Alhadhrami, Esra Ebrahim

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Alhadhrami, Esra Ebrahim
Main Affiliation
SCAI
ORCID
0000-0001-9256-058X

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  • Publication
    Drones Tracking Adaptation Using Reinforcement Learning: Proximal Policy optimization
    (2023)
    Alhadhrami, Esra Ebrahim 
    ;
    Seghrouchni, Amal El Fallah
    ;
    Barbaresco, Frederic
    ;
    Abu Zitar, Raed 
    This paper presents a reinforcement learning approach for automatic adaptation of the process noise covariance (Q). The Q value plays a crucial role in estimating future state values within a Kalman filter tracking system. Proximal Policy Optimization (PPO), a state-of-the-art policy optimization algorithm, was employed to determine the optimal Q value that enhances tracking performance, as measured by Root Mean Square Error (RMSE). Our results demonstrate the successful learning capability of the PPO agent over time, enabling it to suggest the optimal Q value by effectively capturing the policy of appropriate rewards under varying environmental conditions. These outcomes were compared with those of a feed-forward neural network learning, the Castella innovation/ Q values mapping, and fixed Q values. The PPO algorithm yielded promising results. We employed the Stone Soup library to simulate ground truths, measurements, and the Kalman filter tracking process.
      13Scopus© Citations 1
  • Publication
    Optimum sensors allocation for drones multi-target tracking under complex environment using improved prairie dog optimization
    (2024)
    Abu Zitar, Raed 
    ;
    Alhadhrami, Esra Ebrahim 
    ;
    Abualigah, Laith
    ;
    Barbaresco, Frederic
    ;
    Seghrouchni, Amal ElFallah
    This paper presents a novel hybrid optimization method to solve the resource allocation problem for multi-target multi-sensor tracking of drones. This hybrid approach, the Improved Prairie Dog Optimization Algorithm (IPDOA) with the Genetic Algorithm (GA), utilizes the strengths of both algorithms to improve the overall optimization performance. The goal is to select a set of sensors based on norms of weighted distances cost function. The norms are the Euclidean distance and the Mahalanobis distance between the drone location and the sensors. The second one depends on the predicted covariance of the tracker. The Extended Kalman Filter (EKF) is used for state estimation with proper clutter and detection models. Since we use Multi-objects to track, the Joint Probability Distribution Function (JPDA) estimates the best measurement values with a preset gating threshold. The goal is to find a sensor or minimum set of sensors that would be enough to generate high-quality tracking based on optimum resource allocation. In the experimentation simulated with Stone Soup, one radar among five radars is selected at every time step of 50-time steps for 200 tracks distributed over 20 different ground truths. The proposed IPDOA provided optimum solutions for this complex problem. The obtained solution is an optimum offline solution that is used to select one or more sensors for any future flights within the vicinity of the 5 radars. Environment and conditions are assumed to be similar in future drone flights within the radars’ defined zone. The IPDOA performance was compared with the other 8 metaheuristic optimization algorithms and the testing showed its superiority over those techniques for solving this complex problem. The proposed simulated model can find the most relevant sensor(s) capable of generating the best quality tracks based on weighted distance criteria (Euclidean and Mahalanobis ). That would cut down the cost of operating extra sensors and then it would be possible to move them to other vicinity.
      7  2