Behaviour recognition of ground vehicle using airborne monitoring of unmanned aerial vehicles

Oh, Hyondong; Kim, Taegyun; Shin, Hyo‐Sang; Tsourdos, Antonios; White, Brian

doi:10.1080/00207721.2013.772677

Cited by 30 publications

(37 citation statements)

References 29 publications

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“…The target's trajectory is classified to the anomalous trajectory model which has the template with the least edit distance. The edit distance is defined as the minimal number of replacements, insertions and deletions required to change from one string to another [11].…”

Section: F Syntactic Tracker Vs Template Matchingmentioning

confidence: 99%

Syntactic Enhancement to VSIMM for Roadmap Based Anomalous Trajectory Detection: A Natural Language Processing Approach

Krishnamurthy

Gao

2018

IEEE Trans. Signal Process.

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Syntactic tracking aims to classify a target's spatiotemporal trajectory by using natural language processing models. This paper proposes constrained stochastic context free grammar (CSCFG) models for target trajectories confined to a roadmap. We present a particle filtering algorithm that exploits the CSCFG model structure to estimate the target's trajectory. This metalevel algorithm operates in conjunction with a base-level target tracking algorithm. Extensive numerical results using simulated ground moving target indicator (GMTI) radar measurements show useful improvement in both trajectory classification and target state (both coordinates and velocity) estimation.Keywords-syntactic tracking, constrained stochastic context free grammar (CSCFG), particle filter, Earley Stolcke parser 1 Traditionally, given track information, a radar (human) operator examines target trajectories to determine anomalous behavior. This paper develops natural language models and meta-level signal processing algorithms for estimating anomalous trajectories. Such "middleware" forms the interface between the physical signal processing layer and the radar operator; see also [7] for alternative models for intent using bridging distributions.

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Section: F Syntactic Tracker Vs Template Matchingmentioning

confidence: 99%

Syntactic Enhancement to VSIMM for Roadmap Based Anomalous Trajectory Detection: A Natural Language Processing Approach

Krishnamurthy

Gao

2018

IEEE Trans. Signal Process.

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“…Therefore, a two-dimensional space is considered for the UAV flying at a constant altitude. In this study, it is also assumed that initial target information is given by other sources such as search and monitoring UAV [10] and sensor can point at the group center using a gimbal system. Note that data association for multiple targets and communication between UAVs are not the scope of this study, so data association and communication between UAVs are not considered in this study.…”

Section: Problem Formulationmentioning

confidence: 99%

“…In case that multiple moving ground vehicles are identified as targets of interest from reconnaissance or surveillance systems within the ground road traffic [10], [11], it is required to develop strategies on how to deploy multiple UAVs to persistently follow them. Although various different methodologies have been developed for the multi-target tracking using multiple ground [12], [13] or aerial vehicles [14], [15], there is relatively little research on multiple or group target tracking in the context of cooperative standoff LOS tracking considering uncertain dynamic environments and UAV sensing capability.…”

Section: Introductionmentioning

confidence: 99%

Coordinated standoff tracking of groups of moving targets using multiple UAVs

Kim

Shin

et al. 2013

21st Mediterranean Conference on Control and Automation

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This paper presents a coordinated standoff tracking methodology of groups of moving targets using multiple UAVs. The vector field guidance approach is first applied to track a group of targets for a single UAV by defining a variable standoff orbit to be followed, which can keep all targets within the field-of view of the UAV. A new feedforward term is included in the guidance command considering variable standoff distance, and the convergence of the vector field to the standoff orbit is analysed and enhanced by adjusting radial velocity using two active measures associated with vector field generation. Moreover, for multiple group tracking by multiple UAVs, a two-phase approach is proposed as a suboptimal solution for an NP-hard problem, consisting of target clustering/assignment and cooperative standoff group tracking with online local replanning. Lastly, localisation sensitivity to the group of targets is investigated for different angular separations between UAVs and sensing configurations. Numerical simulations are performed using randomly moving ground vehicles with four UAVs to verify the feasibility and benefit of the proposed approach. © 2013 IEEE

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“…One important preliminary step for the GMTI based airborne surveillance is to continuously estimate vehicle's state information. For instance, in order to analyse if the behaviour of a vehicle in a surveillance region is normal, its position and velocity need to be extracted [2]. For the continuous estimation of vehicle states, the Kalman or particle filters based on the Bayesian framework are widely applied [3].…”

Section: Introductionmentioning

confidence: 99%

An improved multiple model particle filtering approach for manoeuvring target tracking using airborne GMTI with geographic information

Chen

2016

Aerospace Science and Technology

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This paper proposes a ground vehicle tracking method using an airborne ground moving target indicator radar where the surrounding geographic information is considered to determine vehicle's movement type as well as constrain its positions. Multiple state models corresponding to different movement modes are applied to represent the vehicle's behaviour in different terrain conditions. Based on geographic conditions and multiple state models, a constrained variable structure multiple model particle filter algorithm is proposed. Compared with the traditional multiple model particle filtering schemes, the proposed algorithm utilises a particle swarm optimisation technique which generates more effective particles and generated particles are constrained into the feasible geographic region. Numerical simulation results in a realistic environment show that the proposed method achieves better tracking performance compared with current state-of-the-art ones for manoeuvring vehicle tracking.

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Behaviour recognition of ground vehicle using airborne monitoring of unmanned aerial vehicles

Cited by 30 publications

References 29 publications

Syntactic Enhancement to VSIMM for Roadmap Based Anomalous Trajectory Detection: A Natural Language Processing Approach

Syntactic Enhancement to VSIMM for Roadmap Based Anomalous Trajectory Detection: A Natural Language Processing Approach

Coordinated standoff tracking of groups of moving targets using multiple UAVs

An improved multiple model particle filtering approach for manoeuvring target tracking using airborne GMTI with geographic information

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