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A novel multi-scale temporal convolutional network (TCN) and long short-term memory network (LSTM) based magnetic localization approach is proposed. To enhance the discernibility of geomagnetic signals, the time-series preprocessing approach is constructed at first. Next, the TCN is invoked to expand the feature dimensions on the basis of keeping the time-series characteristics of LSTM model. Then, a multi-scale time-series layer is constructed with multiple TCNs of different dilation factors to address the problem of inconsistent time-series speed between localization model and mobile users. A stacking framework of multi-scale TCN and LSTM is eventually proposed for indoor magnetic localization.Experiment results demonstrate the effectiveness of the proposed algorithm in indoor localization.

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The conventional MUSIC algorithm demonstrates subpar estimation performance and unreliable results when confronted with the task of estimating coherent signals from multiple targets. Moreover, it suffers from high computational complexity and sluggish processing speed when applied to extensive datasets involving multiple sensors. In order to tackle these challenges, this paper presents an enhanced and expedient MUSIC algorithm for the estimation of multiple coherent signals. Drawing upon the ROOT-MUSIC algorithm based on the propagation operator, this algorithm introduces a spatial smoothing technique by substituting the original covariance matrix with the average of subarray covariance matrices. Through simulation results, it is demonstrated that the proposed algorithm not only resolves the issue of estimating multiple coherent signals but also achieves exceptional performance in terms of robustness and computational speed, even under low signal-to-noise ratio conditions.