An ultra wide band (UWB)-based time-delay indoor human localization scheme is proposed to provide indoor human localization with time-delay measurements. The human position is localized using the UWB-based distance data and the extended finite impulse response (EFIR) estimator employing the timedelay localization model. Only one-step delayed measurements are considered in this paper. We employ the state augmentation method to combine the delayed and not delayed states. To improve the localization robustness for the time-delay localization model, we employ the EFIR filter, which does not require the noise statistics. The experimental results have shown that the EFIR estimator is more robust than the extended Kalman filter-based one for the delayed data.

Accurate indoor localization information of the quadrotor plays an important role in many Internet of Things applications. To improve the estimation accuracy and robustness, a fix-lag extended finite impulse response smoother (FEFIRS) algorithm is proposed for fusing the inertial navigation system (INS) and ultra wide band (UWB) data tightly, which employs distance between the UWB reference nodes and a blind node measured by the INS and UWB. The FEFIRS algorithm consists of an extended unbiased finite impulse response (EFIR) filter and a fix-lag unbiased FIR (UFIR) smoother. The EFIR filter is employed to improve the robustness, and the fix-lag UFIR smoother is capable of improving the accuracy. Based on extensive test investigations employing real data, the proposed FEFIRS has a higher accuracy and robustness than the Kalman-based solutions in tightly integrated INS/UWB-based indoor quadrotor localization.

In indoor robot localization by using Ultra-Wide Band (UWB), the extended Kalman filter (EKF)-based algorithms suffer from the Colored Measurement Noise (CMN) that degrades the localization accuracy and causes the divergence. To overcome this issue, we develop a hybrid colored EKF and colored extended unbiased Finite Impulse Response (EFIR) filter (cEKF/EFIR filter) employing measurement differences. We also develop this algorithm using a filter bank on merged averaging horizons to be adaptive to time-varying CMN and call it the aEKF/EFIR filter. Experimental testing is provided in UWB-based indoor mobile robot localization environments. It is shown that the end-to-end cEKF/EFIR and aEKF/EFIR filtering algorithms have better performances than the EKF, EFIR filter, and their modifications for CMN.

In this paper, an indoor inertial navigation system (INS) integrated with a light detection and ranging (LiDAR) robot localization system is proposed to provide accurate information about the robot location. To achieve a high accuracy and robustness, a cascaded finite-impulse response (FIR) filter is designed and incorporated into the proposed INS/LiDAR localization scheme. The cascaded scheme employs two FIR filters. An unbiased FIR (UFIR) filter is used to estimate the LiDAR-derived position by fusing distances between a robot and detected corner points. An extended FIR (EFIR) filter is used to fuse the LiDAR- and INS-based measurements. An experimental study indicates that the proposed scheme demonstrates higher robustness than traditional methods of localization employing Kalman filtering.

It is known that in seamless inertial navigation system (INS)-based integrated drone localization, once data outage occurs in auxiliary measurement technologies, such as global navigation satellite system and ultrawideband (UWB), traditional integrated navigation data fusion filters may give large errors. To overcome this issue, we develop a dual free-size least squares support vector machines (fLS-SVM) assisted maximum correntropy Kalman filter (KF) (mcKF) for fusing INS and UWB data. In the fLS-SVM structure, the interactive multiple models method is used to improve the adaptability of the training set of LS-SVM. Then, the fLS-SVM method is adopted to estimate position error from INS when the UWB data is outage and then used again to provide the estimation error of the previous LS-SVM algorithm. The mcKF is derived on the basis of the maximum correntropy criterion. One mcKF is to realize data fusion from INS and UWB, and another one is for estimating the INS's position error for the fLS-SVM's output. Experimental testing is done in INS/UWB-based indoor mobile drone localization environments. It is shown that the dual fLS-SVM assisted mcKF algorithm outperforms the KF, general KF with colored measurement noise, mcKF, KF+LS-SVM, and dual KF+LS-SVM it reduces the position errors by an average of about 93.64%, 93.63%, 93.63%, 39.99%, and 94.10%, respectively, in two tests.