When TVAR model signal and reflection coefficients were extended to state vector, Gaussian Particle Filter (GPF) was applied to estimate parameters of TVAR model.
将TVAR模型的信号和反射系数矢量增广为状态矢量后，应用高斯粒子滤波器(GPF)估计TVAR的模型参数，构造了语音增强算法。
Simulation results indicate that the TVAR model for modeling a discharge signal has a good performance for distinguishing the normal discharge and the anti-electric-corona discharge.
对实际信号的建模实验结果表明，该参数信号模型在放电信号建模方面具有优良的性能，特别是在区分正常放电与反电晕放电方面性能较好。
Simulation results indicate that the TVAR model for modeling a discharge signal has a good performance for distinguishing the normal discharge and the anti-electric-corona discharge.
对实际信号的建模实验结果表明，该参数信号模型在放电信号建模方面具有优良的性能，特别是在区分正常放电与反电晕放电方面性能较好。
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