In this thesis, we improve the ranging accuracy by a scheme of a hybrid TOA and RSS. The improvement in ranging will enhance the accuracy of localization. And we have investigated the accuracy of linear least-squares solution for sensor network localization in LOS environment. And the asymptotic MSE for different least-squares techniques were derived. With the derivation in Chapter 3, we can interfere that the theoretical error of TS-approximated LS solution and lower bound of hyperbolic positioning algorithm achieves CRLB asymptotically. On the other hand, the asymptotic MSE is inversely proportional to the number of sensors from the derivation of this thesis.
From simulation results, the proposed NLOS mitigation algorithm with simplified ML outperforms the method proposed in [29] obviously. But there is still a gap between proposed simplified ML and exact ML solution. And if the NLOS has not been identified, the problem will be more challenging. Besides, we have shown the comparisons of proposed adaptive localization scheme and conventional one. In high SNR environment, they have comparable localization performance. But proposed adaptive localization using simplified Kalman Filter has lower
computation cost. While in low SNR environment, the modified of proposed method to enhance performance is an issue which is deserve to discuss in the future.
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