to track the 3D pose of the rover with roll and pitch values used for weighting. An …