CN104614718B - Method for decomposing laser radar waveform data based on particle swarm optimization - Google Patents
Method for decomposing laser radar waveform data based on particle swarm optimization Download PDFInfo
- Publication number
- CN104614718B CN104614718B CN201510010012.7A CN201510010012A CN104614718B CN 104614718 B CN104614718 B CN 104614718B CN 201510010012 A CN201510010012 A CN 201510010012A CN 104614718 B CN104614718 B CN 104614718B
- Authority
- CN
- China
- Prior art keywords
- value
- particle
- waveform
- algorithm
- parameter
- 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
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
- G01S7/48—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S17/00
- G01S7/4802—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S17/00 using analysis of echo signal for target characterisation; Target signature; Target cross-section
-
- 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
-
- 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
- G01S17/89—Lidar systems specially adapted for specific applications for mapping or imaging
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Computer Networks & Wireless Communication (AREA)
- General Physics & Mathematics (AREA)
- Radar, Positioning & Navigation (AREA)
- Remote Sensing (AREA)
- Electromagnetism (AREA)
- Radar Systems Or Details Thereof (AREA)
Abstract
The invention provides a three-dimensional laser etch decomposing algorithm based on the combination of particle swarm optimization and LM algorithm (Levenberg-Marquardt Algorithm). The method comprises the steps of smoothly denoising; detecting the peak value; decomposing the waveform; fitting. According to the method, the threshold is set and the threshold is detected, so as to determine the quantity of smoothly denoised wave with good signal-to-noise ratio; the particle swarm optimization is performed to acquire the rough strength parameter value and the rough wide parameter value of single waveform to be used as the initial values of the LM algorithm to improve the decomposing precision as well as reducing the error influence of the initial value.
Description
Technical field
The invention belongs to laser data processing technology field, specifically refer to a kind of laser radar ripple based on particle cluster algorithm
The method that graphic data is decomposed.
Background technology
The internal earth's surface information of a large amount of laser faculas is contained, by permissible to the analysis of echo waveform in return laser beam waveform
The thin feature of entry target is extracted over the ground.Therefore, one effective and accurate echo decomposition algorithm of searching is one is worth
The problem of research.
Ambient noise can lead to the change at random of wave-shape amplitude, excessive burr may result in detect overdue, therefore
Smothing filtering has large effect for parameter fitting.However, some filtering algorithms can lead to the distortion in amplitude and mistake
Degree is smooth to be made loss in detail or loses the results such as peak point it is therefore desirable to relatively and select wherein preferable algorithm.
In existing algorithm, the precision of LM algorithm depends on initial value, if initial value deviation is larger being so difficult to obtain essence
True fitting effect.In conventional algorithm, the detection often through zero passage flex point to determine width parameter, and practical laser model
And off-gauge Gauss model, and asymmetric, the echo that therefore obtains in this approach in the case of addition of waveforms pulse stretcher
Parameter is as initial value and inaccurate.
In sum, current return laser beam decomposition algorithm parameter more and superposition broadening in the case of effect not
Ideal, therefore effect preferable vondrak smoothing algorithm is more suitable for for echo decomposition, and is obtained by improving particle cluster algorithm
Its parameter value is taken to be a kind of method optimizing its result as the initial value of LM algorithm.
Particle cluster algorithm, also referred to as particle swarm optimization algorithm (Particle Swarm Optimization), are abbreviated as PSO,
It is a kind of new evolution algorithm (Evolutionary Algorithm-EA) developed in recent years.PSO algorithm belongs to evolution
One kind of algorithm, similar with simulated annealing, it is also from RANDOM SOLUTION, finds optimal solution by iteration, it is also logical
Cross fitness to evaluate the quality of solution, but it is more simpler than genetic algorithm rule, it does not have " intersection " of genetic algorithm
(Crossover) with " variation " (Mutation) operate, it by follow current search to optimal value find global optimum.
This algorithm with its realize easily, high precision, convergence cause the attention of academia the advantages of fast, and in solving practical problems
In illustrate its superiority.Particle cluster algorithm is a kind of parallel algorithm.
PSO is initialized as a group random particles (RANDOM SOLUTION).Then optimal solution is found by iteration.In iteration each time
In, particle passes through to follow the tracks of two " extreme values " to update oneself.First is exactly the optimal solution that particle itself is found, this solution
Do individual extreme value pBest.Another extreme value is the optimal solution that whole population is found at present, and this extreme value is global extremum gBest.
In addition can also be the neighbours with a portion as particle without whole population, then the extreme value in all neighbours
It is exactly local extremum.
LM algorithm, full name is Levenberg-Marquard algorithm, and it can be used for solving non-linear least square problem, many
For occasions such as curve matchings.
Content of the invention
The purpose of the present invention is to develop the side that the return laser beam data of complete set is decomposed based on three-dimensional laser imaging system
Method, parameter more and superposition broadening in the case of, using effect preferable vondrak smoothing algorithm, and by improve grain
Swarm optimization obtains its parameter value and optimizes its result as the initial value of LM algorithm.
The technical scheme is that：The method that laser radar waveform data based on particle cluster algorithm decomposes, concrete step
Suddenly as follows：
(1) obtain laser radar Full wave shape echo data；
(2) denoising is carried out to waveform；
(3) waveform is smoothed；
(4) detect the peak point of echo by peakvalue's checking, peak point threshold value is set according to background noise level, remove
The unnecessary peak point producing because of noise, determines echo-peak point position and number；
(5) it is iterated with improvement particle cluster algorithm, obtain the substantially value of single waveform parameter；
(6) parameter obtaining in step (5) is substantially worth the initial value as LM iterative algorithm, is obtained by least square method
Final waveform parameter；Fitting effect calculates according to degree of fitting formula, and described degree of fitting formula is as follows：
Wherein：obsiFor treating the target waveform of matching, i.e. actual waveform data after the process of (1)-(3) step, yiFor intending
Close result, N is actual ghosts sampling number, and degree of fitting is higher when R value is closer to 1.
Further, in step (2), denoising is carried out to waveform using wavelet algorithm.
Further, step (5) is iterated with improving particle cluster algorithm, obtains the substantially value of single waveform parameter；
Idiographic flow is as follows：
A, determine parameter, the position of random initializtion particle colony and speed, recording individual extreme value and colony's extreme value；
B, calculate the adaptive value of each particle；
C, compare each particle adaptive value and individual extreme value, if more excellent, update this particle individuality extreme value；
D, compare each particle adaptive value and colony's extreme value, if more excellent, update this population colony extreme value；
E, the position updating each particle and flying speed；
F, setting iterations, reach and then stop calculating；
Described parameter is time delay, intensity and the width parameter of single waveform, and described adaptive value is by functionCalculate, wherein obsiFor treating the target waveform of matching, that is, after the process of (1)-(3) step
Actual waveform data, yiThe waveform being reconstructed by parameter current particle, N be actual ghosts sampling number, when R value closer to
When 1, effect is better；
Parameter particle is updated to：
Described individuality extreme value refers to the optimal solution that particle finds in itself, that is, in formulaDescribed colony extreme value refers to that the overall situation is looked for
The optimal solution arriving, that is, in formulaFor parameter particle,For flying speed, ω is inertial factor, c1With c2For accelerating
Constant.
Further, using vondrak algorithm, waveform is smoothed in step (3), idiographic flow is as follows:
Wave data sequence (x for measurementi,yi), xiFor sampling time, yiIt is data samples,After being smooth
Value,RepresentThree order derivatives, PiIt is that measured value must be weighed, F is degree of approximation, S is smoothness, 1/ λ2Become smoothing factor；
Smooth function used by vondrak smoothing method to be represented with polynomial form, and specific practice is to four groups of adjacent numbers
According to Represented with the lagrange polynomial of three times, every four
Smooth value just constitutes a lagrange polynomial, represents two middle smooth values, the base of vondrak smoothing method with this formula
This equation group is：
(i=1,2 ..., n)
Total n equation, wherein：
A-3i=ai-3di-3；A-2i=ai-2ci-2+bi-3di-3；A-1i=ai-1bi-1+bi-2ci-2+ci-3di-3；
A1i=aibi+bi-1ci-1+ci-2di-2
A2i=aici+bi-1di-1；A3i=aidi；ε=1/ λ2；Bi=ε Pi；Ai=0 (j+i≤0 or j+i >=n+1)
Wherein：
Resolve this system of linear equations and can obtain smooth rear data.
Further, pass through in step (4) to obtain average and the variance of one section of ambient noise, remove what peakvalue's checking brought
Noise.
The invention has the beneficial effects as follows：Parameter more and superposition broadening in the case of, preferable using effect
Vondrak smoothing algorithm, and obtain its parameter value and optimize its result as the initial value of LM algorithm by improving particle cluster algorithm.Pass
The LM algorithm that system matching is used, initial value design is different, and the precision that final matching obtains is also different, is randomly provided at the beginning of several groups
Value, final Average Quasi is right and to pass through the present invention less than 0.98, by the use of particle cluster algorithm acquisition value as initial value, final matching
Degree is up to 0.989.
Brief description
The invention will be further described with example below in conjunction with the accompanying drawings.
Fig. 1 is the techniqueflow chart of the present invention；
Fig. 2 is laser radar Full wave shape echo data；
Fig. 3 is the Wave data after Wavelet Denoising Method；
Fig. 4 is that the waveform after denoising is made with the result after vondrak smooths, for the waveform of amplifier section, surface in square frame
Loss in details；
Fig. 5 is that the waveform after denoising is made 5 points 3 times with the result after smoothing, for the waveform of amplifier section in square frame, with
Vondrak compares；
Fig. 6 is peakvalue's checking result, and in square frame, the ambient noise for choosing, big according to background noise level given threshold
Little；
Fig. 7 is to decompose final result.
Specific embodiment
Below in conjunction with the accompanying drawings implementer's case of the present invention is described in detail.
As shown in figure 1, the method that the laser radar waveform data based on particle cluster algorithm decomposes, comprise the following steps that：
(1) according to actually used laser system, select corresponding function, obtain laser radar actual ghosts all-wave figurate number
According to as shown in Fig. 2 abscissa is the sampling interval (unit of Wave data：Ns), ordinate is range value.
(2) by wavelet algorithm, the noise of Wave data is processed.Signal in flight course and reflection process by
Wave noise can be produced in air and system noise etc. are multifactor, using Wavelet Denoising Method, Wave data be processed, filtered
In Wave data after process, noise has obtained obvious suppression, and result is as shown in Figure 3.
(3) for the waveform after denoising, however it remains a lot of burr phenomenas, this is to have for the decomposition of Wave data
Serious impact.And simply smooth can lead to the loss of Wave data or smooth effect undesirable it is therefore desirable to choose
The more preferable smoothing algorithm of one relative efficacy.The present invention passes through vondrak algorithm and smooths, and improves signal to noise ratio；The method exists first
It is applied in astronomy it is therefore an objective to be used for reducing the error of astronomical observation instrument and environmental factor introducing to astronomical observation data
Impact.Vondrak data smoothing method is applicable not only to equidistant and equally accurate measurement data, simultaneously can be used for not
Equidistantly and unequal accuracy data smoothing processing, therefore range of application is wider.
Vondrak algorithm smooths and comprises the following steps that：Its basic assumption is
Q=F+ λ2S=is minimum
Wherein
Wave data sequence (x for measurementi,yi), xiFor sampling time, yiIt is data samples,After being smooth
Value,RepresentThree order derivatives, PiIt is that measured value must be weighed, F is degree of approximation, S is smoothness, 1/ λ2Become smoothing factor.
Smooth function used by vondrak smoothing method to be represented with polynomial form, and specific practice is to four groups of adjacent numbers
According to Represented with the lagrange polynomial of three times, every four
Smooth value just constitutes a lagrange polynomial, represents two middle smooth values with this formula.
The Basic equation group (in the matrix form represent) of vondrak smoothing method is：
(i=1,2 ..., n)
Total n equation, wherein：
A-3i=ai-3di-3；A-2i=ai-2ci-2+bi-3di-3；A-1i=ai-1bi-1+bi-2ci-2+ci-3di-3；
A1i=aibi+bi-1ci-1+ci-2di-2
A2i=aici+bi-1di-1；A3i=aidi；ε=1/ λ2；Bi=ε Pi；Ai=0 (j+i≤0 or j+i >=n+1)
Wherein
Resolve this system of linear equations and can obtain smooth rear data, in the present invention, choose ε=1/ λ2For 450.Through
Vondrak smooth after result as shown in figure 4, and by 5 points 3 times conventional smooth results as shown in figure 5, contrast permissible
Find out, after smoothing, burr can be effectively filtered out, the integrality of signal can be ensured simultaneously again well.
(4) peak point is detected by peakvalue's checking, peak point threshold value is set according to background noise level, remove because of noise
The unnecessary peak point producing；For peak value, search for first derivative=0, yet with the impact of noise, the point of search will necessarily
Than actual many many it is therefore desirable to a threshold level is set according to the intensity of ambient noise, noise is removed with this.As
Shown in Fig. 6, by obtaining average and the variance of one section of ambient noise, remove the noise that peakvalue's checking brings.
(5) pass through to improve the substantially value of the parameters that particle cluster algorithm obtains single waveform, and as further
The initial value of matching；
Nonlinear fitting is the core that waveform decomposes, and is the precision of Levenberg-Marquardt algorithm yet with LM
Dependence for initial value is higher, and practical laser waveform asymmetrical Gauss model, if therefore detecting to obtain by flex point
Take half-breadth parameter can produce certain error to result.Particle group optimizing (PSO) algorithm stems to flock of birds or shoal of fish predation
The simulation of behavior, has stronger ability of searching optimum, and its flow process is as follows：
A. parameter, the position of random initializtion particle colony and speed, recording individual extreme value and colony's extreme value are determined.
B. calculate the adaptive value of each particle
C. compare each particle adaptive value and individual extreme value, if more excellent, update this particle individuality extreme value
D. compare each particle adaptive value and colony's extreme value, if more excellent, update this population colony extreme value
E. position and the flying speed of each particle are updated
F. set iterations, reach and then stop calculating
In the present invention, parameter is time delay, intensity and the width parameter of single waveform, and adaptive value is by functionCalculate, wherein obsiFor treating the target waveform of matching, that is, after the process of (1)-(3) step
Actual waveform data, yiThe waveform being reconstructed by parameter current particle, N be actual ghosts sampling number, when R value closer to
When 1, effect is better.Parameter particle is updated to：
Individual extreme value refers to the optimal solution that particle finds in itself, that is, in formulaColony's extreme value refers to the optimum that the overall situation finds
Solution, that is, in formulaFor parameter particle,For flying speed, maximum is set as 0.5, and minimum of a value is set as -0.5.
Better astringency when inertial factor w takes 0.729 through comparing, c1With c2For aceleration pulse, take 1.454 herein, iterations is arranged
For 10000.
(6) using the optimal value obtaining through particle cluster algorithm as the initial value of LM algorithm, cycle-index, fitting effect are set
According to degree of fitting formulaCalculate, wherein obsiFor treating the target waveform of matching, that is, pass through
(1) the actual waveform data after-(3) step is processed, yiFor fitting result, N is actual ghosts sampling number, when R value is closer to 1
When degree of fitting higher, effect is better.Through decomposing, finally give decomposition result.Final result is as shown in Figure 7.For decompose
Each wavelet, its time delay is the elevation information of corresponding pixel points, and its amplitude is the strength information of corresponding pixel points.
The LM algorithm that traditional matching is used, initial value design is different, and the precision that final matching obtains is also different, sets at random
Put several groups of initial values, final Average Quasi is right and to pass through the present invention less than 0.98, by the use of particle cluster algorithm acquisition value as initial value,
Final degree of fitting is up to 0.989.
It should be appreciated that the application of the present invention is not limited to above-mentioned citing, for those of ordinary skills, can
To be improved according to the above description or to convert, all these change and conversion all should belong to the guarantor of claims of the present invention
Shield scope.
Claims (4)
1. based on particle cluster algorithm laser radar waveform data decompose method it is characterised in that：Comprise the following steps that：
(1) obtain laser radar Full wave shape echo data；
(2) denoising is carried out to waveform；
(3) waveform is smoothed；
(4) detect the peak point of echo by peakvalue's checking, peak point threshold value is set according to background noise level, remove because making an uproar
The unnecessary peak point that sound produces, determines echo-peak point position and number；
(5) it is iterated with improvement particle cluster algorithm, obtain the substantially value of single waveform parameter；Idiographic flow is as follows：
A, determine parameter, the position of random initializtion particle colony and speed, recording individual extreme value and colony's extreme value；
B, calculate the adaptive value of each particle；
C, compare each particle adaptive value and individual extreme value, if more excellent, update this particle individuality extreme value；
D, compare each particle adaptive value and colony's extreme value, if more excellent, update this population colony extreme value；
E, the position updating each particle and flying speed；
F, setting iterations, reach and then stop calculating；
Described parameter is time delay, intensity and the width parameter of single waveform, and described adaptive value is by functionCalculate, wherein obsiFor treating the target waveform of matching, that is, after the process of (1)-(3) step
Actual waveform data, yiThe waveform being reconstructed by parameter current particle, N be actual ghosts sampling number, when R value closer to
When 1, effect is better；
Parameter particle is updated to：
Described individuality extreme value refers to the optimal solution that particle finds in itself, that is, in formulaDescribed colony extreme value refers to that the overall situation finds
Optimal solution, that is, in formulaFor parameter particle,For flying speed, ω is inertial factor, c1With c2For aceleration pulse；
(6) parameter obtaining in step (5) is substantially worth the initial value as LM iterative algorithm, is obtained by least square method final
Waveform parameter；Fitting effect calculates according to degree of fitting formula, and described degree of fitting formula is as follows：
Wherein：obsiFor treating the target waveform of matching, i.e. actual waveform data after the process of (1)-(3) step, yiFor matching knot
Really, N is actual ghosts sampling number, and when R value is closer to 1, degree of fitting is higher.
2. the method that the laser radar waveform data based on particle cluster algorithm according to claim 1 decomposes, its feature exists
In：Step carries out denoising using wavelet algorithm to waveform in (2).
3. the method that the laser radar waveform data based on particle cluster algorithm according to claim 1 decomposes, its feature exists
In：Step is smoothed to waveform using vondrak algorithm in (3), and idiographic flow is as follows:
Wave data sequence (x for measurementi,yi), xiFor sampling time, yiIt is data samples,It is the value after smoothing,RepresentThree order derivatives, PiIt is that measured value must be weighed, F is degree of approximation, S is smoothness, 1/ λ2Become smoothing factor；
Smooth function used by vondrak smoothing method to be represented with polynomial form, and specific practice is to four groups of adjacent numbers
According to Represented with the lagrange polynomial of three times, every four
Smooth value just constitutes a lagrange polynomial, represents two middle smooth values, the base of vondrak smoothing method with this formula
This equation group is：
Total n equation, wherein：
A-3i=ai-3di-3；A-2i=ai-2ci-2+bi-3di-3；A-1i=ai-1bi-1+bi-2ci-2+ci-3di-3；
A2i=aici+bi-1di-1；A3i=aidi；ε=1/ λ2；Bi=ε Pi；Ai=0 (j+i≤0 or j+i >=n+1)
Wherein：
Resolve this system of linear equations and can obtain smooth rear data.
4. the method that the laser radar waveform data based on particle cluster algorithm according to claim 1 decomposes, its feature exists
In：Pass through in step (4) to obtain average and the variance of one section of ambient noise, remove the noise that peakvalue's checking brings.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201510010012.7A CN104614718B (en) | 2015-01-08 | 2015-01-08 | Method for decomposing laser radar waveform data based on particle swarm optimization |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201510010012.7A CN104614718B (en) | 2015-01-08 | 2015-01-08 | Method for decomposing laser radar waveform data based on particle swarm optimization |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN104614718A CN104614718A (en) | 2015-05-13 |
| CN104614718B true CN104614718B (en) | 2017-02-22 |
Family
ID=53149252
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201510010012.7A Active CN104614718B (en) | 2015-01-08 | 2015-01-08 | Method for decomposing laser radar waveform data based on particle swarm optimization |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN104614718B (en) |
Families Citing this family (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104993832B (en) * | 2015-07-02 | 2018-04-24 | 中国电子科技集团公司第四十一研究所 | A kind of 3 correlation waveform smoothing methods based on high-speed sample data |
| CN105093207B (en) * | 2015-07-10 | 2018-06-08 | 南京大学 | The method that laser radar waveform based on optimization LM algorithms decomposes |
| CN105785357A (en) * | 2016-05-18 | 2016-07-20 | 南京理工大学 | Method for detecting ultrahigh-speed moving object based on GRFT |
| CN106154247B (en) * | 2016-06-24 | 2018-07-10 | 南京林业大学 | A kind of multiple dimensioned Full wave shape laser radar data optimizes decomposition method |
| CN107894586B (en) * | 2017-10-17 | 2020-01-31 | 南京航空航天大学 | laser radar echo signal denoising method based on synchronous compression transformation |
| CN107798379B (en) * | 2017-11-23 | 2020-07-07 | 东北大学 | Method for improving quantum particle swarm optimization algorithm and application based on improved algorithm |
| CN109655910B (en) * | 2019-01-18 | 2019-12-24 | 吉林大学 | Phase correction-based dual-parameter full waveform inversion method for ground penetrating radar |
| CN111208486B (en) * | 2020-02-27 | 2022-01-28 | 淮阴工学院 | Full-waveform laser radar waveform decomposition method |
| CN112039496B (en) * | 2020-09-10 | 2024-04-19 | 南京航空航天大学 | Rapid Gaussian particle filter data fusion method based on artificial fish swarm optimization |
| CN113433532B (en) * | 2021-07-07 | 2022-09-30 | 清华大学苏州汽车研究院（吴江） | Laser radar attitude calibration method and device based on particle swarm algorithm |
| CN113271099A (en) * | 2021-07-19 | 2021-08-17 | 常州罗盘星检测科技有限公司 | Data processing method for improving measurement precision of instrument |
| CN113484844A (en) * | 2021-08-24 | 2021-10-08 | 扆亮海 | Point cloud classification method driven by LiDAR full-waveform control decomposition |
| CN116312899B (en) * | 2023-05-12 | 2023-08-01 | 中国人民解放军战略支援部队航天工程大学 | Material surface BRDF parameter fitting method and system based on laser radar imaging |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101196562A (en) * | 2007-12-14 | 2008-06-11 | 武汉大学 | Method for laser radar waveshape data decomposition based on improved EM algorithm |
| US8285655B1 (en) * | 2004-02-03 | 2012-10-09 | Hrl Laboratories, Llc | Method for object recongnition using multi-layered swarm sweep algorithms |
| CN103033804A (en) * | 2012-12-14 | 2013-04-10 | 武汉大学 | Laser radar signal processing method based on wavelet entropy threshold value and modulus maximum value method |
| CN103513350A (en) * | 2013-10-09 | 2014-01-15 | 中南大学 | Alignment and coupling method and device of array waveguide component based on particle swarm optimization |
| CN103761443A (en) * | 2014-01-28 | 2014-04-30 | 李奇 | Laser radar waveform data processing method based on wavelet transform denoising |
-
2015
- 2015-01-08 CN CN201510010012.7A patent/CN104614718B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US8285655B1 (en) * | 2004-02-03 | 2012-10-09 | Hrl Laboratories, Llc | Method for object recongnition using multi-layered swarm sweep algorithms |
| CN101196562A (en) * | 2007-12-14 | 2008-06-11 | 武汉大学 | Method for laser radar waveshape data decomposition based on improved EM algorithm |
| CN103033804A (en) * | 2012-12-14 | 2013-04-10 | 武汉大学 | Laser radar signal processing method based on wavelet entropy threshold value and modulus maximum value method |
| CN103513350A (en) * | 2013-10-09 | 2014-01-15 | 中南大学 | Alignment and coupling method and device of array waveguide component based on particle swarm optimization |
| CN103761443A (en) * | 2014-01-28 | 2014-04-30 | 李奇 | Laser radar waveform data processing method based on wavelet transform denoising |
Non-Patent Citations (2)
| Title |
|---|
| Automatic Calibration of Multi-Modal Sensor Systems using a Gradient Orientation Measure;Zachary Taylor etal.;《2013 IEEE/RSJ International Conference on Intelligent Robots and Systems》;20131107;第1293-1300页 * |
| 基于植被表面的激光测高仪回波波形模型化分析;赵欣 等;《光电工程》;20110630;第38卷(第6期);第18-23页 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN104614718A (en) | 2015-05-13 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN104614718B (en) | Method for decomposing laser radar waveform data based on particle swarm optimization | |
| CN108845306B (en) | Laser radar echo signal denoising method based on variational modal decomposition | |
| WO2020191980A1 (en) | Blind calibration method for wireless sensor network data drift | |
| CN103870845B (en) | Novel K value optimization method in point cloud clustering denoising process | |
| CN107831490A (en) | A kind of improved more extension method for tracking target | |
| CN105137498A (en) | Underground target detection and recognition system and method based on feature fusion | |
| CN107630697B (en) | Formation resistivity joint inversion method based on electromagnetic wave resistivity logging while drilling | |
| CN109344812B (en) | Improved cluster-based single photon point cloud data denoising method | |
| CN102819043B (en) | Array signal random noise adaptive model denoising method | |
| CN111190227B (en) | Low signal-to-noise ratio seismic data denoising method based on residual convolution generation countermeasure model | |
| CN108427112A (en) | A kind of improved more extension method for tracking target | |
| CN107144829A (en) | A kind of efficient laser radar echo signal antinoise method | |
| CN110174650B (en) | Meteorological radar wind power plant clutter suppression method based on two-dimensional joint interpolation | |
| CN101685158B (en) | Hidden Markov tree model based method for de-noising SAR image | |
| CN108957403B (en) | Gaussian fitting envelope time delay estimation method and system based on generalized cross correlation | |
| CN105676205A (en) | Airborne LiDAR waveform data Gaussian decomposition method | |
| CN110456351B (en) | Maneuvering target ISAR imaging method based on time-varying amplitude LFM signal parameter estimation | |
| CN114882361B (en) | Deep learning forest aboveground biomass estimation method based on multisource remote sensing fusion | |
| CN112882115A (en) | Magnetotelluric signal denoising method and system based on GWO optimized wavelet threshold | |
| CN105929380B (en) | A kind of laser satellite altimeter Full wave shape laser radar data denoising method | |
| CN108509835B (en) | PolSAR image ground object classification method based on DFIC super-pixels | |
| CN110673138A (en) | Ground penetrating radar image processing method based on singular value decomposition and fuzzy C mean value method | |
| CN108983158A (en) | A kind of Ground Penetrating Radar noise suppressing method based on Hankel Singular Value Decomposition Using | |
| CN111461999A (en) | SAR image speckle suppression method based on super-pixel similarity measurement | |
| CN113866736B (en) | Laser radar echo signal Gaussian inflection point selection decomposition method |
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 |