A small UAV tracking algorithm based on AIMM-UKF

Hou, Zhiwen; Bu, Fanliang

doi:10.1108/aeat-01-2019-0013

Cited by 11 publications

(5 citation statements)

References 29 publications

(33 reference statements)

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“…When a new user-item interaction event is observed, DGHP-LISA first learns the current embedding of the user and the item on the basis of the dynamic graph Hawkes process simultaneously. Secondly, inspired by the Kalman filter ( Julier & Uhlmann, 1997 ; Hou & Bu, 2021 ), we capture the previously updated status and elapsed time through the projection and prediction layer to predict the future embedding of users and items. Thirdly, we calculate the L 2 distance between the predicted item embedding and all other item embeddings, and then recommend items with the smallest distance to the predicted item embedding.…”

Section: Proposed Approachmentioning

confidence: 99%

A dynamic graph Hawkes process based on linear complexity self-attention for dynamic recommender systems

Hou

Zhou

et al. 2023

PeerJ Computer Science

Self Cite

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The dynamic recommender system realizes the real-time recommendation for users by learning the dynamic interest characteristics, which is especially suitable for the scenarios of rapid transfer of user interests, such as e-commerce and social media. The dynamic recommendation model mainly depends on the user-item history interaction sequence with timestamp, which contains historical records that reflect changes in the true interests of users and the popularity of items. Previous methods usually model interaction sequences to learn the dynamic embedding of users and items. However, these methods can not directly capture the excitation effects of different historical information on the evolution process of both sides of the interaction, i.e., the ability of events to influence the occurrence of another event. In this work, we propose a Dynamic Graph Hawkes Process based on Linear complexity Self-Attention (DGHP-LISA) for dynamic recommender systems, which is a new framework for modeling the dynamic relationship between users and items at the same time. Specifically, DGHP-LISA is built on dynamic graph and uses Hawkes process to capture the excitation effects between events. In addition, we propose a new self-attention with linear complexity to model the time correlation of different historical events and the dynamic correlation between different update mechanisms, which drives more accurate modeling of the evolution process of both sides of the interaction. Extensive experiments on three real-world datasets show that our model achieves consistent improvements over state-of-the-art baselines.

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Section: Proposed Approachmentioning

confidence: 99%

A dynamic graph Hawkes process based on linear complexity self-attention for dynamic recommender systems

Hou

Zhou

et al. 2023

PeerJ Computer Science

Self Cite

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“…Pairwise consistency maximization eliminates loop closures [136] by detecting overlaps where the viewpoint and lighting conditions are similar, as they depict the same place viewed by the UAV at the same time. The UAV motion and landmark observations in SLAM are usually nonlinear functions [145]. A UAV capturing images is approximated by non-linear models and the sensor measurements are also nonlinear.…”

Section: Search Space Reduction In Nonlinear Systems Using Extended K...mentioning

confidence: 99%

“…When the UAV is paused, the images are from a single point. The features that are observed in the current timeframe correspond to the mean and covariance [140,145,151]. If there are M landmark points l 1 , .…”

Section: Impact Of Sensor Parameters On Accuracy Of Visual 3d Reconst...mentioning

confidence: 99%

Simultaneous Localization and Mapping (SLAM) and Data Fusion in Unmanned Aerial Vehicles: Recent Advances and Challenges

Gupta

Fernando

2022

Drones

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This article presents a survey of simultaneous localization and mapping (SLAM) and data fusion techniques for object detection and environmental scene perception in unmanned aerial vehicles (UAVs). We critically evaluate some current SLAM implementations in robotics and autonomous vehicles and their applicability and scalability to UAVs. SLAM is envisioned as a potential technique for object detection and scene perception to enable UAV navigation through continuous state estimation. In this article, we bridge the gap between SLAM and data fusion in UAVs while also comprehensively surveying related object detection techniques such as visual odometry and aerial photogrammetry. We begin with an introduction to applications where UAV localization is necessary, followed by an analysis of multimodal sensor data fusion to fuse the information gathered from different sensors mounted on UAVs. We then discuss SLAM techniques such as Kalman filters and extended Kalman filters to address scene perception, mapping, and localization in UAVs. The findings are summarized to correlate prevalent and futuristic SLAM and data fusion for UAV navigation, and some avenues for further research are discussed.

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“…Singer model is a global statistical model that does not require detection during target tracking; therefore, there is no time lag [15,16]. The "current" statistical (CS) model of manoeuvring target assumes that the mean acceleration is a nonzero first-order time-dependent function, and the "current" probability density of acceleration is expressed by the modified Rayleigh distribution [17][18][19], which is more in line with the actual aircraft flight situation.…”

Section: Introductionmentioning

confidence: 99%

Improved Variable Structure Interacting Multimodels for Target Trajectory Tracking and Extrapolation

Ren,

Tang,

Zhou

et al. 2024

International Journal of Aerospace Engineering

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To improve the lengthy computation time of conventional variable structure interacting multiple model (VSIMM) algorithm and increase the precision of target prediction and extrapolation, the target state and flight intent information captured by the Automatic Dependent Surveillance-Broadcast (ADS-B) are used as the model’s prior information; combining this information with VSIMM theoretical framework, we purpose an intent variable structure interacting multiple model (INT-VSIMM) algorithm. Firstly, the motion pattern of the target in the flight phase of the flight path is decomposed, and complete sets of motion models are established. Secondly, according to the principle of directed graph switching, a model set switching method is designed, which is mainly based on “hard” switching and supplemented by “soft” switching. Finally, the INT-VSIMM algorithm is used to track the trajectory of the target aircraft, and short-term trajectory extrapolation is performed based on the target state estimation. The simulation results show that the target tracking performance computational time based on the INT-VSIMM algorithm is superior to the comparative existing methods, and the extrapolated trajectory has less error in the short term, which can satisfy the needs of conflict detection.

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A small UAV tracking algorithm based on AIMM-UKF

Cited by 11 publications

References 29 publications

A dynamic graph Hawkes process based on linear complexity self-attention for dynamic recommender systems

A dynamic graph Hawkes process based on linear complexity self-attention for dynamic recommender systems

Simultaneous Localization and Mapping (SLAM) and Data Fusion in Unmanned Aerial Vehicles: Recent Advances and Challenges

Improved Variable Structure Interacting Multimodels for Target Trajectory Tracking and Extrapolation

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