Human motion tracking could be viewed as a multi-target tracking problem towards numerous body joints. Inertial-measurement-unit-based human motion tracking technique stands out and has been widely used in body are network applications. However, it has been facing the tough problem of accumulative errors and drift. In this paper, we propose a multi-sensor hybrid method to solve this problem. Firstly, an inertial-measurement-unit and time-of-arrival fusion-based method is proposed to compensate the drift and accumulative errors caused by inertial sensors. Secondly, Cramér-Rao lower bound is derived in detail with consideration of both spatial and temporal related factors. Simulation results show that the proposed method in this paper has both spatial and temporal advantages, compared with traditional sole inertial or time-of-arrival-based tracking methods. Furthermore, proposed method is verified in 3D practical application scenarios. Compared with state-of-the-art algorithms, proposed fusion method shows better consistency and higher tracking accuracy, especially when moving direction changes. The proposed fusion method and comprehensive fundamental limits analysis conducted in this paper can provide a theoretical basis for further system design and algorithm analysis. Without the requirements of external anchors, the proposed method has good stability and high tracking accuracy, thus it is more suitable for wearable motion tracking applications.

Non-Gaussian noise may have a negative impact on the performance of the Kalman filter (KF), due to its adoption of only second-order statistical information. Thus, KF is not first priority in applications with non-Gaussian noises. The indoor positioning based on arrival of time (TOA) has large errors caused by multipath and non-line of sight (NLOS). This paper introduces the inequality state constraint to enhance the ranging performance. Based on these considerations, we propose a constrained Kalman filter based on the maximum correntropy criterion (MCC-CKF) to enhance the TOA performance in the extreme environment of multipath and non-line of sight. Pratical experimental results indicate that MCC-CKF outperforms other estimators, such as Kalman filter and Kalman filter based on maximum entropy.

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Localization is one of the most important topics of the cyber-physical system. In the last decades, much attention has been paid to the precise localization and performance evaluation in wireless sensor networks. The inertial measurement unit (IMU) and time-of-arrival (TOA) fusion is a state-of-theart method to solve the accumulative error and drifting problem faced by the sole IMU positioning and navigation method. Many of the existing studies are based on optimization. However, they usually face problems of non-convexity of the objective function, falling into local optimum, and the requirements for the prior/posterior probability distribution of measured values. All these reasons limit its practical applications toward accurate target tracking. This paper presents a Chebyshev-center-based optimization method. Geometrically considering the real position of the target, it aims at improving the target tracking accuracy. Cramér-Rao lower bound (CRLB) and posterior CRLB for IMU/TOA fusion are derived to characterize both the spatial and temporal localization performance of the proposed fusion method. The simulation results show that the proposed fusion method in this paper has obvious spatial-temporal performance advantages in theory. Practical use cases are also conducted, and the experimental results show that the proposed method significantly decreases the drift errors and has a lower tracking error compared with the state of the art. INDEX TERMS Inertial measurement unit (IMU), time of arrival (TOA), Cramér-Rao lower bound (CRLB), posterior Cramér-Rao lower bound (PCRLB), optimization, target tracking.

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