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An improved particle filtering projectile trajectory estimation algorithm fusing velocity information
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AI
DOI：10.1016/j.measurement.2024.115749.png)
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Abstract
En 中文
This study proposes an improved particle-filtering projectile trajectory estimation method that fuses velocity information to address the susceptibility to interference and large fluctuations in position and velocity measurements of a single-antenna-two-dimensional trajectory correction fuze. By considering the Doppler effect in satellite velocity measurements and the residuals between the model-derived velocity and velocity derived from the translational Doppler frequency shift, a sliding time window was established to generate velocity weight coefficients that dynamically adjust the weight of the velocity in filtering. By modifying the likelihood function, a particle-filtering algorithm was designed to achieve a fusion estimation of the projectile trajectory. Tests indicate that this method provides more accurate estimates of the position and velocity of the projectile compared to other existing methods, which can reduce the impact of velocity fluctuations to a certain extent. In addition, the computational burden is not increased and can directly replace existing mature algorithms in engineering.
Keywords:
Fusion state estimation
Particle filtering
Two-dimensional trajectory correction fuze
Sliding time window
Doppler effect