CN105182070A - Signal detection method - Google Patents
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Abstract
The invention discloses a signal detection method, and relates to the technical field of signal detection. The method comprises a data obtaining step, a monitoring frequency adaptive segmentation step, a self-adaptive threshold line determining step and a signal center frequency point obtaining step. The signal detection method overcomes the influences of noise fluctuation and step phenomena in a signal detection process and improves the signal detection accuracy.
Description
Technical field
The present invention relates to signal detection technique field, relate in particular to a kind of signal detecting method.
Background technology
Along with the complexity day by day of space electromagnetic environment, radio monitoring necessitates, and monitoring frequency range detects that signal is the important content of radio monitoring, at present, has three kinds in the main stream approach of frequency domain detection signal.
The first rule of thumb arranges the method for a fixing value as detection signal threshold line, the threshold line of described method arranges simply, but the threshold line of described method is a straight line, the fluctuation of noise can not be adapted to, under existing electromagnetic environment, the effect of described method detection signal cannot reach basic demand.
The second arranges adaptive threshold collimation method in whole monitoring frequency range, the threshold line of described method can rise and fall along with the fluctuation of noise, but the step phenomenon of noise can not be adapted in described method, namely may have in different band noise averages and significantly raise or reduce, especially when monitoring frequency range crosses over different business frequency ranges.
The third is method monitoring frequency range being divided into some sections or according to the difference of business, monitoring frequency range being divided into some sections, certain bandwidth is occupied because each signal has, in this way in average segmentation may a signal be divided in different sections, affect Detection results, reduce the accuracy rate of detection, and also effectively cannot avoid noise step phenomenon, because same traffic segment also may have step phenomenon according to the different segmentation of business.
Summary of the invention
In order to overcome above-mentioned deficiency of the prior art, the invention provides a kind of signal detecting method, the present invention is intended to provide a kind of signal detecting method to overcome the impact of noise fluctuations and step phenomenon in signal detection process, improves the accuracy of input.
In order to solve above-mentioned deficiency of the prior art, the present invention is achieved through the following technical solutions:
A kind of signal detecting method, it is characterized in that, its step is as follows:
I, data acquisition step: obtain the frequency spectrum data of monitoring the scanning of frequency range super band from radio monitoring equipment;
II, frequency band adaptive division step is monitored: find out all crests in the frequency spectrum data obtained in I step and trough, add up the value at all trough places, and calculate the mean value of trough; Mark out the trough that trough value is less than trough mean value, the difference marking the crest value that trough that described trough value is less than trough mean value is adjacent is greater than the trough of empirical value, it can be used as waypoint;
III, determine adaptive threshold line step: adopt C-means clustering algorithm that every section of frequency spectrum data after segmentation in step II is divided into two classes, all values in that larger for spectrum value class is all replaced by the minimum value in such, obtains new frequency spectrum data; Then each data in new frequency spectrum data are added same frequency spectrum shift value, make the minimum value of the frequency spectrum data after moving equal 1; Then logarithm each data in the frequency spectrum data after moving is asked to obtain compressing frequency spectrum data, by the smoothing process of frequency spectrum data after compression, each data of the frequency spectrum data after smoothing processing are asked index, obtain the noise average line with noise fluctuations, then obtain the mean square deviation of this section of frequency spectrum data and described noise average line, described mean square deviation is as noise gate; Described noise average line and noise gate are added and obtain adaptive threshold line, then the adaptive threshold splicing of each section is become the adaptive threshold line in whole monitoring frequency range;
IV, signal center's frequency step is obtained: compared by the adaptive threshold line obtained in the frequency spectrum data that the monitoring frequency range obtained in step I scans and step III, find out the region that described frequency spectrum data is greater than described threshold line, then obtain the frequency of each maximum regional value, described frequency is exactly the center frequency point of a signal.
Smoothing processing in described step II is average smooth, and smoothness period is successively from 1/3 of the 2 frequency spectrum data numbers scanned to monitoring frequency range super band.
In C-means clustering algorithm in described step III, the every segment data after segmentation in step II is divided into two classes, the initial cluster center of a class is the mean value of this section of frequency spectrum data, and another kind of initial cluster center is the maximal value of the frequency spectrum data of this section.
The formula obtaining frequency spectrum shift value in described step III is as follows:
L＝1-min(S(n))；
Wherein L is frequency spectrum shift value, and minimum value is got in min () expression, and S (n) represents one section of frequency spectrum data, and n represents the number of one section of frequency spectrum data intermediate-frequeney point.
In described step III, mean square deviation computing formula is as follows:
Wherein T represents the mean square deviation of one section of frequency spectrum data and this section of noise average line, and V represents the variance of one section of frequency spectrum data and this section of noise average line, and n represents variance number, and i represents i-th variance.
Preferably, before step II, frequency spectrum data pre-treatment step is also comprised; Described frequency spectrum pre-treatment step is as follows:
(a) frequency spectrum data average treatment: the frequency spectrum data that the multiframe monitoring frequency range super band obtained from equipment is scanned, then described multiframe frequency spectrum data average treatment is become a frame frequency modal data, i-th data that the frame frequency namely after process is composed are that i-th data of described multiframe frequency spectrum are averaging gained;
(b) smoothing processing: the smoothing process of the frequency spectrum data that step (a) is obtained.
Preferred: after step IV, also to comprise statistical treatment step; Described statistical treatment step is as follows: will obtain a lot of frame frequency modal data within a period of time, every frame frequency modal data processes to step IV by step I, each frame frequency modal data will obtain multiple signal centers frequency, then add up the center frequency point that a lot of frame frequency modal data obtains and just can show that certain frequency is the number of times of signal center's frequency within described a period of time, when number of times reaches 20% of statistics frame number, certain frequency described is as a significant signal center frequency, and described signal is exactly the signal be detected in monitoring time.
Described empirical value refers to: the frequency exceeding noise 2db-5db is considered as signal frequency point.Whether whether adjacent crest and trough difference are greater than described empirical value by the present invention can be used as waypoint as differentiation.
Compared with prior art, the useful technique effect that the present invention brings shows:
1, principle of the present invention is simple, easy to understand, and has higher efficiency; The present invention is adapted to different electromagnetic environments; The present invention effectively can avoid noise step effect, effectively can complete the Detection task on wide-band; The present invention is added up by the data of long period, can obtain more reliable data accurately; The present invention is used for less empirical value relative to additive method, has higher intelligent.
2, with EM100 receiver and the HE600 antenna of R & S company, the frequency spectrum data that frequency range is the spectrum scan of 87.5MHz ~ 108MHz is acquired in suburb, test by the second method mentioned in the present invention, background technology and the third method respectively, result is as table 1:
| Loss | False alert rate | |
| The inventive method | 4.12％ | 6.25％ |
| Second method | 18.75％ | 6.25％ |
| The third method | 8.33％ | 14.58％ |
Table 1
The experimental code of above-mentioned experiment realizes with MATLAB language; Three kinds of methods are all tested with same group of data; Because the first method mentioned in background technology is not suitable with the situation of noise fluctuations, so not experiment.
3, the present invention is divided into different frequency ranges by adaptive segmentation monitoring frequency range, can avoid noise step phenomenon; Because waypoint is the trough place being in low spot, ensure that waypoint is on noise spot, signal can not be divided in different frequency ranges, improve the accuracy of detection; Before segmentation, carry out pre-service to the frequency spectrum data of frequency spectrum cleaning in monitoring frequency range can level and smooth described frequency spectrum data, decreases ' burr ' of signal area, avoids a signal to differentiate into multiple signal; Adaptive threshold line of the present invention can rise and fall along with the fluctuation of noise, automatically can adapt to noise fluctuations, improves the accuracy of detection; After acquisition signal center frequency, add statistical treatment, there is situation in the signal that can obtain in one period of detection time, adds the fault-tolerance of method, improves the accuracy of detection signal.
Accompanying drawing explanation
The segmented line that Fig. 1 the method for the invention obtains on the frequency spectrum data of band scan and threshold line.
The threshold line that second method described in Fig. 2 background technology obtains on band scan frequency spectrum data
The segmented line that the third method described in Fig. 3 background technology obtains on the frequency spectrum data of band scan and threshold line.
Embodiment
Embodiment 1
As a preferred embodiment of the present invention, present embodiment discloses:
A kind of signal detecting method, is characterized in that: its step is as follows:
I, data acquisition step: obtain the frequency spectrum data of monitoring the scanning of frequency range super band from radio monitoring equipment;
II, frequency band adaptive division step is monitored: find out all crests in the frequency spectrum data obtained in I step and trough, add up the value at all trough places, and calculate the mean value of trough; Mark out the trough that trough value is less than trough mean value, the difference marking the crest value that trough that described trough value is less than trough mean value is adjacent is greater than the trough of empirical value, it can be used as waypoint;
III, determine adaptive threshold line step: adopt C-means clustering algorithm that the every segment data after segmentation in step II is divided into two classes, the spectrum value of a class is comparatively large, and another kind of spectrum value is less; All values in that larger for spectrum value class is all replaced by the minimum value in such, obtains new frequency spectrum data; New frequency spectrum data comprises the larger class of replacement data and does not have the comparatively group of replacement data; Then each data in new frequency spectrum data are added same frequency spectrum shift value, the minimum value of the frequency spectrum data after moving is made to equal 1, then logarithm each data in the frequency spectrum data after moving is asked to obtain compressing frequency spectrum data, by the smoothing process of frequency spectrum data after compression, each data in frequency spectrum data after smoothing processing are asked index, obtain the noise average line with noise fluctuations, then obtain the mean square deviation of this section of frequency spectrum data and described noise average line, described mean square deviation is as noise gate; Described noise average line and noise gate are added and obtain adaptive threshold line, then the adaptive threshold splicing of each section of frequency spectrum data is become the adaptive threshold line in whole monitoring frequency range;
IV, signal center's frequency step is obtained: compared by the adaptive threshold line obtained in the frequency spectrum data that the monitoring frequency range obtained in step I scans and step III, find out the region that described frequency spectrum data is greater than described threshold line, then obtain the frequency of each maximum regional value, described frequency is exactly the center frequency point of a signal.
Embodiment 2
As the another preferred embodiment of the present invention, present embodiment discloses:
A kind of signal detecting method, is characterized in that: its step is as follows:
I, data acquisition step: obtain the frequency spectrum data of monitoring the scanning of frequency range super band from radio monitoring equipment;
II, frequency spectrum data pre-treatment step; Described frequency spectrum pre-treatment step is as follows:
(a) frequency spectrum data average treatment: the frequency spectrum data that the multiframe monitoring frequency range super band obtained from equipment is scanned, then described multiframe frequency spectrum data average treatment is become a frame frequency modal data, i-th data that the frame frequency namely after process is composed are that i-th data of described multiframe frequency spectrum are averaging gained;
(b) smoothing processing: the smoothing process of the frequency spectrum data that step (a) is obtained;
III, frequency band adaptive division step is monitored: find out all crests in the frequency spectrum data obtained in step II and trough, add up the value at all trough places, and calculate the mean value of trough; Mark out the trough that trough value is less than trough mean value, the difference marking the crest value that trough that described trough value is less than trough mean value is adjacent is greater than the trough of empirical value, it can be used as waypoint;
IV, determine adaptive threshold line step: adopt C-means clustering algorithm that the every segment data after segmentation in step III is divided into two classes, the spectrum value of a class is comparatively large, and another kind of spectrum value is less; All values in that larger for spectrum value class is all replaced by the minimum value in such, obtains new frequency spectrum data; New frequency spectrum data comprises the larger class of replacement data and does not have the comparatively group of replacement data; Then each data in new frequency spectrum data are added same frequency spectrum shift value, the minimum value of the frequency spectrum data after moving is made to equal 1, then logarithm each data in the frequency spectrum data after moving is asked to obtain compressing frequency spectrum data, by the smoothing process of frequency spectrum data after compression, each data in frequency spectrum data after smoothing processing are asked index, obtain the noise average line with noise fluctuations, then obtain the mean square deviation of this section of frequency spectrum data and described noise average line, described mean square deviation is as noise gate; Described noise average line and noise gate are added and obtain adaptive threshold line, then the adaptive threshold splicing of each section of frequency spectrum data is become the adaptive threshold line in whole monitoring frequency range;
V, signal center's frequency step is obtained: compared by the adaptive threshold line obtained in the frequency spectrum data that the monitoring frequency range obtained in step I or step II scans and step III, find out the region that described frequency spectrum data is greater than described threshold line, then obtain the frequency of each maximum regional value, described frequency is exactly the center frequency point of a signal.
Embodiment 3
As the another preferred embodiment of the present invention, present embodiment discloses:
A kind of signal detecting method, is characterized in that: its step is as follows:
I, data acquisition step: obtain the frequency spectrum data of monitoring the scanning of frequency range super band from radio monitoring equipment;
II, frequency band adaptive division step is monitored: find out all crests in the frequency spectrum data obtained in step I and trough, add up the value at all trough places, and calculate the mean value of trough; Mark out the trough that trough value is less than trough mean value, the difference marking the crest value that trough that described trough value is less than trough mean value is adjacent is greater than the trough of empirical value, it can be used as waypoint;
III, determine adaptive threshold line step: adopt C-means clustering algorithm that the every segment data after segmentation in step II is divided into two classes, the spectrum value of a class is comparatively large, and another kind of spectrum value is less; All values in that larger for spectrum value class is all replaced by the minimum value in such, obtains new frequency spectrum data; New frequency spectrum data comprises the larger class of replacement data and does not have the comparatively group of replacement data; Then each data in new frequency spectrum data are added same frequency spectrum shift value, the minimum value of the frequency spectrum data after moving is made to equal 1, then logarithm each data in the frequency spectrum data after moving is asked to obtain compressing frequency spectrum data, by the smoothing process of frequency spectrum data after compression, each data in frequency spectrum data after smoothing processing are asked index, obtain the noise average line with noise fluctuations, then obtain the mean square deviation of this section of frequency spectrum data and described noise average line, described mean square deviation is as noise gate; Described noise average line and noise gate are added and obtain adaptive threshold line, then the adaptive threshold splicing of each section of frequency spectrum data is become the adaptive threshold line in whole monitoring frequency range;
IV, signal center's frequency step is obtained: compared by the adaptive threshold line obtained in the frequency spectrum data that the monitoring frequency range obtained in step I or step II scans and step III, find out the region that described frequency spectrum data is greater than described threshold line, then obtain the frequency of each maximum regional value, described frequency is exactly the center frequency point of a signal;
V, statistical treatment step: a lot of frame frequency modal data will be obtained within a period of time, every frame frequency modal data processes to step IV by step I, each frame frequency modal data will obtain multiple signal centers frequency, then the center frequency point that statistics multiframe frequency spectrum data obtains just can show that certain frequency is the number of times of signal center's frequency within described a period of time, when number of times reaches 20% of statistics frame number, certain frequency described is as a significant signal center frequency, and described signal is exactly the signal be detected in monitoring time.
Embodiment 4
As the another preferred embodiment of the present invention, present embodiment discloses:
A kind of signal detecting method, is characterized in that: its step is as follows:
I, data acquisition step: obtain the frequency spectrum data of monitoring the scanning of frequency range super band from radio monitoring equipment;
II, frequency spectrum data pre-treatment step; Described frequency spectrum pre-treatment step is as follows:
(a) frequency spectrum data average treatment: the frequency spectrum data that the multiframe monitoring frequency range super band obtained from equipment is scanned, then described multiframe frequency spectrum data average treatment is become a frame frequency modal data, i-th data that the frame frequency namely after process is composed are that i-th data of described multiframe frequency spectrum are averaging gained;
(b) smoothing processing: the smoothing process of the frequency spectrum data that step (a) is obtained;
III, frequency band adaptive division step is monitored: find out all crests in the frequency spectrum data obtained in step II and trough, add up the value at all trough places, and calculate the mean value of trough; Mark out the trough that trough value is less than trough mean value, the difference marking the crest value that trough that described trough value is less than trough mean value is adjacent is greater than the trough of empirical value, it can be used as waypoint;
IV, determine adaptive threshold line step: adopt C-means clustering algorithm that the every segment data after segmentation in step III is divided into two classes, the spectrum value of a class is comparatively large, and another kind of spectrum value is less; All values in that larger for spectrum value class is all replaced by the minimum value in such, obtains new frequency spectrum data; New frequency spectrum data comprises the larger class of replacement data and does not have the comparatively group of replacement data; Then each data in new frequency spectrum data are added same frequency spectrum shift value, the minimum value of the frequency spectrum data after moving is made to equal 1, then logarithm each data in the frequency spectrum data after moving is asked to obtain compressing frequency spectrum data, by the smoothing process of frequency spectrum data after compression, each data in frequency spectrum data after smoothing processing are asked index, obtain the noise average line with noise fluctuations, then obtain the mean square deviation of this section of frequency spectrum data and described noise average line, described mean square deviation is as noise gate; Described noise average line and noise gate are added and obtain adaptive threshold line, then the adaptive threshold splicing of each section of frequency spectrum data is become the adaptive threshold line in whole monitoring frequency range;
V, signal center's frequency step is obtained: compared by the adaptive threshold line obtained in the frequency spectrum data that the monitoring frequency range obtained in step I or step II scans and step III, find out the region that described frequency spectrum data is greater than described threshold line, then obtain the frequency of each maximum regional value, described frequency is exactly the center frequency point of a signal;
VI, statistical treatment step: a lot of frame frequency modal data will be obtained within a period of time, every frame frequency modal data processes to step V by step I, each frame frequency modal data will obtain multiple signal centers frequency, then the center frequency point that statistics multiframe frequency spectrum data obtains just can show that certain frequency is the number of times of signal center's frequency within described a period of time, when number of times reaches 20% of statistics frame number, certain frequency described is as a significant signal center frequency, and described signal is exactly the signal be detected in monitoring time.
Embodiment 5
As the another preferred embodiment of the present invention, present embodiment discloses:
A kind of signal detecting method, is characterized in that: its step is as follows:
I, data acquisition step: obtain the frequency spectrum data of monitoring the scanning of frequency range super band from radio monitoring equipment;
II, frequency spectrum data pre-treatment step; Described frequency spectrum pre-treatment step is as follows:
(a) frequency spectrum data average treatment: the frequency spectrum data that the multiframe monitoring frequency range super band obtained from equipment is scanned, then described multiframe frequency spectrum data average treatment is become a frame frequency modal data, i-th data that the frame frequency namely after process is composed are that i-th data of described multiframe frequency spectrum are averaging gained;
(b) smoothing processing: the smoothing process of the frequency spectrum data that step (a) is obtained;
III, frequency band adaptive division step is monitored: find out all crests in the frequency spectrum data obtained in step II and trough, add up the value at all trough places, and calculate the mean value of trough; Mark out the trough that trough value is less than trough mean value, the difference marking the crest value that trough that described trough value is less than trough mean value is adjacent is greater than the trough of empirical value, it can be used as waypoint;
IV, determine adaptive threshold line step: adopt C-means clustering algorithm that the every segment data after segmentation in step III is divided into two classes, all values in that larger for spectrum value class is all replaced by the minimum value in such, obtains new frequency spectrum data; Then each data in new frequency spectrum data are added same frequency spectrum shift value, the minimum value of the frequency spectrum data after moving is made to equal 1, then logarithm each data in the frequency spectrum data after moving is asked to obtain compressing frequency spectrum data, by the smoothing process of frequency spectrum data after compression, each frequency spectrum data after smoothing processing is asked index, obtain the noise average line with noise fluctuations, then obtain the mean square deviation of this section of frequency spectrum data and described noise average line, described mean square deviation is as noise gate; Described noise average line and noise gate are added and obtain adaptive threshold line, then the adaptive threshold splicing of each section is become the adaptive threshold line in whole monitoring frequency range;
V, signal center's frequency step is obtained: compared by the adaptive threshold line obtained in the frequency spectrum data that the monitoring frequency range obtained in step I or step II scans and step III, find out the region that described frequency spectrum data is greater than described threshold line, then obtain the frequency that each maximum regional value goes out, described frequency is exactly the center frequency point of a signal;
VI, statistical treatment step: a lot of frame frequency modal data will be obtained within a period of time, every frame frequency modal data processes to step V by step I, each frame frequency modal data will obtain multiple signal centers frequency, then add up the center frequency point that a lot of frame frequency modal data obtains and just can show that certain frequency is the number of times of signal center's frequency within described a period of time, when number of times reaches 20% of statistics frame number, certain frequency described is as a significant signal center frequency, and described signal is exactly the signal be detected in monitoring time.
Smoothing processing in described step II is average smooth, and smoothness period is successively from 1/3 of the 2 frequency spectrum data numbers scanned to monitoring frequency range super band; In C-means clustering algorithm in described step III, the every segment data after segmentation in step II is divided into two classes, the initial cluster center of a class is the mean value of this section of frequency spectrum data, and another kind of initial cluster center is the maximal value of the frequency spectrum data of this section.
The formula obtaining frequency spectrum shift value in described step III is as follows:
L＝1-min(S(n))；
Wherein L is frequency spectrum shift value, and minimum value is got in min () expression, and S (n) represents one section of frequency spectrum data, and n represents the number of one section of frequency spectrum data intermediate-frequeney point.In described step III, mean square deviation computing formula is as follows:
Wherein T represents the mean square deviation of one section of frequency spectrum data and this section of noise average line, and V represents the variance of one section of frequency spectrum data and this section of noise average line, and n represents variance number, and i represents i-th variance.
Claims (8)
1. a signal detecting method, is characterized in that: its step is as follows:
I, data acquisition step: obtain the frequency spectrum data of monitoring the scanning of frequency range super band from radio monitoring equipment;
II, frequency band adaptive division step is monitored: find out all crests in the frequency spectrum data obtained in I step and trough, add up the value at all trough places, and calculate the mean value of trough; Mark out the trough that trough value is less than trough mean value, the difference marking the crest value that trough that described trough value is less than trough mean value is adjacent is greater than the trough of empirical value, it can be used as waypoint;
III, determine adaptive threshold line step: adopt C-means clustering algorithm that the every segment data after segmentation in step II is divided into two classes, all values in that larger for spectrum value class is all replaced by the minimum value in such, obtains new frequency spectrum data; Then each data in new frequency spectrum data are added same frequency spectrum shift value, make the minimum value of the frequency spectrum data after moving equal 1; Then logarithm each data in the frequency spectrum data after moving is asked to obtain compressing frequency spectrum data, by the smoothing process of frequency spectrum data after compression, each data of the frequency spectrum data after smoothing processing are asked index, obtain the noise average line with noise fluctuations, then obtain the mean square deviation of this section of frequency spectrum data and described noise average line, described mean square deviation is as noise gate; Described noise average line and noise gate are added and obtain adaptive threshold line, then the adaptive threshold splicing of each section is become the adaptive threshold line in whole monitoring frequency range;
IV, signal center's frequency step is obtained: compared by the adaptive threshold line obtained in the frequency spectrum data that the monitoring frequency range obtained in step I scans and step III, find out the region that described frequency spectrum data is greater than described threshold line, then obtain the frequency of each maximum regional value, described frequency is exactly the center frequency point of a signal.
2. a kind of signal detecting method as claimed in claim 1, is characterized in that: the smoothing processing in described step II is average smooth, and smoothness period is successively from 1/3 of the 2 frequency spectrum data numbers scanned to monitoring frequency range super band.
3. a kind of signal detecting method as claimed in claim 1 or 2, it is characterized in that: in the C-means clustering algorithm in described step III, every segment data after segmentation in step II is divided into two classes, the initial cluster center of one class is the mean value of this section of frequency spectrum data, and another kind of initial cluster center is the maximal value of the frequency spectrum data of this section.
4. a kind of signal detecting method as claimed in claim 1, is characterized in that: the formula obtaining frequency spectrum shift value in described step III is as follows:
L＝1-min(S(n))；
Wherein L is frequency spectrum shift value, and minimum value is got in min () expression, and S (n) represents one section of frequency spectrum data, and n represents the number of one section of frequency spectrum data intermediate-frequeney point.
5. a kind of signal detecting method as described in claim 1 or 4, is characterized in that: in described step III, mean square deviation computing formula is as follows:
Wherein T represents the mean square deviation of one section of frequency spectrum data and this section of noise average line, and V represents the variance of one section of frequency spectrum data and this section of noise average line, and n represents variance number, and i represents i-th variance.
6. a kind of signal detecting method as claimed in claim 1, is characterized in that: before step II, also comprise frequency spectrum data pre-treatment step; Described frequency spectrum pre-treatment step is as follows:
(a) frequency spectrum data average treatment: the frequency spectrum data that the multiframe monitoring frequency range super band obtained from equipment is scanned, then described multiframe frequency spectrum data average treatment is become a frame frequency modal data, i-th data that the frame frequency namely after process is composed are that i-th data of described multiframe frequency spectrum are averaging gained;
(b) smoothing processing: the smoothing process of the frequency spectrum data that step (a) is obtained.
7. a kind of signal detecting method as claimed in claim 1, is characterized in that: after step IV, also comprise statistical treatment step; Described statistical treatment step is as follows: will obtain a lot of frame frequency modal data within a period of time, every frame frequency modal data processes to step IV by step I, each frame frequency modal data will obtain multiple signal centers frequency, then add up the center frequency point that a lot of frame frequency modal data obtains and just can show that certain frequency is the number of times of signal center's frequency within described a period of time, when number of times reaches 20% of statistics frame number, certain frequency described is as a significant signal center frequency, and described signal is exactly the signal be detected in monitoring time.
8. a kind of signal detecting method as claimed in claim 1, is characterized in that: described empirical value refers to: the frequency exceeding noise 2db-5db is considered as signal frequency point.
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