Qian Song

Arc-scanning synthetic aperture radar (AS-SAR) is an emerging technical means for detecting foreign object debris (FOD). Most FOD are small and appear as weak targets with a low signal-to-noise ratio (SNR) in AS-SAR images. Therefore, image noise is a fundamental challenge in detecting FOD on airport runways that leads to many false alarms. A weak scattering denoising method is proposed to aim at the noise caused by speckle and rough surface scattering. To enhance FOD detection, a transformation parameter concept is offered and adopted, which has different characteristics for the target and background. This paper estimates the transformation parameter through logarithms, normalization, and morphological erosion and optimizes them with edge-preserving filtering. The results show that despeckling and runway scattering suppression can be simultaneously implemented, and that field experiments validate the performance of this method.

Due to the small size and various types of foreign object debris (FOD), radar detection of FOD on airport runways is a great challenge, and there are often a large number of false alarms in the detection results. Arc-scanning synthetic aperture radar (AS-SAR) is an emerging method for detecting FOD targets, which achieves omnidirectional coverage with a very high azimuth resolution. However, this method faces a similar challenge. A direct way to reduce false alarms is to increase the detection threshold based on enhancing the target signal-to-noise ratio (SNR), and in this paper, the coherent accumulation of multiple images is used to improve the target SNR. The stable phase is also an important feature of the target distinguishing background. Therefore, it is important to maintain the stability of the target phase. Aiming at the systematic phase drift (SPD) caused by atmospheric disturbance and system hardware, a spatial and temporal model is established, a corresponding correction approach is proposed, and the performance of the correction approach is validated by field experiments.

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.

Purpose: Aiding information is frequently adopted to calibrate the errors from inertia-generated trajectories in pedestrian positioning. However, existing calibration methods lack interior connections and unanimity, making it difficult to incorporate multiple sources of aiding information. This paper aims to propose a unanimous anchor-based trajectory calibration framework, which is expandable to encompass different types of anchor information. Design/methodology/approach: The concept of anchors is introduced to represent different types of aiding information, which are, in essence, different constraint conditions on inertia-derived raw trajectories. The foundation of the framework is a particle filter which is implemented based on various particle weight updating strategies using diverse types of anchor information. Herein, three representative anchors are chosen to elaborate and validate the proposed framework, namely, ultra-wide-band (UWB) ranging anchors, iBeacons and the building structure-based virtual anchors. Findings: In the simulations, with the particle reweighting strategies of the proposed framework, the positioning errors can be compensated. In the experimental test in an office building in which three anchors, including one UWB anchor, one iBeacon and one building structure-based virtual anchor are deployed; the final positioning error is decreased from 1.9 to 1.2 m; and the heading error is reduced from about 21° to 7°, respectively. Originality/value: Herein, an anchor-based unanimous trajectory calibration framework for inertial pedestrian positioning is proposed. This framework is applicable to the schemes with different configurations of the anchors and can be expanded to adopt as much anchor information as possible.

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.