Joint Sensor Scheduling and Target Tracking with Efficient Bayesian Optimisation
- Resource Type
- Conference
- Authors
- Liu, Xingchi; Lyu, Chenyi; Soleymani, Seyed Ahmad; Wang, Wenwu; Mihaylova, Lyudmila
- Source
- 2023 Sensor Signal Processing for Defence Conference (SSPD) Sensor Signal Processing for Defence Conference (SSPD), 2023. :1-5 Sep, 2023
- Subject
- Aerospace
Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
Computing and Processing
Fields, Waves and Electromagnetics
Photonics and Electrooptics
Robotics and Control Systems
Signal Processing and Analysis
Schedules
Target tracking
Atmospheric measurements
Processor scheduling
Particle measurements
Sensors
Numerical models
Active sensing
Bayesian optimisation
factorised Gaussian process
target tracking
sensor management
unmanned aerial vehicles
hierarchical off-diagonal low-rank (HODLR) factorisation
- Language
The received signal strength measurement has been widely used in search and tracking applications and its benefit is linked with the distance between the transmitter and receiver. This paper proposes an online Bayesian optimisation-based approach that relies on signal strength measurements to schedule multiple sensors for searching and tracking a moving target, without any prior knowledge of the target’s state or motion model. A unique contribution lies in incorporating the Gaussian processes factorisation method into the Bayesian optimisation framework, which enhances the effectiveness of the proposed approach. Numerical results obtained from different sizes of measurements demonstrate that the proposed approach can efficiently schedule two unmanned aerial vehicles. Particularly, it achieves at most 21% lower computational time for deciding measurement locations and 79% lower time for updating the surrogate model as compared to the benchmark approach.