在UPF的基础上引入小波变换，假设系统噪声为高斯分布，而知道目标状态的

The wavelet transform is introduced on the basis of UPF, assuming that the system noise is Gaussian distribution, and the parameters of the target state, such as position, velocity, etc., are in the low frequency band relative to the system noise, and the importance function is selected as the transfer prior. At this time The particles sampled from the transfer a priori are multi-decomposed by wavelet transform to obtain signal components of different frequency bands, and then the high-frequency components of the reaction noise are removed, and the remaining low-frequency signals are used for wavelet reconstruction to obtain the r part of the noise. For particles, the new transition prior of these reconstructed particle reactions has a smaller variance than the original transition prior, so that a sharper probability density function can be obtained, thus reducing the variance of importance weights.

Wavelet transform is introduced on the basis of UPF. Assuming that the system noise is Gaussian distribution and that the parameters of the target state such as position and speed are in the low frequency band relative to the system noise, the importance function is selected as the transfer priori. At this time, the particles sampled from the transfer priori are decomposed by wavelet transform to obtain the signal components of different frequency bands, Then, the high-frequency components of the reaction noise are removed, and the remaining low-frequency signals are used for wavelet reconstruction to obtain the particles that remove r part of the noise. The new transfer priors of these reconstructed particles have smaller variance than the original transfer priors, so as to obtain a more sharp probability density function, which also reduces the variance of the importance weight.<br>

Introducing wavelet transform on the basis of UPF, assuming that the system noise is Gaussian distribution, and knowing that the parameters of the target state, such as position and velocity, are in a low frequency band relative to the system noise, the importance function is selected as the transfer prior. At this time, the particles sampled from the transfer prior are decomposed by wavelet transform to obtain signal components in different frequency bands, and then the high frequency components of the reaction noise are eliminated, and the remaining low frequency signals are used for wavelet reconstruction. The particles with R noise removed are obtained, and the new transfer priors of these reconstructed particles have smaller variance than the original transfer priors, so that a sharper probability density function can be obtained, thus reducing the variance of importance weights.