Filtering method and filtering device for voltage signal

Abstract

The invention discloses a voltage signal filtering method. The method includes the steps: an analog-digital converter samples a voltage signal to be filtered and measures a peak-peak value Vpp of noise; the peak-peak value is transmitted to a microprocessor; the microprocessor calculates a voltage change threshold Vt according to the peak-peak value Vpp; a voltage value of a digital voltage signal is compared to the voltage change threshold Vt; if the voltage value is less than the voltage change threshold Vt, a Kalman digital filter filters the digital voltage signal of a current moment, and X(k|k) of the current moment of the Kalman digital filter is used as X(k-1|k-1) of a next moment; if the voltage value is greater than the voltage change threshold Vt, filtering is not performed, the currently acquired voltage value of the digital voltage signal is used as the X(k-1|k-1) of the next moment of the Kalman digital filter, and Q of the Kalman digital filter is far less than 1, and R is not less than 1. The invention further discloses a voltage signal filtering device. The voltage signal filtering method and device has the advantages that dynamic response of filtering is fast and convergence stability of filtering is good.

Description

technical field [0001] The invention relates to a voltage signal filtering method and a filtering device thereof. Background technique [0002] The Adaptive Front-Lamp System (AFS) can dynamically adjust according to road conditions to improve driving safety. At present, the filtering of the power management module in the AFS controller generally adopts the sliding average method, which inevitably deteriorates the convergence stability in order to have a fast dynamic response. [0003] The Kalman digital filter is an optimal autoregressive data processing algorithm. The five basic formulas of the standard Kalman digital filter are: [0004] X(k|k-1)=A X(k-1|k-1) ……… (1) [0005] P(k|k-1)=A P(k-1|k-1) A’+Q ……… (2) [0006] X(k|k)= X(k|k-1)+Kg(k) (Z(k)-H X(k|k-1)) ……… (3) [0007] Kg(k)= P(k|k-1) H’ / (H P(k|k-1) H’ + R) ……… (4) [0008] P(k|k)=(I-Kg(k) H)P(k|k-1) ……… (5) [0009] In formula (1), X(k|k-1) is the result predicted by the previous state, and X(k-1|k-1) is t...

AI Technical Summary

Problems solved by technology

[0014] The Kalman digital filter is characterized by slow dynamic response and good convergence stability or fast dynamic response and poor convergence stability

For filtering effects that require fast dynamic response and good convergence stability, Kalman filtering cannot be achieved.

Therefore, none of the current filtering methods can guarantee fast dynamic response time and good convergence stability at the same time.

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