In this survey, the currently available ultrawideband-based non-line-of-sight (NLOS) identification and error mitigation methods are presented. They are classified into several categories and their comparison is presented in two tables: one each for NLOS identification and error mitigation. NLOS identification methods are classified based on range estimates, channel statistics, and the actual maps of the building and environment. NLOS error mitigation methods are categorized based on direct path and statistics-based detection.

This study presents an improved joint estimation method of signal parameters via rotational invariance technique (ESPRIT) algorithm for low-complexity simultaneous estimation of direction of departure (DOD) and direction of arrival (DOA) in a multiple-input-multiple-output radar system. The proposed algorithm is based on a data matrix and estimates DOD and DOA without a pairing operation. The computational complexity of the proposed joint ESPRIT algorithm is derived to be less than that of conventional two dimension multiple signal classification (2D-MUSIC) and reduced dimension multiple signal classification. The authors' simulation results demonstrate that the proposed algorithm achieves performance very close to that of 2D-MUSIC and better performance than that of the ESPRIT algorithm.

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We propose a new channel identification parameter that is based on the number of significant paths within the received signal. Our proposed parameter can achieve similar or better results compared to other existing methods with lower complexity. Moreover, our results show that it is possible to use only two channel identification parameters instead of three in joint channel identification techniques, as it was used in conventional methods.

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Abstract-A new improved Least Trimmed Squares (LTS) based algorithm for Non-line-of-sight (NLOS) error mitigation is proposed for indoor localisation systems. The conventional LTS algorithm has hard threshold to decide the final set of base stations (BSs) to be used in position calculations. When the number of Line of Sight (LOS) BSs is more than the number of NLOS BSs the conventional LTS algorithm does not include some of them in position estimation due to principle of LTS algorithm or under heavy NLOS environments it cannot separate least biased BSs to use. To improve the performance of the conventional LTS algorithm in dynamic environments we have proposed a method that selects BSs for position calculation based on ordered residuals without discarding half of the BSs. By choosing a set of BSs which have least residual errors among all combinations as a final set for position calculation, we were able to decrease the localisation error of the system in dynamic environments. We demonstrate the robustness of the new improved method based on computer simulations under realistic channel environments.

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