The pedestrian tracking and motion detection system (P-TMDS) using distributed inertial sensors has broad application potential toward many emerging fields, such as motion tracking, emergency rescue, and others, due to its advanced autonomous navigation capabilities under signal-denied scenarios. The performance of current P-TMDS is constrained by the cumulative error of low-cost sensors, low accuracy of human motion detection, and lack of effective multi-sensor integration algorithms. This paper proposes a motion-constrained P-TMDS based on the adaptive integration of distributed inertial sensors and ultrasonic ranging (MP-TMDS). An enhanced position–attitude update algorithm is developed for the single-sensor module, which integrates the inertial navigation system (INS) mechanization with multi-level constraints and observations. In addition, a bi-directional long short-term memory (Bi-LSTM) structure is adopted to detect the outlier in ultrasonic ranging results and provide accurate distance observations for dual sensor module-based positioning systems. For the overall MP-TMDS, the measurements provided by distributed sensor modules and ultrasonic ranging are adopted as the input vector of designed spatial–temporal network training for human motion detection and walking speed estimation, and the detected human motion modes are further applied as the constraints for multi-module position–attitude update. Finally, an enhanced data and model dual-driven structure is proposed to adaptively integrate motion features acquired from distributed sensor modules and results of velocity and motion detection provided by spatial–temporal network. Real-world experiments in complex scenes represent that the developed MP-TMDS effectively increases the precision of traditional P-TMDS and outperforms existing algorithms under both positioning and motion detection accuracy indexes, and the estimated accuracy improvement is more than 18.4% compared with state-of-the-art algorithms.

Cross-view matching refers to the use of images from different platforms (e.g. drone and satellite views) to retrieve the most relevant images, where the key is that the viewpoints and spatial resolution. However, most of the existing methods focus on extracting fine-grained features and ignore the connection of contextual information in the image. Therefore, we propose a novel ConvNeXt-based multi-level representation learning model for the solution of this task. First, we extract global features through the ConvNeXt model. In order to obtain a joint part-based representation learning from the global features, we then replicated the obtained global features, operating one copy with spatial attention and the other copy using a standard convolutional operation. In addition, the features of different branches are aggregated through the multilevel feature fusion module to prepare for cross-view matching. Finally, we created a new hybrid loss function to better limit these features and assist in mining crucial data regarding global features. The experimental results indicate that we have achieved advanced performance on two common datasets, University-1652 and SUES-200 at 89.79% and 95.75% in drone target matching and 94.87% and 98.80 in drone navigation.