Filtering and estimating methodology in wildlife telemetry



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Texas Tech University


Estimating the location of a free-ranging animal accurately from noisy directional data is treated here in this study by using two widely used filtering techniques namely Extended Kalman filter and Sampling Importance Resample Particle filter. The mathematical model used in this study consists a discrete time linear difference equation. Due to the fact that the measurements are taken every second, time step is taken discrete. Both the process noise and the measurement noise are taken to be Gaussian white noise. Simulations are carried out to compare the performance of both the Extended Kalman filter and the SIR Particle filter for two different sets of data.



Telecommunication -- Research -- Mathematical mode, Electronic noise, Biotelemetry -- Research -- Mathematical models, Global positioning system (GPS), Kalman filtering -- Mathematical models