CN212875757U - Data processing device based on Kalman filter and terahertz system - Google Patents
Data processing device based on Kalman filter and terahertz system Download PDFInfo
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- CN212875757U CN212875757U CN202021772293.2U CN202021772293U CN212875757U CN 212875757 U CN212875757 U CN 212875757U CN 202021772293 U CN202021772293 U CN 202021772293U CN 212875757 U CN212875757 U CN 212875757U
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Abstract
The utility model provides a data processing device and a terahertz system based on a Kalman filter, which are applied to the terahertz system; the method comprises the following steps: the analog-to-digital converter is used for converting analog signals acquired by the terahertz system into digital signals; and the Kalman filter is connected with the analog-to-digital converter and is used for filtering the digital signal. The utility model discloses a data processing device and terahertz system based on kalman filter carries out the filtering through the digital signal that adopts kalman filter to terahertz collection, has improved the SNR effectively.
Description
Technical Field
The utility model relates to a data processing's technical field especially relates to a data processing device and terahertz system now based on kalman filter.
Background
The terahertz Time Domain Spectroscopy (THz-TDS) is a typical representative of the terahertz Spectroscopy and is a newly-developed and very effective coherent detection technology. Terahertz has the following characteristics:
(1) the THz-TDS system is insensitive to black body radiation and has good stability;
(2) nondestructive detection can be carried out;
(3) the amplitude and phase information of various materials, such as dielectric materials, semiconductor materials, gas molecules, biomacromolecules (protein, DNA and the like), superconducting materials and the like can be conveniently and quickly obtained;
(4) terahertz time-domain systems are classified into transmissive and reflective.
In optical systems, data acquisition and processing are critical to improve signal-to-noise ratio. To improve the signal-to-noise ratio, the prior art generally adopts the following ways:
(1) phase-locked amplifiers, such as SR830, NF LI5640, etc., are used. The phase-locked amplifier uses a modulator to transfer the frequency spectrum of a direct current or slowly-changed signal to a modulation frequency and then amplifies the frequency spectrum; then, a correlator is utilized to demodulate the modulation signal, and the frequency and the phase are detected at the same time, so that the probability that the noise and the signal have the same frequency and phase is very small; finally, the low-pass filter is used for restraining noise, the frequency band of the low-pass filter can be made to be narrow, and the signal-to-noise ratio is greatly improved.
(2) And (4) sampling for multiple times by adopting an averaging method, namely using a high-precision analog-to-digital conversion acquisition card, and then averaging.
However, the above method can only reject out-of-band noise and is not effective for in-band noise. The signal generated by the THZ-TDS system requires a certain bandwidth. Therefore, the existing method cannot meet the requirements of the THZ-TDS system.
SUMMERY OF THE UTILITY MODEL
In view of the above prior art's shortcoming, the utility model aims to provide a data processing device and terahertz system now based on kalman filter carries out the filtering through the digital signal that adopts kalman filter to terahertz collection, has improved the SNR effectively.
In order to achieve the above objects and other related objects, the present invention provides a data processing apparatus based on a kalman filter, applied to a terahertz system; the method comprises the following steps: the analog-to-digital converter is used for converting analog signals acquired by the terahertz system into digital signals; and the Kalman filter is connected with the analog-to-digital converter and is used for filtering the digital signal.
In an embodiment of the present invention, the analog signal is a current analog signal.
The utility model provides a terahertz system, which comprises a data processing device and a data generating device based on a Kalman filter; the data generation device is used for generating an analog signal in a terahertz system.
In an embodiment of the present invention, the data generating device includes a laser and a photoelectric receiving device; the laser is used for generating terahertz waves and irradiating an object to be measured; the photoelectric receiving device is used for converting terahertz waves passing through the object to be measured into analog current.
In an embodiment of the present invention, the laser is a femtosecond laser.
In an embodiment of the present invention, the photoelectric receiving device includes a photoelectric converter and a current amplifier; the photoelectric converter is used for converting terahertz waves passing through the object to be measured into analog current, and the current amplifier is used for amplifying the analog current.
As described above, the utility model discloses a data processing device and terahertz system based on kalman filter has following beneficial effect:
(1) filtering a digital signal acquired by terahertz by adopting a Kalman filter in a terahertz system;
(2) the signal-to-noise ratio can be effectively improved under the condition of not increasing the cost.
Drawings
FIG. 1 is a schematic diagram of a Kalman filter-based data processing apparatus according to an embodiment of the present invention
FIG. 2 is a data flow diagram of a Kalman filter of the present invention in one embodiment;
FIG. 3 is a schematic structural diagram of a terahertz system according to an embodiment of the invention;
FIG. 4 is a schematic diagram showing the variation of amplitude with time delay in an embodiment when the Kalman filter based data processing apparatus of the present invention is not used in the prior art;
FIG. 5 is a schematic diagram showing the variation of amplitude with frequency in an embodiment when the Kalman filter based data processing apparatus of the present invention is not used in the prior art;
FIG. 6 is a schematic diagram showing the variation of amplitude with time delay in one embodiment when the Kalman filter based data processing apparatus of the present invention is used;
fig. 7 is a schematic diagram showing the variation of amplitude with frequency in an embodiment when the kalman filter-based data processing apparatus of the present invention is used.
Detailed Description
The following description of the embodiments of the present invention is provided for illustrative purposes, and other advantages and effects of the present invention will be readily apparent to those skilled in the art from the disclosure herein. The present invention can also be implemented or applied through other different specific embodiments, and various details in the present specification can be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention.
It should be noted that the drawings provided in the present embodiment are only for illustrating the basic idea of the invention in a schematic manner, and only the components related to the invention are shown in the drawings rather than being drawn according to the number, shape and size of the components in actual implementation, and the form, quantity and proportion of the components in actual implementation may be changed at will, and the layout of the components may be more complicated.
Kalman filtering (Kalman filtering) is an algorithm that uses a linear system state equation to optimally estimate the state of a system by inputting and outputting observation data through the system. The optimal estimation can also be seen as a filtering process, since the observed data includes the effects of noise and interference in the system. The data filtering is a data processing technology for removing noise and restoring real data, and the Kalman filtering can estimate the state of a dynamic system from a series of data with measurement noise under the condition that the measurement variance is known. Because the method is convenient for realizing computer programming and can update and process the data acquired on site in real time, Kalman filtering is the most widely applied filtering method at present and is better applied to the fields of communication, navigation, guidance, control and the like.
Specifically, kalman filtering consists of the following five formulas:
(1) kalman filter time update equation
(2) Kalman filter state update equation
Wherein the content of the first and second substances,
and
the a posteriori state estimates, representing time k-1 and time k, respectively, are one of the results of the filtering, i.e. the updated result, also called the optimal estimate.
Pk-1And PkRepresenting the posteriori estimated covariance at time k-1 and k, respectively (i.e., the k-time
And
represents the uncertainty of the state) is one of the results of the filtering.
Represents the prior estimated covariance at time k (
Covariance of (d) is the intermediate calculation result of the filtering.
H is a conversion matrix from the state variable to the measurement (observation), which represents the relation connecting the state and the observation, and the Kalman filter is a linear relation, is responsible for converting the m-dimensional measurement value to the n-dimensional measurement value, so that the m-dimensional measurement value accords with the mathematical form of the state variable, and is one of the preconditions of the filtering.
zkRepresenting the measured value (observed value), is the input to the filtering.
KkThe filter gain matrix is represented as an intermediate calculation result of filtering, a kalman gain, or a kalman coefficient.
A represents the state transition matrix, which is actually a guessing model for the target state transition. For example, in moving object tracking, the state transition matrix is often used to model the motion of the object, which may be uniform linear motion or uniform acceleration. When the state transition matrix does not conform to the state transition model of the target, the filtering may quickly diverge.
Q represents the process excitation noise covariance (the covariance of the system process), which is used to represent the error between the state transition matrix and the actual process. Because the process signal cannot be directly observed, the value of Q is difficult to determine, and the Q is noise brought by a Kalman filter used for estimating a state variable of a discrete time process, namely a prediction model.
R denotes the measurement noise covariance. When the filter is actually implemented, the measured noise covariance R is typically observed and is a known condition of the filter.
B denotes a matrix converting the input into states.
The utility model discloses a data processing device and terahertz system based on kalman filter adopt kalman filter to carry out the filtering to the digital signal of terahertz collection to under the prerequisite that does not improve the cost, improved the SNR effectively.
As shown in fig. 1, in an embodiment, the kalman filter-based data processing apparatus of the present invention is applied to a terahertz system; the method specifically comprises the following steps:
the analog-to-digital converter 1 is used for converting an analog signal acquired by the terahertz system into a digital signal. Specifically, in the terahertz system, after a terahertz wave is transmitted or reflected to an object to be measured, the object to be measured is identified by spectral analysis. The terahertz waves passing through the object to be measured are converted into analog signals, so that subsequent analysis and processing are facilitated. The analog-to-digital converter 1 is used for implementing digital conversion of the analog signal.
And the Kalman filter 2 is connected with the analog-to-digital converter 1 and is used for filtering the digital signal. Compared with a filtering method adopting a low-pass filter in the prior art, after filtering is carried out through the Kalman filter 2, the signal-to-noise ratio of the digital signal is greatly improved, and the method is beneficial to acquisition of the signal. The data flow diagram of the kalman filter 2 is shown in fig. 2.
In an embodiment of the present invention, the kalman filter algorithm in the kalman filter 2 is written in C language.
The code is as follows:
as shown in fig. 3, in an embodiment, the terahertz system of the present invention includes the data processing device and the data generating device based on the kalman filter; the data generation device is used for generating an analog signal in a terahertz system.
In an embodiment of the present invention, the data generating device includes a laser 3 and a photo-receiving device 4. The laser 3 is used for generating terahertz waves and irradiating an object to be measured; the photoelectric receiving device 4 is used for converting terahertz waves passing through the object to be measured into analog current. That is, the generated analog signal in the terahertz system is an analog current signal.
Preferably, the laser 3 adopts a femtosecond laser, and the photoelectric receiving device comprises a photoelectric converter and a current amplifier; the photoelectric converter is used for converting terahertz waves passing through the object to be measured into analog current, and the current amplifier is used for amplifying the analog current.
In the kalman filter algorithm, Q is the system covariance. In a terahertz system, data in no light is directly collected and sent to a PC, and matlab is adopted to analyze the data and determine a Q value.
And the R value is a measurement error, a standard signal source is used as a standard, and measurement data is used for calibrating the measurement covariance of the measurement system. Specifically, the analog-to-digital converter directly samples signal source data, directly sends the signal source data to a PC (personal computer) end without processing, and obtains an R value by analyzing the data by using matlab.
Effective parameters are formed through calculation, and the method can be used in an actual terahertz system through debugging. After noise is reduced, low-pass filtering is carried out after data demodulation, and then the signal-to-noise ratio of each frequency component of the signal is analyzed by adopting fast Fourier transform. As shown in fig. 4-7, compared to the situation without the kalman filter, the signal-to-noise ratio is improved by 5 DBs without increasing additional cost after the kalman filter of the present invention is used.
To sum up, the utility model discloses a terahertz system and data processing device based on kalman filter carries out filtering to the digital signal of terahertz collection through adopting scalar kalman filtering algorithm in terahertz system; the signal-to-noise ratio can be effectively improved under the condition of not increasing the cost. Therefore, the utility model effectively overcomes various defects in the prior art and has high industrial utilization value.
The above embodiments are merely illustrative of the principles and effects of the present invention, and are not to be construed as limiting the invention. Modifications and variations can be made to the above-described embodiments by those skilled in the art without departing from the spirit and scope of the present invention. Accordingly, it is intended that all equivalent modifications or changes which may be made by those skilled in the art without departing from the spirit and technical spirit of the present invention be covered by the claims of the present invention.
Claims (6)
1. A data processing device based on a Kalman filter is applied to a terahertz system; the method is characterized in that: the method comprises the following steps:
the analog-to-digital converter is used for converting analog signals acquired by the terahertz system into digital signals;
and the Kalman filter is connected with the analog-to-digital converter and is used for filtering the digital signal.
2. The kalman filter-based data processing apparatus according to claim 1, wherein: the analog signal is a current analog signal.
3. A terahertz system, characterized in that: comprising the kalman filter-based data processing means and the data generating means of claim 1 or 2; the data generation device is used for generating an analog signal in a terahertz system.
4. The terahertz system of claim 3, wherein: the data generation device comprises a laser and a photoelectric receiving device; the laser is used for generating terahertz waves and irradiating an object to be measured; the photoelectric receiving device is used for converting terahertz waves passing through the object to be measured into analog current.
5. The terahertz system of claim 4, wherein: the laser adopts a femtosecond laser.
6. The terahertz system of claim 4, wherein: the photoelectric receiving device comprises a photoelectric converter and a current amplifier; the photoelectric converter is used for converting terahertz waves passing through the object to be measured into analog current, and the current amplifier is used for amplifying the analog current.
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