CN115469273B - Method for improving signal-to-noise ratio - Google Patents
Method for improving signal-to-noise ratio Download PDFInfo
- Publication number
- CN115469273B CN115469273B CN202110653033.6A CN202110653033A CN115469273B CN 115469273 B CN115469273 B CN 115469273B CN 202110653033 A CN202110653033 A CN 202110653033A CN 115469273 B CN115469273 B CN 115469273B
- Authority
- CN
- China
- Prior art keywords
- signal
- frequency domain
- noise ratio
- phase jump
- splicing
- 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
- 238000000034 method Methods 0.000 title claims description 30
- 238000006243 chemical reaction Methods 0.000 claims abstract description 6
- 230000009466 transformation Effects 0.000 claims description 27
- 238000005311 autocorrelation function Methods 0.000 claims description 23
- 238000001228 spectrum Methods 0.000 claims description 18
- 238000003672 processing method Methods 0.000 abstract description 8
- 238000010586 diagram Methods 0.000 description 42
- 230000008569 process Effects 0.000 description 9
- 230000004048 modification Effects 0.000 description 4
- 238000012986 modification Methods 0.000 description 4
- 230000009471 action Effects 0.000 description 3
- 230000035559 beat frequency Effects 0.000 description 2
- 230000006872 improvement Effects 0.000 description 2
- 230000002411 adverse Effects 0.000 description 1
- 238000004458 analytical method Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 230000008034 disappearance Effects 0.000 description 1
- 238000001914 filtration Methods 0.000 description 1
- 230000000737 periodic effect Effects 0.000 description 1
- 238000005070 sampling Methods 0.000 description 1
- 230000001629 suppression Effects 0.000 description 1
- 230000007704 transition Effects 0.000 description 1
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
- G01S7/02—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
- G06F17/14—Fourier, Walsh or analogous domain transformations, e.g. Laplace, Hilbert, Karhunen-Loeve, transforms
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
- G06F17/15—Correlation function computation including computation of convolution operations
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Mathematical Physics (AREA)
- Computational Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Mathematical Analysis (AREA)
- Mathematical Optimization (AREA)
- Pure & Applied Mathematics (AREA)
- Data Mining & Analysis (AREA)
- Algebra (AREA)
- Databases & Information Systems (AREA)
- Software Systems (AREA)
- General Engineering & Computer Science (AREA)
- Computing Systems (AREA)
- Computer Networks & Wireless Communication (AREA)
- Radar, Positioning & Navigation (AREA)
- Remote Sensing (AREA)
- Radar Systems Or Details Thereof (AREA)
Abstract
The invention provides a signal processing method with low signal-to-noise ratio, which is characterized by comprising the following steps: performing phase jump-free splicing on the low signal-to-noise ratio signals to obtain spliced time domain signals; performing frequency domain conversion on the time domain signal to obtain a frequency domain signal; by such design, the signal-to-noise ratio is improved, and a useful signal can be obtained in a weak signal of the signal-to-noise ratio.
Description
Technical Field
The invention relates to the technical field of signal processing, in particular to the technical field of a method for improving signal to noise ratio by weak signals.
Background
The FMCW radar emits the above signal waveform each time, after encountering a stationary target, it is reflected back and received by the radar receiver (like a laser is reflected by a mirror), at which time the received signal and the transmitted signal are identical in waveform, but have a delay, the electromagnetic wave propagates as much as light, and the speed is the speed of light. Delay τ=2r/c, according to this formula. Knowing the delay τ, the distance of the target can be calculated. In practice, there is no method to directly find τ, and the receiver is only responsible for receiving the signal, and it is self-determined when there is no signal or when there is no signal. The received signal is typically further processed, i.e., mixed with the transmitted signal.
From the frequency domain, the transmitted signal and the received signal are subtracted (taking absolute value, the signal has no negative frequency), as shown in fig. 1, two frequencies f b and f c-fb,fc are usually carrier frequencies of several G or tens of G, which are far higher than f b, what is used in f c-fb.fb can be removed by a low-pass filter, as shown in fig. 1, according to a similar triangle, τ/t=f b/B is present, and both B and T are known information, the frequency modulation period is T, and B is bandwidth, so that it is known that f b can be used to calculate τ, thereby obtaining the distance of the target. The process of finding f b is called mixing, and f b is also called beat frequency, which is simply called beat frequency. Mixing means that the mixing is simple in practice, that is, the time domain multiplication of the transmitted signal and the received signal is followed by filtering, and that corresponds to frequency subtraction in the frequency domain. After AD sampling, the mixed signal obtains the original data of the radar which we commonly call.
Ideally, a stationary point target is a fixed frequency sine wave, and the distance between the sine wave and the stationary point target is obtained by calculating the frequency of the sine wave, and if two targets with different distances are obtained, the result is that the sine waves with two different frequencies are added. The processing of FMCW data is in chirp units, and the final output data of the radar is the discrete signal of the sine wave in fig. 2, and if the distance is known, FFT is used. However, in a practical scenario, if the noise is too large, the data processing method in chirp units cannot obtain a useful signal for finding the target distance. Fig. 3a shows a time domain signal of an actually received echo signal, and fig. 3b shows a frequency domain signal obtained by performing fourier transform on the time domain signal. From fig. 3b, it can be seen that the echo signal of the second target is too weak to obtain the distance information of the second target by the existing FFT signal processing method. Therefore, a new signal processing method is needed to quickly improve the signal-to-noise ratio under the weak signal scene to effectively obtain the distance information of the target.
Disclosure of Invention
The present invention is directed to a signal processing method for solving a series of problems caused by the fact that a useful signal cannot be obtained from a low signal-to-noise ratio signal in the related art.
In order to achieve the above purpose, the technical scheme adopted by the embodiment of the invention is as follows:
The embodiment of the invention provides a signal processing method with low signal-to-noise ratio, which is characterized in that,
Comprising the following steps:
performing phase jump-free splicing on the low signal-to-noise ratio signals to obtain spliced time domain signals;
And performing frequency domain conversion on the time domain signal to obtain a frequency domain signal.
Optionally, the splicing without phase jump includes the following steps:
Performing frequency domain conversion on the first signal and the second signal to obtain a frequency domain signal, and taking an amplitude spectrum of the frequency domain signal;
carrying out frequency domain inverse transformation on the amplitude spectrum to obtain a time domain signal;
performing frequency domain inverse transformation on the amplitude spectrum of the second signal to obtain a time domain signal for inversion;
and carrying out frequency domain inverse transformation on the amplitude spectrum of the first signal to obtain a time domain signal, carrying out frequency domain inverse transformation on the amplitude spectrum of the second signal, and then splicing the time domain signal to obtain a spliced signal without phase jump.
Optionally, performing frequency domain transformation on the spliced signal to obtain information of the distance of the acquired target object.
Optionally, repeating the step of splicing without phase jump to finish splicing without phase jump of at least three sections of data.
Optionally, the number of segments of the data subjected to the phase-free stitching is preset.
Optionally, the splicing without phase jump includes the following steps:
respectively carrying out autocorrelation on the first signal and the second signal to obtain a first autocorrelation function and a second autocorrelation function;
Cutting off the first autocorrelation function and the second autocorrelation function respectively;
reversing the cut-off second autocorrelation function;
And splicing the truncated first autocorrelation function with the truncated and inverted second autocorrelation function to obtain a spliced signal without phase jump.
Optionally, performing frequency domain transformation on the spliced signal to obtain information of the distance of the acquired target object.
Optionally, repeating the step of splicing without phase jump to finish splicing without phase jump of at least three sections of data.
Optionally, the number of segments of the data subjected to the phase-free stitching is preset.
Optionally, the truncation is taking half of the symmetry of the autocorrelation function.
The beneficial effects of the invention are as follows: the invention provides a signal processing method with low signal-to-noise ratio,
Characterized by comprising the following steps:
performing phase jump-free splicing on the low signal-to-noise ratio signals to obtain spliced time domain signals;
performing frequency domain conversion on the time domain signal to obtain a frequency domain signal;
By such design, the signal-to-noise ratio is improved, and a useful signal can be obtained in a weak signal of the signal-to-noise ratio.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed in the embodiments will be briefly described below, it being understood that the following drawings only illustrate some embodiments of the present invention and therefore should not be considered as limiting the scope, and other related drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a schematic diagram of an FMCW channel acquisition target provided in the prior art;
FIG. 2 is a schematic diagram of a radar output discrete signal provided by the prior art;
fig. 3a is a time domain signal of an actually received echo signal according to the present embodiment;
fig. 3b is a schematic diagram of the frequency domain signal corresponding to fig. 3a according to the present embodiment;
Fig. 4a is a time domain diagram and a corresponding frequency domain signal schematic diagram of two signal segments with no phase jump after splicing according to the present embodiment;
fig. 4b is a time domain diagram and a corresponding frequency domain signal schematic diagram of the two signal segments with phase jump provided in the present embodiment after splicing;
FIG. 5a is a schematic diagram showing the relationship between the noise power and the signal with random phase and frequency provided by the embodiment of the present application;
FIG. 5b is a graph showing the relationship between the signal-to-noise ratio and the number of connection segments of noise-containing signals with the same frequency and random phase provided by the embodiment of the present application;
FIG. 6 is a schematic diagram of suppressing phase jumps according to an embodiment of the present application;
FIG. 7 is a schematic diagram of multi-segment data stitching provided by an embodiment of the present application;
Fig. 8 is a schematic diagram of a related splicing unit according to an embodiment of the present application;
FIG. 9 is a schematic diagram of a plurality of related splice units according to an embodiment of the present application;
Fig. 10a is a schematic time domain diagram of a noise-free phase hopped three-segment signal splice according to an embodiment of the present application;
FIG. 10b is a schematic diagram of the frequency domain signal of the signal shown in FIG. 10a according to an embodiment of the present application;
FIG. 11a is a schematic time domain diagram of a three-segment signal splice without noise and phase jump according to an embodiment of the present application;
FIG. 11b is a schematic diagram of the frequency domain signal of the signal shown in FIG. 11a according to an embodiment of the present application;
FIG. 12a is a schematic time domain diagram of a noisy phase hopped three-segment signal splice provided by an embodiment of the present application;
FIG. 12b is a schematic diagram of the frequency domain signal of the signal shown in FIG. 12a according to an embodiment of the present application;
fig. 13a is a schematic time domain diagram of a noisy, phase-hop-free, three-segment signal splice provided by an embodiment of the present application;
FIG. 13b is a schematic diagram of the frequency domain signal of the signal shown in FIG. 13a according to an embodiment of the present application;
FIG. 14a is a schematic diagram of signals completely submerged in noise according to an embodiment of the present application;
Fig. 14b is a schematic spectrum diagram of the signal shown in fig. 14a after 50-segment splicing according to the embodiment of the present application;
Fig. 14c is a schematic spectrum diagram of the signal shown in fig. 14a after 500-segment splicing according to the embodiment of the present application;
fig. 15a to 15e are schematic diagrams of spectrum signals obtained after performing correlation splicing once, twice, three times, four times and five times by using the correlation splicing unit respectively;
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments of the present invention. According to the pasmodic theorem, the signal time domain energy is equal to the frequency domain energy, i.e.: e t＝Ef, the energy of which is expressed as for the discrete signal s [ n ]
Where sf represents the amplitude corresponding to frequency f, then the average power is expressed as
Assuming that the total energy of each section of signal is equal, the total energy is E 0, the average power is mu 0, and the length of each section of signal is N 0 points; the energy of the q-segment signal connection is qE 0 so
The result shows that the power is inversely proportional to the number of spliced segments;
signal to noise ratio assuming no phase jump at signal connection
SNR＝qP0 (5)
P 0 is the signal power.
Fig. 4a is a time domain diagram after splicing two segment signals with no phase jump, and a frequency domain diagram after fourier transformation of the spliced signals; fig. 4b is a time domain diagram after splicing two segment signals with phase transitions, and a frequency domain diagram after fourier transformation of the spliced signals. It can be seen from fig. 4 a-4 b that the signal-to-noise ratio can be improved for the frequency domain signal after the two-segment signal without phase jump is spliced, but the signal-to-noise ratio can be adversely affected after the signal with phase jump is spliced.
FIG. 5a is a graph showing the relationship between the noise power and the noise power of a random noise-containing signal with the same frequency and the same phase provided by the embodiment of the present application; fig. 5b shows the relationship between the signal-to-noise ratio and the number of connection segments of noise-containing signals with the same frequency and random phase provided by the embodiment of the present application. It can be seen from fig. 5 a-5 b that the signal to noise ratio of the connection with 1000-segment phase random is only improved by less than 10dB and is far less than 30dB of the theoretical value, and the suppression of phase jump is the key of improving the signal to noise ratio of the connection. It is desirable to provide a method for effectively suppressing phase jumps when splicing multiple weak signals.
Fig. 6 is a schematic diagram of suppressing phase jump according to an embodiment of the present application. The frequency domain transformation of the time domain signal contains the signal amplitude, frequency and phase information, but the amplitude spectrum is irrelevant to the phase, so that the amplitude and the frequency of the time domain signal recovered by the frequency domain inverse transformation of the amplitude spectrum are all unchanged, but the phase is always 0, namely as shown in fig. 6. The ABS implementation in fig. 6 is to take the magnitude after a frequency domain transform (e.g., FFT (fourier transform)).
Then:
for discrete signals, each section of signal is often not an integer multiple of the period, and the two sections of signal are spliced in a reverse way, so that the splicing position is ensured to have no jump break point, a new s (t) is formed, the splicing can be continued by repeating the operation, the splicing process is shown in fig. 6, the obtained spliced phase jump-free signal is s (t) = [ s' 2(t),s′1 (t) ], and fig. 6 shows a frequency domain transformation splicing unit.
Fig. 6 shows the splicing of two signals without phase jump, the number of the spliced segments can be determined according to the requirement in the using process, and the frequency domain transformation splicing unit shown in fig. 6 is spliced again to realize the splicing of the multi-segment data without phase jump. Fig. 7 is a schematic diagram of multi-segment data splicing according to an embodiment of the present application. As shown in fig. 7, S 1 (t) and S 2 (t) finish phase jump-free splicing through the first frequency domain transform splicing unit, the result and S 3 (t) are respectively used as the input of the second frequency domain transform splicing unit to finish phase jump-free splicing of three segments of data, and S q-1 (t) and S q (t) are respectively used as the input of the q-1 frequency domain transform splicing unit to finish phase jump-free splicing of q segments of data. The signal to noise ratio of the signal can be obviously improved after the frequency domain transformation of the time domain signal spliced without phase jump. Finally, frequency domain transformation is carried out to obtain the required parameters for obtaining the distance information. The frequency domain shown in fig. 6 and 7 is only an exemplary frequency domain transform method, and is not limited to a simple fourier transform, and other frequency domain transform methods such as laplace transform, wavelet transform, Z, and the like may be used.
Autocorrelation is a data processing method that can find useful information hidden in a scrambled signal. The signals in engineering practice inevitably suffer from various interferences, and in severe cases, completely drown out the truly useful data. The autocorrelation can find out repeated information (periodic signal masked by noise) or identify fundamental frequencies implicit in the disappearance of the harmonic frequencies of the signal, which is commonly used for analysis of time-domain signals. An autocorrelation function is an average measure of the signal's characteristics in the time domain, which is used to describe the dependence of the signal's value at one time on another. Taking a segment of the time domain signal (e.g., a segment of the signal in fig. 3 a) the autocorrelation function is as follows:
The autocorrelation function is left-right symmetric,
After the cutting off:
From equation (8) and equation (9), it can be seen that if two signals obtained from the autocorrelation stage are directly spliced, there is a phase jump, so that it is necessary to splice one spliced signal after reversing the direction, as shown in fig. 8. As shown in fig. 8, S1 (t) and S2 (t) are respectively auto-correlated to obtain respective auto-correlation functions R1S1 (τ) and R2S2 (τ), then the stages are respectively performed on R1S1 (τ) and R2S2 (τ) to obtain truncated auto-correlation functions Rs1 (τ) and Rs2 (τ), if Rs1 (τ) and Rs2 (τ) are directly spliced, the spliced signals have phase jump, which is unfavorable for improving the signal to noise ratio. It is necessary to reverse one of the truncated autocorrelation functions, rs2 (τ) is reversed in fig. 8, but Rs1 (τ) may be reversed, which is only schematically illustrated and not particularly limited. And finally, splicing the two sections of signals to obtain a spliced signal without phase jump.
The only process shown in fig. 8 is the splicing of two pieces of data, and we refer to the splicing process shown in fig. 8 as the relevant splicing unit. If more than two segments of data are to be spliced without phase jumps, a plurality of associated splice units are required as shown in fig. 9. S1 (t) and S2 (t) complete splicing without phase jump through a first relevant splicing unit, and the splicing process is shown in FIG. 8, and will not be repeated here. As a result, R1 (2) (τ) and another autocorrelation function R2 (2) (τ) are respectively used as inputs of the second correlation splicing unit to complete the phase jump-free splicing of the three-segment data, and similarly, R1 (q) (τ) and R2 (q) (τ) are respectively used as inputs of the q-th correlation splicing unit to complete the phase jump-free splicing of the q-segment data. The signal to noise ratio of the signal can be obviously improved after the frequency domain is changed for the time domain signal spliced without phase jump. Finally, frequency domain transformation is carried out to obtain the required parameters for obtaining the distance information. The frequency domain transform shown in fig. 9 is not limited to the simple fourier transform. Other frequency domain transform methods such as laplace transform, wavelet transform, Z, etc. are also possible.
Fig. 10a is a schematic diagram of phase jump splicing of three noise-free time domain signals, and fig. 10b is a schematic diagram of frequency domain signals shown in fig. 10 a. Fig. 11a is a schematic diagram of phase jump free splicing of three segments of the noise free time domain signal, and fig. 11b is a schematic diagram of a frequency domain signal corresponding to the time domain signal shown in fig. 11 a. The phase-free splicing implemented in fig. 11a is implemented using the principle of the frequency domain splicing transformation unit shown in fig. 7, and the implementation procedure thereof will not be described herein. As can be seen from the frequency domain signal diagrams of fig. 11b and fig. 10b, the signal-to-noise ratio of the signal can be effectively improved by using the principle of the frequency domain stitching transformation unit described in fig. 7 to implement the phase jump free stitching.
Fig. 12a is a schematic diagram of phase hopping concatenation of three noisy time-domain signals, and fig. 12b is a schematic diagram of the frequency-domain signal shown in fig. 12 a. Fig. 13a is a schematic diagram of phase jump free splicing of three noisy time domain signals, and fig. 13b is a schematic diagram of a frequency domain signal corresponding to the time domain signal shown in fig. 13 a. The phase-free splicing implemented in fig. 13a is implemented using the principle of the frequency domain splicing transformation unit shown in fig. 7, and the implementation procedure thereof will not be described herein. As can be seen from the frequency domain signal diagrams of fig. 13b and fig. 12b, the signal-to-noise ratio of the signal can be effectively improved by using the principle of the frequency domain stitching transformation unit described in fig. 7 to implement the phase jump free stitching. Especially for signals with signals submerged in noise, useful signals can be finally obtained through a splicing principle of a plurality of times without phase jump.
The useful signal shown in fig. 14a is completely submerged in noise, the useful signal cannot be obtained by the prior art, fig. 14b is a schematic spectrum diagram after 50 segments of the signal shown in fig. 14a are spliced by using the principle of the frequency domain splicing transformation unit shown in fig. 7, the useful signal can be detected from fig. 14b, and the useful signal can be easily detected as the signal segment number is larger, the useful signal can be easily detected as the signal-to-noise ratio is higher, and the signal-to-noise ratio of fig. 14c is high as the signal-to-noise ratio is shown in fig. 14 c. While the greater the number of signal segments spliced, the higher the signal-to-noise ratio that results, the more complex and time consuming the computation needs to be considered. Fig. 15a to 15e are schematic diagrams of spectrum signals obtained after performing correlation splicing once, twice, three times, four times and five times, respectively, using the correlation splicing unit principle shown in fig. 8. From fig. 15a to fig. 15e, it can be seen that the signal-to-noise ratio of the signal spectrum after the splicing can be effectively improved by the related splicing unit principle, so as to obtain a useful signal, and of course, the more the number of related splices is, the higher the signal-to-noise ratio is, but the complexity and time consumption of calculation need to be considered at the same time.
It should be noted that in this document, relational terms such as "first" and "second" and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
The above description is only of the preferred embodiments of the present application and is not intended to limit the present application, but various modifications and variations can be made to the present application by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the protection scope of the present application. It should be noted that: like reference numerals and letters denote like items in the following figures, and thus once an item is defined in one figure, no further definition or explanation thereof is necessary in the following figures. The above description is only of the preferred embodiments of the present application and is not intended to limit the present application, but various modifications and variations can be made to the present application by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the protection scope of the present application.
Claims (8)
1. A method of improving signal-to-noise ratio comprising:
performing phase jump-free splicing on the low signal-to-noise ratio signals to obtain spliced time domain signals;
performing frequency domain conversion on the time domain signal to obtain a frequency domain signal;
the phase jump free stitching comprises:
Performing frequency domain conversion on the first signal and the second signal to obtain a frequency domain signal, and taking an amplitude spectrum of the frequency domain signal;
carrying out frequency domain inverse transformation on the amplitude spectrum to obtain a time domain signal;
performing frequency domain inverse transformation on the amplitude spectrum of the second signal to obtain a time domain signal for inversion;
performing frequency domain inverse transformation on the amplitude spectrum of the first signal to obtain a time domain signal, performing frequency domain inverse transformation on the amplitude spectrum of the second signal, and performing inverse transformation to obtain a time domain signal to splice to obtain a spliced signal without phase jump;
Or comprises:
respectively carrying out autocorrelation on the first signal and the second signal to obtain a first autocorrelation function and a second autocorrelation function;
Cutting off the first autocorrelation function and the second autocorrelation function respectively;
reversing the cut-off second autocorrelation function;
And splicing the truncated first autocorrelation function with the truncated and inverted second autocorrelation function to obtain a spliced signal without phase jump.
2. The method of claim 1, wherein the spliced signal is subjected to frequency domain transformation to obtain information of the distance of the object.
3. The method of claim 1, wherein the step of phase jump free stitching is repeated to complete phase jump free stitching of at least three pieces of data.
4. A method for improving signal to noise ratio according to claim 3, wherein the number of segments of data subjected to phase-free stitching is preset.
5. The method of claim 1, wherein the spliced signal is subjected to frequency domain transformation to obtain information of the distance of the object.
6. The method of claim 1, wherein the step of phase jump free stitching is repeated to complete phase jump free stitching of at least three pieces of data.
7. The method of improving signal-to-noise ratio according to claim 6, wherein the number of segments of data subjected to phase-free stitching is preset.
8. The method of improving signal-to-noise ratio according to claim 1, wherein the truncation is taken as half based on symmetry of the autocorrelation function.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110653033.6A CN115469273B (en) | 2021-06-11 | 2021-06-11 | Method for improving signal-to-noise ratio |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110653033.6A CN115469273B (en) | 2021-06-11 | 2021-06-11 | Method for improving signal-to-noise ratio |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN115469273A CN115469273A (en) | 2022-12-13 |
| CN115469273B true CN115469273B (en) | 2024-06-21 |
Family
ID=84363525
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110653033.6A Active CN115469273B (en) | 2021-06-11 | 2021-06-11 | Method for improving signal-to-noise ratio |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115469273B (en) |
Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101365214A (en) * | 2008-09-27 | 2009-02-11 | 华为技术有限公司 | Method and apparatus for reverse access channel detection signal |
Family Cites Families (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP2709105B1 (en) * | 2012-09-13 | 2014-11-19 | Nxp B.V. | Method, system and computer program product for reducing impulsive noise disturbance in an audio signal |
| CN103954963A (en) * | 2014-05-05 | 2014-07-30 | 北京理工大学 | Step frequency SAR imaging method based on CS algorithm |
| CN105024959A (en) * | 2015-08-03 | 2015-11-04 | 南京林业大学 | Segmented noise reduction method for aiming at no-frequency-jump broadband signals |
| CN107231169B (en) * | 2017-05-31 | 2019-04-02 | 北京理工大学 | A kind of anti-symbol jump Direct-Spread signal dynamics focus method based on KT-LVT |
| CN107907878B (en) * | 2017-11-08 | 2021-05-11 | 零八一电子集团有限公司 | Method for obtaining FMCW radar distance measurement value with high precision |
| CN108881082B (en) * | 2018-06-26 | 2019-09-24 | 中国人民解放军国防科技大学 | Signal-to-noise ratio determining method and device and channel equalization method and device |
| CN111147102B (en) * | 2019-12-25 | 2021-08-03 | 南京理工大学 | Low signal-to-noise ratio code capturing method based on frequency modulation signal |
| CN112114326B (en) * | 2020-09-21 | 2022-10-04 | 哈尔滨工业大学 | Frequency sweep signal splicing method and device for FMCW distance measurement |
| CN112379379A (en) * | 2020-11-10 | 2021-02-19 | 河南大学 | Channel phase deviation estimation method and device of TOPS SAR system |
-
2021
- 2021-06-11 CN CN202110653033.6A patent/CN115469273B/en active Active
Patent Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101365214A (en) * | 2008-09-27 | 2009-02-11 | 华为技术有限公司 | Method and apparatus for reverse access channel detection signal |
Non-Patent Citations (1)
| Title |
|---|
| 基于改进SPWVD的DS/FH信号跳频参数估计方法;钱怡等;《舰船电子对抗》;20150225;第38卷(第01期);58-59 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN115469273A (en) | 2022-12-13 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Shi et al. | A novel fractional wavelet transform and its applications | |
| Ipanov et al. | Radar signals with ZACZ based on pairs of D-code sequences and their compression algorithm | |
| Dwyer | Fourth‐order spectra of Gaussian amplitude‐modulated sinusoids | |
| AU2009201431A1 (en) | Radar Receiver and a Method for Processing Radar Returns | |
| US8022863B1 (en) | Spatially variant apodization (SVA) filter | |
| Goldfinger | Estimation of spectra from speckled images | |
| Zhang et al. | Joint delay and Doppler shift estimation for multiple targets using exponential ambiguity function | |
| Kulpa et al. | Filter-based design of noise radar waveform with reduced sidelobes | |
| DE102011088346B4 (en) | Device for detecting acoustic signals and the associated method | |
| Thakur et al. | A novel pulse compression technique for side-lobe reduction using woo filter concepts | |
| EP1369703B1 (en) | Irregular pulse repetition time (PRT) deconvolution method and systems, Doppler and clutter processing | |
| JP2005085167A (en) | Correlation processing device and method, and pulse compression processing device and method | |
| EP1521097B1 (en) | Pulse compression processor | |
| CN115469273B (en) | Method for improving signal-to-noise ratio | |
| Baghel et al. | Development of an efficient hybrid model for range sidelobe suppression in pulse compression radar | |
| Ehara et al. | Weak radar signal detection based on wavelet transform | |
| Hague | Generating waveform families using multi-tone sinusoidal frequency modulation | |
| DeFerrari et al. | Ideal signals and processing for continuous active sonar | |
| Thakur et al. | Signal generation employing Chebyshev polynomial for pulse compression with small relative side-lobe level | |
| Tierney et al. | Adaptive waveform design with least-squares system identification for interference mitigation in SAR | |
| Dwyer | Range and Doppler information from fourth-order spectra | |
| EP2997394A1 (en) | Coherent radar | |
| Zhuo et al. | A subbandadaptive filter for DTV based passive radar | |
| JP2004264284A (en) | Method for increasing distance which is not ambiguity in fsk radar | |
| Parajuli et al. | Interference-Resistant Photonically Generated Orthogonal Pair Multi-Chirp-LFM Waveform |
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 |