CN104062642B - A kind of method that laser radar waveform data is carried out Gauss echo decomposition - Google Patents
A kind of method that laser radar waveform data is carried out Gauss echo decomposition Download PDFInfo
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- CN104062642B CN104062642B CN201310597059.9A CN201310597059A CN104062642B CN 104062642 B CN104062642 B CN 104062642B CN 201310597059 A CN201310597059 A CN 201310597059A CN 104062642 B CN104062642 B CN 104062642B
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- 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
- G01S7/48—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S17/00
- G01S7/483—Details of pulse systems
- G01S7/486—Receivers
- G01S7/487—Extracting wanted echo signals, e.g. pulse detection
- G01S7/4876—Extracting wanted echo signals, e.g. pulse detection by removing unwanted signals
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- 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
- G01S17/00—Systems using the reflection or reradiation of electromagnetic waves other than radio waves, e.g. lidar systems
- G01S17/88—Lidar systems specially adapted for specific applications
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- Computer Networks & Wireless Communication (AREA)
- General Physics & Mathematics (AREA)
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- Electromagnetism (AREA)
- Radar Systems Or Details Thereof (AREA)
- Optical Radar Systems And Details Thereof (AREA)
Abstract
The present invention relates to remote sensing technology and mapping science, disclose a kind of method that laser radar waveform data is carried out Gauss echo decomposition.Specifically include: first laser radar waveform data is carried out noise reduction process, then flex point is utilized quickly to find out all of flex point in the effective data intervals of described Wave data about the character that neighbouring curve near tangent is interior point or exterior point, and remove owing to numerical fluctuations is mistaken for the data point of flex point, obtain several Gauss crests in Wave data followed by two flex points that odd even is adjacent, finally the Gauss crest of gained is processed to satisfactory Gauss echo decomposition result.Present invention achieves the most accurately processing laser radar waveform data.
Description
Technical field
The present invention relates to remote sensing technology and mapping science, be specifically related to a kind of to laser radar waveform number
According to the method carrying out Gauss echo decomposition.
Background technology
Laser radar is taken as the active remote sensing equipment of detecting technique means, with its high score
The feature of resolution is widely used in the field such as forestry, mapping science.Such as GLAS(Geoscience
Laser Altimeter System, geoscience laser-measured height instrument system) it is mounted in the U.S. 2003
The First satellite-bone laser radar sensor on scientific experiment satellite ICEsat that year launches, can connect
Continuous acquisition air and the echo data on ground.Digitizer record therein is from satellite to earth's surface 765
Echo-signal (time-intensity curves) in the range of km, can extract with analyzing after filtering
Launch pulse and surface echo.1064nm wave band echo data is extractible plants such as the height of tree etc. for it
By vertical stratification information, can preferably evaluate whole world vegetation biomass and carbon through such data
Circulation.Laser radar echo mathematical form is expressed as the superposition of several Gaussian function by researcher
Plus an estimated bias, the corresponding elevation information of the crest location of each Gaussian function component.
First step hence for the extraction of elevation information carries out height to laser radar waveform data exactly
This echo decomposes, it is possible to be referred to as the process to laser radar waveform data.
In existing laser radar waveform data processing scheme, mainly with theory calculate, non-linear
Fit to main, solve the best fit parameters of each Gaussian component.There is research group to the method
In weight improved with estimation error, it is thus achieved that reasonable result.
But in actual laser radar data, wavy curve does not has concrete function representation
Formula；And owing to these data are affected by multiple extraneous factor, the theoretical function expression that calculates is deposited
In computationally intensive and that error is big shortcoming.In the processing scheme of the studies above group, algorithm is with theory
It is calculated as main, calculates complexity, and in the execution step of actual algorithm, crest and trough are had
Wrongheaded phenomenon.
Summary of the invention
(1) solve the technical problem that
For the deficiencies in the prior art, the present invention provides a kind of and carries out laser radar waveform data
The method that Gauss echo decomposes, it is achieved that the most accurately processing laser radar waveform data.
(2) technical scheme
A kind of laser radar waveform data is carried out Gauss echo decomposition method, its feature exists
In comprising the following steps:
Utilize flex point about the character that neighbouring curve near tangent is interior point or exterior point at described waveform number
According to effective data intervals in quickly find out all of flex point；
Remove owing to numerical fluctuations is mistaken for the data point of flex point；
Two flex points utilizing odd even adjacent obtain several Gauss crests in Wave data；
The Gauss crest of gained is processed to satisfactory Gauss echo decomposition result.
It is preferred that described, to utilize flex point be interior point or the character of exterior point about neighbouring curve near tangent
The effective data intervals of described Wave data quickly finds out all of flex point step include: root
In described Wave data, an effective data intervals is estimated according to waveform shape；To in this interval
Each, find with continuous four data points that this point is the 3rd point；Judge that this point is the fullest
Foot condition: this point is the interior point of first and second some place straight line and the 4th point is second point
With the exterior point of this place straight line, or, this point is the exterior point of first and second some place straight line
And the interior point that the 4th point is second point and this place straight line；If meeting this condition, judge
This point is flex point.
It is preferred that the data point step that described removal is mistaken for flex point due to numerical fluctuations includes:
When the flex point ordinate ordinate than adjacent 2 the biggest or the least time, it is determined that this flex point be due to
Numerical fluctuations is mistaken for the data point of flex point, and is removed.
It is preferred that described two flex points utilizing odd even adjacent obtain several in Wave data
Gauss crest step includes: two flex points utilizing odd even adjacent estimate ripple the most that may be present
The initial value of the peak each key parameter under Gaussian function form；Character according to crest is to institute
The crest that there may exist screens, and obtains several Gauss crests.
It is preferred that the described character according to crest carries out screening step to all crests that may be present
Suddenly include: judge that peak point is the most overlapping with one of two flex points, if then giving up this peak point,
Then judge whether the value of the ordinate of 2 adjacent with peak point is both less than peak point ordinate
Value, if not then giving up this peak point.
It is preferred that before all described steps, farther include laser radar waveform data
Carry out noise reduction process.
It is preferred that described noise reduction process includes by Gaussian filter laser radar waveform data
Point carries out LPF.
It is preferred that described noise reduction process farther includes based on histogram filtered waveform number
According to carrying out ambient noise estimation.
It is preferred that described, the Gauss crest of gained is processed to the decomposition of satisfactory Gauss echo
Result step includes merging Gauss crest and it being intended relative to smoothing filter function
Close.
It is preferred that described to its method being fitted relative to smoothing filter function it is
Lenvenberg-Marquardt non-linear fitting method.
(3) beneficial effect
The present invention at least has a following beneficial effect:
The present invention directly solves flex point from discrete laser radar waveform data point, without
First its Function Fitting carrying out complexity calculated process, to greatly reduce amount of calculation；Solve and turn
Only judge to obtain flex point, especially for laser radar waveform with simple a few step computings during point
This algorithm of data that this abscissa of data equidistantly changes is simpler quickly；The present invention utilizes and turns
Put and promptly extract the effective Gaussian component in laser radar waveform data, and as
Original function is fitted compared to initial data, can quickly obtain remaining main information
Fitting function；In sum, the present invention is compared with existing laser radar waveform data process side
Case is carried out more quickly accurately.
Certainly, arbitrary product or the method for implementing the present invention must be not necessarily required to reach above simultaneously
Described all advantages.
Accompanying drawing explanation
In order to be illustrated more clearly that the embodiment of the present invention or technical scheme of the prior art, below
The accompanying drawing used required in embodiment or description of the prior art will be briefly described, aobvious and
Easily insight, the accompanying drawing in describing below is only some embodiments of the present invention, for this area
From the point of view of those of ordinary skill, on the premise of not paying creative work, it is also possible to according to these
Accompanying drawing obtains other accompanying drawing.
Fig. 1 is in one embodiment of the invention, laser radar waveform data to be carried out Gauss echo to divide
The method flow diagram solved；
Fig. 2 is the schematic diagram of flex point evaluation algorithm in one embodiment of the invention；
Fig. 3 is in the flex point found from laser radar waveform data in one embodiment of the invention
Owing to numerical fluctuations is mistaken for the schematic diagram (circle marks) of the data point of flex point；
Fig. 4 is that find out from laser radar waveform data in one embodiment of the invention all turn
The schematic diagram of point；
Fig. 5 is that two flex points utilizing odd even adjacent in one embodiment of the invention obtain laser thunder
Reach the schematic diagram of several high bass wave peak-to-peak value points in Wave data；
Fig. 6 is the showing of laser radar waveform data wave merging result in one embodiment of the invention
It is intended to；
Fig. 7 is Lenvenberg-Marquardt nonlinear fitting result in one embodiment of the invention
Schematic diagram；
Fig. 8 is that in the present invention is better simply to carry out Gauss to laser radar waveform data and returns
The method flow diagram of Wave Decomposition.
Detailed description of the invention
Below in conjunction with the accompanying drawing of the present invention, technical scheme is carried out clear, complete
Ground describe, it is clear that described embodiment be only a part of embodiment of the present invention rather than
Whole embodiments.Based on the embodiment in the present invention, those of ordinary skill in the art are not having
Make the every other embodiment obtained under creative work premise, broadly fall into present invention protection
Scope.
It should be noted that laser radar waveform data be with discrete data point (time m-signal
Intensity) form exists, and wherein contains and affected by various actual physics factors and produce
Noise signal, these noise signals broadly fall into invalid signals certainly.It addition, laser radar waveform
Data only the part in waveform can embody elevation information (pass under the purpose measuring height
In the judgement of validity can be estimated a valid data district substantially according to waveform shape
Between), thus before and after these useful signals the data point outside certain limit all with elevation carrection without
Close, naturally also broadly fall into invalid signals.
Embodiment 1: the embodiment of the present invention proposes one and laser radar waveform data is carried out Gauss
The method that echo decomposes, sees Fig. 1.The present embodiment demonstrate the different terrain to Sanxia area with
Four groups of GLAS Wave datas of vegetation pattern (in Figure of description, it is expressed as (a) vegetation:
Gently, (b) is without vegetation: tableland, (c) are without vegetation: level land, (d) vegetation: abrupt slope) carry out height
The process that this echo decomposes, finally gives corresponding result.The method includes:
Step 101: GLAS Wave data point set is carried out LPF by Gaussian filter.
Select the filter width of Gaussian filter and the width (4ns) of laser firing pulses herein
The Gaussian function caused makees filter function (LPF), itself and original waveform is done convolution and realizes flat
Sliding filtering, the function obtained is referred to as smoothing filter function.
This step eliminates the high-frequency noise in a part of Wave data, remain therein effectively
Composition, improves the signal to noise ratio of Wave data.
Step 102: filtered Wave data point set is carried out ambient noise estimation.
Based on histogram estimating background noise comprising: to each waveform from signal start to finish segmentation pair
The value of data point carries out statistics with histogram.Then statistics with histogram data are carried out Gauss curve fitting,
Select the peak-peak ambient noise estimation as this section.
The two step is all the noise reduction process to GLAS Wave data, it is therefore an objective to improve signal
Signal to noise ratio.
Step 103: utilize flex point about the character that neighbouring curve near tangent is interior point or exterior point in institute
State and the effective data intervals of Wave data quickly finds out all of flex point, and remove due to number
Value fluctuation is mistaken for the data point of flex point.
Concretely comprise the following steps: first in laser radar waveform data, estimate significant figure according to waveform shape
According to interval, concrete grammar can be to estimate an interval according to signal strength signal intensity so that it is comprises all
Signal strength signal intensity is higher than the point of certain threshold value.
If P3(x3,y3) it is certain point in this effective data intervals, finding with this point is the 3rd point
Continuous four data points P1(x1,y1)、P2(x2,y2)、P3(x3,y3)、P4(x4,y4), and carry out as
The judgement of lower condition:
L12(x3,y3)L23(x4,y4) < 0 and δ3=(y3-y2)(y3-y4)<0
Wherein,
Lab(x,y)=(xb-xa)(y-ya)+(ya-yb)(x-xa)
δk=(yk-yk-1)(yk-yk+1)
If meeting this condition, then judge P3(x3,y3) it is a flex point；Again by P2(x2,y2)、P3(x3,y3)、
P4(x4,y4)、P5(x5,y5) to P4(x4,y4) it is made whether the judgement into flex point；By that analogy, for
Put P arbitrarilyk(xk,yk), can be by being continuous four data points of the 3rd point with point
Pk-2(xk-2,yk-2)、Pk-1(xk-1,yk-1)、Pk(xk,yk)、Pk+1(xk+1,yk+1) to Pk(xk,yk) be made whether
Judgement for flex point；So can obtain all of flex point in effective data intervals.
In this step, Lab(x, y) be mathematically in the expression of exterior point, if its value is more than zero, then
(x y) is vector P to some PaPbExterior point；If its value is less than zero, then (x y) is vector P to PaPbInterior
Point.
It is known that the flex point concavo-convex point of interface that is curve, not 3 on same vector permissible
Determine the concavity and convexity of certain section of curve.Utilize this character, can be to the 3rd in continuous 4 points
Point determines whether flex point.Owing to convex (or recessed) curve is the envelope of its all tangent lines, because of
This, in less scope, the point on convex (or recessed) curve is all in tangent line race same on it
Side.Assume given point set from each other very close to curve point set, then curve near tangent is permissible
Replaced by the forward straight lines of in succession two, thus can classify with the point set about forward curve
Determine flex point.
See Fig. 2, for GLAS Wave data, owing to the change of its data point is not as figure
1. with 2. such regular in 2, a mainly some P4(x4,y4) or P4‘(x4,y4) and vector P2P3Folder
Angle the least (in Fig. 2 3. with 4.), adds difficulty to the determination of flex point.Pass through
It has been observed that often as a P4(x4,y4) or P4’(x4,y4) Building Y mark more than or equal to some a P3(x3,y3)
Building Y timestamp, algorithm above is correct；Such as fruit dot P4(x4,y4) or P4‘(x4,y4) Building Y
Mark is less than some P3(x3,y3) Building Y mark, it is determined that flex point P3(x3,y3) often at crest location.
About this wrongheaded situation of flex point occurred in the case of special convex concave as shown in Figure 3
(cross symbol represents the flex point quickly found, and circle marks mistake flex point), such data
Point is to cause fluctuation due to noise logarithm value, thus can be mistaken for flex point by data fluctuations.
According to this phenomenon, add δ in this step3=(y3-y2)(y3-y4) < judgement of 0, the most just
It is to judge to change the ordinate of this some ordinate whether than adjacent 2 the biggest or the least, only
When meeting this condition, just judge P3(x3,y3) it is flex point, otherwise it is judged as due to numerical value ripple
The dynamic data point being mistaken for flex point, and be removed.Add this judgement so can keep away
Exempt from the erroneous judgement that waveform change acutely causes.
Use this algorithm that GLAS Wave data is carried out flex point and ask calculation, obtain tens to hundreds of
Flex point (cross symbol marks) as shown in Figure 4.The advantage of the method is that calculating error is little, effect
Rate is high.To x coordinate be especially the GLAS Wave data of equidistant change (i.e.
xi=x1+ ih, i=2,3 ...., n, step-length h > 0), this algorithm is the easiest.
Step 104: two flex points utilizing odd even adjacent estimate that crest the most that may be present is at height
The initial value of each key parameter under this functional form.
Need slightly to explain, for single Gauss crest, can be by it be asked twice
The method of derivative finds out two flex points present in it；That is each Gauss crest has
Two corresponding flex points.Thus in the case of supposing to there is Gauss crest between two flex points, can
With by the two flex point estimate crest the most that may be present under Gaussian function form each
The initial value of key parameter.
Estimate that by flex point the method for Gaussian function key parameter initial value includes: set calculate strange
Even adjacent two flex points are respectively at T2m-1With T2mPlace, then the amplitude of the Gaussian Profile estimated is flat
Sliding filter function is at interval [T2m-1, T2mMaximum in], the point obtaining maximum is i.e. defined as
Peak point Tm.Corresponding half-breadth is typically calculated by following formula:
σm=min{|T2m-1-Tm|,|T2m-Tm|}
As long as it is to say, the maximum of points quickly found between adjacent two flex points of odd even, it is possible to
Obtain the corresponding center of Gaussian peak, half-breadth and amplitude.
But in GLAS data, due to when exist saturated with forward scattering time wavy curve can send out
Raw distortion, there is certain error, therefore, meter in the Gaussian function half-breadth therefore resolved by above formula
Must use correction algorithm when calculating Gauss half-breadth, specific algorithm (Brenner et al.2003) is as follows:
It is located at peak swing Am80% at position be T2m-1_80With T2m_80, wherein distance
TmNearer point is T80；Am60.653% at position be T2m-1_61With T2m_61, wherein
Distance TmNearer point is T61, by Gaussian function formula
Work as wm=0.8AmTime:
|T80-Tm|=0.668*σm_80σm_80=|T80-Tm|/0.668
Work as wm=0.60653AmTime:
|T61-Tm|=σm_61σm_61=|T61-Tm|
Therefore, σ during revised half-breadth is above-mentioned formulam_80.And calculate second estimate
σm_61It is mainly used for can not producing during Gauss curve fitting preferably when first estimate
The situation of standard deviation.
From GLAS Wave data, thus isolate several Gaussian component, and estimate to obtain
The initial value of the key parameter of corresponding each component.But in Practical Calculation, this step does not has
Whether be a little crest test, it is possible to the situation not having crest between flex point occur if having being worth most,
Need this situation is rejected.
Step 105: all crests that may be present are screened according to the character of crest.
Concretely comprise the following steps: judge interval [T2m-1, T2mPeak point T in]mWhether with two flex points it
One is overlapping, if then giving up this peak point；Judge the ordinate of two adjacent with peak point
Whether value is both less than the value of peak point ordinate, if not then giving up this peak point.
Carry out this to judge that when being owing to using algorithm described in step 104, counted peak point has can
The change of the waveform before and after the character of crest, i.e. crest and can be unsatisfactory for should meet from positive to negative
The rule of change, therefore need the peak point being unsatisfactory for condition to be rejected, after being screened
Some Gauss crests.
Through step 104 and 105, the peak point tried to achieve (triangle marks) as shown in Figure 5,
All there is no peak point between visible a lot of flex point, illustrate to have been carried out in step 105 effectively
Reject.
Above-mentioned steps has been obtained in GLAS Wave data comprising several of useful signal
Gauss crest, the most just should process the Gauss crest of gained to satisfactory Gauss echo
Decomposition result.Described meeting the requirements generally refers to the final reserving degree to effective information,
Refer specifically to further screening effective information being carried out under certain rule, and be the side of subsequent treatment
Just it is carried out the merging of Gauss crest.
Step 106: according to the initial value estimated to the invalid signals in these several Gauss crests
Reject.
If Gaussian minimum amplitude is:
Amin=ε+σnoise+d_nPeak_min
Wherein, ε is the noise level of Wave data, σnoiseFor the standard deviation estimate of noise, the two
Numerical value obtains the most in a step 102；D_nPeak_min be in a GLAS secondary file about
The numerical value that this judgement is given.
Each Gauss crest obtained is judged, and whether its amplitude is more than Amin, if not then
This Gaussian peak is rejected.
For laser radar system, the amplitude of an effective ground echo should receive echo
DC component more than.Therefore, the Gaussian component that adjacent two flex points of odd even determine is big at its numerical value
Effective ground echo it is only when echo-signal.In the Gauss crest more than solved, major part
Remain the random fluctuation generation of ambient noise, belong to invalid signals, therefore to be rejected.
Step 107: the crest of all remaining Gaussian function forms is ranked up by area, and
By in crest bigger for wave merging minimum for area to closest area, until crest
Number less than or equal to certain fixed value.
The method of wave merging is as follows:
1) if the area of a Gauss crest is less than or equal to the 5% of another PeakArea,
Then rejected；
2) otherwise, Gauss crest is merged by area weight or mean value；
Wherein, subscript n ew represents the Gauss crest after merging, and subscript 1 and 2 represents conjunction respectively
And the front Gauss crest that area is less and area is bigger；Area represents PeakArea；A represents
The amplitude of Gauss crest；Wt represents area weight, it is possible to all take 0.5；σ represents Gauss crest
Standard deviation or width；T represents the abscissa positions of Gauss crest.
The step for actually the measured value near several height values has been taken averagely, thus may be used
To extract the main information in GLAS Wave data, facilitate follow-up Function Fitting and letter
Breath extracts.By wave merging to six in the present embodiment, amalgamation result is (relatively low as shown in Figure 6
Smoother curve is amalgamation result, and another curve is smothing filtering waveform).
Step 108: using the superposition of these Gauss crests as original function, with smothing filtering letter
Number is object function, and substitutes into the uncertainty of measurement, by Lenvenberg-Marquardt non-thread
Property approximating method draws one group of best fit parameters, thus obtains the Gauss of GLAS Wave data
The result that echo decomposes.
In regions with complex terrain, there is certain broadening in the waveform after above-mentioned steps processes,
And the amplitude of each Gaussian component has reduced, this does not utilize the extraction of elevation information, therefore,
Have to be fitted these waveforms optimizing.Lenvenberg-Marquardt nonlinear fitting side
Method is a kind of nonlinear least square method, by the χ minimized in fit procedure2Value, can obtain
To one group of best fit parameters.In order to improve precision, the present embodiment combines some actual conditions and carries
Go out certain constraints, if the amplitude of Gaussian component is more than Gaussian minimum amplitude Amin, intend
The half-breadth of the Gaussian component closed is more than or equal to half-breadth etc. of System planes echo etc..Meanwhile, for
Making the fitting result meet the requirement of precision, each Gaussian component is added by the present embodiment one by one,
Until fitting result meets required precision.
(realization represents fitting result to the final fitting result to four groups of Wave datas as shown in Figure 7
Function, dotted line represents smothing filtering waveform), two suite lines represent original smothing filtering letter respectively
Number and final matching Gauss echo decomposition result out.Visible, matching makes because of the influence of topography
And the broadening of the onset wave caused is weakened, also make much noise obtain flat plan simultaneously,
This extracts being all of value to follow-up height, reduces the error brought because of influence of noise.Certainly
The present embodiment has only selected 6 crests to carry out matching, and hypsography is relatively big in addition, and echo is not only
Whole waveform is widened and echo waveform contains many high-frequency components, therefore fitting result
And some deviation between original waveform data, but this has no effect on the extraction to waveform principal character.
So far the process that GLAS Wave data is carried out Gauss echo decomposition is completed.See Fig. 8,
Generally speaking, the present embodiment first carries out noise reduction process to laser radar waveform data, then utilizes
Flex point is about effective at described Wave data of the character that neighbouring curve near tangent is interior point or exterior point
Data interval quickly finds out all of flex point, and removes owing to numerical fluctuations is mistaken for flex point
Data point, obtain several in Wave data followed by two flex points that odd even is adjacent high
This crest, finally processes the Gauss crest of gained to satisfactory Gauss echo decomposition result.
It is further to note that owing to laser radar is determined its Wave data all by its operation principle
Can be expressed as the estimated bias form plus several Gauss echo superpositions, hence for
The processing method of GLAS Wave data can be used for processing other laser radar waveform data.
In addition to the beneficial effect that the present embodiment described above having, the most at least having following has
Benefit effect:
1, it can be seen that the method in the present embodiment is for difference the Fig. 7 after having processed
The GLAS data that landform and different vegetation types are returned have preferable treatment effect, explanation
The embodiment of the present invention has wider applicability.
2, in the method for the present embodiment, repeatedly carried out the screening of the data obtained, on the one hand gone
Except wrong or invalid information, on the other hand also reduce amount of calculation for subsequent calculations, have
Higher treatment effeciency.
It should be noted that in this article, the relational terms of such as first and second or the like is only
Only it is used for separating an entity or operation with another entity or operating space, and not necessarily
Require or imply and there is the relation of any this reality or suitable between these entities or operation
Sequence.And, term " includes ", " comprising " or its any other variant are intended to non-exclusive
Comprising, so that include the process of a series of key element, method, article or equipment not of property
Only include those key elements, but also include other key elements being not expressly set out, or also wrap
Include the key element intrinsic for this process, method, article or equipment.There is no more restriction
In the case of, statement " including ... " key element limited, it is not excluded that described in including
The process of key element, method, article or equipment there is also other identical element.
Above example only in order to technical scheme to be described, is not intended to limit；Although
With reference to previous embodiment, the present invention is described in detail, those of ordinary skill in the art
It is understood that the technical scheme described in foregoing embodiments still can be modified by it,
Or wherein portion of techniques feature is carried out equivalent；And these amendments or replacement, not
The essence making appropriate technical solution departs from the spirit and scope of various embodiments of the present invention technical scheme.
Claims (8)
1. method laser radar waveform data being carried out Gauss echo decomposition, its feature exists
In comprising the following steps:
Utilize flex point about the character that neighbouring curve near tangent is interior point or exterior point at described waveform number
According to effective data intervals in quickly find out all of flex point；
Remove owing to numerical fluctuations is mistaken for the data point of flex point；
Two flex points utilizing odd even adjacent obtain several Gauss crests in Wave data；
The Gauss crest of gained is processed to satisfactory Gauss echo decomposition result；
Described utilize flex point about waveform described in the character that neighbouring curve near tangent is interior point or exterior point
The effective data intervals of data quickly finds out all of flex point step include:
In described Wave data, an effective data intervals is estimated according to waveform shape；
To each point in this interval, find with continuous four data points that this point is the 3rd point；
Judge whether this point meets condition: this point be first and second some place straight line interior point and
4th point is second point and the exterior point of this place straight line, or, this point is first and second
The exterior point of individual some place straight line and the 4th point are the interior point of second point and this place straight line；
If meeting this condition, judge that this point is as flex point；
The data point step that described removal is mistaken for flex point due to numerical fluctuations includes:
When the flex point ordinate ordinate than adjacent 2 the biggest or the least time, it is determined that this flex point is
Owing to numerical fluctuations is mistaken for the data point of flex point, and it is removed.
Method the most according to claim 1, it is characterised in that described utilize odd even adjacent
Several Gauss crest steps of obtaining in Wave data of two flex points include:
Two flex points utilizing odd even adjacent estimate that crest the most that may be present is at Gaussian function number form
The initial value of each key parameter under formula；
All crests that may be present are screened by the character according to crest, obtain several Gausses
Crest.
Method the most according to claim 2, it is characterised in that the described property according to crest
All crests that may be present of verifying carry out screening step and include:
Judging that peak point is the most overlapping with one of two flex points, if then giving up this peak point, then sentencing
The value of the value the most both less than peak point ordinate of the ordinate of disconnected adjacent with peak point 2,
If not then giving up this peak point.
Method the most according to claim 1, it is characterised in that all described steps it
Before, farther include laser radar waveform data is carried out noise reduction process.
Method the most according to claim 4, it is characterised in that described noise reduction process includes
By Gaussian filter, laser radar waveform data point is carried out LPF.
Method the most according to claim 5, it is characterised in that described noise reduction process enters one
Step includes, based on histogram, filtered Wave data is carried out ambient noise estimation.
Method the most according to claim 1, it is characterised in that described by the Gauss of gained
Crest processes extremely satisfactory Gauss echo decomposition result step and includes closing Gauss crest
And and it is fitted relative to smoothing filter function.
Method the most according to claim 7, it is characterised in that described to it relative to flat
The method that sliding filter function is fitted is Lenvenberg-Marquardt non-linear fitting method.
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