volume | PIER Journals

Abstract

To obtain three-dimensional (3-D) high-precision and real-time through-wall location under ambiguous wall parameters, an approach based on the extreme learning machine (ELM) which is a neural network is proposed. The wall's ambiguity and propagation effects are both included in the hidden layer feedforward network, and then the through-wall location problem is converted to a regression problem. The relationship between the scattered signals and the target properties are determined after the training process. Then the target properties are estimated using the ELM approach. Numerical results demonstrate good performance in terms of effectiveness, generalization, and robustness, especially for the kernel extreme learning machine (KELM) approach. Noiseless and noisy measurements are performed to further demonstrate that the approach can provide good performance in terms of stability and reliability. The location time, including the training time and the test time, is also discussed, and the results show that the KELM approach is very suitable for real-time location problems. Compared to the machine learning approach, the KELM approach is better not only in the aspect of accuracy but also in location time.

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Dr. Hua-Mei Zhang

Dr. Sheng Zhou

Dr. Cheng Xu

Dr. Jiao Jie Zhang