6.1 Conclusion
In the radio propagation, when the transmitter emits the signal to the receiver in the space, we can find that all different propagation paths have their own signal strength attenuate rates which we call path loss exponent (PLE). On the other hand, if we calculate the distance by the RSS-to-distance equation with right PLE on the T-R path, we can get the more accurate distance estimation. And in the RSS-based positioning system which uses the relationship between the T-R distance and RSS to do positioning, if most estimated T-R distances are more precise, we can get the more precise positioning result.
In this thesis, we propose the method to calibrate all PLEs for all T-R paths by two signals with two different frequencies in the RSS-based positioning system. Therefore, we can use these estimated PLEs to calculate all T-R distances and do positioning by
trilateration with these estimated distances. The method what we propose is called robust trilateration positioning method. And by using our method in simulations, we can get the result that the accuracy of the positioning result is more precise than the traditional
positioning method with static PLE. In our simulation of the indoor room which is 8 meter by 8 meter, it shows that the mean error is in 0.5 meter to 1 meter. This result of
simulation-1 is better than the simulation with the same conditions but static PLE for calculating all T-R distances. The simulation with static PLE calculation shows that the mean error is in 2.1 meter to 3.3 meter. Furthermore in simulation-2 of the indoor floor which is 60 meter by 60 meter, the result shows that the mean error is in 2.5 meter to 4 meter due to its lower node density compared to simulation-1.
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6.2 Future Work
Our proposed scheme in this thesis is using two signals with two different frequencies to do positioning. And in our simulation we just use frequency spectrums in 914MHz and 2.4GHz. However, if one of two signals is destroyed by its specific characteristics of the signal, PLE, the estimated distance, and the positioning result all may be huge false. So in the future work, we think that if more signals with more different frequencies are used, we may avoid some impacts or mistakes by comparing with many signals. And we can also do some actions like eliminating wrong signals or tuning the information of the signal for calibrating more precise PLE or calculating the more accurate T-R distance.
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