Cross-view geo-localization has been widely used as an important technique for determining the geographical location of unmanned aerial vehicles (UAV). Despite various image retrieval methods proposed, drone and satellite image cross-view geo-localization still remains challenging due to their wildly inconsistent view angles. In this paper, we propose a new framework, Swin-Radial-Locality Network (SRLN), to extract robust image feature representations. Specifically, SRLN is based on a pruned version of the Swin Transformer, which integrates multi-scale feature aggregation within a Siamese network structure, featuring shared weights and equipped with multi-classification heads. SRLN is mainly comprised of a Radial-Slicer-Network (RSN) and a Local-Pattern-Network (LPN), which is designed to effectively harmonize directional information from drone-captured images and broader environmental features from satellite imagery, crucial for capturing angle and feature details between drone and satellite images. The RSN part focuses on capturing fine-grained features that represent the drone’s directional information, while the LPN is utilized for a more comprehensive analysis of broader environmental features. Extensive experiments are carried out on widely used public benchmark datasets, i.e., University-1652 and SUES-200. With more than 3% improvement over existing methods in both drone-view target localization tasks and drone navigation applications, the results validate the superior performance of our multi-scale feature fusion model, achieving a state-of-the-art performance record.

Accurately estimating origin-destination (OD) network traffic is crucial for network management and capacity planning. However, the potential network anomaly and complex noise make this goal difficult to achieve. Existing network traffic estimation methods usually impute network traffic independent of anomaly detection, which ignores the potential relationship between the two tasks to help each other in achieving better performance. Moreover, these approaches can only be suitable for simple Gaussian or outlier noise assumptions, which cannot be applied to more complex noise distributions in practical applications. To address these issues, we propose a novel anomaly-tolerant network traffic estimation approach for simultaneously estimating network traffic and detecting network anomaly. Specifically, by utilizing the inherent low-rank property and temporal characteristic of traffic matrix, we formulate the network traffic estimation problem as a noise-immune temporal matrix completion (NiTMC) model, where the complex noise is fitted by mixture of Gaussian (MoG), and the network anomaly is smoothed by the L2,1-norm regularization. In addition, we also design a convergence-guaranteed optimization algorithm based on the expectation maximization (EM) and block coordinate update (BCU) methods to solve the proposed model. Furthermore, to deal with large-scale network problems, we develop a scalable and memory-efficient algorithm by employing stochastic proximal gradient descent (SPGD) method. Finally, the extensive experiments performed on real datasets demonstrate that our proposed NiTMC model outperforms the previously widely used network traffic estimation methods.

WiFi technology has fostered numerous mobile computing applications, e.g. indoor localization, gesture and activity recognition, device-free localization, etc., due to its ubiquity. The awareness of LOS and NLOS is a prerequisite for WiFi-based methods, since the WiFi signals received under NLOS conditions may contain a lot of noise and multipath effects, exerting great influences on the accuracy of location or identification. Traditional schemes based on commodity WiFi devices can achieve real-time LOS/NLOS identification. However, these methods face the challenges of limited bandwidth and coarse multipath resolution. In this work, we explore the amplitude feature of PHY layer information, and accordingly propose AmpN, a real-time LOS identification scheme based on commodity WiFi infrastructure that is applicable in both static and mobile scenarios. AmpN employs BP neural network algorithm in static scenario and K-Mean method in dynamic scenario, respectively. Experimental results demonstrate that AmpN outperforms existing approaches, achieving overall LOS and NLOS detection rates of 94.2% and 97.6% in static case, and above 97% LOS and NLOS detection rates in mobile context. In addition, the detection delay is less than 0.4s when the link state switches from LOS to NLOS.

Due to its indispensability for device-free passive (DfP) sensing, DfP human detection has attracted numerous research efforts during the past years. Although previous works have achieved considerable detection performance, they mainly focus on moving human detection, making mobility a prerequisite for reliable detection. Besides, existing static human detection systems usually require dense deployment or controlled settings. In this paper, we propose a robust respiration-rate-estimation-based passive static human detection system, R-PSHD. Specifically, different from recent works which leverage the amplitude of channel state information (CSI) for DfP sensing, we resort to the more sensitive phase information for minute respiration detection. To deal with the randomness of raw phase, R-PSHD exploits phase difference between antennas for feature extraction. Moreover, due to varying sensitivity of different subcarriers, R-PSHD tries to identify the useful subcarriers and only uses them for accurate estimation. Experimental results with different people during a week demonstrate that R-PSHD achieves great performance with both TP and TN rate higher than 90%.

Due to rapid developments of smart devices and mobile applications, there is an urgent need for a new human-in-the-loop architecture with better system efficiency and user experience. Compared with conventional device-based human-computer interactive (HCI) methods, device-free technology with WiFi provides a new HCI method and is promising for providing better user-perceived quality-of-experience. Being essential for device-free applications, device-free human detection has gained increasing interest, of which through-the-wall (TTW) human detection is of great challenge. Existing TTW detection systems either rely on massive deployment of transceivers or require specialized WiFi monitors, making them inapplicable for real-world applications. Recently, more and more researchers have tapped into the physical layer for more robust and reliable human detection, ever since channel state information (CSI) can be exported with commodity devices. Despite great progress achieved, there have been few works studying TTW detection. In this paper, we propose a novel scheme for robust device-free TTW detection (R-TTWD) of a moving human with commodity devices. Different from the time dimension-based features exploited in the previous works, R-TTWD takes advantage of the correlated changes over different subcarriers and extracts the first-order difference of eigenvector of CSI across different subcarriers for TTW human detection. Instead of direct feature extraction, we first perform a PCA-based filtering on the preprocessed data, since a simple low-pass filtering is insufficient for noise removal. Furthermore, the detection results across different transmit-receive antenna pairs are fused with a majority-vote-based scheme for more robust and accurate detection. We prototype R-TTWD on commodity WiFi devices and evaluate its performance both in different environments and over long test period, validating the robustness of R-TTWD with both detection rates for moving human and human absence over 99% regardless of different wall materials, dynamic moving speeds, and so on.

Robust Device-free passive (Dfp) detection is an essential primitive for a broad range of applications such as intrusion detection and smart space. Most recent works focus on finer-grained Channel State Information (CSI), instead of the variable Received Signal Strength (RSS). However, existing solutions have some limitations, being feasible only in the line of sight (LOS) or for more than one targeted entities. Moreover, space diversity supported by the MIMO systems hasn't been fully investigated. Motivated by this observation, we propose a novel scheme for Robust Passive Motion Detection (R-PMD). In our scheme, the variance of CSI amplitude feature is extracted as a new metric and the earth mover's distance (EMD) is utilized to determine the detection results. Besides, CSIs across multiantennas are further exploited to improve the detection precision and robustness. We prototype R-PMD on commercial WiFi devices and evaluate it in a typical indoor scenario. Experiment results show R-PMD can achieve great performance in terms of sensitivity and robustness.