Search strategy in a complex and dynamic environment: the MH370 case
This addresses the problem of inefficient search operations in complex, dynamic ocean environments for agencies and responders, though it is an incremental improvement on existing methods.
The paper tackles the challenge of searching for objects on the ocean surface, such as in the MH370 case, by proposing an improved algorithm based on ergodic theory, which achieves an order of magnitude improvement in success rate compared to conventional methods.
Search and detection of objects on the ocean surface is a challenging task due to the complexity of the drift dynamics and lack of known optimal solutions for the path of the search agents. This challenge was highlighted by the unsuccessful search for Malaysian Flight 370 (MH370) which disappeared on March 8, 2014. In this paper, we propose an improvement of a search algorithm rooted in the ergodic theory of dynamical systems which can accommodate complex geometries and uncertainties of the drifting search areas on the ocean surface. We illustrate the effectiveness of this algorithm in a computational replication of the conducted search for MH370. In comparison to conventional search methods, the proposed algorithm leads to an order of magnitude improvement in success rate over the time period of the actual search operation. Simulations of the proposed search control also indicate that the initial success rate of finding debris increases in the event of delayed search commencement. This is due to the existence of convergence zones in the search area which leads to local aggregation of debris in those zones and hence reduction of the effective size of the area to be searched.