CN105488260A - Aerial noise identification and non aerial noise filtration method - Google Patents
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Abstract
The invention discloses an aerial noise identification and non aerial noise filtration method. On the premise of generally meshed airport noise perception, densely and uniformly distributed noise monitoring devices are arranged in a monitoring region; and according to position relationships among the monitoring devices and real-time noise monitoring values generated by the monitoring devices, the generally meshed airport noise perception based aerial noise identification and non aerial noise filtration are realized.
Description
Technical field
The present invention relates to a kind of aircraft noise identification and non-aircraft noise filter method, belong to noise monitoring technical field.
Background technology
In recent years, although the career development of civil aviaton of China is very swift and violent, air transport resource was still very nervous.No matter be infrastructure aspect, or airport quantity and capacity have been in the state of wretched insufficiency all.According to statistics, 2010 the end of the years first 50 of whole nation handling capacity rank airport in, have 26 aerodrome capacities saturated, estimate that other 24 airports also will reach capacity in 2015.In order to solve the problem of air transport resource anxiety, the whole nation is planned for " 12 ", and civil transport in latter stage number reaches more than 230.And actual newly-increased airport in 2011 only has 5,2012 3, estimate the airport of newly-increased 14 at the beginning of 2013, but reality only builds up 10.Tracing it to its cause, is the construction speed that have impact on China's civil airport because airport noise is seriously polluted.Research report according to CAAC is added up: in China, has and there is problem of noise pollution more than 150 civil airports, and wherein the airport noise pollution problem of 1/3rd is nearly very serious for this.Along with the raising day by day that the people requires living environment quality, day by day serious airport noise pollution problem has caused the tide of opposition airport construction among the people, make airport operator gradually passive, the pressure from public opinion born is also increasing, has a strong impact on and govern the sustainable development of China's Civil Aviation Industry.
The main airports of most of developed country has all been installed and has been applied airport noise monitoring system in the world at present, as Frankfurt, Germany airport (Fraport) adds two monitoring stations around airport, and some mobile detection apparatus, monitor aircraft noise situation; Airport, Belgian Brussels is provided with the noise situations that 21 noise monitoring device provide surrounding incessantly, can generate average noise size distribution ratio and the isogram of one day.The main airports such as domestic Beijing Capital International Airport, Hong Kong International Airport, Taiwan Taoyuan International Airport also use similar monitoring system.Airport noise monitoring system can realize monitoring the whole day of airport noise, and the reliable basis that its Monitoring Data is polluted as airport noise, greatly facilitates airport administrator and implement comprehensive management and control to noise.But existing airport noise monitoring system all adopts traditional monitoring pattern, monitoring node with high costs, node is high to layout environmental requirement, and system stability is poor, and data-transmission mode is fixed, and the cost of transmission line laying and later maintenance is very high.For above-mentioned reasons, traditional monitoring pattern can not realize the extensive dense deployment of airport noise monitoring node, can not realize the overall measure to airport and circumference noise thereof.
For comprehensive real-time perception demand of airport noise perception, existing Internet of Things theoretical result is that the research of airport noise perception provides theoretical foundation and practical experience.But these researchs, mostly for general technology of Internet of things or application-specific scene, also do not have investigation and application precedent in airport noise perception.Theoretical based on Internet of Things, even, intensive with a large amount of noise monitoring device, widely layout around airport environment, form the layout that the Ubiquitous network is formatted, to providing noise perception data real-time in all directions around airport.
Current general noise recognizing method carries out analysis and distinguishing to the frequency of noise, mainly contains by the method such as wavelet decomposition, noise reduction, isolates with this noise signal needing to identify, and carry out identifying to it and judge; Or on this basis, in being excavated by usage data, the method such as classification, cluster, carries out recognition and classification to noise situations.But the noise data that above-mentioned noise recognizing method only used single noise monitoring point is analyzed, the characteristic of monitoring points numerous under not only have ignored general grid environment, do not consider the correlativity between each data of monitoring point yet.If consider many data of monitoring point, the noise data of each monitoring point is all analyzed, will greatly increase its calculated amount, but due to the similarity after the conversion of each check point noise data in frequency higher, thus can not significantly improve the identification level of its noise.
Summary of the invention
Technical matters to be solved by this invention is to provide a kind of aircraft noise identification and non-aircraft noise filter method, even, intensive, the noise monitoring device widely of layout around airport, and each monitoring point can be real-time generation noise figure, obtain the real-time Ubiquitous network with this and to format airport noise Monitoring Data.Because non-aircraft noise is less relative to the aircraft noise sound intensity, its noise effect greatly obviously decays with the change of distance and eliminates, noise effect radius is shorter, the monitoring point number that can affect is less, therefore can according to the noise figure difference of target monitoring point and neighbouring monitoring point, calculate the similarity relation of noise between adjacent monitoring point, differentiate that whether belonging to non-aircraft noise covers, and then carry out filter operation.And the radius of influence of aircraft noise is comparatively far away, often can several kilometers more than be reached.Thus, when aircraft noise, the noise monitoring point of abundant extensive distribution can be affected, and differentiates the existence of aircraft noise with this.According to the distribution situation of monitoring point noise, calculate the noise rise vector between each monitoring point, differentiate whether noise rise direction is pointed to same direction or whether be gathered in same position, calculates its aircraft noise possibility size, differentiate whether there is aircraft noise with this, general its scope of estimation.
The present invention is for solving the problems of the technologies described above by the following technical solutions:
On the one hand, the invention provides a kind of non-aircraft noise filter method, by target monitoring point and the noise figure size of closing between monitoring point, set up noise monitoring grid, and the noise fluctuations situation respectively in calculated level direction, vertical direction and two diagonals; Based on the isolatism of interference noise point, amplification pre-service is carried out to noise fluctuations situation, choose most suitable direction and to the party upwards noise situations carry out similarity analysis, if similarity is lower than setting threshold value, then there is the interference of non-aircraft noise, filter operation is carried out to it, otherwise it is not filtered.
As further prioritization scheme of the present invention, target monitoring point with close on monitoring point and form 3 × 3 noise monitoring grids centered by target monitoring point.
On the other hand, the invention provides a kind of aircraft noise recognition methods, by the noise figure of noise monitoring point in whole monitored area, calculate each target monitoring point relative to the noise figure difference of closing on monitoring point, and calculate its noise rise vector; Calculate monitoring point, whole monitored area noise rise vector arrives the distance vector of this position, monitoring point inner product with any, and obtain its maximal value as what there is aircraft noise source and there is degree size, if there is degree size to be greater than setting threshold value, then show that monitored area exists aircraft noise source, this point is then the estimated position of place, aircraft noise source floor projection.
On the other hand, the invention provides a kind of aircraft noise identification and non-aircraft noise filter method, first, adopt a kind of non-aircraft noise filter method as claimed in claim 1, filter non-aircraft noise; Secondly, adopt a kind of aircraft noise recognition methods as claimed in claim 3, estimate the position in aircraft noise source.
As further prioritization scheme of the present invention, if disappearance noise figure in certain monitoring point place in monitored area, then substitute with the inverse distance-weighting average of its nearest N number of spot noise figure, N is positive integer.
The present invention adopts above technical scheme compared with prior art, has following technique effect:
1, the present invention analyzes according to the real-time noise monitor value of the mutual alignment relation between the equipment of each monitoring point and its generation, can realize the aircraft noise identification based on airport noise perception and the filtration of non-aircraft noise.The present invention substantially increases the recognition correct rate of aircraft noise, for the reasonable management of aircraft noise provides important evidence;
2, the non-aircraft noise source that the present invention can produce periphery monitoring region, airport identifies filters non-aircraft noise source simultaneously; Identification decision is carried out to the aircraft noise source in monitored area, determines whether airport periphery monitoring scope has takeoff and landing; Estimate aircraft Position Approximate.The present invention not only can effectively filter non-aircraft noise, also can provide reliable basis for the flight course planning taken off for estimating of aircraft Position Approximate;
3, the present invention can for realize the real-time isoplethes drawing of airport noise further, for aircraft track reproduction etc. provides criterion and theoretical foundation, for making tremendous contribution to China's airport construction, significant to China's aviation development.
Embodiment
Below technical scheme of the present invention is described in further detail:
The noise General Influence scope of aircraft is comparatively wide, and when aircraft enters airport noise monitoring scope, due to the characteristic of Internet of Things perception on a large scale, multiple noise monitoring points all can monitor the fluctuation of noise, and noise profile has obvious trend.And if general vehicle whistle or animal cry etc., because its intensity of sound is little compared with aircraft noise many, the scope that can affect in noise attentuation process is less, often around a sound source, the noise figure of 2,3 monitoring point monitorings can change, and other noise monitoring points then can not produce obvious change.
Non-aircraft noise filter algorithm
By the association similarity analysis of monitor value between contiguous monitoring point, the interference noise source filtering that regional area can be monitored, can improve the degree of accuracy of aircraft noise identification.Algorithm detailed process is as follows:
In the monitoring point layout that the Ubiquitous network is formatted, monitoring point according to the distribution of grid situation, but allows the position of monitoring point to there is the disappearance etc. of deviation and monitoring point.If certain some place disappearance noise figure situation, first substitutes with the inverse distance-weighting average of its nearest N number of spot noise figure.
During for judging whether certain some P (x, y) exists non-aircraft noise, select 3 × 3 noise monitoring grid W of point centered by P (x, y),
W＝{P(i-1,j-1),P(i-1,j),P(i-1,j+1),P(i,j-1),P(i,j),P(i,j+1),P(i+1,j-1),P(i+1,j),P(i+1,j+1)}
Wherein, i represents the horizontal ordinate of noise monitoring grid, and j represents the ordinate of grid.Table 1 travel direction mark is pressed to the monitoring node in this noise monitoring grid W.
Table 1 noise monitoring grid W
| NW | N | NE |
| W | P(x,y) | E |
| SW | S | SE |
Get 4 subset (i in this grid
dfor the noise figure of monitoring point contiguous in all directions in monitoring grid, d represents 8 directions represented in monitoring grid):
W
1＝{i
NW,P(x,y)，i
SE}
W
2＝{i
W,P(x,y)，i
E}
W
3＝{i
SW,P(x,y)，i
NE}
W
4＝{i
N,P(x,y)，i
S}
The standard deviation of 4 subsets asked for, and find out that minimum group, be designated as Wmin.Standard deviation is less, then illustrate that the fluctuation of these group data is less, and their noise monitoring value is more close.
Carry out reinforcement process to the noise figure of the central point of that minimum group, i.e. Wmin*L, wherein L=[-1,3 ,-1], obtain new central point monitor value P (x, y) '.If central point is interference noise source, by above-mentioned weighting process, the noise figure of this monitoring point can be given prominence to further, increase the difference between this monitoring point and surrounding normal monitoring point, the existence of interference noise source can be detected in conjunction with Similarity measures hereinafter better.If center monitors point is normal noise, namely there is not non-aircraft noise source near, then its noise figure detected should with the noise figure of contiguous monitoring point closely, strengthen rear center's spot noise figure almost constant, therefore Similarity measures hereinafter can not be impacted, also can not be mistaken for interference noise point.
Through above-mentioned process, grid element center point becomes P (x, y) ', other monitor values in grid W outside center point P (x, y) ' are formed new sequence, obtains new sequence B={ B (1), B (2), B (3), B (4), B (5), B (6), B (7), B (8) }, and will wherein each element and P (x, y) ' subtract each other, taking absolute value to all differences obtains
D
j＝|B(j)-P(x,y)'|
Wherein 1≤j≤8.Consider the fault-tolerance of considering algorithm, and there is not non-aircraft noise source around center monitors point, and when there is non-aircraft noise source around monitoring point around, this noise spot is influenced, other 7 noise monitoring Dian Ji center noise monitoring points are then influenced less, and therefore, we are from D
jin select minimum 4 values and form new one dimension matrix, can remove with this and affect noise spot by limit.
D={D
i, wherein 1≤i≤4.
For monitoring grid W, if central point is interference noise point, so based on the isolatism of noise spot, in D, the value of 4 elements will be larger; Otherwise if central point is normal monitoring point, then the value of 4 elements in D is less.In order to represent the degree of similarity between central point and contiguous monitoring point, defining a new function T, being expressed as follows shown in formula:
Wherein: D
irepresent the element in above-mentioned new matrix D, t is the threshold value judging noise.If T<t, then P (x, y) be normal monitoring point, otherwise P (x, y) be interference noise point, can think that the noise that this point monitors is not aircraft noise, in aircraft noise monitoring and processing procedure, the monitor value of this point can be ignored, mark this noise figure and be subject to non-aircraft noise effect.
Aircraft noise recognizer
Identical with non-aircraft noise filter algorithm, the identification of aircraft noise needs the similarity each target monitoring point being calculated to itself and periphery monitoring point.Because the coverage of aircraft noise is wider, the logarithm of the spacing of noise attentuation and noise monitoring point and noise source is relevant, and aircraft noise source and monitoring point distance can not be too near, it is at least the distance of hundred meters, therefore, the noise figure of several adjacent to one another noise monitoring point is more or less the same, and can not be identified by non-aircraft noise filter algorithm by mistake.Meanwhile, the distance of the spacing of each monitoring point and noise source, can affect the size on noise figure.W
1={ i
nW, P (x, y), i
sE, W
2={ i
w, P (x, y), i
e, W
3={ i
sW, P (x, y), i
nE, W
4={ i
n, P (x, y), i
sin, each monitoring point is due to the distance of its range noise source position, unless noise source is just near P point, or these monitoring point distance aircraft noise spacings are too far away, the distance that distance between these monitoring points arrives aircraft noise source is relatively too little, causes its noise figure to be all more or less the same; Otherwise W
1, W
2, W
3, W
4sequence can form increasing or decreasing sequence.According to the degree that noise figure in all directions increases or reduces, the direction of noise rise can be differentiated, or the direction of noise source.Specific algorithm step is as follows:
Suppose that airport periphery monitoring point layout is the general meshed network of a M*N.For the net point that there is walkaway equipment near each, non-aircraft noise is calculated to it and differentiates and filter algorithm, the grid after filtering is set to matrix A=[a
ij], (1≤i≤M, 1≤j≤N), wherein a
ijthe noise figure of corresponding net point.
When there is aircraft noise, the overlay area of aircraft noise is with aircraft noise source for the center of circle, the radius of influence more than 1Km border circular areas.The noise figure that interior monitoring point monitors on a large scale all can be subject to the impact of aircraft noise, and the size of its noise figure mainly relies on the distance of monitoring point to aircraft noise source.Therefore we use matrix
for representing the noise rise vector that each net point is corresponding.
For each net point after filtration in A, to the W of the 3*3 grid centered by it
1, W
2, W
3, W
4judge, if W
i(1≤i≤4) are increasing or decreasing sequences, then mark its noise rise direction, otherwise do not mark it.To W
1, W
2, W
3, W
4noise rise direction carry out addition of vectors,
wherein
w
tnoise rise vector.
In compute matrix B, each noise rise vector points to the degree of the same area:
((x
i, y
j)) representing that the coordinate of the i-th row jth row net point, α represent distance balance weight, net point distance (a, b) distance is far away, and weight is less, and represent the degree size of its sensing the same area, namely aircraft noise exists the size of possibility.T represents that it exists the threshold value of aircraft noise, and namely T>t shows to there is aircraft noise, and T<t shows to there is not aircraft noise.When there is aircraft noise source, (a, b) is then the estimated position of place, aircraft noise source floor projection.
The above; be only the embodiment in the present invention; but protection scope of the present invention is not limited thereto; any people being familiar with this technology is in the technical scope disclosed by the present invention; the conversion or replacement expected can be understood; all should be encompassed in and of the present inventionly comprise within scope, therefore, protection scope of the present invention should be as the criterion with the protection domain of claims.
Claims (5)
1. a non-aircraft noise filter method, it is characterized in that, by target monitoring point and the noise figure size of closing between monitoring point, set up noise monitoring grid, and the noise fluctuations situation respectively in calculated level direction, vertical direction and two diagonals; Based on the isolatism of interference noise point, amplification pre-service is carried out to noise fluctuations situation, choose most suitable direction and to the party upwards noise situations carry out similarity analysis, if similarity is lower than setting threshold value, then there is the interference of non-aircraft noise, filter operation is carried out to it, otherwise it is not filtered.
2. the non-aircraft noise filter method of one according to claim 1, is characterized in that, target monitoring point with close on monitoring point and form 3 × 3 noise monitoring grids centered by target monitoring point.
3. an aircraft noise recognition methods, is characterized in that, by the noise figure of noise monitoring point in whole monitored area, calculates each target monitoring point relative to the noise figure difference of closing on monitoring point, and calculates its noise rise vector; Calculate monitoring point, whole monitored area noise rise vector arrives the distance vector of this position, monitoring point inner product with any, and obtain its maximal value as what there is aircraft noise source and there is degree size, if there is degree size to be greater than setting threshold value, then show that monitored area exists aircraft noise source, this point is then the estimated position of place, aircraft noise source floor projection.
4. aircraft noise identification and a non-aircraft noise filter method, is characterized in that, first, adopts a kind of non-aircraft noise filter method as claimed in claim 1, filter non-aircraft noise; Secondly, adopt a kind of aircraft noise recognition methods as claimed in claim 3, estimate the position in aircraft noise source.
5. a kind of aircraft noise identification according to claim 4 and non-aircraft noise filter method, is characterized in that, if disappearance noise figure in certain monitoring point place in monitored area, then substitute with the inverse distance-weighting average of its nearest N number of spot noise figure, N is positive integer.
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| CN201510829721.8A CN105488260B (en) | 2015-11-25 | 2015-11-25 | A kind of aircraft noise identification and non-aircraft noise filter method |
| CN201811220563.6A CN109405960B (en) | 2015-11-25 | 2015-11-25 | Aviation noise identification method |
| CN201811220571.0A CN109408945B (en) | 2015-11-25 | 2015-11-25 | Non-aviation noise filtering method |
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| CN115326193A (en) * | 2022-10-12 | 2022-11-11 | 江苏泰洁检测技术股份有限公司 | Intelligent monitoring and evaluation method for factory operation environment |
| CN115638876A (en) * | 2022-12-14 | 2023-01-24 | 北京英视睿达科技股份有限公司 | Noise calculation method, device, equipment and storage medium based on high-density grid |
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| CN113295265B (en) * | 2021-03-31 | 2022-06-14 | 国网河北省电力有限公司电力科学研究院 | Transformer noise detection method |
| CN113887590B (en) * | 2021-09-22 | 2023-06-09 | 中国电子科技集团公司第二十九研究所 | Target typical track and area analysis method |
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| US4114135A (en) * | 1975-06-20 | 1978-09-12 | The United States Of America As Represented By The Secretary Of The Navy | Acoustic device |
| US20100079342A1 (en) * | 1999-03-05 | 2010-04-01 | Smith Alexander E | Multilateration enhancements for noise and operations management |
| US6885340B2 (en) * | 2000-02-29 | 2005-04-26 | Rannoch Corporation | Correlation of flight track data with other data sources |
| US7092853B2 (en) * | 2001-10-25 | 2006-08-15 | The Trustees Of Dartmouth College | Environmental noise monitoring system |
| KR100967084B1 (en) * | 2008-07-01 | 2010-07-01 | 한국항공우주연구원 | Crack Monitoring System, Crack Monitoring Method and Computer Readable Medium on which Crack Monitoring Program is Recorded |
| CN102735332B (en) * | 2012-07-16 | 2014-06-18 | 中国民航大学 | Airplane noise monitoring, covering and optimizing method and device |
| CN102820034B (en) * | 2012-07-16 | 2014-05-21 | 中国民航大学 | Noise sensing and identifying device and method for civil aircraft |
| CN103337248B (en) * | 2013-05-17 | 2015-07-29 | 南京航空航天大学 | A kind of airport noise event recognition based on time series kernel clustering |
| KR101552981B1 (en) * | 2015-05-08 | 2015-09-15 | 한국디지탈콘트롤 주식회사 | A method for analyzing noise by aircraft by adjusting cut-off frequency |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN115326193A (en) * | 2022-10-12 | 2022-11-11 | 江苏泰洁检测技术股份有限公司 | Intelligent monitoring and evaluation method for factory operation environment |
| CN115326193B (en) * | 2022-10-12 | 2023-08-25 | 江苏泰洁检测技术股份有限公司 | Intelligent monitoring and evaluating method for factory operation environment |
| CN115638876A (en) * | 2022-12-14 | 2023-01-24 | 北京英视睿达科技股份有限公司 | Noise calculation method, device, equipment and storage medium based on high-density grid |
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| CN105488260B (en) | 2019-01-18 |
| CN109405960A (en) | 2019-03-01 |
| CN109408945A (en) | 2019-03-01 |
| CN109408945B (en) | 2023-02-07 |
| CN109405960B (en) | 2020-04-24 |
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