Research on the Application of Improved Salp Swarm Algorithm in Time Difference of Arrival of Passive Location

According to the shortcomings of slow positioning speed and low detection accuracy of passive positioning that uses time difference of arrival (TDOA), an innovative location model of extreme learning machine (ELM) which is improved by Salp Swarm Algorithm (SSA) in view of Logistic Mapping, Opposition-Based Learning and Cauchy Mutation (LOCSSA) is put forward. The method firstly initializes the population by Logistic mapping and improves SSA by Opposition Based Learning and Cauchy mutation. Then uses LOCSSA to look for the optimal weights and biases of ELM. Finally, LOCSSA is used to locate the target. The results show that the ELM positioning model of LOCSSA has better accuracy and stability for target positioning, which demonstrate that the method is feasible.