CN114519373B - Interference signal denoising method adopting infrared long-wave focal plane detector - Google Patents
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Abstract
The invention discloses an interference signal denoising method adopting an infrared long-wave focal plane detector. And acquiring interference signals through an infrared focal plane detector and a Fourier spectrometer built by a Michelson interferometer. Filtering the output of each detecting element of the detector at each moment by using a Kalman filtering algorithm, then averaging the filtering result value of each section, and further filtering other noise; and finally, sequencing and reconstructing the interference pattern. The whole denoising method is simple and easy to implement, and compared with the method for directly averaging a plurality of groups of spectrum data to improve the spectrum signal-to-noise ratio, the method suppresses noise in interference signals before Fourier transformation of the interference patterns into the spectrum patterns, and can further filter random noise and retain original spectrum characteristics. Provides a technical foundation for Fourier spectrometer interference signal processing by adopting a long-wave focal plane detector in the future.
Description
Technical Field
The invention belongs to the technical field of atmospheric remote sensing, and particularly relates to an interference signal denoising method adopting an infrared long-wave focal plane detector.
Background
In the next half of 2016, china successfully launched a second generation of stationary meteorological satellite No. four, and an interference infrared hyperspectral atmospheric vertical detector working on a geostationary orbit is mounted on a satellite. The detector employs a light-guide type detector to measure radiation in two infrared bands from the earth in a high spectral resolution. Through physical inversion, an atmospheric temperature and humidity profile can be obtained, and more accurate initial field data is provided for numerical weather forecast. To meet the atmospheric detection requirements of higher spatial-temporal resolution, acquiring spectral data of greater data volume requires the use of a larger-scale detector. The scale of the detecting element of the detector is continuously enlarged, the number of required signal amplifying and processing circuits is increased, and the factors such as the number of leads, the volume, the weight, the power consumption, the parameter consistency and the reliability of the detector lead the detecting element of the detector to be controlled within a certain range, so that the application of the infrared detection technology in space remote sensing is severely restricted. However, with the continuous appearance and development of new technologies of various new materials, infrared detection technology is developed towards large-scale focal plane detection with larger scale, longer distance and faster response. The infrared long-wave interference signal is acquired by adopting a tellurium-cadmium-mercury infrared focal plane detector, and the infrared long-wave interference signal is specially developed by Shanghai technology physical research institute of China academy of sciences aiming at the detection requirement of Fourier spectrum. The response speed of the focal plane detector used for Fourier spectrum detection on the photosensitive device is far higher than that of the focal plane detector generally used for infrared image detection, and the focal plane detector needs to have the capability of real-time high-speed response to high-frequency external trigger signals and can realize large dynamic range output. Therefore, some random and discrete noise is inevitably generated in the processes of detector output, numerical quantization and the like of the signals, and the noise mainly sources of the random and discrete noise include detector noise, preamplifier noise, AD sampling noise, noise introduced by sampling errors caused by the shaking of the scanning speed of the moving mirror and the like. The superposition of noise and signals results in the inability to reconstruct an accurate interferogram directly, requiring prior processing of the interference signal, i.e., suppression of noise in the interference signal, to reduce the impact of noise on data accuracy.
Disclosure of Invention
The invention aims to provide an interference signal denoising method adopting an infrared long wave focal plane detector, which realizes filtering denoising of interference signals in a time domain and reduces the influence of various random discrete noises on the accuracy of the interference signals.
The invention is realized mainly by the following technical scheme:
step 1: interference signal data preprocessing. Dividing digital signals acquired by an infrared long-wave focal plane detector according to the output of detection elements of the detector, grouping the sequences according to acquisition time, wherein one group contains data z k, k=1, 2,3.
Step 2: establishing a Kalman filtering state equation and a measurement equation according to the interference data characteristics:
The process equation is:
Xk＝Fk-1Xk-1+wk-1 (1)
The measurement equation is:
zk＝HkXk+vk (2)
State vector Is a state vector at time K, where I k is the detection state quantity at time K, b k is the detector noise state quantity at time K,/>Is a state vector at time k-1, I k-1 is a detection state quantity at time k-1, and b k-1 is a random noise state quantity at time k-1; z k is the detection value at time k, definingFor state transition matrices, based on interference signal characteristics, whereH k is a measurement matrix, and H k is defined as 1; the process noise w k-1 at the time of k-1 and the measurement noise v k at the time of k are independent Gaussian white noise, and the average value is zero.
Step 3: setting initial conditions according to groups;
state estimate at time k=1, where/> Initially the mean of all data of the set,/>Initially z 1 and/>P 1 is the state estimation error covariance matrix at the initial time, the initial is the identity matrix, and Q k and R k respectively represent the process noise covariance matrix and the measurement noise covariance matrix at the k time,/>K represents a kth filtering period;
Step 4: starting to enter a filtering period, superposing the Kalman filtering period, and updating a covariance matrix of a predicted state quantity and a state prediction error;
predicting state quantity:
covariance matrix of state prediction error:
Pk|k-1＝Fk-1Pk-1Fk-1 T+Qk-1 (4)
F k-1 is a state transition matrix, State estimation for time k-1, wherein/>For detecting estimated values,/>For detector noise estimation,/>For one-step prediction of the state at time k, P k-1 is the state estimation error covariance matrix at time k-1, Q k-1 is the process noise covariance matrix at time k-and P k|k-1 is the covariance matrix of the one-step state prediction errors calculated by equations (1) and (3).
And 5, further calculating covariance matrixes of the Kalman gain, the state estimation value and the state estimation error according to the recursive least square method.
Kalman gain:
Gk＝Pk|k-1Hk T(HkPk|k-1Hk T+Rk)-1 (5)
State estimation value
Covariance matrix of state estimation error
Pk＝(I-GkHk)Pk|k-1 (7)
Wherein G k is Kalman gain, which represents the proportion of the current detection value to be introduced with new information in the state estimation; for the state estimate at time k,/> For detection estimate at time k,/>The estimated value of the detector noise at the moment k; /(I)The method is characterized in that the method is one-step prediction of the state at the moment k, and I is an identity matrix; the covariance matrix P k of the state estimation error is used for calculating a prediction covariance matrix of the next filtering period;
Step 6: and after the execution of the k filtering periods is finished, repeatedly filtering the next group of data for k periods until the execution of the m groups of data is finished. Finally for each group of data And (5) averaging for reconstructing the interference pattern.
The invention has the beneficial effects that: compared with the prior art, the invention provides the interference signal denoising method based on time domain analysis, the method can recover the interference pattern image information to the maximum extent from the interference signal containing noise, the standard deviation of one interference pattern with the standard deviation of 1404.84743 can be reduced to 231.5462 after being processed by the denoising method, and the result shows that the noise can be restrained to a larger extent. The method provides a basis for the interference signal processing collected by the large-area array long-wave focal plane detector, and has guiding significance for weather detection of the future atmosphere vertical interferometer.
Drawings
FIG. 1 is a flow chart of an interference signal denoising method of the present invention;
FIG. 2 is a diagram of interference signal denoising prior to interference signal denoising according to the present invention;
FIG. 3 is an interference pattern of the denoising method of the present invention after processing an interference signal.
Detailed Description
The invention will be further described with reference to the accompanying drawings
1) Interference signal data preprocessing module
All detection values forming an interference pattern are obtained from the acquired digital signals. All outputs of one detection element are used for grouping detection values according to the acquisition time, one interferogram detection value can be divided into 20012 groups, and one group of data comprises z k, k=1, 2, 3..21;
2) Time domain Kalman filtering
As shown in FIG. 1, for the conventional Kalman filtering, a system process equation and a measurement equation are required to be established, and then a filtering initial value is set to enter an iterative period
Xk＝Fk-1Xk-1+wk-1 (1)
The measurement equation is:
zk＝HkXk+vk (2)
State transition matrix H k is 1. After the process equation and the measurement equation of the system are established, a state transition matrix and a measurement matrix during the calculation of the filtering iteration period can be obtained
3) Setting initial conditions according to groups;
state estimation for an initial moment of a set of data, wherein/> For the average of all data of the group,/>For z 1 and/>P 1 is the covariance matrix of the prediction error at the initial time of the group, the initial is the identity matrix, and Q k and R k respectively represent the process noise covariance matrix and the measurement noise covariance matrix at the k time,/>K represents a kth filtering period;
4) Entering a filtering period, updating Kalman filtering time, and updating a covariance matrix of a predicted state quantity and a state prediction error;
Pk|k-1＝Fk-1Pk-1Fk-1 T+Qk-1 (4)
5) Updating the filtered measurement, and calculating covariance matrix of Kalman gain, state estimation value and state estimation error
Kalman gain:
Gk＝Pk|k-1Hk T(HkPk|k-1Hk T+Rk)-1 (5)
state estimation value:
covariance matrix of state estimation errors:
pk＝(I-GkHk)Pk|k-1 (7)
Wherein G k is Kalman gain, which represents the proportion of the current measurement value in the new information and state estimation; for the state estimation value of the Kalman filtering at the k moment,/> For detecting estimated values,/>Is a random noise estimate; the method is characterized in that the method is one-step prediction of the state at the moment k, and I is an identity matrix; p k is used for calculating a prediction covariance matrix of the next filtering period;
6) After the execution of 21 filtering periods is finished, repeating the filtering of 21 periods on the next group of data; until 20012 sets of data are executed. Finally for each group of data And (5) averaging for reconstructing the interference pattern.
Fig. 2 and fig. 3 are respectively a comparison of the front and rear of an interference pattern to which an interference signal denoising method is applied, and it can be seen through comparison that the denoising algorithm provided by the invention can effectively remove random noise in an original interference signal.
Claims (1)
1. The interference signal denoising method adopting the infrared long-wave focal plane detector is characterized by comprising the following steps of:
Step 1: preprocessing interference signal data, namely dividing digital signals acquired by an infrared long-wave focal plane detector according to the output of detection elements of the detector, grouping the sequences according to acquisition time into m groups, wherein the data contained in one group is z k, k=1, 2,3.
Step 2: establishing a Kalman filtering process equation and a measurement equation according to the interference data characteristics:
The process equation is:
Xk＝Fk-1Xk-1+wk-1 (1)
The measurement equation is:
zk＝HkXk+vk (2)
State vector Is a state vector at time K, where I k is the detection state quantity at time K, b k is the detector noise state quantity at time K,/>Is a state vector at time k-1, I k-1 is a detection state quantity at time k-1, and b k-1 is a random noise state quantity at time k-1; z k is the detected value at time k, definition/>For state transition matrices, based on interference signal characteristics, here/>H k is a measurement matrix, and H k is defined as 1; the process noise w k-1 at the moment k-1 and the measurement noise v k at the moment k are mutually independent Gaussian white noise, and the average value is zero;
step 3: setting initial conditions according to groups;
state estimate at time k=1, where/> Initially the mean of all data of the set,/>Initially z 1 and/>P 1 is the state estimation error covariance matrix at the initial time, the initial is the identity matrix, and Q k and R k respectively represent the process noise covariance matrix and the measurement noise covariance matrix at the k time,/>K represents a kth filtering period;
Step 4: starting to enter a filtering period, superposing the Kalman filtering period, and updating a covariance matrix of a predicted state quantity and a state prediction error;
predicting state quantity:
covariance matrix of state prediction error:
F k-1 is a state transition matrix, State estimation for time k-1, wherein/>For detecting estimated values,/>For detector noise estimation,/>For one-step prediction of the state at time k, P k-1 is the state estimation error covariance matrix at time k-1, Q k-1 is the process noise covariance matrix at time k-and P k|k-1 is the covariance matrix of the one-step state prediction errors calculated by equations (1) and (3);
step 5: further calculating a covariance matrix of the Kalman gain, the state estimation value and the state estimation error according to a recursive least square method;
Kalman gain:
Gk＝Pk|k-1Hk T(HkPk|k-1Hk T+Rk)-1 (5)
State estimation value
Covariance matrix of state estimation error
Pk＝(I-GkHk)Pk|k-1 (7)
Wherein G k is Kalman gain, which represents the proportion of the current detection value to be introduced with new information in the state estimation; for the state estimate at time k,/> For detection estimate at time k,/>The estimated value of the detector noise at the moment k; /(I)The method is characterized in that the method is one-step prediction of the state at the moment k, and I is an identity matrix; the covariance matrix P k of the state estimation error is used for calculating a prediction covariance matrix of the next filtering period;
Step 6: after the execution of k filtering periods is finished, repeatedly filtering the next group of data for k periods until the execution of m groups of data is finished; finally for each group of data And (5) averaging for reconstructing the interference pattern.
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