CN103871525B - Rhodium self-powered detector signal delay removing method based on Kalman filtering - Google Patents

Abstract

The present invention relates to nuclear reactor core and measure system detector signal processing technology field, specifically disclose a kind of rhodium self-powered detector signal delay removing method based on Kalman filtering.Concretely comprising the following steps of the method: 1, set up the nuclear reaction model of rhodium and neutron；2, Kalman filter model is set up；3, Kalman filtering is utilized to make rhodium self-power neutron detector current signal to postpone to eliminate；3.1, obtain the systematic procedure white noise variance matrix Q in Kalman filtering algorithm and systematic observation white noise variance matrix is R；3.2, gather rhodium self-powered detector current value, after carrying out analog digital conversion, utilize Kalman filtering to make rhodium self-power neutron detector current signal to postpone to eliminate；A kind of rhodium self-powered detector signal delay removing method based on Kalman filtering of the present invention, can carry out noise reduction process to measuring current signal, it is ensured that in the case of response time is sufficiently small, noise amplification suppresses at 1 ~ 8 times.

Description

Rhodium self-powered detector signal delay removing method based on Kalman filtering

Technical field

The invention belongs to nuclear reactor core and measure system detector signal processing technology field, be specifically related to a kind of based on card The rhodium self-powered detector signal delay removing method of Kalman Filtering.

Background technology

As the rhodium self-power neutron detector of detector, its sensitive material rhodium and neutron in advanced In-core Instrumentation system heap The secondary nucleic that reaction produces occurs decay to produce electric current, and under stable situation, this size of current is directly proportional to position flux, Therefore its position neutron flux can be deduced by measurement rhodium self-powered detector.Owing to such detector current mainly becomes Divide and produced by the decay of secondary nucleic, under reactor transient condition (situation of neutron-flux level change), such detection Device electric current can not reflect the change of flux level in real time, but has certain delay, and delay time parameter declines with secondary nucleic Become consistent.Therefore, rhodium self-power neutron detector is utilized to make the advanced reactor core measuring system of neutron measurement device, in ensureing The accuracy of sub-flux measurement, needs the current signal that rhodium self-sufficiency can be visited device to make to postpone Processing for removing.

Owing to being always attended by noise (process noise and measurement noise) during actual measurement, utilize direct mathematical reverse Method of drilling is made to postpone elimination and can detector current signal noise be amplified, and maximum is scalable to 20 times, the precision that impact is measured.Cause This is during postponing Processing for removing, it is desirable to have the amplification of effect suppression noise.

Kalman filtering belongs to a kind of software filtering method, and its basic thought is: with least mean-square error as best estimate Criterion, uses the state-space model of signal and noise, utilizes the estimated value of previous moment and the observation of current time to come more The new estimation to state variable, obtains the estimated value of current time, algorithm according to the system equation set up and observational equation to need Signal to be processed makes the estimation meeting least mean-square error.

Summary of the invention

It is an object of the invention to be to provide a kind of rhodium self-powered detector signal delay based on Kalman filtering to disappear Except method, can carry out the current signal of rhodium self-power neutron detector postponing Processing for removing, and effectively suppress noise so that Rhodium self-power neutron detector also can normally use when reactor transient condition.

Technical scheme is as follows: a kind of rhodium self-powered detector signal delay elimination side based on Kalman filtering Method, the method specifically includes following steps:

Step 1, set up the nuclear reaction model of rhodium and neutron；

Under reactor transient condition, the change of flux causes change the difference of rhodium self-power neutron detector electric current Step, the latter has certain delayed compared with the former, and the concrete formula describing above-mentioned reaction is as follows:

$\frac{{\∂m}_2\left(t\right)}{\∂t}=a_{2}n\left(t\right)-{\mathrm{\λ}}_2{m}_2\left(t\right)---\left(1\right)$

$\frac{{\∂m}_1\left(t\right)}{\∂t}=a_{1}n\left(t\right)+{\mathrm{\λ}}_2{m}_2\left(t\right)-{\mathrm{\λ}}_1{m}_1\left(t\right)---\left(2\right)$

I (t)=cn (t)+λ₁m₁(t) (3)

Wherein, m₁(t), m₂T () represents detector¹⁰⁴Rh and^104mThe current component that Rh directly causes, n (t) represents detector Detector current under the detector poised state that place's neutron flux is corresponding；λ₁, λ₂Represent¹⁰⁴Rh and^104mThe decay constant of Rh；c Represent the transient response composition of detector current；a₁、a₂Represent respectively¹⁰⁴Rh and^104mThe electric current share that Rh causes；I (t) represents rhodium Self-supporting energy electric current；

Step 2, set up Kalman filter model；

For the system of a discrete control process, this system can describe with a state equation:

X (k+1)=F (k+1 | k) X (k)+GW (k) (4)

The measured value of system:

Z (k)=H (k) X (k)+IV (k) (5)

System amount to be asked:

Y (k)=LX (k) (6)

Wherein, X (k) is that the n of kth time sampled point ties up state vector, W (k) systematic procedure white noise, and its variance matrix is Q, V (k) is systematic observation white noise, its variance matrix be R, F (k+1 | k) be n*n state-transition matrix, Z (k) is kth time sampling The m of point ties up measured value, and H (k) is m*n observing matrix, and G is n*n control system matrix, and I is the noise control matrix of m*n.Y (k) is l Tieing up and wait to seek vector, L is that l*n ties up matrix；

Step 3, utilize Kalman filtering to rhodium self-power neutron detector current signal make postpone eliminate；

Step 3.1, the systematic procedure white noise variance matrix Q obtained in Kalman filtering algorithm and systematic observation white noise Variance matrix is R；

Obtain the white noise variance R and systematic procedure white noise variance matrix Q of self-supporting moderate energy neutron detector measurement electric current；

Step 3.2, collection rhodium self-powered detector current value, after carrying out analog digital conversion, utilize Kalman filtering to rhodium certainly Make to postpone to eliminate to moderate energy neutron detector current signal；

Formula (1), formula (2), formula (3) are made Laplace transform, obtain following equation:

$\frac{I\left(s\right)}{n\left(s\right)}=c+\frac{a_1\·{\mathrm{\λ}}_1}{s+{\mathrm{\λ}}_1}+\frac{a_2\·{\mathrm{\λ}}_1\·{\mathrm{\λ}}_2}{s^2+({\mathrm{\λ}}_1+{\mathrm{\λ}}_2)\·s+{\mathrm{\λ}}_1\·{\mathrm{\λ}}_2}---\left(7\right)$

During equilibrium state, equation becomes:

$\frac{I_0}{n_0}=c+a_1+a_2=1---\left(8\right)$

Then formula (7) becomes:

$I\left(s\right)=n\left(s\right)\·\frac{I_0}{n_0}(c+\frac{a_1\·{\mathrm{\λ}}_1}{s+{\mathrm{\λ}}_1}+\frac{a_2\·{\mathrm{\λ}}_1\·{\mathrm{\λ}}_2}{s^2+({\mathrm{\λ}}_1+{\mathrm{\λ}}_2)\·s+{\mathrm{\λ}}_1\·{\mathrm{\λ}}_2}))---\left(9\right)$

Formula (9) is carried out inverse Laplace transformation, obtains following state equation:

$\frac{{\∂x}_1\left(t\right)}{\∂t}=\frac{1}{c}(a_1\·{\mathrm{\λ}}_1-a_2\·g)\·n\left(t\right)-{\mathrm{\λ}}_1{x}_1\left(t\right)---\left(10\right)$

$\frac{{\∂x}_2\left(t\right)}{\∂t}=\frac{1}{c}{a}_2\·g\·n\left(t\right)-{\mathrm{\λ}}_2{x}_2\left(t\right)---\left(11\right)$

I (t)=[c, c, c] X (t) (12)

Wherein,

$g=\frac{{\mathrm{\λ}}_1\·{\mathrm{\λ}}_2}{{\mathrm{\λ}}_1-{\mathrm{\λ}}_2}---\left(13\right)$

$X\left(t\right)=\left[\begin{array}{c}n\left(t\right)\\ x_1\left(t\right)\\ x_2\left(t\right)\end{array}\right]---\left(14\right)$

Initial value:

$X\left(0\right)=\left[\begin{array}{c}n\left(0\right)\\ \frac{1}{c}(a_1-a_2\·g/{\mathrm{\λ}}_1)\·n\left(0\right)\\ \frac{1}{c}(a_2\·g)\·n\left(0\right)\end{array}\right]---\left(15\right)$

Discrete state equations is:

$X(k+1)=\left[\begin{array}{ccc}1& 0& 0\\ \frac{1}{c}(a_1-a_2\·g/{\mathrm{\λ}}_1)\·(1-e^{{\mathrm{\λ}}_1\·\mathrm{Ts}})& e^{{-\mathrm{\λ}}_1\·\mathrm{Ts}}& 0\\ \frac{1}{c}{a}_2\·g\·(1-e^{-\mathrm{\λ}2\·\mathrm{Ts}})/{\mathrm{\λ}}_2& 0& e^{{-\mathrm{\λ}}_2\·\mathrm{Ts}}\end{array}\right]\·X\left(k\right)+\left[\begin{array}{ccc}1& 0& 0\\ 0& 1& 0\\ 0& 0& 1\end{array}\right]\·W\left(k\right)---\left(16\right)$

I (k)=[c c c] X (k) (17)

N (k)=[1 0 0] X (k) (18)

Wherein, $X\left(k\right)=\left[\begin{array}{c}n\left(k\right)\\ x_1\left(k\right)\\ x_2\left(k\right)\end{array}\right]$

Initial value: $X\left(0\right)=\left[\begin{array}{c}I\left(0\right)\\ \frac{1}{c}(a_1-a_2\·g/{\mathrm{\λ}}_1)\·I\left(0\right)\\ \frac{1}{c}(a_2\·g)\·I\left(0\right)\end{array}\right]---\left(19\right)$

$P\left(0\right)=\left[\begin{array}{ccc}1& 0& 0\\ 0& 0& 0\\ 0& 0& 0\end{array}\right]---\left(20\right)$

Formula (4) in formula (16), (17), (18) the most corresponding Kalman model, (5), (6).Then postpone to eliminate step Rapid as follows:

Collection k moment rhodium self-powered detector current value I (k), the Z (k) of corresponding formula (5), continuation below step:

Carry out pre-estimation:

XP (k+1)=F (k+1 | k) X (k) (21)

Obtain covariance matrix

P (k+1)=F (k+1 | k) P (k) * F (k+1 | k) '+G*Q*G'(22)

Obtain Kalman gain

K (k+1)=P (k+1) * H (k) ' * (H (k) * P (k+1) * H (k) '+R)-1 (23)

Obtain next step state value

X (k+1)=XP (k+1)+K (k+1 | k) * (Z (k+1)-H (k) * XP (k+1)) (24)

Update covariance matrix

P (k+2)=P (k+1)-K (k+1) * H (k) * P (k+1) (25)

Calculating obtains amount to be asked

Y (k)=LX (k) (26)

Wherein, Y (k) is k moment rhodium self-powered detector current value I(k) corresponding postpone the value after eliminating.

In described step 3.1, the white noise variance R of electric current measured by self-power neutron detector is defeated by state of statistical control The white noise exporting electric current under artificial situation obtains.

Systematic procedure white noise variance matrix Q in described step 3.1 particularly as follows:

Q is 3*3 matrix, is process white noise $W\left(k\right)=\left[\begin{array}{c}{w}_1\left(k\right)\\ w_2\left(k\right)\\ w_3\left(k\right)\end{array}\right]$ Variance matrix, w here₁(k), w₂K () is 0, w₃ K () is the increment of k sample moment n (k), therefore Q has a following form:

$Q=\left[\begin{array}{ccc}{q}_{11}& 0& 0\\ 0& q_{22}& 0\\ 0& 0& q_{33}\end{array}\right]$

Wherein, q here₁₁、q₂₂Close to 0, for formula (1), the model variance of formula (2), q₃₃Increment side for n (k) Difference.

The remarkable result of the present invention is: a kind of rhodium self-powered detector based on Kalman filtering of the present invention is believed Number delay eliminating method, can carry out noise reduction process to measuring current signal, it is ensured that in the case of response time is sufficiently small, Noise amplification suppresses at 1 ~ 8 times.

Accompanying drawing explanation

Fig. 1 is a kind of rhodium self-powered detector signal delay removing method stream based on Kalman filtering of the present invention Cheng Tu.

Detailed description of the invention

Below in conjunction with the accompanying drawings and the present invention is described in further detail by specific embodiment.

As described in Figure 1, a kind of rhodium self-powered detector signal delay removing method based on Kalman filtering, it specifically walks Rapid as follows:

Step 1, set up the nuclear reaction model of rhodium and (hot) neutron；

Under reactor transient condition, the change of flux causes change the difference of rhodium self-power neutron detector electric current Step, the latter has certain delayed compared with the former, and the concrete formula describing above-mentioned reaction is as follows: $\frac{{\∂m}_2\left(t\right)}{\∂t}=a_{2}n\left(t\right)-{\mathrm{\λ}}_2{m}_2\left(t\right)$ Formula 1 $\frac{{\∂m}_1\left(t\right)}{\∂t}=a_{1}n\left(t\right)+{\mathrm{\λ}}_2{m}_2\left(t\right)-{\mathrm{\λ}}_1{m}_1\left(t\right)$ Formula 2

I (t)=cn (t)+λ₁m₁(t) formula 3

Wherein, m₁(t), m₂T () represents detector¹⁰⁴Rh and¹⁰⁴The current component that mRh directly causes, n (t) represents detector Detector current under the detector poised state that place's (hot) neutron flux is corresponding；λ₁, λ₂Represent¹⁰⁴Rh and^104mThe decay of Rh is normal Number (λ₁=ln2/42.3s^-1=0.016386s^-1,λ₂=ln2/4.34/60s^-1=0.00266186s^-1)；C represents detector current Transient response composition；a₁、a₂Represent respectively¹⁰⁴Rh and^104m(a kind of situation is c=0.06 to the electric current share that Rh causes, a₁=0.879, a₂=0.061)；I (t) represents SPND electric current.

Step 2, set up Kalman filter model

For the system of a discrete control process, this system can describe with a state equation:

X (k+1)=F (k+1 | k) X (k)+GW (k) formula 4

The measured value of system:

Z (k)=H (k) X (k)+IV (k) formula 5

System amount to be asked:

Y (k)=LX (k) formula 6

Wherein, X (k) is the n dimension state vector of kth time sampled point, W (k) systematic procedure white noise (n dimension), its variance square Battle array be Q, V (k) be systematic observation white noise (n dimension), its variance matrix be R, F (k+1 | k) be n*n state-transition matrix, Z (k) M for kth time sampled point ties up measured value, and H (k) is m*n observing matrix, and G is n*n control system matrix, and I is that m*n is noise control Matrix.Y (k) is that l ties up and waits to ask vector, and L is that l*n ties up matrix.

Step 3, utilize Kalman filtering to rhodium self-power neutron detector current signal make postpone eliminate

Step 3.1, the systematic procedure white noise variance matrix Q arranged in Kalman filtering algorithm and systematic observation white noise Variance matrix is R, wherein,

R: be the white noise variance of self-power neutron detector measurement electric current, in the case of this value can be exported by state of statistical control The white noise of output electric current is obtained.

Q:Q is 3*3 matrix, is process white noise $W\left(k\right)=\left[\begin{array}{c}{w}_1\left(k\right)\\ w_2\left(k\right)\\ w_3\left(k\right)\end{array}\right]$ Variance matrix, w here₁(k), w₂K () is 0, w₃K () is the increment of k sample moment n (k), therefore Q has a following form:

$Q=\left[\begin{array}{ccc}{q}_{11}& 0& 0\\ 0& q_{22}& 0\\ 0& 0& q_{33}\end{array}\right]$ Formula 7

Wherein, q here₁₁、q₂₂Close to 0, for formula 1, the model variance of formula 2, q₃₃Increment variance for n (k).

Step 3.2, collection rhodium self-powered detector current value, and utilize Kalman filtering to rhodium self-power neutron detector Current signal is made to postpone to eliminate

Formula 1, formula 2, formula 3 are made Laplace transform, obtain following equation:

$\frac{I\left(s\right)}{n\left(s\right)}=c+\frac{a_1\·{\mathrm{\λ}}_1}{s+{\mathrm{\λ}}_1}+\frac{a_2\·{\mathrm{\λ}}_1\·{\mathrm{\λ}}_2}{s^2+({\mathrm{\λ}}_1+{\mathrm{\λ}}_2)\·s+{\mathrm{\λ}}_1\·{\mathrm{\λ}}_2}$ Formula 8

During equilibrium state, equation becomes:

$\frac{I_0}{n_0}=c+a_1+a_2=1$ Formula 9

Then formula 8 becomes:

$I\left(s\right)=n\left(s\right)\·\frac{I_0}{n_0}(c+\frac{a_1\·{\mathrm{\λ}}_1}{s+{\mathrm{\λ}}_1}+\frac{a_2\·{\mathrm{\λ}}_1\·{\mathrm{\λ}}_2}{s^2+({\mathrm{\λ}}_1+{\mathrm{\λ}}_2)\·s+{\mathrm{\λ}}_1\·{\mathrm{\λ}}_2}))$ Formula 10

Formula 10 is carried out inverse Laplace transformation, obtains following state equation:

$\frac{{\∂x}_1\left(t\right)}{\∂t}=\frac{1}{c}(a_1\·{\mathrm{\λ}}_1-a_2\·g)\·n\left(t\right)-{\mathrm{\λ}}_1{x}_1\left(t\right)$ Formula 11

$\frac{{\∂x}_2\left(t\right)}{\∂t}=\frac{1}{c}{a}_2\·g\·n\left(t\right)-{\mathrm{\λ}}_2{x}_2\left(t\right)$ Formula 12

I (t)=[c, c, c] X (t) formula 13

Wherein,

$g=\frac{{\mathrm{\λ}}_1\·{\mathrm{\λ}}_2}{{\mathrm{\λ}}_1-{\mathrm{\λ}}_2}$ Formula 14

$X\left(t\right)=\left[\begin{array}{c}n\left(t\right)\\ x_1\left(t\right)\\ x_2\left(t\right)\end{array}\right]$ Formula 15

Initial value:

$X\left(0\right)=\left[\begin{array}{c}n\left(0\right)\\ \frac{1}{c}(a_1-a_2\·g/{\mathrm{\λ}}_1)\·n\left(0\right)\\ \frac{1}{c}(a_2\·g)\·n\left(0\right)\end{array}\right]$ Formula 16

Discrete state equations is:

$X(k+1)=\left[\begin{array}{ccc}1& 0& 0\\ \frac{1}{c}(a_1-a_2\·g/{\mathrm{\λ}}_1)\·(1-e^{{\mathrm{\λ}}_1\·\mathrm{Ts}})& e^{{-\mathrm{\λ}}_1\·\mathrm{Ts}}& 0\\ \frac{1}{c}{a}_2\·g\·(1-e^{-\mathrm{\λ}2\·\mathrm{Ts}})/{\mathrm{\λ}}_2& 0& e^{{-\mathrm{\λ}}_2\·\mathrm{Ts}}\end{array}\right]\·X\left(k\right)+\left[\begin{array}{ccc}1& 0& 0\\ 0& 1& 0\\ 0& 0& 1\end{array}\right]\·W\left(k\right)---\left(16\right)$ Formula 17

I (k)=[c c c] X (k) formula 18

N (k)=[1 0 0] X (k) formula 19

Wherein, $X\left(k\right)=\left[\begin{array}{c}n\left(k\right)\\ x_1\left(k\right)\\ x_2\left(k\right)\end{array}\right]$

Initial value: $X\left(0\right)=\left[\begin{array}{c}I\left(0\right)\\ \frac{1}{c}(a_1-a_2\·g/{\mathrm{\λ}}_1)\·I\left(0\right)\\ \frac{1}{c}(a_2\·g)\·I\left(0\right)\end{array}\right]$ Formula 20

$P\left(0\right)=\left[\begin{array}{ccc}1& 0& 0\\ 0& 0& 0\\ 0& 0& 0\end{array}\right]$ Formula 21

Formula 4,5,6 in the most corresponding Kalman model of formula 17,18,19.Then postpone removal process as follows:

Collection k moment rhodium self-powered detector current value I (k), the Z (k) of corresponding formula 5, continuation below step:

Carry out pre-estimation:

XP (k+1)=F (k+1 | k) X (k) formula 22

Obtain covariance matrix

P (k+1)=F (k+1 | k) P (k) * F (k+1 | k) '+G*Q*G' formula 23

Obtain Kalman gain

K (k+1)=P (k+1) * H (k) ' * (H (k) * P (k+1) * H (k) '+R)-1 formula 24

Obtain next step state value

X (k+1)=XP (k+1)+K (k+1 | k) * (Z (k+1)-H (k) * XP (k+1)) formula 25

Update covariance matrix

P (k+2)=P (k+1)-K (k+1) * H (k) * P (k+1) formula 26

Calculating obtains amount to be asked

Y (k)=LX (k) formula 27

Wherein, Y (k) is k moment rhodium self-powered detector current value I(k) corresponding postpone the value after eliminating.

Claims (3)

1. a rhodium self-powered detector signal delay removing method based on Kalman filtering, it is characterised in that: the method has Body comprises the steps:

Step 1, set up the nuclear reaction model of rhodium and neutron；

Under reactor transient condition, the change of flux causes the change of rhodium self-powered detector electric current asynchronous, and the latter is relatively The former has certain delayed, and the concrete formula describing above-mentioned reaction is as follows:

I (t)=cn (t)+λ₁m₁(t) (3)

Wherein, m₁(t), m₂T () represents detector respectively¹⁰⁴Rh and^104mThe current component that Rh directly causes, n (t) represents detector Detector current under the detector poised state that place's neutron flux is corresponding；λ₁, λ₂Represent respectively¹⁰⁴Rh and^104mThe decay of Rh is normal Number；C represents the transient response composition of detector current；a₁、a₂Represent respectively¹⁰⁴Rh and^104mThe electric current share that Rh causes；I (t) table Show rhodium self-powered detector electric current；

Step 2, set up Kalman filter model；

For the system of a discrete control process, this system can describe with a state equation:

X (p+1)=F (p+1 | p) X (p)+GW (p) (4)

The measured value of system:

Z (p)=H (p) X (p)+IV (p) (5)

System amount to be asked:

Y (p)=LX (p) (6)

Wherein, X (p) is that the n of pth time sampled point ties up state vector, and W (p) systematic procedure white noise, its variance matrix is Q, V (p) For systematic observation white noise, its variance matrix be R, F (p+1 | p) be n*n state-transition matrix, Z (p) is the m of pth time sampled point Dimension measured value, H (p) is m*n observing matrix, and G is n*n control system matrix, and I is the noise control matrix of m*n, and Y (p) is that l dimension is treated Seeking vector, L is that l*n ties up matrix；

Step 3, utilize Kalman filtering to rhodium self-powered detector current signal make postpone eliminate；

Step 3.1, the systematic procedure white noise variance matrix Q obtained in Kalman filtering algorithm and systematic observation white noise variance Matrix is R；

Obtain rhodium self-powered detector and measure the white noise variance R and systematic procedure white noise variance matrix Q of electric current；

Step 3.2, collection rhodium self-powered detector current value, after carrying out analog digital conversion, utilize Kalman filtering to rhodium self-sufficiency energy Detector current signal is made to postpone to eliminate；

Formula (1), formula (2), formula (3) are made Laplace transform, obtain following equation:

During equilibrium state, equation becomes:

Then formula (7) becomes:

Formula (9) is carried out inverse Laplace transformation, obtains following state equation:

I (t)=[c, c, c] X (t) (12)

Wherein,

Initial value:

Discrete state equations is:

I (k)=[c c c] X (k) (17)

N (k)=[1 0 0] X (k) (18)

Wherein,

Initial value:

Formula (4) in formula (16), (17), (18) the most corresponding Kalman model, (5), (6), then postpone removal process such as Under:

Collection k moment rhodium self-powered detector current value I (k), the Z (k) of corresponding formula (5), continuation below step:

Carry out pre-estimation:

XP (k+1)=F (k+1 | k) X (k) (21)

Obtain covariance matrix

P (k+1)=F (k+1 | k) P (k) F (k+1 | k) '+G Q G'(22)

Obtain Kalman gain

K (k+1)=P (k+1) H (k) ' (H (k) P (k+1) H (k) '+R)^-1 (23)

Obtain next step state value

X (k+1)=XP (k+1)+K (k+1 | k) (Z (k+1)-H (k) XP (k+1)) (24)

Update covariance matrix

P (k+2)=P (k+1)-K (k+1) H (k) P (k+1) (25)

Calculating obtains amount to be asked

Y (k)=LX (k) (26)

Wherein, Y (k) is the value after delay corresponding for k moment rhodium self-powered detector current value I (k) eliminates.

A kind of rhodium self-powered detector signal delay removing method based on Kalman filtering the most according to claim 1, It is characterized in that: in described step 3.1, the white noise variance R of electric current measured by rhodium self-powered detector is defeated by state of statistical control The white noise exporting electric current under artificial situation obtains.

A kind of rhodium self-powered detector signal delay removing method based on Kalman filtering the most according to claim 1, It is characterized in that: systematic procedure white noise variance matrix Q in described step 3.1 particularly as follows:

Q is 3*3 matrix, is systematic procedure white noiseVariance matrix, w here₁(k), w₂K () is 0, w₃ K () is the increment of k sample moment n (k), therefore Q has a following form:

Wherein, q here₁₁、q₂₂Close to 0, respectively formula (1), the model variance of formula (2), q₃₃Increment side for n (k) Difference.