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The algorithm flow of an inertial-based Pedestrian Navigation System (PNS) can be divided into a trajectory-generation stage and trajectory-calibration stage. The Zero-velocity UPdaTe (ZUPT)-aided Extended Kalman Filter (EKF) algorithm is commonly used to resolve the trajectory of a walking person, but it still suffers from long-term drift. Many methods have been developed to suppress these drifts and thus to calibrate the trajectory generated by the previous stage. However, these methods have certain requirements, such as explicit map information or frequent location revisits, which are hard to satisfy in such situations as Search and Rescue (SAR) operations. A new approach is proposed in this paper that requires no explicit presupposition. This approach is based on a particle filter framework, with the weight of particles being adaptively adjusted according to the a priori knowledge of building structures and human behaviours. The distribution of particle weights is designed with awareness of the regular structures of buildings. The time-varying parameter of the distribution is acquired from a Hidden Markov Model (HMM) based on the foregoing odometry, which has a close relation with human behaviour. HMM is trained offline based on samples acquired in advance. Many real-world experiments under various scenarios were performed, and the results indicate good accuracy and robustness of the proposed approach. [ABSTRACT FROM AUTHOR]

This paper studies the performance of the frequency-domain algorithms (FDAs) for low-frequency ultra-wideband synthetic aperture radar (UWB SAR) data processing. First, a generalized theoretical derivation of the FDAs is presented from the viewpoint of SAR signal processing. The derivation not only provides a deeper understanding to the imaging principle of the extended Omega-K algorithm (EOKA), but also makes it compatible and comparable with the other FDAs. Second, the performance comparison on different FDAs is made based on theoretical analysis, simulation and experimental data. The comparison results show that the Omega-K algorithm (ωKA) has the highest imaging precision in the ideal case (i.e, no motion error), but its application is limited by the poor ability of compensating motion errors. In contrast, the EOKA and nonlinear chirp scaling algorithm (NCSA) have excellent performance on dealing with the motion error, but they can only be applied under specific preconditions. Besides, as cetner frequency gets lower, the fractional bandwidth and integration angle get larger, the imaging precision of NCSA greatly decreases, while the ωKA and EOKA still keep high precision. [ABSTRACT FROM PUBLISHER]

The zero velocity update (ZUPT) algorithm is an effective way to suppress the error growth for a foot-mounted pedestrian navigation system. To make ZUPT work properly, it is necessary to detect zero velocity intervals correctly. Existing zero velocity detection methods cannot provide good performance at high gait speeds or stair climbing. An adaptive zero velocity detection approach based on multi-sensor fusion is proposed in this paper. The measurements of an accelerometer, gyroscope and pressure sensor were employed to construct a zero-velocity detector. Then, the adaptive threshold was proposed to improve the accuracy of the detector under various motion modes. In addition, to eliminate the height drift, a stairs recognition method was developed to distinguish staircase movement from level walking. Detection performance was examined with experimental data collected at varying motion modes in real scenarios. The experimental results indicate that the proposed method can correctly detect zero velocity intervals under various motion modes. [ABSTRACT FROM AUTHOR]

In inertial-based pedestrian navigation, anchors can effectively compensate the positioning errors originating from deviations of Inertial Measurement Units (IMUs), by putting constraints on pedestrians' motions. However, these anchors often need to be deployed beforehand, which can greatly increase system complexity, rendering it unsuitable for emergency response missions. In this paper, we propose an anchor-based pedestrian navigation approach without any additional sensors. The anchors are defined as the intersection points of perpendicular corridors and are considered characteristics of building structures. In contrast to these real anchors, virtual anchors are extracted from the pedestrian's trajectory and are considered as observations of real anchors, which can accordingly be regarded as inferred building structure characteristics. Then a Rao-Blackwellized particle filter (RBPF) is used to solve the joint estimation of positions (trajectory) and maps (anchors) problem. Compared with other building structure-based methods, our method has two advantages. The assumption on building structure is minimum and valid in most cases. Even if the assumption does not stand, the method will not lead to positioning failure. Several real-scenario experiments are conducted to validate the effectiveness and robustness of the proposed method.