CN102508219A - Turbulent current target detection method of wind profiler radar - Google Patents
Turbulent current target detection method of wind profiler radar Download PDFInfo
- Publication number
- CN102508219A CN102508219A CN2011103140749A CN201110314074A CN102508219A CN 102508219 A CN102508219 A CN 102508219A CN 2011103140749 A CN2011103140749 A CN 2011103140749A CN 201110314074 A CN201110314074 A CN 201110314074A CN 102508219 A CN102508219 A CN 102508219A
- Authority
- CN
- China
- Prior art keywords
- spectrum
- power spectrum
- turbulent flow
- detection
- detection method
- 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.)
- Granted
Links
Images
Classifications
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
- Y02A90/00—Technologies having an indirect contribution to adaptation to climate change
- Y02A90/10—Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation
Landscapes
- Radar Systems Or Details Thereof (AREA)
Abstract
The invention relates to a turbulent current target detection method of a wind profiler radar. The method comprises the following steps of inputting a power spectrum of the wind profiler radar; performing pretreatment on the input power spectrum; determining noise level by a segmental averaging method; performing preliminary detection on the power spectrum, wherein the preliminary detection comprises amplitude detection, spectrum width detection and envelope detection; performing classification detection on the power spectrum, wherein the classification detection comprises exploratory excision of a spectral line, classification judgment according to the excision result and spectral peak separation according to categories; performing speed correction on a turbulent current target power spectrum; performing continuous modification on the turbulent current target power spectrum; and performing spectral moment calculation and outputting a result. According to the method disclosed by the invention, the target detection quality of the wind profiler radar can be improved.
Description
Technical field
The present invention relates to a kind of wind profile radar turbulent flow object detection method, be used for the target detection of wind profile radar.
Background technology
Wind profile radar (wind profiling radar/wind profiler radar) is a kind of novel windfinding radar; Can the distribution of meteorological elements such as atmospheric level wind field, uprush, air index textural constant with height be provided unmanned continuously in 24 hours; Have the good characteristics of spatial and temporal resolution height, continuity and real-time; It is the visual plant that carries out aerological sounding; Being the important supplement of current conventional pilot balloon observation system, is the new tool of carrying out weather forecast and meteorological support.
In the echoed signal that radar receives, echo signal is arranged not only, also have various undesired signals such as noise and clutter, the target detection of radar just is meant under the situation of various interference, goes for out real target.Therefore Radar Targets'Detection is to improve an important ring of the data confidence level and the quality of data.
The target that radar will be surveyed is body in motion normally; The theoretical foundation that detects moving target and fixed clutter is their difference on translational speed; Cause that owing to movement velocity is different the Doppler frequency of echoed signal is unequal; Thereby can distinguish different objects, this is the basic thought that adopts moving-target demonstration and moving target detection technique in the radar.
The target detection of wind profile radar and other radar have different characteristics.At present, the simplest way of wind profile radar target detection is to the peaked detection method of power spectrum amplitude, and this method thinks that signal is always the strongest, therefore on power spectrum, finds out the position at maximal value place, and to be taken as be turbulent flow target spectrum peak.But when strong noise jamming was arranged, this method can cause very mistake.
Owing to a little less than the turbulent flow echoed signal, mix when wind profile radar is surveyed with very easily receiving, the pollution of bird etc.The multiple method of from the wind profile radar spectrum, removing clutter has been proposed at present, for example, main NIMA method (the NCAR Improved Moment Algorithm) method of using.The NIMA method is mainly used in rejects the land clutter influence, and it thinks that whether certain point is that the principal character of land clutter is symmetry and curvature, in composite formula of these eigenwert substitutions, according to the size of output valve, judges signal or clutter.Parameter in the NIMA algorithm needs to be configured according to different equipment, different measurement places, and is more loaded down with trivial details.
General in the current operation wind transmission profile radar is " method objectifies "." method objectifies " thinks that spectral density value all is a signal greater than the frequency of noise level, carries out integrating meter to whole spectrum then and calculates spectral moment.When on interference makes power spectrum, a plurality of spectrums peak occurring, this method is actually the weighting of spectrum peak and shared width, and what the result reflected is total effect of signal and various interference.More intense when echoed signal, when spectrum peak envelope was apparent in view, the detection of target was to be relatively easy to; But the echoed signal of wind profile radar is all more weak usually, and noise jamming is usually arranged, and a lot of clutters can both be covered real echo.In this case, adopt " method objectifies " to carry out target detection and calculate with each rank square, effect is relatively poor.
Therefore, need a kind of new wind profile radar turbulent flow object detection method of research, can satisfy real-time of handling and the requirement that detects effect two aspects simultaneously, to improve the quality of wind profile radar target detection.
Summary of the invention
The object of the present invention is to provide a kind of wind profile radar turbulent flow object detection method, this method is treated to target with real time business, does not carry out loaded down with trivial details calculating, has eliminated the influence of noise jamming simultaneously again preferably.
According to main aspect of the present invention, this wind profile radar turbulent flow object detection method may further comprise the steps:
The power spectrum of a, input wind profile radar;
B, the power spectrum of being imported is carried out pre-service;
C, confirm noise level with the segmental averaging method;
D, power spectrum is carried out Preliminary detection, detect and envelope detected comprising amplitude detection, spectrum width;
E, power spectrum is carried out classification and Detection, comprising spectral line being carried out exploratory excision, classifying according to the excision result and judge and compose the peak according to classification and separate;
F, turbulent flow target power spectrum is carried out speed correct;
G, turbulent flow target power spectrum is carried out the continuity correction; And
H, carry out spectral moment and calculate, and the output result.
According to an aspect of the present invention, pre-service comprises interpolation processing.
According to an aspect of the present invention, pre-service also comprises the running mean processing.
According to an aspect of the present invention, interpolation processing is that the zero velocity point interpolation is handled.
According to an aspect of the present invention, adopt during interpolation processing about zero velocity point the mean value of 2 spectral density value to replace the zero velocity point value.
According to an aspect of the present invention, the running mean processing is that 3 running means are handled.
Will be appreciated that the characteristic in the above each side of the present invention is independent assortment within the scope of the invention, and do not receive the restriction of its order---as long as the technical scheme after the combination drops in the connotation of the present invention.
Description of drawings
In order to be illustrated more clearly in the technical scheme among the present invention, will do to introduce simply to accompanying drawing of the present invention below, wherein:
Fig. 1 has shown the process flow diagram according to wind profile radar turbulent flow object detection method of the present invention;
Fig. 2 is for carrying out the exploratory synoptic diagram that excises front and back according to the present invention to spectral line;
Fig. 3 (a)-3 (f) has shown according to spectral line of the present invention and has carried out several kinds of situation after the exploratory excision.
Embodiment
Hereinafter will combine the preferred embodiments of the present invention that technical scheme of the present invention is elaborated.
Need to understand that the description of hereinafter (comprising accompanying drawing) only is exemplary, but not the description of limitation of the present invention property.Can relate to the concrete quantity of parts in the following description, yet also need should be appreciated that, these quantity also only are exemplary, and those skilled in the art can choose the parts of right quantity with reference to the present invention arbitrarily.
According to the first embodiment of the present invention, the power spectrum that wind profile radar is surveyed is input in the signal processor, carries out pre-service then.Pre-service preferably mainly comprises interpolation processing and running mean and handles.
Interpolation processing generally is that the zero velocity point interpolation is handled.In zero velocity point interpolation processing procedure; When wind speed hour; Even the turbulence signal spectrum envelope has just comprised the zero velocity point near zero velocity point; Can make Power Spectrum Distribution fracture occur at zero velocity point place like this, therefore preferably adopt the alternative zero velocity point value of mean value of 2 spectral density value about zero velocity point to carry out interpolation processing.
The main effect that running mean is handled is to reject for some " flying spot data ".In the spectrum data, exist some to be worth king-sized data point sometimes, this belongs to interference once in a while, and their distribution is general more scattered, and spectral line is very narrow, promptly only occurs in some individual point.Spectrum is carried out running mean handle, preferably 3 running means are handled and can be removed these spike points.
Then, confirm noise level with the segmental averaging method.Be not arranged under the too little situation in maximum Doppler speed; In the whole long power spectrum chart of wind profile radar output; The spectrum of signal and various noise jamming generally all only accounts for the sub-fraction of spectrum on horizontal ordinate spectrum axle, therefore can whole spectrum be divided into plurality of sections, calculates the mean value of each section; Because in stronger signal spectrum and disturbance spectrum coexistence a certain section in plurality of sections, so can be with the minimum value in the mean value of all the other sections as noise level.In a preferred embodiment of the invention, whole spectrum is divided into eight sections.Noise level is employed in subsequent step after confirming.
Follow, power spectrum is carried out Preliminary detection, this detects and envelope detected comprising amplitude detection, spectrum width.No matter how faint the atmospheric turbulence echoed signal is, on Power Spectrum Distribution figure, turbulent flow echoed signal spectrum peak all should be higher than cogongrass, and its spectral density value can not be a maximal value, but must be the maximum point with certain spectrum width.Therefore; On power spectrum, at first detect the spectrum peak maximum, search for respectively toward both sides then, find first point (or power spectral value is by the turning point that progressively reduces to begin to increase) less than the noise level value; Promptly think to have hunted out whole spectrum peak envelope, in envelope, calculate the spectrum width value.If the spectrum width value not in certain presetting range, then thinks not to be turbulence signal, should compose the peak and envelope all is set to the noise level value, remove to seek next polarographic maximum point then, up to finding the spectrum peak envelope that meets the demands.If the whole piece spectral line all fails to search out, seek again after then suitably enlarging the threshold range of spectrum width.
After Preliminary detection, need proceed classification and Detection.This is because the spectrum peak that Preliminary detection goes out is positioned at the zero velocity point sometimes, and a little spectrum peak envelope is arranged near zero velocity point, and the position and the detected spectrum of a last height layer (or next height layer) peak at this place, little spectrum peak have better height continuity.Human eye can think intuitively that this little spectrum peak should be only turbulent flow echoed signal spectrum peak, and just because the turbulent flow echoed signal of place height is weaker than land clutter, so computing machine has detected the land clutter position of zero velocity point when composing the peak Preliminary detection.If can cut away near the one section spectrum of Power Spectrum Distribution zero velocity point on these height, computing machine just can detect this little spectrum peak.But, for the automatic time of being born by computer program detects, be to let the people which specify highly can excise near one section spectrum of zero velocity point, therefore must after Preliminary detection, proceed classification and Detection.
Classification and Detection is one step of key of rejecting the land clutter influence, and main thought is near the assorted different shape that zero velocity point, is mingled in signal in base area, the judgement of classifying.Its key step comprises: judge whether the spectrum peak envelope that Preliminary detection goes out has crossed over curve of zero velocity, if do not cross over curve of zero velocity, think that the turbulence signal of this height is more intense, and not disturbed by land clutter, be labeled as credible; If spectrum peak envelope has been crossed over the height layer of curve of zero velocity, will detect as follows according to spectrum peak and the distance of zero line, the concrete shape of envelope:
Near one section spectral line to the Power Spectrum Distribution curve of zero velocity of this height layer carries out exploratory excision, according to " otch " shape that cuts out, the judgement of classifying.The width of exploratory spectral line excision will confirm that generally selected wide than the width of all land clutters around the curve of zero velocity both sides, still a width narrower than general turbulent flow Echo width excises according to the situation of local land clutter spectrum.Preferably, the excision width is about 1m/s, also is about each 0.5m/s of curve of zero velocity both sides.
When carrying out the excision of exploratory land clutter interference spectral line, replace being excised spectrum value a little with the noise level value of this height layer.
Fig. 2 is the synoptic diagram before and after the exploratory spectral line excision, the Far Left and the farthest right position of one section spectrum of left and right sides straight line representative excision among the figure, and middle straight line is represented the position of curve of zero velocity.First row's expression original spectrum, it possibly exist land clutter to disturb, and the spectrum after the exploratory spectral line excision is carried out in second row's expression, and the 3rd row's expression is to the otch both sides value of composing gradient calculation.The leftmost Grad of otch is that A point spectrum value deducts the spectrum value that B is ordered, and the rightmost Grad of otch is that the spectrum value that C is ordered deducts the spectrum value that D is ordered.Obtain after these two gradients, utilize them to judge that otch is precipitous or relaxes that the precipitous representative of otch has the influence of turbulent flow echo here, otch relaxes representative and does not exist the influence of turbulent flow echo or the influence of turbulent flow echo to ignore here.
Can know referring to Fig. 3 (a)-3 (f); After carrying out the exploratory excision of spectral line,, can be divided into following three kinds of situation according to otch both sides Grad size; Every kind of situation all possibly comprise different land clutters and turbulent flow echo mixed type, so will carry out following classification and Detection.
First kind of situation is that both sides Grad all very little (less than certain prevalue) at this moment possibly exist two kinds of situations again.A kind of is shown in Fig. 3 (a), and what cut away is the land clutter peak really, has found the secondary maximum value peak with certain spectrum width in all the other positions of spectral line, can be used as turbulent flow target spectrum peak.Another kind of as Fig. 3 (b) shown in, in this case, exploratory excision be the stack echo of turbulent flow echo and land clutter, what excision afterwards was left all is the very little values in original both sides, can not find the spectrum peak with turbulent flow target property.This explanation target and land clutter are excised simultaneously, should recover the data of being excised this moment, compose the peak as the turbulent flow target.
Second kind of situation be, both sides otch Grad is very big (greater than certain prevalue) all, at this moment also possibly have two kinds of situations.A kind of is because the land clutter spectrum is wide unusually, and causes otch both sides Grad all very big, but the secondary maximum value peak with certain spectrum width has been found in all the other positions of the spectral line after excision, can be used as turbulent flow target spectrum peak, like Fig. 3 (c).Another kind is to can not find the spectrum peak with turbulent flow target property in the spectral line remaining after excision, and this explanation target is excised with land clutter simultaneously, should recover the data of being excised this moment, composes the peak as the turbulent flow target, like Fig. 3 (d).
The third situation is that otch both sides Grad is big on one side little on one side, at this moment still possibly have two kinds of situations.A kind of is that the turbulent flow echo separates with the spectrum peak of land clutter, and distance is also far away, and preferably at least greater than 0.5m/s, but the spectral pattern envelope is not overlapping, shown in Fig. 3 (e).Can be in original spectrum for this situation; Through finding in this side otch first smaller or equal to the spectrum point position of noise level value; Separation as land clutter and turbulent flow echo spectrum; Left side straight line adjusted to carry out the land clutter excision behind the separation again, and then hunt out target spectrum peak and envelope (referring to the following row of Fig. 3 (e)) thereof.Second kind of situation is that the turbulent flow echo separates with the spectrum peak of land clutter, but the spectral pattern envelope overlaps, shown in Fig. 3 (f).For this situation; Can in original spectrum, find the position of minimum spectrum value point in this side otch; (can not find the minimal noise point when overlapping as the separation of land clutter and turbulent flow echo spectrum; So can only look for minimum spectrum value point), also be to carry out the land clutter excision again after this side straight line is adjusted to separation, detect target spectrum peak and envelope thereof (referring to the following row of Fig. 3 (e)) again.
After classification and Detection, turbulent flow target radar spectrum is carried out speed correct.The all corresponding maximal rate that velocity ambiguity does not take place of each mode of operation of wind profile radar; If the actual radial velocity of target surpasses this maximal rate; It is fuzzy then can to test the speed; Thereby on the spectral density image, produce folding phenomenon, therefore need from the low layer to the high level, carry out the velocity ambiguity detection one by one and move back Fuzzy Processing, promptly carry out speed and correct.
Then, turbulent flow target power spectrum is carried out the continuity correction.Because wind field has certain continuity on the determined space scale of detection volume with on the determined time scale of time sense; If the difference of therefore searching the spectrum peak position that comes out between the height layer up and down is within certain threshold value; Then this data fit continuity requirement does not meet the continuity requirement otherwise should be identified as.According to this principle the radial velocity value between each height layer of each beam position detection being carried out continuity detects.Detect if all pass through continuity between all height layers, that is optimal.But because the intermittence of turbulent flow perhaps is interfered, usually there are some height layers spectrum distribution image cogongrass the centre, and the confidence level at the target spectrum peak of detection is poor, can't through and other height layer between continuity detect.For this situation; Can utilize the radial velocity value of the height layer that has passed through the continuity detection; Produce a radial velocity variation range; On the power spectrum of the height layer that does not detect, search the spectrum peak,, thereby accomplish continuity reparation tomography as the new target peak position of this height layer through continuity.
At last, carry out spectral moment and calculate, and the output result.After identifying turbulent flow target spectrum peak position, only within turbulent flow target spectrum peak envelope, calculate, in the hope of data such as signal to noise ratio (S/N ratio), doppler velocity and speed spectrum widths.The formula that calculates is following:
Wherein, P
rBe the power of echoed signal,
Ψ (v) be the power spectrum density distribution function,
V
rBe average doppler velocity, and
σ
vIt is the doppler velocity spectrum width.
In computation process, other spectral line beyond the turbulent flow target spectrum peak envelope is not calculated, be higher than of the influence of the clutter spike of noise level to overcome other spectrum point place to each rank square result of calculation.After the doppler velocity of trying to achieve each beam position detection, just can calculate the air-out profile.
Above basis has preferred embodiment been done detailed description to the present invention; But it will be appreciated that; Scope of the present invention is not limited to these concrete embodiments, but comprises that those skilled in the art are according to any change and the change that openly can make of the present invention.
Claims (6)
1. wind profile radar turbulent flow object detection method said method comprising the steps of:
The power spectrum of a, input wind profile radar;
B, the power spectrum of being imported is carried out pre-service;
C, confirm noise level with the segmental averaging method;
D, power spectrum is carried out Preliminary detection, detect and envelope detected comprising amplitude detection, spectrum width;
E, power spectrum is carried out classification and Detection, comprising spectral line being carried out exploratory excision, classifying according to the excision result and judge and compose the peak according to classification and separate;
F, turbulent flow target power spectrum is carried out speed correct;
G, turbulent flow target power spectrum is carried out the continuity correction; And
H, carry out spectral moment and calculate, and the output result.
2. wind profile radar turbulent flow object detection method according to claim 1 is characterized in that said pre-service comprises interpolation processing.
3. wind profile radar turbulent flow object detection method according to claim 1 is characterized in that, said pre-service comprises the running mean processing.
4. wind profile radar turbulent flow object detection method according to claim 2 is characterized in that, interpolation processing is that the zero velocity point interpolation is handled.
5. wind profile radar turbulent flow object detection method according to claim 4 is characterized in that, adopts about zero velocity point the mean value of 2 spectral density value to replace the zero velocity point value during interpolation processing.
6. wind profile radar turbulent flow object detection method according to claim 3 is characterized in that, it is that 3 running means are handled that said running mean is handled.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN2011103140749A CN102508219B (en) | 2011-10-17 | 2011-10-17 | Turbulent current target detection method of wind profiler radar |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN2011103140749A CN102508219B (en) | 2011-10-17 | 2011-10-17 | Turbulent current target detection method of wind profiler radar |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN102508219A true CN102508219A (en) | 2012-06-20 |
| CN102508219B CN102508219B (en) | 2013-08-07 |
Family
ID=46220325
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN2011103140749A Expired - Fee Related CN102508219B (en) | 2011-10-17 | 2011-10-17 | Turbulent current target detection method of wind profiler radar |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN102508219B (en) |
Cited By (20)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102890272A (en) * | 2012-11-05 | 2013-01-23 | 中国航天科工集团第二研究院二十三所 | Method for processing millimeter wave cloud radar signal |
| CN103149552A (en) * | 2013-02-04 | 2013-06-12 | 南京信息工程大学 | Retreating dim method of Doppler weather radar radial velocity field |
| CN104316929A (en) * | 2014-09-12 | 2015-01-28 | 何平 | Power spectrum identification method and apparatus |
| CN105259537A (en) * | 2015-11-10 | 2016-01-20 | 武汉大学 | Doppler spectrum center frequency estimation method based on frequency shift iteration |
| CN105353378A (en) * | 2015-10-15 | 2016-02-24 | 中国气象科学研究院 | Cloud-seeding operation effect radar detection time sequence contrast analysis method and system |
| CN106054195A (en) * | 2016-05-31 | 2016-10-26 | 中国民航大学 | Turbulence spectrum width estimation method based on space-time optimal processor |
| CN106324601A (en) * | 2016-08-08 | 2017-01-11 | 北京无线电测量研究所 | Wind profile radar networking-based three dimension wind field inversion method |
| CN107870332A (en) * | 2016-09-26 | 2018-04-03 | 北京无线电测量研究所 | A kind of wind profile radar atmospheric turbulance energy detection system and method |
| CN107907864A (en) * | 2017-10-27 | 2018-04-13 | 北京无线电测量研究所 | A kind of wind profile radar precipitation disturbance restraining method and system |
| CN108469609A (en) * | 2018-06-11 | 2018-08-31 | 成都纳雷科技有限公司 | A kind of detection information filtering method for radar target tracking |
| CN109254273A (en) * | 2018-11-01 | 2019-01-22 | 中国气象科学研究院 | The treating method and apparatus of wind profile radar echo-signal |
| CN109324328A (en) * | 2018-12-17 | 2019-02-12 | 北京无线电测量研究所 | The method and device of wind profile radar vertical beam spectrum of turbulence when extracting precipitation |
| CN109828276A (en) * | 2019-03-06 | 2019-05-31 | 南京微麦科斯电子科技有限责任公司 | A kind of wind profile radar based on Non-Interference Algorithm |
| JP2020008536A (en) * | 2018-07-12 | 2020-01-16 | 日本無線株式会社 | Device and program for target detection |
| CN111239704A (en) * | 2020-02-12 | 2020-06-05 | 中国科学院大气物理研究所 | Atmospheric detection radar target echo identification processing method, device, equipment and medium |
| CN112859031A (en) * | 2021-02-05 | 2021-05-28 | 北京城市气象研究院 | Method and system for inverting raindrop spectrum |
| CN114114273A (en) * | 2021-12-08 | 2022-03-01 | 天津市气象台 | Wind profile radar signal processing method |
| CN115356787A (en) * | 2022-10-21 | 2022-11-18 | 南京信息工程大学 | Calculation and prediction method for atmospheric coherence length |
| CN115980744A (en) * | 2022-11-10 | 2023-04-18 | 国家卫星海洋应用中心 | Method for separating spectrum of multi-peak sea wave image of satellite-borne SAR image data without overlapping |
| RU2824853C1 (en) * | 2023-06-08 | 2024-08-14 | Дмитрий Николаевич Дудин | Air perimeter radar system |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US5592171A (en) * | 1995-08-17 | 1997-01-07 | The United States Of America As Represented By The Secretary Of Commerce | Wind profiling radar |
| US5689444A (en) * | 1995-06-07 | 1997-11-18 | The United States Of America, As Represented By The Secretary Of Commerce | Statistical quality control of wind profiler data |
| CN101881824A (en) * | 2009-05-05 | 2010-11-10 | 何平 | Objective and fast determination method of noise threshold of power spectrum density data |
| CN101988963A (en) * | 2010-04-19 | 2011-03-23 | 南京恩瑞特实业有限公司 | Method for acquiring three-dimensional wind field by using wind profiler radar |
-
2011
- 2011-10-17 CN CN2011103140749A patent/CN102508219B/en not_active Expired - Fee Related
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US5689444A (en) * | 1995-06-07 | 1997-11-18 | The United States Of America, As Represented By The Secretary Of Commerce | Statistical quality control of wind profiler data |
| US5592171A (en) * | 1995-08-17 | 1997-01-07 | The United States Of America As Represented By The Secretary Of Commerce | Wind profiling radar |
| CN101881824A (en) * | 2009-05-05 | 2010-11-10 | 何平 | Objective and fast determination method of noise threshold of power spectrum density data |
| CN101988963A (en) * | 2010-04-19 | 2011-03-23 | 南京恩瑞特实业有限公司 | Method for acquiring three-dimensional wind field by using wind profiler radar |
Non-Patent Citations (4)
| Title |
|---|
| CLOTHIAUX, E. E等: "A First-Guess Feature-Based Algorithm for Estimating Wind Speed in Clear-Air Doppler Radar Spectra", 《JOURNAL OF ATMOSPHERIC AND OCEANIC TECHNOLOGY》 * |
| GRIESSER T.等: "Multiple peak processing algorithm for identification of atmospheric signals in Doppler radar wind profiler spectra", 《JOURNAL OF ATMOSPHERIC AND OCEANIC TECHNOLOGY》 * |
| 张雯雯等: "风廓线雷达去地杂波方法研究", 《北京大学学报(自然科学版)》 * |
| 董德保等: "风廓线雷达地物杂波抑制研究", 《大气与环境光学学报》 * |
Cited By (28)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102890272B (en) * | 2012-11-05 | 2013-12-04 | 中国航天科工集团第二研究院二十三所 | Method for processing millimeter wave cloud radar signal |
| CN102890272A (en) * | 2012-11-05 | 2013-01-23 | 中国航天科工集团第二研究院二十三所 | Method for processing millimeter wave cloud radar signal |
| CN103149552A (en) * | 2013-02-04 | 2013-06-12 | 南京信息工程大学 | Retreating dim method of Doppler weather radar radial velocity field |
| CN103149552B (en) * | 2013-02-04 | 2015-11-18 | 南京信息工程大学 | A kind of Doppler radar radial velocity field move back blur method |
| CN104316929A (en) * | 2014-09-12 | 2015-01-28 | 何平 | Power spectrum identification method and apparatus |
| CN105353378A (en) * | 2015-10-15 | 2016-02-24 | 中国气象科学研究院 | Cloud-seeding operation effect radar detection time sequence contrast analysis method and system |
| CN105259537A (en) * | 2015-11-10 | 2016-01-20 | 武汉大学 | Doppler spectrum center frequency estimation method based on frequency shift iteration |
| CN106054195B (en) * | 2016-05-31 | 2018-07-06 | 中国民航大学 | The turbulent flow spectrum width method of estimation of optimal processor during based on sky |
| CN106054195A (en) * | 2016-05-31 | 2016-10-26 | 中国民航大学 | Turbulence spectrum width estimation method based on space-time optimal processor |
| CN106324601A (en) * | 2016-08-08 | 2017-01-11 | 北京无线电测量研究所 | Wind profile radar networking-based three dimension wind field inversion method |
| CN106324601B (en) * | 2016-08-08 | 2019-04-09 | 北京无线电测量研究所 | A kind of dimensional wind inversion method based on wind profile radar networking |
| CN107870332A (en) * | 2016-09-26 | 2018-04-03 | 北京无线电测量研究所 | A kind of wind profile radar atmospheric turbulance energy detection system and method |
| CN107907864A (en) * | 2017-10-27 | 2018-04-13 | 北京无线电测量研究所 | A kind of wind profile radar precipitation disturbance restraining method and system |
| CN107907864B (en) * | 2017-10-27 | 2019-11-15 | 北京无线电测量研究所 | A kind of wind profile radar precipitation disturbance restraining method and system |
| CN108469609A (en) * | 2018-06-11 | 2018-08-31 | 成都纳雷科技有限公司 | A kind of detection information filtering method for radar target tracking |
| JP2020008536A (en) * | 2018-07-12 | 2020-01-16 | 日本無線株式会社 | Device and program for target detection |
| CN109254273A (en) * | 2018-11-01 | 2019-01-22 | 中国气象科学研究院 | The treating method and apparatus of wind profile radar echo-signal |
| CN109324328A (en) * | 2018-12-17 | 2019-02-12 | 北京无线电测量研究所 | The method and device of wind profile radar vertical beam spectrum of turbulence when extracting precipitation |
| CN109324328B (en) * | 2018-12-17 | 2022-12-20 | 北京无线电测量研究所 | Method and device for extracting wind profile radar vertical beam turbulence spectrum in rainfall |
| CN109828276A (en) * | 2019-03-06 | 2019-05-31 | 南京微麦科斯电子科技有限责任公司 | A kind of wind profile radar based on Non-Interference Algorithm |
| CN109828276B (en) * | 2019-03-06 | 2023-04-21 | 南京微麦科斯电子科技有限责任公司 | Wind profile radar based on interference algorithm |
| CN111239704A (en) * | 2020-02-12 | 2020-06-05 | 中国科学院大气物理研究所 | Atmospheric detection radar target echo identification processing method, device, equipment and medium |
| CN112859031A (en) * | 2021-02-05 | 2021-05-28 | 北京城市气象研究院 | Method and system for inverting raindrop spectrum |
| CN114114273A (en) * | 2021-12-08 | 2022-03-01 | 天津市气象台 | Wind profile radar signal processing method |
| CN115356787A (en) * | 2022-10-21 | 2022-11-18 | 南京信息工程大学 | Calculation and prediction method for atmospheric coherence length |
| CN115980744A (en) * | 2022-11-10 | 2023-04-18 | 国家卫星海洋应用中心 | Method for separating spectrum of multi-peak sea wave image of satellite-borne SAR image data without overlapping |
| CN115980744B (en) * | 2022-11-10 | 2024-03-22 | 国家卫星海洋应用中心 | Method for separating satellite-borne SAR image data from non-overlapping masking peak sea wave image spectrum |
| RU2824853C1 (en) * | 2023-06-08 | 2024-08-14 | Дмитрий Николаевич Дудин | Air perimeter radar system |
Also Published As
| Publication number | Publication date |
|---|---|
| CN102508219B (en) | 2013-08-07 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN102508219B (en) | Turbulent current target detection method of wind profiler radar | |
| EP2419755B1 (en) | Methods and apparatus for integration of distributed sensors and airport surveillance radar to mitigate blind spots | |
| CN105488770B (en) | A kind of airborne laser radar point cloud filtering method of object-oriented | |
| US7515087B1 (en) | Weather radar system and method using data from a lightning sensor | |
| CN202433521U (en) | Wind profile radar turbulence target detection processing plate | |
| KR101258668B1 (en) | Korea local radar processing system | |
| US7948429B2 (en) | Methods and apparatus for detection/classification of radar targets including birds and other hazards | |
| CN103176187B (en) | A kind of airborne early warning radar ground surface high speed highway goal filtering method | |
| CN111123212B (en) | Signal processing method of scene surveillance radar based on complex clutter background | |
| JP2010181406A (en) | System and method for displaying radar-measured turbulence intensity on vertical display | |
| US9459348B2 (en) | Millimeter wave radar system for and method of weather detection | |
| CN104714225B (en) | Dynamic programming tracking-before-detection method based on generalized likelihood ratios | |
| CN112051568B (en) | Pitching angle measurement method of two-coordinate radar | |
| CN106772387A (en) | A kind of wind shear recognition methods | |
| CN109188430B (en) | Target extraction method based on ground monitoring radar system | |
| CN102243298A (en) | Method for eliminating ground clutter of airborne weather radar based on digital elevation model (DEM) | |
| CN113267779B (en) | Target detection method and system based on radar and image data fusion | |
| CN113281716A (en) | Photon counting laser radar data denoising method | |
| KR101426226B1 (en) | Signal processing method of radar | |
| Shen et al. | Sea ice type classification based on random forest machine learning with Cryosat-2 altimeter data | |
| CN104199002B (en) | The radar information processing method of suppression wind-power electricity generation vane false-alarm targets | |
| JP3084483B2 (en) | In-flight Doppler weather radar windshear detection system | |
| CN104914421A (en) | Low-altitude wind shear wind speed estimating method based on sum-difference beam | |
| CN105260591B (en) | A kind of MIGFA Gust fronts identification innovatory algorithm at more elevations angle | |
| CN115436966A (en) | Batch extraction method for laser radar reference water depth control points |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| C14 | Grant of patent or utility model | ||
| GR01 | Patent grant | ||
| CF01 | Termination of patent right due to non-payment of annual fee |
Granted publication date: 20130807 Termination date: 20141017 |
|
| EXPY | Termination of patent right or utility model |