CN111812404B - Signal processing method and processing device - Google Patents
Signal processing method and processing device Download PDFInfo
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- CN111812404B CN111812404B CN202010957736.3A CN202010957736A CN111812404B CN 111812404 B CN111812404 B CN 111812404B CN 202010957736 A CN202010957736 A CN 202010957736A CN 111812404 B CN111812404 B CN 111812404B
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Abstract
The embodiment of the application provides a signal processing method and a processing device, wherein the method comprises the following steps: acquiring signal data; calculating to obtain frequency domain data and a power spectrum of the signal data; detecting the power spectrum to obtain frequency domain parameters; designing a frequency domain filter according to the frequency domain parameters, and performing frequency domain filtering on the frequency domain data through the frequency domain filter; converting the frequency domain data after frequency domain filtering into time domain data; and processing the time domain data to obtain time domain parameters. By the design, time domain parameters and frequency domain parameters of the signals can be quickly given, and the frequency domain parameters are detected through the power spectrum, so that the signal-to-noise ratio of the frequency domain is improved, and the frequency domain parameters such as bandwidth, center frequency and frequency band number are more easily obtained. In addition, before the frequency domain data and the time domain data are converted, frequency domain filtering is performed firstly, out-of-band noise and interference are filtered, and the signal to noise ratio can be improved, so that a better detection effect is obtained under the condition of a lower signal to noise ratio.
Description
Technical Field
The embodiment of the present application relates to the field of signal processing technologies, and in particular, to a signal processing method and a signal processing apparatus.
Background
The different angles used to analyze the signal are called domains, the Time domain (Time domain) describing the signal versus Time, and the frequency domain (frequency domain) describing the peak amplitude versus frequency. In the prior art, the time domain parameters such as the pulse width, the repetition frequency, the arrival time and the like of the measured signal generally adopt a time domain direct detection method, the time domain direct detection method is easy to realize in engineering, and a better result can be obtained when the signal-to-noise ratio is higher. But when the noise amplitude is comparable to the signal amplitude, i.e. the signal-to-noise ratio is low, the detection will be poor or even impossible.
In addition to the time domain direct detection method, a frequency domain method (time frequency analysis method) is also commonly adopted in the prior art, the frequency domain method adopts short-time frequency measurement, and when a frequency measurement result exceeds a detection threshold, a pulse signal is considered to exist; and continuously measuring the frequency for a short time, and if the threshold signal is continuously detected, determining that the pulse signal exists. Meanwhile, the time span of the continuous over-detection threshold is the pulse width. Although the frequency domain method can adapt to lower signal-to-noise ratio than the time domain direct detection method, the method cannot easily distinguish a plurality of targets existing simultaneously, and the time domain parameter measurement accuracy is not high.
In view of this, a signal processing method with less influence of signal-to-noise ratio and better detection effect of time domain parameters and frequency domain parameters is needed.
Disclosure of Invention
An object of the embodiments of the present application is to provide a signal processing method and a signal processing apparatus, which are used for adapting to a lower signal-to-noise ratio and obtaining time domain parameters and frequency domain parameters with a better detection effect.
In view of the foregoing, in a first aspect, an embodiment of the present application provides a signal processing method, including:
acquiring signal data;
calculating to obtain frequency domain data and a power spectrum of the signal data;
detecting the power spectrum to obtain frequency domain parameters;
designing a frequency domain filter according to the frequency domain parameters, and performing frequency domain filtering on the frequency domain data through the frequency domain filter;
converting the frequency domain data after frequency domain filtering into time domain data;
and processing the time domain data to obtain time domain parameters.
By adopting the signal processing method provided by the embodiment, the time domain parameters and the frequency domain parameters of the signal can be rapidly given, and the frequency domain parameters are detected through the power spectrum, so that the signal-to-noise ratio of the frequency domain is improved, and the frequency domain parameters such as the bandwidth, the central frequency, the frequency band number and the like can be more easily obtained. In addition, before the frequency domain data and the time domain data are converted, frequency domain filtering is performed firstly, out-of-band noise and interference are filtered, and the signal to noise ratio can be improved, so that a better detection effect is obtained under the condition of a lower signal to noise ratio. Therefore, the signal processing method provided by the embodiment is more easily adapted to a lower signal-to-noise ratio, and can more easily obtain more accurate time domain parameters.
In one possible embodiment, the step of calculating the frequency domain data and the power spectrum of the signal data comprises:
performing fast Fourier transform on the signal data to obtain the frequency domain data;
calculating an absolute value of the frequency domain data;
and performing non-coherent accumulation on the frequency domain data after the absolute value is taken to obtain the power spectrum.
In a possible implementation, the step of detecting the power spectrum to obtain frequency domain parameters includes: performing constant false alarm detection on the power spectrum to obtain a plurality of target signals;
the step of designing a frequency domain filter according to the frequency domain parameters and performing frequency domain filtering on the frequency domain data through the frequency domain filter comprises:
selecting a required target signal in the power spectrum;
forming a frequency domain filter according to the bandwidth of the target signal, wherein the frequency domain filter is a rectangular frame framing the target signal in the power spectrum;
dot-multiplying the frequency domain data with the frequency domain filter.
In a possible implementation, the converting the frequency-domain data after frequency-domain filtering into time-domain data is implemented by inverse fast fourier transform.
In a possible implementation manner, the step of processing the time domain data to obtain time domain parameters includes:
performing modulus extraction on the time domain data to obtain a signal envelope;
and carrying out constant false alarm detection on the signal envelope to obtain a time domain parameter.
In a possible implementation, after the modulus of the time domain data is obtained as a signal envelope, the method further includes:
performing time domain decimation on the signal envelope;
and low-pass filtering the signal envelope after decimation.
In one possible embodiment, the low-pass filtering is implemented by an IIR filter.
In one possible embodiment, the frequency domain parameters include bandwidth, center frequency and number of signals; the time domain parameters include the number of detected pulses, pulse width, arrival time, and repetition frequency.
In one possible implementation, the signal processing method further includes:
selecting a target signal in the power spectrum;
shifting the power spectrum of the target signal to 0 frequency according to the central frequency of the target signal;
performing time domain extraction and outputting;
and/or:
and outputting the power spectrum and performing visual PSD display.
In a second aspect, an embodiment of the present application provides a signal processing apparatus, including:
an acquisition module configured to acquire signal data;
a calculation module configured to calculate frequency domain data and a power spectrum of the signal data;
a detection module configured to detect the power spectrum resulting in frequency domain parameters;
a filtering module configured to design a frequency domain filter according to the frequency domain parameters and to perform frequency domain filtering on the frequency domain data through the frequency domain filter;
a conversion module configured to convert the frequency domain data after frequency domain filtering into time domain data;
and the processing module is configured to process the time domain data to obtain time domain parameters.
The apparatus of this embodiment may be configured to implement the technical solution of the first aspect, and the implementation principle and the technical effect are similar, which are not described herein again.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are only the embodiments of the present application, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a flowchart of a signal processing method according to an embodiment of the present application;
FIG. 2 is a signal envelope plot of signal data;
FIG. 3 is a power spectrum of calculated signal data;
FIG. 4 is a schematic diagram of constant false alarm detection of the power spectrum of FIG. 3;
FIG. 5 is a schematic diagram of a frequency domain filter;
FIG. 6 is a graph of a time domain waveform after frequency domain filtering;
FIG. 7 is a schematic diagram after time-domain low-pass filtering;
FIG. 8 is a schematic diagram of time domain constant false alarm detection;
FIG. 9 is a graph of the detection of the signal envelope of FIG. 2 by a prior art time domain direct detection method;
fig. 10 is a schematic structural diagram of a signal processing apparatus according to an embodiment of the present application.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are some embodiments of the present invention, but not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
An embodiment of the present application provides a signal processing method, as shown in fig. 1, the signal processing method includes the following steps:
step S10: acquiring signal data;
fig. 2 is a signal envelope diagram of signal data, which is raw signal data acquired by a data acquisition device, and is denoted as s (n) and read in during a signal processing process.
Step S20: calculating to obtain frequency domain data and a power spectrum of the signal data;
the signal data s (n) is subjected to fourier transform and other related operations to obtain frequency domain data, and Power Spectral Density (PSD) can be obtained by accumulating the frequency domain data, which is referred to as a power spectrum or PSD spectrum for short.
Step S30: detecting the power spectrum to obtain frequency domain parameters;
and performing constant false alarm detection on the power spectrum, wherein the part exceeding the detection threshold indicates that a signal exists, and frequency domain parameters such as the bandwidth, the center frequency, the number of the signals and the like of the signal can be output at the moment. The bandwidth of the signal is defined as the difference between the starting point frequency and the end point frequency of the signal over-detection threshold; the center frequency is defined as the midpoint between the start point frequency and the end point frequency; in addition, the spectrum from the power spectrum of the signal to the point that the signal is not over-detected to the power spectrum of the signal is defined as a signal, and the number of the signals can be obtained by distinguishing different signals.
Step S40: designing a frequency domain filter according to the frequency domain parameters, and performing frequency domain filtering on the frequency domain data through the frequency domain filter;
a frequency domain filter can be designed according to the frequency domain parameters, and the frequency domain filter is adopted to perform frequency domain filtering on the frequency domain data so as to remove noise and interference in the frequency domain data, thereby improving the signal-to-noise ratio, so that the signal processing method in the embodiment can be more suitable for a lower signal-to-noise ratio.
Step S50: converting the frequency domain data after frequency domain filtering into time domain data;
the filtered signal spectrum may be converted to time domain data by an inverse fourier transform or the like.
Step S60: and processing the time domain data to obtain time domain parameters.
Obtaining signal envelope by modulus of time domain data, and obtaining time domain parameters such as pulse number, pulse width, pulse arrival time and repetition frequency parameters by carrying out constant false alarm detection on the signal envelope; the pulse width is defined as the time difference from the moment when the pulse signal starts to pass through the detection threshold to the moment when the pulse signal is just lower than the detection threshold; the arrival time is defined as the moment when the signal just starts to pass the detection threshold; the repetition frequency is defined as the difference of the arrival time of two adjacent pulse signals.
By adopting the signal processing method provided by the embodiment, the time domain parameters and the frequency domain parameters of the signal can be rapidly given, and the frequency domain parameters are detected through the power spectrum, so that the signal-to-noise ratio of the frequency domain is improved, and the frequency domain parameters such as the bandwidth, the central frequency, the frequency band number and the like can be more easily obtained. In addition, before the frequency domain data and the time domain data are converted, frequency domain filtering is performed firstly, out-of-band noise and interference are filtered, and the signal to noise ratio can be improved, so that a better detection effect is obtained under the condition of a lower signal to noise ratio. Therefore, the signal processing method provided by the embodiment is more easily adapted to a lower signal-to-noise ratio, and can more easily obtain more accurate time domain parameters and frequency domain parameters.
In some possible embodiments, step S20 includes:
performing fast Fourier transform on the signal data to obtain the frequency domain data;
calculating an absolute value of the frequency domain data;
and performing non-coherent accumulation on the frequency domain data after the absolute value is taken to obtain the power spectrum.
Fast Fourier Transform (FFT), a general term for an efficient and Fast calculation method for calculating Discrete Fourier Transform (DFT) by using a computer, particularly, the more the number N of transformed samples, the more significant the saving of the calculation amount of the FFT algorithm. In the embodiment, the FFT with a large number of points is adopted to measure the frequency for a long time in the frequency domain, and accumulation is carried out simultaneously, so that the design can improve the signal-to-noise ratio of the frequency domain.
Specifically, the signal data s (N) is grouped according to the number N of FFT points, that is, one group of data is provided every N points, and each group is subjected to N-point FFT. The result of FFT is frequency domain data, and the frequency domain data is accumulated in a non-coherent mode after taking the absolute value to form a PSD spectrum. The number of FFTs required for non-coherent accumulation is M, i.e. N × M sample points are required to complete calculation of a PSD spectrum, and N and M are both positive integer powers of 2, for example, N =8192, and M =256 in this embodiment.
Optionally, the result of non-coherent accumulation, i.e., the PSD spectrum result, may be output for visual PSD display, and fig. 3 is a power spectrum of the calculated signal data, and the visual PSD spectrum may be conveniently observed by the user.
Then, constant false alarm detection is performed on the PSD spectrum to obtain frequency domain parameters, which include bandwidth, center frequency, number of signals, and the like. Fig. 4 is a schematic diagram of constant false alarm detection of the power spectrum in fig. 3, as shown in fig. 4, a dotted line in the diagram is a detection threshold, and a portion exceeding the dotted line indicates that a signal exists, and at this time, the bandwidth, the center frequency, and the number of signals of the output signal are present. The signal bandwidth is defined as the difference between the starting point frequency and the end point frequency of the signal over-detection threshold, the center frequency is defined as the middle point between the starting point frequency and the end point frequency, the spectrum from the PSD spectrum of the signal over-detection threshold to the signal without over-detection threshold is a signal, and the number of the signals can be obtained by identifying different signals.
As can be seen from fig. 4, in the embodiment of fig. 4, there are 3 target signals, the bandwidths are 0.226MHz, 9.48MHz and 0.07MHz from left to right, and the center frequencies are 7.63MHz, 26.53MHz and 50.02MHz, respectively.
In one possible implementation, step S40 includes:
selecting a required target signal in the power spectrum;
forming a frequency domain filter according to the bandwidth of the target signal, wherein the frequency domain filter is a rectangular frame framing the target signal in the power spectrum;
dot-multiplying the frequency domain data with the frequency domain filter.
In step S20, the frequency domain data is obtained and stored in the memory, and in this step, the frequency domain data in the memory is read and the frequency domain filtering is performed.
Specifically, a required target signal is selected for a plurality of target signals in a PSD spectrum constant false alarm detection result. And framing the PSD spectrum of the selected target signal by using a rectangular frame according to the bandwidth of the selected target signal obtained in the step, namely forming a frequency domain filter, wherein the number of points of the frequency domain filter is N and is equal to the number of points of FFT (fast Fourier transform). All points of the filter have only two values of 0, 1. The signal present has a magnitude of 1 (or other constant) and the signal absent has a magnitude of 0.
Illustratively, fig. 5 is a schematic diagram of a frequency domain filter, as shown in fig. 5, in the present embodiment, only the middle target signal of fig. 5 is selected; if only the intermediate signal is selected as the target signal, the filter values corresponding to the two previous and next target signals may be set to 0.
In this step, the selection of the target signal may be implemented by setting a set bandwidth threshold, and automatically filtering out the small bandwidth target signal smaller than the set bandwidth threshold, for example, when the set bandwidth threshold is 0.5MHz, only the target signal with the bandwidth greater than 0.5MHz is selected.
In another possible embodiment, each target signal is filtered separately. That is, the target signals are sequentially selected and the filtering steps are repeated until all the target signals are filtered.
And after the design of the frequency domain filter is finished, performing frequency domain filtering on the frequency domain data in the memory, reading out N-point frequency domain data from the memory each time in the filtering process, and then performing point multiplication on the frequency domain data and the frequency domain filter to finish the N-point frequency domain filtering. The frequency-domain filtering is continued every N points until M filtering times are needed for the entire readout, i.e. the frequency-domain filtering of N x M sample points needs to be completed.
The frequency domain data after the frequency domain filtering can be converted into time domain data through Inverse Fast Fourier Transform (IFFT). Corresponding to FFT, the IFFT points in this embodiment are N, M operations are performed, and the conversion result is as shown in fig. 6, comparing fig. 6 and fig. 2, it can be found that 4 impulse signals originally submerged in noise can now be clearly resolved.
After the time domain data are obtained, performing modulus operation on the time domain data to obtain signal envelope data of the signal; in order to reduce the burden of subsequent calculation, time domain extraction is carried out on the signal envelope data, the extraction multiple is determined as D, namely, one point is reserved for each D point. After the extraction is finished, low-pass filtering is carried out, so as to filter out high-frequency components of the signal envelope. In this embodiment, the low-pass filtering is an IIR filter.
Fig. 7 is a schematic diagram after time-domain low-pass filtering, and it is obvious from comparing fig. 7 and fig. 6 that the envelope of the signal after low-pass filtering is already obvious, and the detection is easy.
Then, constant false alarm detection is performed on the low-pass filtered signal envelope, and the detection result is shown in fig. 8. In the embodiment shown in fig. 8, the number of detected pulses is 4, the pulse widths are respectively 9.87ms, 9.86ms, 9.85ms and 9.86ms, and the pulse arrival time and the repetition frequency parameter are also easily obtained from the detection result; the definition of the pulse width is the time difference from the moment when the pulse signal starts to pass the detection threshold to the moment when the pulse signal is just lower than the detection threshold; the time of arrival is defined as the time at which the signal just begins to cross the detection threshold. The repetition frequency is defined as the difference between the arrival times of two adjacent pulses.
Fig. 9 is a detection result of the signal envelope in fig. 2 by the conventional time domain direct detection method, and it can be seen from comparing fig. 8 and fig. 9 that the detection signal-to-noise ratio of the conventional time domain direct detection method is low, which results in a missing detection situation (the 3 rd pulse in fig. 9 is missed), and at the same time, the pulse width measurement accuracy is not high (the second pulse width in fig. 9 is inaccurate).
Optionally, the signal processing method further includes:
selecting a target signal in the power spectrum;
shifting the power spectrum of the target signal to 0 frequency according to the central frequency of the target signal;
performing time domain extraction and outputting; and the data volume is reduced and finally the data volume is sent to other algorithm modules, so that the application of a signal processing result is facilitated.
It should be noted that the method of the embodiments of the present application may be executed by a single device, such as a computer, an embedded processor or a server. The method of the embodiment can also be applied to a distributed scene and completed by the mutual cooperation of a plurality of devices. In such a distributed scenario, one of the multiple devices may only perform one or more steps of the method of the embodiment, and the multiple devices interact with each other to complete the method.
In addition, specific embodiments of the present specification have been described above. Other embodiments are within the scope of the following claims. In some cases, the actions or steps recited in the claims may be performed in a different order than in the embodiments and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing may also be possible or may be advantageous.
Fig. 10 is a schematic structural diagram of a signal processing apparatus according to an embodiment of the present application, and as shown in fig. 10, the apparatus according to the embodiment may include:
an acquisition module 100 configured to acquire signal data;
a calculation module 200 configured to calculate frequency domain data and a power spectrum of the signal data;
a detection module 300 configured to detect the power spectrum resulting in frequency domain parameters;
a filtering module 400 configured to design a frequency domain filter according to the frequency domain parameters and perform frequency domain filtering on the frequency domain data through the frequency domain filter;
a converting module 500 configured to convert the frequency domain data after frequency domain filtering into time domain data;
a processing module 600 configured to process the time domain data to obtain time domain parameters.
The apparatus of this embodiment may be used to implement the technical solution of the method embodiment shown in fig. 1, and the implementation principle and the technical effect are similar, which are not described herein again.
For convenience of description, the above devices are described as being functionally divided into various modules, respectively. Of course, the functions of the modules may be implemented in the same or multiple software and/or hardware when implementing the embodiments of the present application.
Those of ordinary skill in the art will understand that: all or a portion of the steps of implementing the above-described method embodiments may be performed by hardware associated with program instructions. The program may be stored in a computer-readable storage medium. When executed, the program performs steps comprising the method embodiments described above; and the aforementioned storage medium includes: various media that can store program codes, such as ROM, RAM, magnetic or optical disks.
Finally, it should be noted that: the above embodiments are only used to illustrate the technical solution of the present invention, and not to limit the same; while the invention has been described in detail and with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; and the modifications or the substitutions do not make the essence of the corresponding technical solutions depart from the scope of the technical solutions of the embodiments of the present invention.
Claims (10)
1. A signal processing method, comprising:
acquiring signal data;
calculating to obtain frequency domain data and a power spectrum of the signal data;
detecting the power spectrum to obtain frequency domain parameters, and performing constant false alarm detection on the power spectrum to obtain a plurality of target signals;
selecting the required target signal in the power spectrum;
forming a frequency domain filter according to the bandwidth of the target signal, wherein the frequency domain filter is a rectangular frame framing the target signal in the power spectrum;
performing point multiplication on the frequency domain data and the frequency domain filter to realize that the frequency domain filter performs frequency domain filtering on the frequency domain data;
converting the frequency domain data after frequency domain filtering into time domain data;
and processing the time domain data to obtain time domain parameters.
2. The signal processing method according to claim 1, characterized in that: the step of calculating the frequency domain data and the power spectrum of the signal data comprises:
performing fast Fourier transform on the signal data to obtain the frequency domain data;
calculating an absolute value of the frequency domain data;
and performing non-coherent accumulation on the frequency domain data after the absolute value is taken to obtain the power spectrum.
3. The signal processing method according to claim 1 or 2, characterized in that: the step of detecting the power spectrum to obtain frequency domain parameters comprises: performing constant false alarm detection on the power spectrum to obtain a plurality of target signals;
the step of designing a frequency domain filter according to the frequency domain parameters and performing frequency domain filtering on the frequency domain data through the frequency domain filter comprises:
selecting the required target signal in the power spectrum;
forming a frequency domain filter according to the bandwidth of the target signal, wherein the frequency domain filter is a rectangular frame framing the target signal in the power spectrum;
dot-multiplying the frequency domain data with the frequency domain filter.
4. The signal processing method according to claim 1 or 2, characterized in that: and converting the frequency domain data after frequency domain filtering into time domain data by inverse fast Fourier transform.
5. The signal processing method according to claim 1 or 2, characterized in that: the step of processing the time domain data to obtain time domain parameters comprises:
performing modulus extraction on the time domain data to obtain a signal envelope;
and carrying out constant false alarm detection on the signal envelope to obtain a time domain parameter.
6. The signal processing method according to claim 5, characterized in that: after the modulus of the time domain data is obtained to obtain the signal envelope, the method further comprises the following steps:
performing time domain decimation on the signal envelope;
and low-pass filtering the signal envelope after decimation.
7. The signal processing method according to claim 6, characterized in that: the low-pass filtering is realized by an IIR filter.
8. The signal processing method according to claim 1, characterized in that: the frequency domain parameters comprise bandwidth, center frequency and signal number; the time domain parameters include the number of detected pulses, pulse width, arrival time, and repetition frequency.
9. The signal processing method according to claim 1, characterized in that: the signal processing method further includes:
selecting a target signal in the power spectrum;
shifting the power spectrum of the target signal to 0 frequency according to the central frequency of the target signal;
performing time domain extraction and outputting;
and/or:
and outputting the power spectrum and performing visual PSD display.
10. A signal processing apparatus, characterized by comprising:
an acquisition module configured to acquire signal data;
a calculation module configured to calculate frequency domain data and a power spectrum of the signal data;
the detection module is configured to detect the power spectrum to obtain frequency domain parameters, and perform constant false alarm detection on the power spectrum to obtain a plurality of target signals;
a filtering module configured to select the desired target signal in the power spectrum; forming a frequency domain filter according to the bandwidth of the target signal, wherein the frequency domain filter is a rectangular frame framing the target signal in the power spectrum; performing point multiplication on the frequency domain data and the frequency domain filter to realize that the frequency domain filter performs frequency domain filtering on the frequency domain data;
a conversion module configured to convert the frequency domain data after frequency domain filtering into time domain data;
and the processing module is configured to process the time domain data to obtain time domain parameters.
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