Abstract
Wireless fingerprinting localization (FL) systems identify locations by building radio fingerprint maps, aiming to provide satisfactory location solutions for the complex environment. However, the radio map is easy to change, and the cost of building a new one is high. One research focus is to transfer knowledge from the old radio maps to a new one. Feature-based transfer learning methods help by mapping the source fingerprint and the target fingerprint to a common hidden domain, then minimize the maximum mean difference (MMD) distance between the empirical distributions in the latent domain. In this paper, the optimal transport (OT)-based transfer learning is adopted to directly map the fingerprint from the source domain to the target domain by minimizing the Wasserstein distance so that the data distribution of the two domains can be better matched and the positioning performance in the target domain is improved. Two channel-models are used to simulate the transfer scenarios, and the public measured data test further verifies that the transfer learning based on OT has better accuracy and performance when the radio map changes in FL, indicating the importance of the method in this field.
Highlights
In the mode of pervasive computing, people can acquire and process information at any time, any place, and in any way
The results indicate that optimal transport (OT) technology is significant to the transfer learning problem in fingerprint localization (FL)
To verify the performance of OT-based transfer learning in FL, two models described in Section 5 are used for numerical simulation, and the performance of the algorithm is verified with the public data set
Summary
In the mode of pervasive computing, people can acquire and process information at any time, any place, and in any way. According to the measurement techniques, the existing methods include time of arrival (TOA). The existing methods include triangulation, direct positioning, fingerprint localization (FL), and so on. The model’s accuracy obtained from the old training data will decrease or even fail This can be addressed by seeking transfer learning techniques. No matter which kind of transfer is used, the essential learning is that the fingerprint distribution has changed from one state to another. By introducing the Laplacian regularization and jointly learning mechanism, a smoother mapping function can be learned to improve the algorithm’s robustness further We use both the free space channel model and the multi-wall model to simulate the proposed method’s performance and analyze the reason why the OT-based transfer learning performance is good. The results indicate that OT technology is significant to the transfer learning problem in FL
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