Browsing by Author "Khan, Irfan"

Now showing 1 - 6 of 6
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    A two-stage classification method for improved positioning using low-cost inertial sensors
    (IEEE, 2024-08-08) Maton, Dariusz; Economou, John; Galvao Wall, David; Khan, Irfan; Cooper, Robert; Ward, David; Trythall, Simon
    The two-stage subtractive clustering Takagi-Sugeno (2SC-TS) method is proposed which completely replaces the established method of inertial navigation with classification models. The classifiers are designed by the subtractive clustering algorithm, an unsupervised learning method. The accuracy of the trajectories is compared against three competitive data-driven methods on three independent experimental datasets. The results show how 2SC-TS generates trajectories with approximately 20% lower positional error compared with the single-stage version (SC-TS), and halves the error produced by competitive deep learning methods. The proposed method may help improve the positioning of people and robots carrying low-cost inertial sensors.
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    Dynamic UAV flight simulator utilizing a Stewart platform
    (IEEE, 2024-11-06) Mukherjee, Anurag; Khan, Irfan; Turner, William; Sullivan, Peter; Mahony, Chris; Gilbert, Oliver; Economou, John T.
    This paper focuses on replicating the motion profile of a UAV flying in different conditions on a Stewart platform testbed. The resulting system provides a safe platform to operate indoors reproducing the same signals to those of a UAV flying outdoors. This approach results in more frequent use of all flying abilities while retaining high safety standards. To illustrate the effectiveness of the testbed, experimental results are presented. Six linear actuators are used in the testbed to achieve the desired orientation and position. The desired length of each actuator is calculated via an inverse kinematics algorithm. To validate the algorithm, Inertial Measurement Unit (IMU) data from three aerial platforms of different configurations/size are used. The IMU data captured from flying/hovering the aerial platforms indoors, with and without disturbance, is pre-processed using Fast Fourier Transform (FFT) and used as an input to the Stewart platform.
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    Energy harvesting technologies on high-speed railway infrastructure: review and comparative analysis of the potential and practicality
    (Elsevier, 2025-02-01) Sun, Wenjing; Thompson, David J.; Yurchenko, Daniil; Zhao, Dong; Luo, Zhenhua; Khan, Irfan
    A comprehensive quantitative analysis is provided of the potential applications of energy harvesting (EH) technologies tailored to high-speed railway infrastructure. The study compares the various energy sources within railway infrastructure and identifies suitable EH technologies. Feasible designs and scales of EH are assessed based on the installation location; the overall power availability and energy yield are compared for a notional high-speed railway. For resonant EH devices an assessment is also given of the optimal tuning frequency. Vibration-based EH, when applied to the track or bridge structures, can provide sufficient power for individual low-power sensors; however, its output is insufficient for higher-power applications or for data transmission unless energy storage devices are incorporated. Despite the elevated noise levels generated by high-speed trains, the energy available from this acoustic source is negligible and impractical for EH. Small vertical axis wind turbines installed close to the track and driven by passing trains show great potential, capable of harvesting several orders of magnitude more energy than vibration-based EH. Solar photovoltaic panels can generate significantly more energy than other methods, although their output is confined to daylight conditions and is contingent upon weather conditions.
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    Indirect tuning of a complementary orientation filter using velocity data and a genetic algorithm
    (Taylor and Francis, 2024-04-23) Maton, Dariusz; Economou, John T.; Galvão Wall, David; Khan, Irfan; Cooper, Robert; Ward, David; Trythall, Simon
    In this paper, the accuracy of inertial sensor orientation relative to the level frame is improved through optimal tuning of a complementary filter by a genetic algorithm. While constant filter gains have been used elsewhere, these may introduce errors under dynamic motions when gyroscopes should be trusted more than accelerometers. Optimal gains are prescribed by a Mamdani fuzzy rule base whose membership functions are found using a genetic algorithm and experimental data. Furthermore, model fitness is not based directly on orientation but the error between estimated and ground truth velocities. This paper has three interrelated novel elements. The main novelty is the indirect tuning method, which is simple, low-cost and requires a single camera and inertial sensor. The method is shown to increase tracking accuracy compared with popular baseline filters. Secondary novel elements are the bespoke genetic algorithm and the time agnostic velocity error metric. The contributions from this work can help improve the localization accuracy of assets and human personnel. This research has a direct impact in command and control by improving situational awareness and the ability to direct assets to safe locations using safer routes. This results in increasing safety in applications such as firefighting and battlespace.
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    Low-cost IMU Sensor Temperature Humidity Zero Bias Data
    (Cranfield University, 2024-05-13 11:08) Maton, Dariusz; Economou, John; Galvao Wall, David; Khan, Irfan; Cooper, Rob
    Dataset containing the responses of three inertial measurement units (IMUs) of the same model (MPU-6050s) under varying temperature and relative humidity conditions in a Sanyo Gallenkamp environmental chamber.
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    Tuning of a Complementary Orientation Filter Using Velocity Data and a Genetic Algorithm
    (Cranfield University, 2024-01-08 14:59) Maton, Dariusz; Economou, John; Khan, Irfan; Galvao Wall, David; Cooper, Rob
    The data uploaded here contains the experimental and simulation data used to demonstrate the utility of the optimisation of a complementary orientation filter using a genetic algorithm (GA). Implementation of the GA in MATLAB is provided as well as supporting functions such as the zero velocity update and weighted-relative velocity error metric (W-RVE). The novelty of the work is the optimal tuning of the complementary filter gain using a GA and velocity data of an object moving in the locally level frame. Optimal filter gains are encoded a Takagi-Sugeno (TS) fuzzy inference system with four Gaussian membership functions. This offers a transparent and traceable encoding.