The greedy approach in the EDM FDE algorithm, while efficient in quickly identifying and excluding faulty measurements, may have limitations compared to other FDE methods. One potential drawback is the lack of optimality guarantees, as the greedy strategy aims to find a locally optimal solution in each iteration without considering the global optimum. This can lead to suboptimal fault exclusion decisions, especially in complex scenarios with multiple faults and noisy measurements.
To address this limitation, one possible solution is to incorporate a backtracking mechanism in the greedy algorithm to backtrack and reconsider previous decisions if a better solution is found later in the process. By introducing backtracking, the algorithm can explore alternative paths and potentially reach a more optimal fault exclusion outcome.
Additionally, the greedy approach may struggle with scalability when dealing with a large number of measurements and faults, as the iterative nature of the algorithm can become computationally intensive. One way to mitigate this limitation is to implement heuristics or adaptive strategies that dynamically adjust the threshold values or the number of faults removed in each iteration based on the complexity of the dataset. By incorporating adaptive mechanisms, the greedy EDM FDE algorithm can adapt to different scenarios and improve its performance in challenging environments.