This paper investigates the problem of cooperative standoff tracking using multiple fixed-wing unmanned aerial vehicles (UAVs) with control input constraints. In order to achieve accurate moving target tracking in the presence of unknown background wind, a coordinated standoff target tracking algorithm is proposed. The objective of the research is to steer multiple UAVs to fly a circular orbit around a moving target with prescribed intervehicle angular spacing. To achieve this goal, two control laws are proposed, including relative range regulation and space phase separation. On one hand, a heading rate control law based on a Lyapunov guidance vector field is proposed. The convergence analysis shows that the UAVs can asymptotically converge to a desired circular orbit around the target, regardless of their initial position and heading. Through a rigorous theoretical proof, it is concluded that the command signal of the proposed heading rate controller will not violate the boundary constraint on the heading rate. On the other hand, a temporal phase is introduced to represent the phase separation and avoid discontinuity of the wrapped space phase angle. On this basis, a speed controller is developed to achieve equal phase separation. The proposed airspeed controller meets the requirements of the airspeed constraint. Furthermore, to improve the robustness of the aircraft during target tracking, an estimator is developed to estimate the composition velocity of the unknown wind and target motion. The proposed estimator uses the offset vector between the UAV’s actual flight path and the desired orbit, which is defined by the Lyapunov guidance vector field, to estimate the composition velocity. The stability of the estimator is proved. Simulations are conducted under different scenarios to demonstrate the effectiveness of the proposed cooperative standoff target tracking algorithm. The simulation results indicate that the temporal-phase-based speed controller can achieve a fast convergence speed and small phase separation error. Additionally, the composition velocity estimator exhibits a fast response speed and high estimation accuracy.

By using ionosonde data recorded at Chiang Mai (18.8° N, 98.9° E, magnetic latitude is 9.1° N), Puer (22.7° N, 101.1° E, magnetic latitude is 12.9° N), and Leshan (29.6° N, 103.7° E, magnetic latitude is 19.8° N), the statistical features of different types of spread F (SF) occurrence at low and middle latitudes were analyzed in this study. The results showed that the SF occurrence had obvious local time, latitude, and SF-type variations. The range spread F (RSF) occurrence in equinox months decreased with the increase in latitude, while the frequency spread F’s (FSF) occurrence rate in the summer months increased and the onset time of FSF became earlier when the latitude increased. The generation of SF depends on the SF type. A plasma bubble excited by the generalized Rayleigh–Taylor instability (GRT) at the equator is more likely to produce RSF, while nighttime medium-scale traveling ionospheric disturbances (MSTIDs) induced by Perkins instability at middle latitudes is the main reason for the generation of FSF.

Video anomaly detection (VAD) aims to identify abnormal events based on video-level annotations. In recent years, multimodal data has helped to address the limitations of relying solely on the visual modality in complex scenarios. However, existing methods exploit multimodal information relatively coarsely. Meanwhile, current methods introduce long-range noise when modeling temporal relationships in videos. To address these issues, this paper proposes a triple-modal-guided video anomaly detection method (TMG-VAD). Specifically, we design an audio optimization module (AOM) that uses visual information to model and enhance raw audio signals in the time–frequency domain, producing more discriminative audio features. Furthermore, we introduce an Abnormal Weight Generation Module (AWGM), which employs a multilayer perceptron (MLP) to generate temporal guidance maps from audio features. The temporal guidance maps reflect the anomaly probability for each video segment. In addition, we propose a Video–Text Alignment Module (VTAM) that incorporates the temporal guidance maps into a selective state-space model (SSM) to produce more representative video features while reducing the influence of long-range noise. It also achieves accurate alignment between visual and textual features through contrastive learning. Extensive experiments conducted on the benchmark datasets UCF-Crime and XD-Violence demonstrate the effectiveness of TMG-VAD.