CN110958196B - Optimal sampling point acquisition method for burst system timing synchronization algorithm - Google Patents
Optimal sampling point acquisition method for burst system timing synchronization algorithm Download PDFInfo
- Publication number
- CN110958196B CN110958196B CN201910624658.2A CN201910624658A CN110958196B CN 110958196 B CN110958196 B CN 110958196B CN 201910624658 A CN201910624658 A CN 201910624658A CN 110958196 B CN110958196 B CN 110958196B
- Authority
- CN
- China
- Prior art keywords
- sampling
- symbol
- power
- sampling point
- right half
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Images
Classifications
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L25/00—Baseband systems
- H04L25/02—Details ; arrangements for supplying electrical power along data transmission lines
- H04L25/03—Shaping networks in transmitter or receiver, e.g. adaptive shaping networks
- H04L25/03006—Arrangements for removing intersymbol interference
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L25/00—Baseband systems
- H04L25/02—Details ; arrangements for supplying electrical power along data transmission lines
- H04L25/03—Shaping networks in transmitter or receiver, e.g. adaptive shaping networks
- H04L25/03006—Arrangements for removing intersymbol interference
- H04L2025/03592—Adaptation methods
- H04L2025/03598—Algorithms
Abstract
The invention discloses an optimal sampling point acquisition method for a burst system timing synchronization algorithm, and belongs to the field of communication. Base of the inventionBased on the symmetric characteristic of left and right half symbol power of the matched filter, the idea of smoothing noise by multi-path data accumulation is utilized, firstly, the length of a symbol is taken as a range, corresponding sampling point power values in each symbol are accumulated to obtain n paths of sampling power values, and then, the nth sampling point is taken as a center to obtain the power difference delta between the left and right half symbols n And according to the symmetrical characteristic of the power of the left and right half symbols, the position of the final optimal sampling point is the path of sampling point corresponding to the minimum difference value of the power of the left and right half symbols. The invention inherits the advantage of low computation complexity of the maximum average power algorithm and easy realization, and simultaneously has stronger anti-noise performance due to the influence of multipath data accumulation smoothing noise, better stability and higher synchronization precision no matter under the conditions of high and low signal-to-noise ratios.
Description
Technical Field
The invention relates to an optimal sampling point acquisition method for a burst system timing synchronization algorithm, and belongs to the field of communication.
Background
At present, short burst communication systems have been widely used in the fields of military communication, satellite communication, deep space exploration, high-speed mobile communication, and the like. In a burst communication system, in order to obtain transmitted information data, a signal needs to be sampled at a symbol rate at an output end of a matched filter in a demodulator, so that how to accurately determine at what time within a symbol interval to sample the signal is very important, when a timing deviation exists in the signal and a sampling point of the signal deviates from an optimal sampling position, the signal-to-noise ratio of the received signal is reduced, inter-symbol interference is caused to influence the decision of the signal, and finally, the error rate is improved. Therefore, it is necessary to determine the best sampling decision time of the received signal by performing timing estimation on the signal.
Currently, timing synchronization methods in short burst communication are generally divided into two types, one is to directly adjust the current signal sampling time through a previous signal feedback error value to realize synchronization, and the method is called as a closed loop or feedback algorithm; another method for timing recovery directly from a received discrete data signal using digital signal processing is known as open loop or forward algorithm. The classical feedback algorithm is a Gardener timing synchronization algorithm, timing synchronization can be carried out by only needing two sampling points for each symbol, and the method is simple, high in precision and easy to implement. But because the feedback algorithm has a long capture time, the convergence speed is slow and a 'false lock' phenomenon occurs, so that the feedback algorithm is not suitable for timing recovery of burst signals. Compared with a feedback algorithm, the forward algorithm has high calculation complexity and low estimation precision, but has shorter acquisition time and no convergence process, and is more suitable for timing recovery of short-time burst signals. Therefore, in short burst communication systems, a forward algorithm is generally selected for timing synchronization. The classical forward algorithm includes a nonlinear transformation algorithm and a maximum average power algorithm. The nonlinear transformation algorithm is based on the maximum likelihood estimation theory, the baseband signals after passing through the matched filter are subjected to nonlinear transformation after being subjected to modulus extraction, then are transformed to a frequency domain, and a timing deviation value is obtained according to a frequency spectrum complex angle relation. After the signal is subjected to square transformation by the maximum average power algorithm, timing error extraction is carried out by judging the value of the maximum average power of the sampling points, the optimal sampling point is directly determined, the algorithm is low in complexity and easy to implement, the synchronization precision is good, but the performance is poor in a system for high-speed data transmission.
In practical use, since the rate of the short-time burst signal is not high and the sampling rate is high, the maximum average power criterion is generally selected to complete the timing estimation. The design and implementation of the EVM algorithm in the GMR-13G terminal tester, published by Luzongchen et al, indicates that the algorithm with the minimum amplitude variance is provided based on the maximum average power thought, and has the advantages of higher stability and more accurate estimation precision than the maximum average power algorithm under the condition that the signal-to-noise ratio of the algorithm is more than 0 dB. However, the algorithm only uses a single path of data for estimation and does not consider the influence caused by noise, so that the synchronization accuracy is low due to the fact that the algorithm is easily interfered by the noise in the environment with low signal to noise ratio.
Disclosure of Invention
In view of this, the present invention provides an optimal sampling point obtaining method for a burst system timing synchronization algorithm, which has the advantages of low computation complexity of a maximum average power algorithm and easy implementation, and at the same time has better anti-noise performance, better stability and higher synchronization precision no matter under the conditions of high and low signal-to-noise ratios. The technical scheme adopted by the invention for solving the technical problems is as follows:
an optimal sampling point acquisition method for a burst system timing synchronization algorithm is specifically realized by the following steps:
Step one, sampling the signals after passing through the matched filter to obtain the power values of the sampled data of all the symbols to form a first array P 1 ,P 2 ,...P n ,...P M (ii) a Wherein n is more than or equal to 1 and less than or equal to M, and M is the total number of sampling paths; p n Sampling power for the nth path, namely the accumulated sum of the squares of the amplitudes of the sampled data of the nth path of all the symbols;
step two, copying and expanding the first array to form a second data group P:
P＝P 1 ,P 2 ...,P n ...,P M ,P M+1 ,P M+2 ,...P M+n ,...P 2M and P is M+n ＝P n ；
Step three, calculating the power difference of the left and right half symbols of the nth path of sampling, wherein the power difference of the left and right half symbols of the nth path of sampling is as follows: in a second data group P, taking the nth to (n-1 + M) th paths of sampling data as a calculation range, and calculating the absolute value of the difference between the sum of the first half power values and the sum of the second half power values in the calculation range; and traversing M paths of sampling, and taking the sampling corresponding to the minimum left-right half symbol power difference as an optimal sampling point.
Preferably, the nth sampling data power value P n The acquisition method comprises the following steps:
In the formula R n Sampling data amplitude values for the nth path of all symbols; | R x (n) | is the nth sampling data amplitude of the xth symbol;
step 3, according to the formula (5),
obtaining the power value P of the sampled data of the nth path of all the symbols n 。
Preferably, in the third step, the method for obtaining the optimal sampling point includes:
when M is an even number, according to the formula (3), obtaining the power difference delta of the left and right half symbols of the nth path n Wherein n is more than or equal to 1 and less than or equal to M;
traversing M sampling points, and obtaining the minimum left-right half symbol power difference delta n The corresponding sample is the optimal sampling point.
Preferably, when M is an odd number, the nth left and right half-symbol power difference Δ n Comprises the following steps:
wherein n is more than or equal to 1 and less than or equal to M, traversing M sampling points, and minimizing the power difference delta between the left half symbol and the right half symbol n The corresponding sample is the optimal sampling point.
Has the advantages that:
the invention is based on the left and right half symbol power pairs of the matched filterThe characteristic is that the thought of smoothing noise by multi-path data accumulation is utilized, firstly the length of a symbol is taken as a range, the power values of corresponding sampling points in each symbol are accumulated to obtain n paths of sampling power values, and then the power difference delta between the left half symbol and the right half symbol is obtained by taking the nth path of sampling points as the center n And according to the symmetrical characteristic of the power of the left and right half symbols, the position of the final optimal sampling point is the path of sampling point corresponding to the minimum difference value of the power of the left and right half symbols. The invention inherits the advantage of low computation complexity of the maximum average power algorithm and is easy to realize, and simultaneously has stronger anti-noise performance due to the influence of multi-path data accumulation smoothing noise, better stability and higher synchronization precision no matter under the conditions of high and low signal-to-noise ratios.
Drawings
FIG. 1 is a flow chart of the present invention;
FIG. 2 is a comparison of the stability of the present invention and two other algorithms at SNR of 0;
FIG. 3 is a comparison of the mean square error of the algorithm of the present invention with two other algorithms under different SNR conditions.
Detailed Description
The invention is described in detail below by way of example with reference to the accompanying drawings.
Based on the symmetric characteristics of the left and right half-symbol power values at the optimal sampling time of the matched filter, the invention performs difference processing on the left and right half-symbol power values, wherein the minimum value of the difference value corresponds to the optimal sampling time, and the specific implementation mode is shown in fig. 1:
step one, after a signal passes through a matched filter, enabling the sampling data R of the nth sampling point of the x-th symbol x (n) the expression is as follows:
wherein j is an imaginary unit, W (n) and
respectively representing the amplitude and phase angle of white Gaussian noise, the amplitude and phase angle of the noise are independent and are all zero mean valuesGaussian random process. A. the x (n) and
respectively representing the amplitude and phase angle of the signal; wherein:
wherein L is the symbol length, m is the mth symbol position in the symbol length L, a m τ is the symbol width, g (i) is the root-raised cosine filter response, and M is the number of samples in each symbol.
Step two, traversing each sampling point in the L symbols, obtaining the amplitude of each sampling data of each symbol, and expressing the amplitude in a matrix form:
in the formula R n Sampling data amplitude values for the nth path of all symbols; | R x (n) | is the nth sampling data amplitude of the xth symbol, and is:
step three, obtaining the power value P of the nth path of sampling data of all symbols according to the formula (9) and the formula (10) n
In the formula pow { R } n The expression is the sum of the squares of the elements in the row vector of formula (7), i.e.:
it can be seen that the nth sample power sum is composed of three parts, i.e., signal power, noise power, and signal-to-noise cross-correlation.
Step four, the P obtained in the step three 1 ,P 2 ,...P n ,...P M Performing copy expansion to form a data group P ═ P 1 ,P 2 ...,P n ...,P M ,P M+1 ,P M+2 ,...P M+n ,...P 2M And P is M+n ＝P n (ii) a Taking the nth path of sampling data as an example, according to the formula (11), when M is an even number, the nth path of left and right half-symbol power difference Δ is obtained n Wherein n is more than or equal to 1 and less than or equal to M;
when M is odd number, the power difference of left and right half symbols of the nth path is delta n The following formula:
traversing M sampling points, and obtaining the minimum left-right half symbol power difference delta n The corresponding sampling point is the best sampling point. When n is the optimal sampling point, the value of the power difference part of the left and right half-symbol signals in the above formula is zero according to the symmetric characteristic of the left and right half-symbol power values of the matched filter; in addition, the noise power value of any k value is subjected to zero mean value and N variance 0 Gaussian distribution of/2, N 0 The power values are the single-side power spectrum density of noise, so the left and right half symbol noise power values are accumulated and then subjected to difference in the formula, the noise influence is smoothed, and the result can be approximate to zero; for the product part of the signal amplitude and the noise amplitude in the above formula, because the correlation between the signal and the noise is poor, the product result is very small, the correlation values of the full burst length can be approximate to zero after being accumulated, and in addition, the difference value is taken after the correlation values of the left and right half symbols are accumulated to further inhibit the influence of the noise, so the result value of the part can also be approximate to zero.
When n deviates from the optimal sampling point position, the left and right half symbol power values are asymmetric due to sampling, so that the value of the power difference part of the above-mentioned signal is increased; in addition, for noise and signal correlation parts, although the correlation is poor, the final accumulation and further difference result is not zero because the signal amplitude of the noise and the signal amplitude are not consistent in weighting magnitude. When n deviates from the optimal sampling point position, the left-right half-symbol power difference is increased finally. The position of the optimal sampling point N is:
the left-and-right half-symbol power difference minimum algorithm provided by the invention inherits the advantages of low calculation complexity and easy realization of the maximum average power algorithm, and improves the anti-noise performance, the algorithm stability and the estimation precision on the basis, so that the stability is better and the synchronization precision is higher no matter in the high-low signal-to-noise ratio environment
As shown in fig. 2, in the maximum average power method in the simulation process performed 50 times under the condition of SNR being 0, the maximum sampling point deviation is 3 sampling points, the number of sampling point deviations is 38, and the number of deviations is the largest. The maximum sampling point deviation of the amplitude variance minimum method is also 3 sampling points, the sampling point deviation times is 35 times, and the deviation times is less. And the left half and the right half symbol power difference value is the minimum method, the maximum sampling point deviation is 2 sampling points, the sampling point deviation frequency is 25 times, and the occurrence deviation frequency is minimum. The analysis shows that the algorithm stability is higher by the left-right half-symbol power difference minimum method.
As shown in FIG. 3, under the conditions of high and low signal-to-noise ratios, the algorithm of the invention has strong anti-noise performance, smaller mean square error value and higher estimation precision.
In summary, the above description is only a preferred embodiment of the present invention, and is not intended to limit the scope of the present invention. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (4)
1. An optimal sampling point acquisition method for a burst system timing synchronization algorithm is characterized by comprising the following specific implementation modes:
Step one, sampling the signals after passing through the matched filter to obtain the power values of the sampled data of all the symbols to form a first array P 1 ,P 2 ,...P n ,...P M (ii) a Wherein n is more than or equal to 1 and less than or equal to M, and M is the total number of sampling paths; p n Sampling power for the nth path, namely the accumulated sum of the squares of the amplitudes of the sampled data of the nth path of all the symbols;
step two, copying and expanding the first array to form a second data group P:
P＝P 1 ,P 2 ...,P n ...,P M ,P M+1 ,P M+2 ,...P M+n ,...P 2M and P is M+n ＝P n ；
Step three, calculating the power difference of the left and right half symbols of the nth path of sampling, wherein the power difference of the left and right half symbols of the nth path of sampling is as follows: in a second data group P, taking the nth to (n-1 + M) th paths of sampling data as a calculation range, and calculating the absolute value of the difference between the sum of the first half power values and the sum of the second half power values in the calculation range; and traversing M paths of sampling, and taking the sampling corresponding to the minimum left-right half symbol power difference as an optimal sampling point.
2. The optimal sampling point acquisition method as claimed in claim 1, wherein the nth sampled data power value P n The acquisition method comprises the following steps:
step 1, after the signal passes through a matched filter, obtaining each path of sampling data corresponding to each symbol, and recording the data as R x (n)，R x (n) is the nth sampling data of the xth symbol, x is more than or equal to 1 and less than or equal to L, and L is the symbol length;
step 2, traversing each sampling point in the L symbols, obtaining the amplitude of each sampling data of each symbol, and expressing the amplitude in a matrix form:
In the formula R n Sampling data amplitude values for the nth path of all symbols; | R x (n) | is the nth sampling data amplitude of the xth symbol;
step 3, according to the formula (2),
wherein A is x (n) represents the amplitude of the signal, W (n) is the amplitude of Gaussian white noise, and the power value P of the sampled data of the nth path of all the symbols is obtained n 。
3. The method for obtaining the optimal sampling point according to claim 1, wherein in the third step, the method for obtaining the optimal sampling point comprises:
when M is an even number, according to the formula (3), obtaining the power difference delta of the left and right half symbols of the nth path n Wherein n is more than or equal to 1 and less than or equal to M;
traversing M sampling points, and obtaining the minimum left-right half symbol power difference delta n The corresponding sample is the optimal sampling point.
4. The method for acquiring optimal sampling points according to claim 1, wherein when M is an odd number, the nth left and right half-symbol power difference Δ n Comprises the following steps:
wherein n is more than or equal to 1 and less than or equal to M, traversing M sampling points, and minimizing the power difference delta between the left half symbol and the right half symbol n The corresponding sample is the optimal sampling point.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910624658.2A CN110958196B (en) | 2019-07-11 | 2019-07-11 | Optimal sampling point acquisition method for burst system timing synchronization algorithm |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910624658.2A CN110958196B (en) | 2019-07-11 | 2019-07-11 | Optimal sampling point acquisition method for burst system timing synchronization algorithm |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN110958196A CN110958196A (en) | 2020-04-03 |
| CN110958196B true CN110958196B (en) | 2022-07-29 |
Family
ID=69976198
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910624658.2A Active CN110958196B (en) | 2019-07-11 | 2019-07-11 | Optimal sampling point acquisition method for burst system timing synchronization algorithm |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN110958196B (en) |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105162570A (en) * | 2015-09-11 | 2015-12-16 | 北京华清瑞达科技有限公司 | Timing synchronization method and device for signal parallel processing |
| CN105245303A (en) * | 2015-08-28 | 2016-01-13 | 北京理工大学 | High-speed burst demodulation synchronization system |
| CN108768909A (en) * | 2018-06-15 | 2018-11-06 | 中国电子科技集团公司第四十研究所 | A kind of 2FSK symbol timing synchronization methods and system based on minimum variance |
| CN109756968A (en) * | 2019-01-25 | 2019-05-14 | 北京睿信丰科技有限公司 | The precise synchronization timing method and precise synchronization timing means of single carrier spread spectrum system |
-
2019
- 2019-07-11 CN CN201910624658.2A patent/CN110958196B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105245303A (en) * | 2015-08-28 | 2016-01-13 | 北京理工大学 | High-speed burst demodulation synchronization system |
| CN105162570A (en) * | 2015-09-11 | 2015-12-16 | 北京华清瑞达科技有限公司 | Timing synchronization method and device for signal parallel processing |
| CN108768909A (en) * | 2018-06-15 | 2018-11-06 | 中国电子科技集团公司第四十研究所 | A kind of 2FSK symbol timing synchronization methods and system based on minimum variance |
| CN109756968A (en) * | 2019-01-25 | 2019-05-14 | 北京睿信丰科技有限公司 | The precise synchronization timing method and precise synchronization timing means of single carrier spread spectrum system |
Also Published As
| Publication number | Publication date |
|---|---|
| CN110958196A (en) | 2020-04-03 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109039960A (en) | A kind of underwater sound condition of sparse channel estimation variable step degree of rarefication Adaptive matching method for tracing | |
| CN108848043B (en) | Low-complexity underwater sound sparse time-varying channel estimation method based on compressed sensing | |
| US6505053B1 (en) | Method for sinusoidal modeling and prediction of fast fading processes | |
| CN113259291B (en) | Phase compensation method realized by dynamic Doppler tracking of underwater sound continuous signals | |
| CN110138459A (en) | Sparse underwater sound orthogonal frequency division multiplexing channel estimation methods and device based on base tracking denoising | |
| CN112953863B (en) | Channel parameter estimation method suitable for ultralow signal-to-noise ratio multipath transmission environment | |
| CN114268902B (en) | Pulse ultra-wideband direction finding method based on PDOA | |
| Zhang et al. | Meta-learning-aided orthogonal frequency division multiplexing for underwater acoustic communications | |
| CN110958196B (en) | Optimal sampling point acquisition method for burst system timing synchronization algorithm | |
| CN110290084B (en) | Short wave channel blind symbol synchronization method based on data frequency energy peak value | |
| CN110059401B (en) | OFDM system underwater sound channel impulse response reconstruction method | |
| CN110233687B (en) | Multi-channel data signal joint decision detection method | |
| CN110445740A (en) | Frequency deviation estimating method and system based on repetitive sequence | |
| CN107231169B (en) | A kind of anti-symbol jump Direct-Spread signal dynamics focus method based on KT-LVT | |
| CN110133628A (en) | A kind of restoration methods of LFM Signal Embeded in Strong Interference in short-term | |
| CN107276654B (en) | Signal processing method and system | |
| CN111953626B (en) | Orthogonal-chirp-multiplex-modulation-oriented low-complexity frequency-selective channel estimation method | |
| CN111431823B (en) | Sub-path underwater acoustic channel tracking method | |
| CN110661735B (en) | Time delay estimation method based on orthogonal matching pursuit sparse channel estimation | |
| CN108833320B (en) | Keystone transformation-based broadband DS/FH signal parameter estimation method and system | |
| Manzoor et al. | Novel SNR estimation technique in Wireless OFDM systems | |
| CN109302365B (en) | TD-SCDMA analyzer frequency offset estimation method based on multistage difference method | |
| CN110417699A (en) | A method of the ofdm system timing synchronization based on machine learning | |
| Bellili et al. | Low-complexity DOA estimation from short data snapshots for ULA systems using the annihilating filter technique | |
| CN107294889B (en) | Carrier synchronization method and device |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |