CN114374448A - Signal-to-noise ratio resolving method based on interference avoidance - Google Patents
Signal-to-noise ratio resolving method based on interference avoidance Download PDFInfo
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- H04B17/30—Monitoring; Testing of propagation channels
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Abstract
The invention belongs to the field of satellite signal tracking, and particularly relates to a signal-to-noise ratio resolving method based on interference avoidance. The method comprises the steps of sequencing the spectrum power by a histogram statistical sequencing method, calculating a noise spectrum estimation value according to a sequencing result, correcting the noise spectrum estimation value by an FCME algorithm, and calculating a signal-to-noise ratio according to the corrected noise spectrum value and the signal power. The algorithm not only improves the real-time performance and stability of calculation, but also has the function of avoiding interference, and meets the requirement of calculating a stable signal-to-noise ratio in real time in an interference environment.
Description
Technical Field
The invention belongs to the field of satellite signal tracking, and particularly relates to a signal-to-noise ratio resolving method for avoiding interference.
Background
In signal tracking demodulation, a user can conveniently acquire the quality of a signal to judge whether the signal meets the tracking demodulation requirement, so that the signal-to-noise ratio of the signal needs to be calculated. However, with the development of the satellite communication field, the air interference signals are increasingly complex and changeable, and most algorithms have the problems of inaccurate calculated noise spectrum, large fluctuation, high complexity and the like, so that the practical requirements cannot be met.
Disclosure of Invention
Aiming at the defects, the invention provides a signal-to-noise ratio calculation method based on interference avoidance. The method comprises the steps of sequencing the spectrum power by a histogram statistical sequencing method, calculating a noise spectrum estimation value according to a sequencing result, correcting the noise spectrum estimation value by an FCME algorithm, and calculating a signal-to-noise ratio according to the corrected noise spectrum value and the signal power. The algorithm not only improves the real-time performance and stability of calculation, but also has the function of avoiding interference, and meets the requirement of calculating a stable signal-to-noise ratio in real time in an interference environment.
The purpose of the invention is realized by the following steps:
a signal-to-noise ratio resolving method based on interference avoidance comprises the following steps:
step 1: converting the analog signal into a digital signal, carrying out down-conversion, then selecting sampling data according to the lowest signal-to-noise ratio requirement of the tracking signal, and carrying out fast Fourier transform to form frequency spectrum data;
step 2: selecting data participating in signal-to-noise ratio calculation according to the monitoring bandwidth, and splicing the frequency spectrum data to obtain a new data sequence;
and step 3: finding the maximum power value p in the new data sequencemaxAnd a minimum power value pminAnd calculating Δmax＝10lgpmax-10lgpmin；
And 4, step 4: if ΔmaxAbove a maximum threshold, the gradient hdBIs 1dB, if ΔmaxBetween the maximum threshold and the minimum threshold, then hdB＝(10lgpmax-10lgpmin) /64, then go to step 6 if ΔmaxIf the signal-to-noise ratio is smaller than the minimum threshold value, the signal-to-noise ratio is directly set to be 0;
and 5: dividing the power value into t intervals, using the gradient hdBCalculating the belonged point of each frequency spectrumAnd calculating the average power P of each intervalj(ii) a Wherein t is the maximum threshold value in the step 4;
step 6: if the tracking signal is a single beacon signal, the number of the noise spectrum points is mnoiseN x l, the number of noise spectrum points is m if the tracking signal is a broadband signalnoise(N-BW/Sample) × l, where BW is the tracking signal bandwidth, Sample is the data frequency, N is the number of data in the new data sequence, and l is the coefficient;
and 7: according to the histogram statistics, obtaining the satisfaction
And calculating a noise spectrum estimate
Wherein q represents the minimum number of intervals satisfying the requirement;
and 8: comparing all frequency spectrums with a threshold T which is K multiplied by noise, selecting one noise spectral line to carry out noise spectrum estimation correction
m is m +1, wherein pxIs a spectral point less than a threshold;
and step 9: updating noise, selecting another noise spectral line, and repeating the step 9 until no spectral line is smaller than a threshold T or the maximum allowable iteration number is reached;
step 10: if a single beacon signal is tracked, the signal-to-noise ratio results in
If the broadband signal is tracked, the signal-to-noise ratio results in
Wherein
∑pi(N/2+ BW/(2 × Sample)) < i < (N/2+ B) for all spectral power sums within the wideband signalW/(2 × Sample)), M is the number of all spectra in the wideband signal, pmax-1And pmax+1Respectively, the previous data and the next data adjacent to the data corresponding to the maximum power value.
Wherein the number C of the sampling data selected in the step 1 is 2rAnd r is an integer.
Wherein, the number of data participating in the calculation of the signal to noise ratio in the step 2
Wherein, B is the monitoring bandwidth, Sample is the data frequency, and if N is an even number, the value of N is increased by 1.
In the step 2, the splicing method includes:
1) sequentially fetching spectral data LiLast (N-1)/2 data L ofC-N/2+1,LC-N/2+2…LC；
2) Sequentially fetching spectral data LiThe first (N-1)/2+1 data L of1,L2…LN/2+1；
3) Mixing L withC-N/2+1,LC-N/2+2…LC,L1,L2…LN/2+1As a new data sequence participating in the calculation of the signal-to-noise ratio, the total number of the spectrum data is N, and the power value of each spectrum data is piAnd i is 1,2,3 … N.
Wherein, the step 5 specifically comprises the following steps:
step 5.1: dividing the power value into t intervals, and selecting a spectrum point pi(ii) a Wherein t is the maximum threshold value in the step 4;
step 5.2: and calculating the section j to which the selected spectrum point belongs:
and calculating the average power P of the interval jj：
Wherein n isjTaking j as an integer for the number of power frequency points in the interval j, and if j is less than 1, taking j as 1, Pj-1The average power calculated for the last frequency point;
step 5.3: selecting another spectral point piAnd 5.2, returning to the step until all the frequency point intervals are divided.
Compared with the background technology, the invention has the following advantages:
1. the invention adopts a histogram statistical sorting method, the complexity of the calculation time is N, the calculation efficiency is high, and the real-time performance is good.
2. The invention adopts FCME algorithm, can carry on the rational correction to the spectrum estimation of the noise, make the noise calculate more accurately.
3. According to the invention, through the combination of the histogram statistical sorting method and the FCME algorithm, the signal-to-noise ratio calculation method has anti-interference capability, and the calculation result is more stable.
4. The invention is suitable for the signal-to-noise ratio calculation of single beacon signals and broadband signals, and is also suitable for various tracking receiver platforms.
Detailed Description
A signal-to-noise ratio resolving method based on interference avoidance comprises the following steps:
step 1: the analog-to-digital converter converts the received analog signals into digital signals and outputs the digital signals to the FPGA, the FPGA carries out down-conversion on the digital signals, the data frequency is Sample, and then the appropriate number C of sampling data is selected to be 2 according to the requirement of the lowest signal-to-noise ratio of the tracking signalsr(r is an integer) performing a fast fourier transform to form spectral data;
step 2: selecting data and number participating in signal-to-noise ratio calculation according to monitoring bandwidth B
Splicing the frequency spectrum data to obtain a new data sequence; the splicing method comprises the following steps:
1) sequentially fetching spectral data LiLast (N-1)/2 data L ofC-N/2+1,LC-N/2+2…LC；
2) Sequentially fetching spectral data LiThe first (N-1)/2+1 data L of1,L2…LN/2+1；
3) Mixing L withC-N/2+1,LC-N/2+2…LC,L1,L2…LN/2+1As a new data sequence participating in the calculation of the signal-to-noise ratio, the total number of the spectrum data is N, and the power value of each spectrum data is piAnd i is 1,2,3 … N.
And step 3: finding the maximum power value p in the new data sequencemaxAnd a minimum power value pminAnd calculating Δmax＝10lgpmax-10lgpmin；
And 4, step 4: if ΔmaxGreater than the maximum threshold (65 dB in this example), the gradient hdBIs 1dB, if ΔmaxBetween a maximum threshold and a minimum threshold (3dB < delta)max< 64dB), then hdB＝(10lgpmax-10lgpmin) /64, then go to step 6 if ΔmaxLess than the minimum threshold (3dB in this embodiment), the signal-to-noise ratio is set to 0 directly;
and 5: the power values are divided into t (64) intervals, using the gradient hdBCalculating the interval j of each frequency spectrum point and calculating the average power P of each intervalj；
The method specifically comprises the following steps:
step 5.1: dividing the power value into t intervals, and selecting a spectrum point pi(ii) a Wherein t is the maximum threshold value in the step 4;
step 5.2: and calculating the section j to which the selected spectrum point belongs:
and calculating the average power P of the interval jj：
Wherein n isjTaking j as an integer for the number of power frequency points in the interval j, and if j is less than 1, taking j as 1, Pj-1The average power calculated for the last frequency point;
step 5.3: selecting another spectral point piAnd 5.2, returning to the step until all the frequency point intervals are divided.
Step 6: if the tracking signal is a single beacon signal, the number of the noise spectrum points is mnoiseN x l, the number of noise spectrum points is m if the tracking signal is a broadband signalnoise(N-BW/Sample) × l, where BW is the tracking signal bandwidth, Sample is the data frequency, N is the number of data in the new data sequence, 0.2 < l < 0.3;
and 7: according to the histogram statistics, obtaining the satisfaction
And calculating a noise spectrum estimate
Wherein q represents the minimum number of intervals satisfying the requirement;
and 8: comparing all frequency spectrums with a threshold T which is K multiplied by noise, selecting one noise spectral line to carry out noise spectrum estimation correction
m is m +1, wherein pxThe spectrum point is less than the threshold, and the threshold factor is more than 1.5 and less than 2.5;
and step 9: updating noise, selecting another noise spectral line, and repeating the step 9 until no spectral line is smaller than the threshold T or the maximum number of allowed iterations (5 in this embodiment) is reached;
step 10: if a single beacon signal is tracked, the signal-to-noise ratio results in
If the broadband signal is tracked, the signal-to-noise ratio results in
Wherein
∑piIs the sum of all spectral powers in the wideband signal, (N/2+ BW/(2 × Sample)) < i < (N/2+ BW/(2 × Sample)), M is the number of all spectra in the wideband signal, pmax-1And pmax+1Respectively, the previous data and the next data adjacent to the data corresponding to the maximum power value.
Claims (5)
1. A signal-to-noise ratio resolving method based on interference avoidance is characterized by comprising the following steps:
step 1: converting the analog signal into a digital signal, carrying out down-conversion, then selecting sampling data according to the lowest signal-to-noise ratio requirement of the tracking signal, and carrying out fast Fourier transform to form frequency spectrum data;
step 2: selecting data participating in signal-to-noise ratio calculation according to the monitoring bandwidth, and splicing the frequency spectrum data to obtain a new data sequence;
and step 3: finding the maximum power value p in the new data sequencemaxAnd a minimum power value pminAnd calculating Δmax＝10lg pmax-10lg pmin；
And 4, step 4: if ΔmaxAbove a maximum threshold, the gradient hdBIs 1dB, if ΔmaxBetween the maximum threshold and the minimum threshold, then hdB＝(10lg pmax-10lg pmin) /64, then go to step 6 if ΔmaxIf the signal-to-noise ratio is smaller than the minimum threshold value, the signal-to-noise ratio is directly set to be 0;
and 5: dividing the power value into t intervals, using the gradient hdBCalculating the interval j of each frequency spectrum point and calculating the average power P of each intervalj(ii) a Wherein t is the maximum threshold value in the step 4;
step 6: if the tracking signal is a single beacon signal, the number of the noise spectrum points is mnoiseN x l, the number of noise spectrum points is m if the tracking signal is a broadband signalnoise＝(N-BW/Sample)*lWherein BW is tracking signal bandwidth, Sample is data frequency, N is data number in new data sequence, and l is coefficient;
and 7: according to the histogram statistics, obtaining the satisfaction
And calculating a noise spectrum estimate
Wherein q represents the minimum number of intervals satisfying the requirement;
and 8: comparing all frequency spectrums with a threshold T which is K multiplied by noise, selecting one noise spectral line to carry out noise spectrum estimation correction
m is m +1, wherein pxIs a spectrum point less than the threshold, and K is a threshold factor;
and step 9: updating noise, selecting another noise spectral line, and repeating the step 9 until no spectral line is smaller than a threshold T or the maximum allowable iteration number is reached;
step 10: if a single beacon signal is tracked, the signal-to-noise ratio results in
If the broadband signal is tracked, the signal-to-noise ratio results in
Wherein
∑piIs the sum of all spectral powers in the wideband signal, (N/2+ BW/(2 × Sample)) < i < (N/2+ BW/(2 × Sample)), M is the number of all spectra in the wideband signal, pmax-1And pmax+1Respectively, the previous data and the next data adjacent to the data corresponding to the maximum power value.
2. The interference avoidance-based signal-to-noise ratio calculation method according to claim 1, wherein the number of the sampling data C-2 selected in the step 1rAnd r is an integer.
3. The interference avoidance-based signal-to-noise ratio calculation method according to claim 1, wherein the number of data participating in signal-to-noise ratio calculation in step 2 is
Wherein, B is the monitoring bandwidth, Sample is the data frequency, and if N is an even number, the value of N is increased by 1.
4. The interference avoidance-based signal-to-noise ratio calculation method according to claim 1, wherein in the step 2, the splicing method comprises the following steps:
1) sequentially fetching spectral data LiLast (N-1)/2 data L ofC-N/2+1,LC-N/2+2…LC；
2) Sequentially fetching spectral data LiThe first (N-1)/2+1 data L of1,L2…LN/2+1；
3) Mixing L withC-N/2+1,LC-N/2+2…LC,L1,L2…LN/2+1As a new data sequence participating in the calculation of the signal-to-noise ratio, the total number of the spectrum data is N, and the power value of each spectrum data is piAnd i is 1,2,3 … N.
5. The signal-to-noise ratio calculation method based on interference avoidance according to claim 1, wherein the step 5 specifically comprises the following steps:
step 5.1: dividing the power value into t intervals, and selecting a spectrum point pi(ii) a Wherein t is the maximum threshold value in the step 4;
step 5.2: and calculating the section j to which the selected spectrum point belongs:
and calculating the average power P of the interval jj：
Wherein n isjTaking j as an integer for the number of power frequency points in the interval j, and if j is less than 1, taking j as 1, Pj-1The average power calculated for the last frequency point;
step 5.3: selecting another spectral point piAnd 5.2, returning to the step until all the frequency point intervals are divided.
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| CN111934795A (en) * | 2020-08-18 | 2020-11-13 | 电子科技大学 | Method for estimating signal-to-noise ratio and interference power in interference environment |
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Patent Citations (5)
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| US20030011835A1 (en) * | 2001-03-09 | 2003-01-16 | Joerg-Peter Elbers | Method for determining signal quality in optical transmission systems |
| CN102238114A (en) * | 2010-05-07 | 2011-11-09 | 中兴通讯股份有限公司 | Method and device for determining valid signal-to-noise ratio |
| CN102904841A (en) * | 2011-07-28 | 2013-01-30 | 无锡物联网产业研究院 | Method for estimating signal to noise ratio |
| CN111130630A (en) * | 2019-12-31 | 2020-05-08 | 南京御通信息技术有限公司 | Communication satellite frequency spectrum monitoring equipment and frequency spectrum acquisition and feature identification method thereof |
| CN111934795A (en) * | 2020-08-18 | 2020-11-13 | 电子科技大学 | Method for estimating signal-to-noise ratio and interference power in interference environment |
Non-Patent Citations (1)
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