JP2005114416A - Disturbance detection device - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To provide a disturbance detection device improving detection precision for each disturbance phenomenon by increasing information for danger by performing detection process for the various practically problematic disturbance. <P>SOLUTION: The disturbance detection device comprises: the radio-wave transmitter-receiver part 1 for receiving reflected waves from the disturbance body in the air by transmitting radio waves to the air in the observation area; the Doppler velocity distribution calculation part 2 for calculating the Doppler velocity distribution by using the receiving signal from the radio-wave transmitter-receiver 1; the characteristics calculation part 3 for doppler velocity distribution for forming a characteristic space by calculating the characteristics of the wind direction and wind velocity of the doppler velocity distribution; the disturbance characteristic determination part 4 for detecting the spatial distribution of the characteristics in the characteristic space; and the disturbance determination part 5 for determining whether the disturbance is prescribed disturbance phenomenon or not is determined based on the spatial distribution of characteristic values. <P>COPYRIGHT: (C)2005,JPO&NCIPI

Description

  The present invention relates to a disturbance detection device for detecting disturbances in the atmosphere. In particular, the present invention relates to a disturbance detection apparatus that detects a plurality of disturbance phenomena by applying a feature calculation filter and map transformation to a received signal from an electromagnetic wave transmission / reception unit.

  2. Description of the Related Art Conventionally, in a transportation system that uses the atmosphere as an operation route or a transportation system that is affected by an atmospheric phenomenon, particularly in the aviation field, it is necessary to accurately grasp the atmospheric phenomenon from the viewpoint of safety. In the aviation field, it is known that there is a high risk of aircraft taking off and landing due to atmospheric phenomena, and atmospheric conditions around airports are observed by radar and the like. As air turbulence at the time of aircraft takeoff and landing, wind shear, microburst / downburst, backward turbulence and the like are known. The conventional wind shear detection device detects the spatial distribution data of wind speed and direction by analyzing the Doppler component of the reflected light and the optical transmission / reception means that transmits while scanning the laser beam to the observation target area and receives the reflected light. Wind detection means for analyzing the wind speed and wind direction spatial distribution data, and detecting wind shear from the wind direction distribution pattern. The generated wind shear is observed (for example, refer to Patent Document 1).

  Also, the conventional wake turbulence detection device is a lidar that receives reflected light from the atmosphere, converts the frequency and outputs it, a velocity distribution estimation means that estimates and outputs the Doppler velocity distribution of the atmosphere using the output of the lidar, and is specific to the wake turbulence A speed distribution template output means for outputting a speed distribution template, which is a typical example of the speed distribution, input the outputs of the speed distribution estimation means and the speed distribution template output means, Velocity distribution comparison means for finding and outputting the distribution of the similarity, and a wake turbulence search means for searching for the maximum point of the similarity and outputting the position and intensity of the wake turbulence with the output of the speed distribution comparison means as an input (For example, refer to Patent Document 2).

Japanese Patent Laid-Open No. 2002-267553 (first page, FIG. 1) Japanese Unexamined Patent Publication No. 2000-310680 (first page, FIG. 2)

  In the conventional wind shear detection device as described above, only the wind shear is detected, and in the conventional wake turbulence detection device, only the wake turbulence is detected. However, various turbulence phenomena in the atmosphere may exist at the same time, and there are some that are difficult to distinguish individually. For this reason, it is conceivable that the detection accuracy is deteriorated due to the indistinguishability, the other disturbance phenomenon becomes an obstacle to detection of the desired disturbance phenomenon, and the detection accuracy is deteriorated. Moreover, in general, various disturbance phenomena can be actual threats, and the amount of information is insufficient for devices that can be detected individually, and a large-scale device configuration is required to deal with all threats. .

  The present invention has been made in order to solve the above-described problems. The purpose of the present invention is to increase the amount of information on threats by performing detection processing on various disturbance phenomena that are actually problematic, and to each individual A disturbance detection device capable of improving the detection accuracy of the disturbance phenomenon is obtained.

  The disturbance detection apparatus according to the present invention uses an electromagnetic wave transmitting / receiving unit that radiates an electromagnetic wave in the atmosphere of an observation region and receives a reflected wave from a scatterer in the atmosphere, and a reception signal from the electromagnetic wave transmitting / receiving unit. A Doppler velocity distribution calculating unit for calculating a Doppler velocity distribution in the observation region; a Doppler velocity distribution feature calculating unit for calculating a wind direction and a wind speed characteristic of the Doppler velocity distribution to generate a feature space; and a feature value on the feature space. A disturbance feature detection unit for detecting the spatial distribution of the characteristic value, and a disturbance determination unit for determining whether or not a predetermined disturbance phenomenon is caused based on the spatial distribution of the feature values.

  The disturbance detection apparatus according to the present invention is capable of increasing the amount of information on threats by performing detection processing on various disturbance phenomena that are actually problematic, and improving the detection accuracy of individual disturbance phenomena. Play.

  Embodiments of the present invention will be described below with reference to the drawings. In addition, in each figure, the same code | symbol is attached | subjected to a common element and the overlapping description is abbreviate | omitted. In general, the observation value by the radar is often expressed by an r-θ-φ polar coordinate system based on an elevation angle θ, an azimuth angle φ, and a distance r, but hereinafter, it is expressed as a three-dimensional orthogonal coordinate system (xy−). (Z coordinate system) is used (generality is not lost with respect to the content of the present invention).

Embodiment 1 FIG.
A disturbance detection apparatus according to Embodiment 1 of the present invention will be described with reference to the drawings. FIG. 1 is a diagram showing a configuration of a disturbance detection apparatus according to Embodiment 1 of the present invention.

  In FIG. 1, the disturbance detection apparatus uses an electromagnetic wave transmission / reception unit 1 that radiates an electromagnetic wave to the atmosphere of an observation region and receives a reflected wave from a scatterer in the atmosphere, and an observation signal using the reception signal from the electromagnetic wave transmission / reception unit 1. A Doppler velocity distribution calculating unit 2 for calculating a Doppler velocity distribution of the region, a Doppler velocity distribution feature calculating unit 3 for generating a feature space by calculating a wind direction and a wind speed feature of the Doppler velocity distribution, and a feature value on the feature space A disturbance feature detection unit 4 for detecting the spatial distribution of the characteristic value, and a disturbance determination unit 5 for determining whether or not there is a predetermined disturbance phenomenon based on the spatial distribution of the feature values.

  Next, the operation of the disturbance detection apparatus according to the first embodiment will be described with reference to the drawings.

  The electromagnetic wave transmission / reception unit 1 transmits an electromagnetic wave into the atmosphere in the desired observation region and receives a reflected wave from a scatterer in the atmosphere. As the electromagnetic wave transmission / reception unit 1, for example, a laser radar (light wave radar, lidar) that transmits and receives an optical pulse can be considered. By using the laser radar, the atmosphere is subjected to, for example, vertical scanning, horizontal scanning, conical scanning, or a combination thereof. Observed values of the entire desired observation region can be obtained by beam scanning and observation from a plurality of points and observation devices having different installation positions.

  Using the received signal obtained from the electromagnetic wave transmission / reception unit 1, the Doppler velocity distribution calculation unit 2 calculates the Doppler velocity distribution in the observation region. Since the spatial resolution of the Doppler velocity distribution is generally determined by the observation specifications of the electromagnetic wave transmission / reception unit 1, the disturbance (feature) detection process of the subsequent disturbance feature detection unit 4 and the observation specifications are determined according to the desired resolution. Interpolate Doppler velocity distribution after observation. In general, since an observed value includes noise, noise suppression processing is performed as necessary. Examples of the noise suppression process include a smoothing filter such as an average value filter and a median filter, and a screening process described in JP-A-2002-48866.

  Next, the Doppler velocity distribution feature calculation unit 3 calculates the wind direction and wind velocity characteristics of the Doppler velocity distribution from the obtained Doppler velocity distribution in the observation region. As a method for calculating the wind direction and wind speed characteristics of the Doppler velocity distribution, for example, a matching process using a specific speed pattern that models a specific disturbance phenomenon can be considered.

  As a specific disturbance phenomenon, for example, in the case of wind shear (shear), there is a change in the wind speed in the vertical or horizontal direction with respect to the traveling direction of a certain wind. Use a pattern.

  In the case of microburst, strong downflow appears in a plane perpendicular to the ground, and radial wind appears in a horizontal plane relatively close to the ground. Use the speed pattern.

  In addition, in the case of wake turbulence, two vortices that are inward from each other centering on both wingtips of the aircraft appear along the flight path in a plane substantially perpendicular to the flight path after passing through the aircraft. Use velocity patterns that model changes. Hereinafter, filters for calculating features such as speed patterns are collectively referred to as feature calculation filters.

  The atmospheric turbulence phenomenon is a phenomenon in a three-dimensional space. As a velocity pattern for extracting the features, a two-dimensional plane is assumed in a three-dimensional space in addition to a three-dimensional velocity pattern. A method using a two-dimensional velocity pattern, a method using a one-dimensional straight line in a three-dimensional space, and a velocity pattern on the straight line are conceivable. Furthermore, in addition to a three-dimensional space, a multi-dimensional feature space considering time and the like can also be considered. In general, as the number of dimensions and the spatial size of the speed pattern increase, the amount of information increases and the detection accuracy improves. However, the calculation load also increases, so select the appropriate processing according to the desired detection accuracy and device configuration. To do. In the following, a process assuming a two-dimensional plane in a three-dimensional space will be described, but a one-dimensional or three-dimensional to multi-dimensional process can be similarly considered.

  For example, in order to extract Doppler velocity distribution features such as vertical wind shear and microburst / downburst in the vertical section, consider the vertical section as shown by the diagonal lines in FIG. The Doppler velocity distribution is matched using a velocity pattern as shown in FIG. 3A and 3B are examples of velocity change patterns in the vertical direction (in the case of a vertical section). The numbers enclosed in circles mean that the entire area has numerical values, and the specific configuration is as shown in FIGS. 3 (c) and 3 (d). 3 (a) and 3 (c) are basic configurations of the velocity change pattern in the vertical direction, and FIGS. 3 (b) and 3 (d) emphasize the boundary of the velocity change (for example, as described later). 3 (a) and 3 (c), the boundary is between cells, and the wind speed distribution near the boundary is an obstacle to speed change detection. This is an extended configuration.

  Further, k, l, m, and n in FIG. 3 are the size of the feature calculation filter. For example, when the magnitude of the disturbance phenomenon is large, the size of the filter is also increased. Decide by etc. Alternatively, it is determined from the relationship between the observation target and the observation specifications (resolution). For example, in the case of wake turbulence, if the wing tip of the aircraft is Xm, the size of the wake turbulence feature calculation filter that detects two vortices is (X / 2) m to ((X + 1) / 2. ) Determine the size of the filter so that it is about m. The vertical section and the feature calculation filter (speed pattern) are divided by mesh. The size of the vertical section is assumed to be X × Y mesh, and the size of the feature calculation filter is assumed to be k × (m + n) mesh. However, the mesh has sufficient resolution to capture the desired disturbance phenomenon. Further, the value of the Doppler velocity distribution at the position (p, q) is V (p, q), and the value of the feature calculation filter is F (p, q). At this time, the characteristic value T (x, y) at a certain position (x, y) in the vertical cross section is obtained as follows. Note that i = 1 to k and j = 1 to (m + n). However, this is a case where a feature calculation filter as shown in FIGS. 3A and 3C is used.

T (x, y) = ΣΣF (i, j) ×
V {x− (k / 2) + i, y − ((m + n) / 2) + j} (1)

  If the wind direction from far (far) to the sensor side (this) is positive and the opposite is negative, the velocity pattern shown in FIGS. 3 (a) and 3 (b) will be stronger in the vertical downward direction. The characteristic value T is negative in a region where there is such a wind speed change, and is positive in a region where the reverse wind speed change is present, and the magnitude of the feature value is proportional to the difference in speed change. Similarly, in order to calculate Doppler velocity distribution characteristics such as horizontal wind shear and microburst / downburst in the vertical section, matching using velocity patterns as shown in FIG. 4 is performed. 4, the configurations of (a), (c), (b), and (d) are the same as the configurations of (a), (c), (b), and (d) in FIG. The velocity patterns in the vertical direction and the horizontal direction in the vertical section described above can be used for the horizontal section and an arbitrary section. In the case of wake turbulence, the wake turbulence template described in Patent Document 2 can be used as a velocity pattern.

  As described above, by extracting the feature quantity of the Doppler velocity distribution using the feature calculation filter (specific velocity pattern) that models the specific disturbance phenomenon, it is possible to perform the optimum process for detecting the target disturbance phenomenon. And detection accuracy can be improved. In addition, when the target disturbance phenomenon is known, processing in a later-described disturbance feature detection unit 4 described later can be simplified, and detection at higher speed and higher accuracy can be performed. Further, by specializing in a specific disturbance phenomenon, the size of the feature calculation filter can be optimized, the sensitivity to noise can be reduced, and the detection accuracy can be improved. Furthermore, since it is not necessary to configure a large number of feature calculation filters, the apparatus configuration can be simplified.

  As another method for calculating the wind direction and wind speed characteristics of the Doppler velocity distribution, a feature calculation filter (directional velocity pattern) that extracts the spatial direction characteristics of the Doppler velocity distribution is considered as the direction. The matching process used can be considered. FIG. 5 is an example of a feature calculation filter that extracts the spatial direction feature of such a Doppler velocity distribution value. In the configuration of this example, feature calculation filters corresponding to eight directions (a) to (h) are shown. However, the feature value can be reduced to half by allowing the feature value to be positive and negative, and the diagonal direction is detected in the vertical and horizontal directions. As described above, the number of filters can be reduced by narrowing down only in the two directions of up, down, left, and right, and the calculation load and the apparatus configuration can be simplified. As for the size of each filter, when the filter is made larger, the sensitivity to small noise can be reduced and the detection accuracy can be improved, while the sensitivity to a weak speed change is also reduced. If the filter is made smaller, the opposite tendency occurs. Therefore, the size of the filter is determined according to the target disturbance phenomenon, device configuration, and feature value.

  By extracting the spatial directional features of the Doppler velocity distribution as described above, it is not necessary to have information about specific disturbance phenomena in advance, the system configuration can be simplified, and various atmospheric disturbance phenomena in general can be detected. Is possible. This process has the same effect as applying a spatial differential filter to the Doppler velocity distribution, and can efficiently detect a changed portion (direction and intensity of change) of the Doppler velocity distribution.

  Another method for calculating the wind direction and wind speed characteristics of the Doppler velocity distribution is to extract a Doppler velocity distribution within a predetermined range from a predetermined value above, below, or from a predetermined value by threshold processing using a predetermined Doppler velocity value as a threshold value. A way to do this is considered. In addition, as an application, for example, a background wind or a predetermined reference Doppler velocity value is provided, and the value that changes from time to time is used as a reference (threshold value). A process for extracting the space of the Doppler velocity of the above can be considered. For example, in the case of wake turbulence, the size of wake turbulence can be estimated in advance based on the weight, wing tip, speed, etc. of the target aircraft. By extracting the characteristics of the Doppler velocity distribution in this way, only when a Doppler velocity value that can be taken by the target disturbance phenomenon is known, or only a disturbance phenomenon having a Doppler velocity value that is above, below, or within a certain range. In this case, the feature calculation process can be realized with a simple configuration.

  As another method for calculating the wind direction and wind speed feature of the Doppler velocity distribution, a method of mapping the Doppler velocity distribution to the feature space by a predetermined mapping transformation is conceivable. Examples of the mapping transformation include Fourier transformation, Hough transformation, Radon transformation, wavelet transformation, and the like, and these are often used for extracting feature quantities such as straight lines and curves. For example, in wind shear, speed change portions in the atmosphere appear in a line, and by using these conversions, a (linear) Doppler speed change portion can be extracted. In addition, for example, when using Fourier transform, it is expected that the background frequency portion other than the disturbance, the disturbance portion, and the noise portion have different spatial frequency characteristics, and an effect of improving the detection accuracy of the disturbance phenomenon can be expected. In addition, the conversion method as described above is easy to implement because the calculation processing technology on the computer has been established, such as FFT (Fast Fourier Transform) for Fourier transform, for example, and the disturbance detection processing is performed. Speeding up can be performed. In addition, these mapping conversions are normally performed on the entire target area, but by dividing the target area and performing the processing, it can be parallelized and the processing speed can be increased.

  In addition, when applying the above feature calculation filter and map transformation, it is important to apply the filter and map transformation depending on the disturbance phenomenon of interest and other factors such as background wind, weather conditions at the time of observation, observation specifications, etc. It is possible to change the size. For example, in the case of wake turbulence, the size can be estimated to some extent depending on the aircraft model, and therefore the detection accuracy can be improved by adjusting the size of the feature calculation filter or the like to the size. Hereinafter, the size of the area to which the feature calculation filter and the mapping transformation are applied is referred to as a mask size and a window. In general, a filter and map conversion using a small mask size and window are highly sensitive to minute changes in the target area, and a sensitivity using a large mask size and window is low. Therefore, while it is possible to extract fine features with a small mask size and window, it becomes sensitive to noise and the like, but with a large mask size and window, the sensitivity to noise and the like can be reduced, but the target phenomenon There is a risk of missing the fine features. Therefore, the detection accuracy can be improved by changing the mask size and the window according to, for example, the amount of change in the target phenomenon and the specifications of the noise removal processing in the subsequent stage.

  In addition, when applying the above feature calculation filter and mapping transformation, in addition to a configuration that extracts only the target disturbance phenomenon, a configuration that extracts other disturbance phenomena, or all of the target region It is conceivable to adopt a configuration for extracting the speed change in the direction and size. Narrowing down the target has the advantage of simplifying the device configuration. On the other hand, in addition to the target disturbance phenomenon, an apparatus configuration that targets all possible disturbance phenomena or extracts speed changes in all directions and sizes, for example, the target disturbance phenomenon In addition to being able to reduce the uncertainty of the target and improving the detection accuracy of the target disturbance phenomenon, there is a possibility of detecting a disturbance phenomenon that can be an unknown threat.

  A specific processing method when detecting a plurality of disturbance phenomena using a plurality of feature calculation filters is as follows. For example, the vertical velocity pattern (feature calculation filter) in the vertical section shown in FIG. 3 and the horizontal velocity pattern (feature calculation filter) in the vertical section shown in FIG. 4 are used. From the Doppler velocity distribution in the observation area calculated by the Doppler velocity distribution calculation unit 2, the Doppler velocity distribution feature calculation unit 3 generates feature spaces individually or in parallel with the vertical velocity pattern and the horizontal velocity pattern.

  As described above, the feature value is detected by the disturbance feature detection unit 4 in the feature space generated by the feature calculation filter and the mapping transformation. For example, the disturbance phenomenon appears as a positive or negative peak, or a point or line element having a certain value. Therefore, as a means for detecting the disturbance phenomenon, such a method of detecting a positive or negative peak value (maximum minimum value, maximum / minimum value, etc.), a distribution having a certain value, or a linear distribution It is conceivable to detect this. FIGS. 6A and 6B show the case where the characteristic of the disturbance phenomenon takes a positive peak value in the feature space. The figure (a) is the figure which looked at the feature space from the top, and the figure (b) is a perspective view of the feature space. When the feature value takes a positive or negative peak value as described above, the presence / absence and intensity of the disturbance phenomenon can be detected by detecting the peak value in the feature space. Further, when the feature space and the original observation area have a one-to-one correspondence, the position can be detected at the same time. For example, when template matching or the like is used, the characteristic value of the predetermined disturbance phenomenon is a certain value or a range of values from the multiplication result of the model value and speed value of the disturbance phenomenon and the coefficient of the template and the time change thereof. Can be expected. Therefore, by detecting a feature value having a predetermined value or value range, it is possible to reduce the influence of other disturbance phenomena and noise, and to detect only the desired disturbance phenomenon.

  In addition, when the characteristic values of the predetermined disturbance phenomenon are distributed in a linear shape, the predetermined disturbance phenomenon can be detected by detecting the line elements constituting the linear distribution. For example, when the above-described filter for extracting the spatial direction feature is applied to wind shear, a feature value having a linear peak appears in the feature space. FIGS. 7A and 7B are examples showing a feature space having a linear positive feature value. The figure (a) is the figure which looked at the feature space from the top, and the figure (b) is a perspective view of the feature space. Therefore, in such a case, the presence / absence and intensity of a predetermined disturbance phenomenon is detected by detecting a linear peak, and if the real space and the feature space have a one-to-one correspondence, the position is determined. Can be detected. As a detection method, for example, a method of detecting a connected component on a feature space of a feature value having a certain value can be considered. At that time, the connection state can be easily detected by performing the thinning process. In addition, for example, when mapping transformation such as Fourier transformation is performed, the line element appears as a line segment on the straight line passing through the origin in the feature space. Therefore, the feature amount is detected using such a property. . In this method, the search space is reduced and the calculation load is reduced.

  The disturbance determination unit 5 determines whether or not the distribution of feature values in the feature space detected as described above is a predetermined disturbance phenomenon. For example, it can be determined by considering the spatial size of the distribution. When the features of the disturbance appear as dots and the spatial positions of the real space and the feature space have a one-to-one correspondence, the spatial size occupied by the feature value distribution on the feature space is the actual size. This is consistent with the spatial magnitude of the disturbance phenomenon in space. Therefore, when the spatial distribution of feature values is larger or smaller than a predetermined size obtained from the physical properties of the disturbance phenomenon, it can be excluded as other disturbance phenomenon or noise, for example. By performing such processing, it is possible to improve the determination accuracy for the predetermined disturbance phenomenon.

  When the feature value in the feature space can be estimated in advance by using a model of a predetermined disturbance phenomenon, the disturbance determination unit 5 determines only the characteristic value having such a value as the predetermined disturbance phenomenon, By excluding those, it is possible to improve the determination accuracy for the predetermined disturbance phenomenon.

  In addition, when the spatial position information between the real space and the feature space has a one-to-one correspondence, for example, the distribution shape of the target disturbance phenomenon may be preserved even in the feature space. Thus, the disturbance determination unit 5 can determine the disturbance phenomenon. For example, in a microburst / downburst etc., a wind speed distribution on radiation appears in a plane that is horizontal to the ground in real space. On the other hand, when the direction feature calculation filter is applied, the detection result has a special shape such as a circular element (ellipse). Therefore, it is possible to accurately determine a predetermined disturbance phenomenon using such a property. .

  When detection processing is performed for a plurality of disturbance phenomena and a plurality of disturbance phenomena are determined at the same position in a certain space, the disturbance determination unit 5 includes, for example, the magnitude of the feature value and the actual influence level in the space. A method of uniquely determining the presence / absence of a predetermined disturbance phenomenon for each predetermined small region (cell) in the observation space is conceivable so that only the presence of only one type of disturbance phenomenon is satisfied. Thus, by determining the disturbance phenomenon (presence / absence), the configuration (display items and the like) of the display unit 6 at the subsequent stage can be simplified.

  Further, as a different method, when a plurality of disturbance phenomena are determined at the same position in a certain space, the disturbance determination unit 5 assumes that all these disturbance phenomena may exist, and a plurality of types of disturbance phenomena are detected at that position. A method that allows existence is conceivable. In this way, by allowing the presence of a plurality of types of disturbance phenomena, it is possible to display disturbance phenomena that are difficult to discriminate from each other, and there is an effect of improving the amount of information as a whole process.

  Further, as an extension of the detection process for a plurality of disturbance phenomena, it is conceivable to perform determination by providing an index such as probabilistic expression / reliability including information such as no disturbance or determination impossible. FIG. 8 is an example of determination results for a plurality of disturbance phenomena at the same position in the space. As a method of expressing the determination result, the method is expressed by a classical probability method (the sum of rejection events is 1), but for example, a method expressed by a fuzzy number or a method expressed by Dempster-Shafer theory. (The sum of the discharge events is not always 1). As a calculation standard for each probability / reliability, for example, a characteristic value or observation result information of the previous time / adjacent position is used as a calculation standard. For example, the characteristic value is high, and the disturbance phenomenon occurs at the previous time or the adjacent position. In the case where the existence of the target is confirmed, there is a high possibility that the target disturbance phenomenon exists in the region of interest, and a method of adding points is considered. As described above, by representing the determination result stochastically, it is possible to display disturbance phenomena / determination possibilities that are difficult to discriminate from each other, and the amount of information as a whole process, particularly the amount of information on the existence of each disturbance phenomenon, can be displayed. Can be improved.

  The disturbance phenomenon information in the observation area determined as described above is displayed by, for example, the display unit 6 shown in FIG. As a display method, for example, as shown in FIG. 10, a method of showing the spatial existence state (shape) of each disturbance phenomenon on the display 6 </ b> A of the display unit 6 can be considered. FIG. 10 shows the airport as an observation area as viewed from the sky. The upper part of the figure is a shaerline indicated by a broken line in wind shear, and the middle part is indicated by a one-dot chain line. A vortex (vortex wake) is displayed below and a microburst indicated by a dotted circle is displayed below. A rectangular runway is shown in the middle. According to the method of indicating the spatial existence state, it is easy to understand the distribution state of each disturbance phenomenon.

  Further, as shown in FIG. 11, a method of displaying on the display 6B of the display unit 6 by a symbol (symbol) indicating a disturbance phenomenon is conceivable. According to this method, display items are reduced as compared with the method showing the spatial distribution, the device configuration can be simplified, and since it does not depend on a specific display form, connection with other existing display devices is facilitated. FIG. 11 shows the airport as an observation area as viewed from above, with S (shaerline) in the upper part, V (vortex) in the middle, and M (lower) in the middle. (microbarst: microburst) Each is displayed. A rectangular runway is shown in the middle. Further, as shown in FIG. 11, it is conceivable to display the reliability (a numerical value in parentheses on the right side of the symbol indicating the disturbance phenomenon) together with the disturbance phenomenon. For example, (3.2), (0.7), and (8.9) on the right side of each of S (shear line), V (backward turbulence), and M (microburst) are the reliability. This reliability is expressed by a method expressed in terms of Dempster-Shafer theory (the sum of rejection events is not always 1). S (3.2) indicates that the possibility of shear line is 3.2, and the larger the numerical value, the greater the possibility of shear line. Other display items include rise / fall status, duration, disturbance risk, and the like. According to this method, it is possible to provide disturbance information with a higher information amount.

Embodiment 2. FIG.
A disturbance detection apparatus according to Embodiment 2 of the present invention will be described with reference to the drawings. FIG. 12 is a diagram showing a configuration of a disturbance detection apparatus according to Embodiment 2 of the present invention.

  The difference from the first embodiment is that, in the second embodiment, after a disturbance is determined by the disturbance determination unit 5 for a certain feature amount, the feature amount of the feature space in which the disturbance is present is converted into the disturbance feature removal processing unit 7. This is a point to be removed. For example, as shown in FIG. 6, the disturbance feature removal processing unit 7, when the disturbance determination unit 5 determines that the characteristic of the disturbance phenomenon has a positive peak value in the feature space, the positive peak value. (Spatial distribution of feature values) is removed. By removing the feature quantity related to the disturbance phenomenon identified in this way, the search space is reduced, and there is an effect that detection of other disturbance phenomena is facilitated and an operation load is reduced.

Embodiment 3 FIG.
A disturbance detection apparatus according to Embodiment 3 of the present invention will be described with reference to the drawings. FIG. 13 is a diagram showing a configuration of a disturbance detection apparatus according to Embodiment 3 of the present invention.

  The difference from the second embodiment is that in the third embodiment, after a disturbance is determined by the disturbance determination unit 5 for a certain feature amount, the Doppler velocity distribution in the space where the disturbance is present is subjected to a Doppler velocity distribution smoothing process. This is a point to be smoothed in the part 8. As the smoothing method, for example, a method of replacing the Doppler velocity of the space where the previous disturbance phenomenon is detected with the average value of the entire observation region or a predetermined region, or a predetermined spatial smoothing filter such as an average value filter or a median filter is used. Application is possible. By smoothing the Doppler velocity value of the space in which the disturbance phenomenon is identified in this way, the search space is reduced, and there is an effect that the detection of other disturbance phenomenon is facilitated and an operation load is reduced.

Embodiment 4 FIG.
A disturbance detection apparatus according to Embodiment 4 of the present invention will be described with reference to the drawings. FIG. 14 is a diagram showing a configuration of a disturbance detection apparatus according to Embodiment 4 of the present invention.

  The difference from each of the above embodiments is that, in the fourth embodiment, for each disturbance phenomenon (including no disturbance) determined by the disturbance determination unit 5, their spatial positional relationship, the determination result of the previous time, The determination result correction processing unit 9 corrects the determination result using additional information such as flight route information of the aircraft, weather information, and terrain information. For example, when the disturbance determination unit 5 determines that the wind shear probability is 0.6 and the wake turbulence probability is 0.3 as a plurality of disturbance phenomena at the same position in the observation region, the determination result correction processing unit 9 If the disturbance phenomenon is backward turbulence using the flight path information of the aircraft, the determination result of the disturbance determination unit 5 is corrected. With this configuration, for example, erroneous determination results can be removed, and determination accuracy in the determination results can be improved.

Embodiment 5 FIG.
A disturbance detection apparatus according to Embodiment 5 of the present invention will be described with reference to the drawings. FIG. 15 is a diagram showing a configuration of a disturbance detection apparatus according to Embodiment 5 of the present invention.

  The difference from each of the above embodiments is that, in the fifth embodiment, the detection processing control unit 10 makes the presence state of the previous time of the disturbance phenomenon (target), the spatial positional relationship, and the flight path information of the aircraft. Further, by using additional information such as weather information and topographic information, the observation specifications of the electromagnetic wave transmission / reception unit 1, the application area in the Doppler velocity distribution feature calculation unit 3, and the processing application range in the disturbance feature detection unit 4 are limited. In addition, the determination result in the disturbance determination unit 5 is corrected. For example, as shown in FIG. 8, at the same position in the observation region, when the disturbance determination unit 5 determines that the wind disturbance probability is 0.3 and the wake turbulence probability is 0.6, as a plurality of disturbance phenomena, The detection processing control unit 10 corrects the determination result of the disturbance determination unit 5 when the disturbance phenomenon is wind shear using the presence state (determined as wind shear) of the previous time of the disturbance phenomenon. With this configuration, the processing / search space can be reduced, the overall processing and calculation load can be reduced, and the determination accuracy can be improved.

Embodiment 6 FIG.
A disturbance detection apparatus according to Embodiment 6 of the present invention will be described.

  In each of the above embodiments, the Doppler velocity of the atmosphere is calculated, the characteristic amount is calculated therefrom, and the turbulence phenomenon is detected. However, when the turbulence phenomenon exists, the wind speed and the wind direction are more constant than when the wind speed and the wind direction are constant. The spectrum spreads and changes in the Doppler velocity range also appear. Therefore, the Doppler speed width can be used together with the Doppler speed or instead of the Doppler speed. As a method for calculating the Doppler velocity width, for example, a moment method can be cited. In this case, the velocity width is obtained as a secondary moment. That is, the Doppler velocity width calculation unit (Doppler velocity distribution calculation unit) 2A that calculates the Doppler velocity width of the observation region using the reception signal from the electromagnetic wave transmission / reception unit 1, and calculates the feature of the Doppler velocity width by calculating the feature space. Based on the generated Doppler velocity width feature calculating unit (Doppler velocity distribution feature calculating unit) 3A, the disturbance feature detecting unit 4 for detecting the spatial distribution of the feature values on the feature space, and the spatial distribution of the feature values. A disturbance determination unit 5 that determines whether or not a predetermined disturbance phenomenon occurs is provided. As described above, when the Doppler velocity width is used, for example, when the distance resolution of the observation value is large with respect to the magnitude of the disturbance phenomenon, the velocity change of the disturbance phenomenon is buried, but the change in the Doppler velocity width changes. Appears, so it is possible to properly capture the disturbance phenomenon.

Embodiment 7 FIG.
A disturbance detection apparatus according to Embodiment 7 of the present invention will be described.

  In each of the above embodiments, the Doppler velocity or Doppler velocity width of the atmosphere is used. However, in the space where the disturbance phenomenon exists, the atmosphere is compressed and the received signal intensity also changes. From this, it is conceivable to detect the disturbance phenomenon using the intensity of the received signal. The intensity of the received signal is calculated as a zero-order moment in the moment method. That is, a reception intensity calculation unit (Doppler velocity distribution calculation unit) 2B that calculates the reception intensity of the observation region using a reception signal from the electromagnetic wave transmission / reception unit 1, and a reception that calculates a feature of the reception intensity and generates a feature space An intensity feature calculation unit (Doppler velocity distribution feature calculation unit) 3B, a disturbance feature detection unit 4 that detects a spatial distribution of feature values on the feature space, and a predetermined disturbance phenomenon based on the spatial distribution of the feature values And a disturbance determination unit 5 for determining whether or not. As described above, when the disturbance is determined, the received intensity information is used as an index added to the Doppler velocity or the Doppler velocity width information, so that the amount of information related to the disturbance phenomenon can be increased and the determination accuracy can be improved. .

It is a figure which shows the structure of the disturbance detection apparatus which concerns on Embodiment 1 of this invention. It is a figure which shows the observation area | region and vertical cross section (shaded part) of the disturbance detection apparatus which concerns on Embodiment 1 of this invention. It is a figure which shows the fundamental structure and extended type | mold structure of the speed change pattern of the perpendicular direction in the vertical cross section of the disturbance detection apparatus which concerns on Embodiment 1 of this invention. It is a figure which shows the fundamental structure and extended type | mold structure of the horizontal speed change pattern in the vertical cross section of the disturbance detection apparatus which concerns on Embodiment 1 of this invention. It is a figure which shows the structure of the feature calculation filter which extracts the spatial direction characteristic of the Doppler velocity distribution of the disturbance detection apparatus which concerns on Embodiment 1 of this invention. It is a figure which shows the spatial distribution of the feature space and the feature value on a feature space which were produced | generated by the Doppler velocity distribution feature calculation part of the disturbance detection apparatus which concerns on Embodiment 1 of this invention. It is a figure which shows the spatial distribution of the feature value on another feature space and feature space which were produced | generated by the Doppler velocity distribution feature calculation part of the disturbance detection apparatus which concerns on Embodiment 1 of this invention. It is a figure which shows the example which has a several disturbance phenomenon in the same position in an observation area | region by the determination result of the disturbance determination part of the disturbance detection apparatus which concerns on Embodiment 1 of this invention. It is a figure which shows another structure of the disturbance detection apparatus which concerns on Embodiment 1 of this invention. It is a figure which shows the example of a display by the method which shows the spatial presence condition (shape) of each disturbance phenomenon on the display of the display part of the disturbance detection apparatus which concerns on Embodiment 1 of this invention. It is a figure which shows the example of a display by the method which shows each disturbance phenomenon with a symbol (symbol) on the display of the display part of the disturbance detection apparatus which concerns on Embodiment 1 of this invention. It is a figure which shows the structure of the disturbance detection apparatus which concerns on Embodiment 2 of this invention. It is a figure which shows the structure of the disturbance detection apparatus which concerns on Embodiment 3 of this invention. It is a figure which shows the structure of the disturbance detection apparatus which concerns on Embodiment 4 of this invention. It is a figure which shows the structure of the disturbance detection apparatus which concerns on Embodiment 5 of this invention.

Explanation of symbols

  1 electromagnetic wave transmission / reception unit, 2 Doppler velocity distribution calculation unit, 3 Doppler velocity distribution feature calculation unit, 4 disturbance feature detection unit, 5 disturbance determination unit, 6 display unit, 7 disturbance feature removal processing unit, 8 Doppler velocity distribution smoothing processing unit , 9 Determination result correction processing unit, 10 Detection processing control unit.

Claims (21)

An electromagnetic wave transmitting and receiving unit that radiates electromagnetic waves into the atmosphere of the observation region and receives reflected waves from scatterers in the atmosphere;
A Doppler velocity distribution calculating unit that calculates a Doppler velocity distribution of the observation region using a reception signal from the electromagnetic wave transmitting / receiving unit;
A Doppler velocity distribution feature calculating unit for calculating a wind direction and a wind velocity feature of the Doppler velocity distribution to generate a feature space;
A disturbance feature detection unit for detecting a spatial distribution of feature values on the feature space;
A disturbance detection unit comprising: a disturbance determination unit that determines whether or not a predetermined disturbance phenomenon is based on a spatial distribution of the feature values. The Doppler velocity distribution feature calculation unit generates the feature space by matching a velocity pattern obtained by modeling a specific disturbance phenomenon and the Doppler velocity distribution calculated by the Doppler velocity distribution calculation unit. The disturbance detection device according to claim 1. The Doppler velocity distribution feature calculation unit is configured to match the Doppler velocity distribution calculated by the Doppler velocity distribution calculation unit with the direction velocity pattern corresponding to the spatial direction with respect to the level of the value of the Doppler velocity distribution. The disturbance detection device according to claim 1, wherein: 2. The Doppler velocity distribution feature calculation unit generates the feature space by threshold processing using a predetermined Doppler velocity value as a threshold for the Doppler velocity distribution calculated by the Doppler velocity distribution calculation unit. Disturbance detection device. The disturbance detection device according to claim 1, wherein the Doppler velocity distribution feature calculation unit maps the Doppler velocity distribution calculated by the Doppler velocity distribution calculation unit to the feature space by a predetermined mapping conversion. The Doppler velocity distribution feature calculating unit is configured to match a first velocity pattern modeling a first specific disturbance phenomenon with the Doppler velocity distribution calculated by the Doppler velocity distribution calculating unit, and a second specific The disturbance detection device according to claim 2, wherein the feature space is generated by matching a second velocity pattern modeling a disturbance phenomenon and a Doppler velocity distribution calculated by the Doppler velocity distribution calculation unit. . The Doppler velocity distribution feature calculation unit matches the direction velocity pattern corresponding to the vertical direction and the Doppler velocity distribution calculated by the Doppler velocity distribution calculation unit, and also matches the direction velocity pattern corresponding to the horizontal direction and the Doppler velocity distribution. The disturbance detection device according to claim 3, wherein the feature space is generated by matching the Doppler velocity distribution calculated by the calculation unit. The disturbance detection device according to claim 1, wherein the disturbance feature detection unit detects a positive or negative peak value of a feature value on the feature space. The disturbance detection device according to claim 1, wherein the disturbance feature detection unit detects a plurality of positive or negative peak values that are continuous in a line of feature values in the feature space. The said disturbance determination part determines whether it is a predetermined disturbance phenomenon based on the spatial magnitude | size of the spatial distribution of the said feature value detected by the said disturbance feature detection part. Disturbance detection device. The disturbance detection device according to claim 1, wherein the disturbance determination unit determines whether or not a predetermined disturbance phenomenon is caused based on a feature value on the feature space detected by the disturbance feature detection unit. The disturbance detection device according to claim 1, wherein the disturbance determination unit determines whether or not the disturbance is a predetermined disturbance phenomenon based on a shape of a spatial distribution of the feature values detected by the disturbance feature detection unit. . The disturbance detection device according to claim 1, wherein the disturbance determination unit limits the disturbance phenomenon to one type when determining a plurality of disturbance phenomena occupying the same predetermined space in the observation region. The disturbance detection device according to claim 1, wherein the disturbance determination unit sets a plurality of disturbance phenomena when determining a plurality of disturbance phenomena occupying the same predetermined space in the observation region. The disturbance detection device according to claim 1, further comprising: a display unit that displays a predetermined disturbance phenomenon determined by the disturbance determination unit with a shape or a symbol. Disturbance feature removal that removes the spatial distribution of feature values corresponding to the predetermined disturbance phenomenon determined by the disturbance determination unit from the spatial distribution of feature values on the feature space detected by the disturbance feature detection unit The disturbance detection device according to claim 1, further comprising a processing unit. The Doppler velocity distribution smoothing processing unit that smoothes the Doppler velocity distribution in a predetermined space in the observation region corresponding to the predetermined disturbance phenomenon determined by the disturbance determination unit. The disturbance detection device described. The disturbance detection device according to claim 1, further comprising: a determination result correction processing unit that corrects a determination result of the disturbance determination unit based on additional information related to a predetermined disturbance phenomenon determined by the disturbance determination unit. . At least one of the electromagnetic wave transmission / reception unit, the Doppler velocity distribution feature calculation unit, the disturbance feature detection unit, and the disturbance determination unit based on additional information so as to reduce a search space for the disturbance phenomenon in the observation region. The disturbance detection device according to claim 1, further comprising a detection processing control unit configured to control processing. The disturbance detection device according to any one of claims 1 to 19, wherein the disturbance detection is performed using a Doppler velocity width instead of the Doppler velocity distribution. The disturbance detection device according to any one of claims 1 to 19, wherein the disturbance detection is performed using the intensity of the received signal instead of the Doppler velocity distribution.

Images