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For localization, the CSI may be obtained, based on either uplink or downlink transmission, for sending to a localization processor.
As using the uplink CSI for the localization processor residing in the infrastructure is less cumbersome compared with using the downlink CSI, we assume that the location of a MD is determined by processing the CSI obtained by the MIMO-OFDM receivers of all BSs upon successful reception of a data packet from that MD.
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In this paper, we are only interested in localization with processor residing in the network infrastructure to avoid burdening the generally resource-limited MDs.
To facilitate large scale computations the algorithm is designed to retain strict data localization on processors.
From Figures 2(a) and 2(b), the EMD-MFP and Bartlett processor localization results are all able to accurately localize target too, when SNR is 10 dB.
Then, we compared the underwater target localization performance of this processor with Bartlett processor to get the source location in the low SNR case.
Moreover, the internal sensors and the processor used for self-localization are generally inexpensive.
The integration was successfully implemented in a parallel paradigm on a multi-core computer processor to solve a simultaneous 3D localization problem.
In the study presented here, the localization algorithms were always executed offline on standard processors.
To make indoor autonomous robots inexpensive, it is essential to develop the self-localization method which does not require expensive sensor or processor and have just enough accuracy.
However, suitable self-localization method which does not require expensive sensor or processor and does not accumulate the error over time have not been realized.
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