Browsing by Author "Khan, Irfan"

Now showing 1 - 4 of 4
  • Thumbnail Image
    ItemOpen Access
    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.
  • Thumbnail Image
    ItemOpen Access
    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.
  • No Thumbnail Available
    ItemOpen Access
    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.
  • No Thumbnail Available
    ItemOpen Access
    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.