Omnidirectional slant visibility prediction method and device based on laser radar

Abstract

The invention relates to the technical field of laser radars, in particular to an omnidirectional slant visibility prediction method and device based on a laser radar. The method comprises the following steps: determining a laser emission path according to a measurement area, and respectively acquiring scattered echo signal data, slant visibility data and wind data under the same range gate on the laser emission path through a micro-pulse laser radar and a coherent Doppler wind measurement laser radar; storing and processing the acquired data; and inputting the processed data into a vector autoregression exogenous variable prediction model to obtain a predicted slope visibility value at a future moment. Meanwhile, the invention discloses a device for executing the method, and the method and the device for predicting the omnidirectional slant visibility based on the laser radar are adopted, so that the slant visibility on a laser emitting path can be predicted, and technical support is provided for follow-up.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of laser radar technology, and in particular to an all-directional slant range visibility prediction method and device based on laser radar. BACKGROUND

[0002] As an active optical remote sensing device, laser radar has the advantages of high space-time resolution, high precision and long distance measurement. Micro pulse laser radar is an effective means to obtain slant range visibility, and coherent Doppler wind lidar is an advanced atmospheric wind speed and direction monitoring technology, which has significant advantages in the field of precise measurement of wind speed and direction. Atmospheric visibility, as an important meteorological parameter, mainly includes horizontal visibility and slant range visibility. Slant range visibility is one of the important meteorological factors affecting traffic. For example: the visibility requirement for airplane take-off is at least 600 meters, and the landing requirement is higher than the take-off requirement. Low visibility can easily cause accidents during airplane take-off and landing. The prediction of high-speed fog also directly affects traffic safety. The existing visibility prediction method has low precision and cannot measure and predict the slant range visibility on the traffic planning path in real time and with high precision, which limits the decision support provided to staff. SUMMARY

[0003] The purpose of the present application is to provide an all-directional slant range visibility prediction method and device based on laser radar, which solves the above technical problems.

[0004] To achieve the above purpose, the present application provides an all-directional slant range visibility prediction method based on laser radar, and the specific steps are as follows: Step S1: determining the laser emission path according to the measurement area, and obtaining the scattering echo signal data, slant range visibility data and wind data under the same distance gate on the laser emission path by micro pulse laser radar and coherent Doppler wind lidar respectively; Step S2: storing and processing the data obtained in step S1; Step S3: inputting the processed data into the vector autoregressive exogenous variable prediction model with distance gate space correlation term and dynamic exogenous variable weight adjustment to obtain the predicted slant range visibility value at the future time.

[0005] Preferably, in step S1, the micro pulse laser radar and the coherent Doppler wind lidar are powered on and start working at the same time, and the space-time resolution is uniformly set; The micro pulse laser radar measures the backscattering echo signal under each distance gate on the laser emission path, and calculates the slant range visibility in real time according to the scattering echo signal; The wind data obtained by the coherent Doppler wind lidar includes wind speed and wind direction.

[0006] Preferably, in step S2, the data processing of the scattering echo signal comprises photoelectric detector nonlinear correction, geometric factor correction, range square correction and normalization processing; The processed scattering echo signal and the wind data are unified in time and space resolution.

[0007] Preferably, in step S3, the scattering echo signal unified in time and space resolution and the wind data are input into a vector autoregressive exogenous variable prediction model. The endogenous variable is the scattering echo signal under each distance gate of the micro pulse laser radar, and the exogenous variable is the wind data under each distance gate of the coherent Doppler wind lidar; the stationarity of the input time sequence is tested by using sample autocorrelation function test.

[0008] Preferably, the endogenous variable vector of the vector autoregressive exogenous variable prediction model is as follows: ; wherein, is the range square corrected signal acquired by the micro pulse laser radar at time , distance gate ; ; is the dimension of the endogenous variable vector, representing the range square corrected signal corresponding to each distance gate; The exogenous variable vector is as follows: ; wherein, represents the eastward wind speed acquired by the coherent Doppler wind lidar at time , distance gate ; represents the northward wind speed acquired by the coherent Doppler wind lidar at time , distance gate ; the dimension of the exogenous variable vector is 2 , representing the eastward and northward wind speed components corresponding to each distance gate; The expression of the basic vector autoregressive exogenous variable prediction model is as follows: ; wherein, is the model lag order, determined by Akaike information criterion, is a dimension endogenous variable coefficient matrix, characterizing the influence of the lagged term of the endogenous variable on the current term; is a dimension exogenous variable coefficient matrix, characterizing the influence of the exogenous variable and its lagged term on the endogenous variable; for A dimensional random error vector, satisfying , Here is the error covariance matrix; Visibility and scattered echo signal exhibit a monotonically decreasing relationship. The mapping model between visibility and scattered echo signal is as follows: ; in, For at any time Distance Gate Visibility, This is the mapping function between visibility and the scattered echo signal; Predicting endogenous variables as follows: ; in, For micropulse lidar at time Distance Gate The predicted distance squared correction signal obtained at the location, To predict the step size; The predicted visibility is as follows: ; in, For at any time Distance Gate Predicted visibility.

[0009] Preferably, the vector of endogenous variables is corrected by introducing spatial correlation terms. as follows: ; in, The spatial correlation coefficient is determined through cross-validation. For The dimensional weight matrix is ​​obtained using distance decay weights, and the expression for distance decay weights is as follows: ; in, For the first The distance between the gates is the first The spatial influence weight of each distance gate For spatial correlation threshold, Represented as distance gate and distance door The distance between them.

[0010] Preferably, a dynamic weight matrix is ​​introduced. The exogenous variables at each time point are weighted and adjusted, and the adjusted exogenous variable vector is as follows: ; wherein, is a diagonal matrix, the diagonal elements is expressed as follows: ; wherein, is the th element of the exogenous variable vector, corresponding to the wind component of the distance gate, the weight is obtained by normalizing the absolute value of the covariance between the endogenous variable and each exogenous variable in real time, realizing automatic weighted enhancement of the exogenous variable with significant influence, is the covariance function, is the maximum value of the absolute value of the covariance obtained by traversing exogenous variables; The absolute value of the covariance between the endogenous variable and each exogenous variable is calculated in real time, and the weight is obtained by normalizing the absolute value, realizing automatic weighted enhancement of the exogenous variable.

[0011] Preferably, the expression of the vector autoregressive exogenous variable prediction model after introducing the distance gate space correlation term and the dynamic exogenous variable weight adjustment is as follows: ; wherein, is the modified endogenous variable at lag time, is the modified exogenous variable at lag time.

[0012] The device for the above-mentioned all-directional slant range visibility prediction method based on laser radar comprises: a micro pulse laser radar for acquiring scattered echo signal data and calculating slant range visibility data; a coherent Doppler wind measurement laser radar for acquiring wind data; a data acquisition and processing module for collecting and processing the scattered echo signal data, the slant range visibility data and the wind data; a prediction module based on a vector autoregressive exogenous variable model for predicting the slant range visibility value at a future time according to the scattered echo signal data, the slant range visibility data and the wind data; The micro pulse laser radar and the coherent Doppler wind measurement laser radar are both connected with the data acquisition and processing module, and the data acquisition and processing module is connected with the prediction module based on the vector autoregressive exogenous variable model.

[0013] Therefore, the application adopts the above-mentioned all-directional slant range visibility prediction method and device based on a laser radar, and has the beneficial effects that: the scattering echo signal data, the slant range visibility data and the wind data at the same range gate on the laser emission path are respectively acquired by the micro pulse laser radar and the coherent Doppler wind measuring laser radar, the predicted slant range visibility value at the future time is obtained through the vector autoregressive exogenous variable prediction model, the distance gate space correlation term and the dynamic exogenous variable weight are introduced to realize high-precision prediction of the visibility on the laser emission path, the slant range visibility on the traffic planning path is measured and predicted in real time, the travel safety can be effectively improved, and the decision support is provided for the staff.

[0014] The technical solutions of the application will be further described in detail below with reference to the drawings and embodiments. BRIEF DESCRIPTION OF DRAWINGS

[0015] Figure 1 A flow chart of the all-directional slant range visibility prediction method based on a laser radar according to the application; Figure 2 A principle block diagram of the device according to the application; Figure 3 A micro pulse laser radar echo energy pseudo-color map; Figure 4 A visibility data pseudo-color map measured by the micro pulse laser radar; Figure 5 A predicted echo energy pseudo-color map of the technical solution of the embodiment; Figure 6 A predicted future time visibility pseudo-color map of the technical solution of the embodiment; Figure 7 A comparison chart of the visibility precision of the micro pulse laser radar measurement and the technical solution of the embodiment. DETAILED DESCRIPTION

[0016] In the description of the present application, it should be noted that the terms "upper", "lower", "inner", "outer" and the like indicate the orientation or positional relationship shown in the drawings, or the orientation or positional relationship in which the product of the present application is usually placed, and are only for the convenience of describing the present application and simplifying the description, and do not indicate or imply that the device or element referred to must have a particular orientation, be constructed and operated in a particular orientation, and therefore cannot be understood as a limitation on the present application. In the description of the present application, it should be noted that, unless otherwise specified and limited, the terms "arrangement", "installation", "connection" should be understood broadly, for example, it can be fixedly connected, or it can be detachably connected, or integrally connected; it can be mechanically connected, or it can be electrically connected; it can be directly connected, or it can be indirectly connected through an intermediate medium, or it can be connected inside two elements. For those skilled in the art, the specific meaning of the above terms in the present application can be understood according to the specific circumstances.

[0017] The embodiments of the present application will be described in detail below with reference to the drawings.

[0018] As Figure 1 shown, a laser radar-based omnidirectional slant range visibility prediction method, the specific steps are as follows: Step S1: Determine the laser emission path according to the measurement area, and obtain the scattering echo signal data, slant range visibility data and wind data under the same distance gate on the laser emission path by micro-pulse laser radar and coherent Doppler wind measurement laser radar respectively.

[0019] The micro-pulse laser radar and the coherent Doppler wind measurement laser radar are powered on and start working at the same time, and the space-time resolution is uniformly set; The micro-pulse laser radar measures the backscattering echo signal under each distance gate on the laser emission path, and calculates the slant range visibility in real time according to the scattering echo signal; The wind data obtained by the coherent Doppler wind measurement laser radar includes wind speed and wind direction.

[0020] Step S2: Store and process the data obtained in step S1. In step S2, the data processing of the scattering echo signal includes nonlinear correction of photodetector, geometric factor correction, distance square correction and normalization processing; The processed scattering echo signal and the wind data are unified in space-time resolution.

[0021] Step S3: Input the processed data into the vector autoregressive exogenous variable prediction model with distance gate space correlation term and dynamic exogenous variable weight adjustment.

[0022] The vector autoregressive exogenous variable prediction model obtains the predicted future time slope visibility value. The scattered echo signal and wind data after unifying the space-time resolution are input into the vector autoregressive exogenous variable prediction model.

[0023] The endogenous variable is the scattered echo signal under each distance gate of the micro pulse laser radar, and the exogenous variable is the wind data under each distance gate of the coherent Doppler wind lidar; the stationarity test of the input time sequence is performed by using the sample autocorrelation function test; and the lag order of the model is determined to be 5 by using the Akaike information criterion.

[0024] The endogenous variable vector of the vector autoregressive exogenous variable prediction model As follows: ; Wherein, is the range square correction signal obtained by the micro pulse laser radar at time , distance gate ; ; The dimension of the endogenous variable vector represents the range square correction signal corresponding to each distance gate; The exogenous variable vector As follows: ; Wherein, represents the eastward wind speed wind amount obtained by the coherent Doppler wind lidar at time , distance gate ; represents the northward wind speed wind amount obtained by the coherent Doppler wind lidar at time , distance gate ; the dimension of the exogenous variable vector is 2 , corresponding to the eastward and northward wind speed components of each distance gate; The expression of the basic vector autoregressive exogenous variable prediction model is as follows: ; Wherein, is the model lag order, which is determined by the Akaike information criterion, is the dimension endogenous variable coefficient matrix, characterizing the influence of the endogenous variable lag term on the current term; is the dimension exogenous variable coefficient matrix, characterizing the influence of the exogenous variable and its lag term on the endogenous variable; is the dimension random error vector, satisfying , is the error covariance matrix; Visibility and scattering echo signal exists a monotone decreasing relationship, the mapping model of visibility and scattering echo signal is as follows: ; Wherein, is the visibility at time , distance gate , is the mapping function of visibility and scattering echo signal; the calculation of aerosol extinction coefficient from the predicted range square correction signal adopts the classic Selnerde method, and the visibility calculation adopts the conventional calculation method, which is not described here.

[0025] The predicted endogenous variable is as follows: ; Wherein, is the predicted range square correction signal obtained by the micro pulse laser radar at time , distance gate , is the prediction step; The predicted visibility is as follows: ; Wherein, is the predicted visibility at time , distance gate .

[0026] Through the characteristics of laser radar data and atmospheric data, the model is improved by introducing the distance gate space correlation term and dynamic exogenous variable weight adjustment.

[0027] Atmospheric aerosol and wind field have spatial continuity, and the data of adjacent distance gates are strongly correlated. The existing prediction module does not consider this feature, which limits the prediction accuracy. The endogenous variable vector is corrected by introducing the spatial correlation term as follows: ; Wherein, is the spatial correlation coefficient, which is determined by cross validation, and the value range is 0.3-0.6, which is adjusted according to the atmospheric uniformity of the observation area, is a dimensional weight matrix, which is obtained by using distance attenuation weight, and the expression of distance attenuation weight is as follows: ; Wherein, is the spatial influence weight of the th distance gate on the th distance gate,For the spatial correlation threshold, take 5-10 distance gate intervals, The distance between the distance gate and the distance gate .

[0028] The influence degree of different distance gates and wind fields at different times on visibility is different (such as the near-surface wind field has a greater influence on low-altitude visibility), and the existing prediction module uses a fixed coefficient matrix, which cannot adapt to this dynamic characteristic. Introducing a dynamic weight matrix , the adjusted exogenous variable vector is as follows: ; Wherein, is a dimensional diagonal matrix, and the diagonal elements are as follows: ; Wherein, is the th element of the exogenous variable vector, corresponding to the wind component of the distance gate, and the weight is obtained by normalizing the absolute value of the covariance between the endogenous variable and each exogenous variable, realizing automatic weighted enhancement of the exogenous variable with significant influence, is the covariance function, is the maximum value of the covariance absolute value obtained by traversing exogenous variables; The absolute value of the covariance between the endogenous variable and each exogenous variable is obtained by normalizing the absolute value of the covariance between the endogenous variable and each exogenous variable, realizing automatic weighted enhancement of the exogenous variable with significant influence.

[0029] The expression of the vector autoregressive exogenous variable prediction model after introducing the distance gate spatial correlation term and adjusting the dynamic exogenous variable weight is as follows: ; Wherein, is the modified endogenous variable at lag time, is the modified exogenous variable at lag time. Through the dual improvement of the distance gate spatial correlation term and the dynamic exogenous variable weight adjustment, the prediction accuracy is improved.

[0030] As shown in Figure 2 , an apparatus for executing the above-mentioned all-directional oblique-range visibility prediction method based on a laser radar comprises: A micro-pulse laser radar is used to obtain scattered echo signal data and calculate oblique-range visibility data.

[0031] A coherent Doppler wind measurement laser radar is used to obtain wind data.

[0032] The data acquisition and processing module is used for the acquisition and processing of scattered echo signal data, slant path visibility data, and wind data.

[0033] The prediction module based on the vector autoregressive exogenous variable model is used to predict the slant visibility value at future times based on the scattered echo signal data, slant visibility data, and wind data.

[0034] Both the micropulse lidar and the coherent Doppler wind lidar are connected to the data acquisition and processing module, which in turn is connected to the prediction module based on a vector autoregression exogenous variable model.

[0035] The model fully leverages the complementary advantages of multi-instrument collaborative observations, quantifies the coupling relationship between atmospheric aerosols and wind fields through mathematical modeling, and solves the problems of insufficient accuracy and poor robustness of traditional univariate or linear prediction models, providing an efficient mathematical tool and implementation path for atmospheric visibility prediction.

[0036] To verify the effectiveness of the technical solution in this embodiment, the following tests were conducted.

[0037] Visibility is measured using micropulse lidar, such as... Figure 3-4 As shown, the prediction data of the technical solution in this embodiment is as follows: Figure 5-6 As shown, Figure 7 As shown, a forward-scattering visibility meter is used to improve visibility prediction accuracy. Near-ground measurement data (at 150m) from the lidar is extracted from the visibility profile and compared with the forward-scattering visibility meter results. Starting from 15:53, the figure shows good consistency between the model's predicted future visibility and the forward-scattering visibility meter's measurement results. The technical solution adopted in this embodiment utilizes a coherent Doppler wind lidar to accurately acquire the atmospheric dynamics change trend (wind data) along the laser path, while simultaneously combining it with a micro-pulse lidar to provide the aerosol content change trend along the path. Through joint analysis of these two trends and model establishment, high-precision prediction of future visibility changes along the laser emission path is achieved.

[0038] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the technical solutions of the present invention, and these modifications or equivalent substitutions cannot cause the modified technical solutions to deviate from the spirit and scope of the technical solutions of the present invention.

Claims

1. A method for predicting omnidirectional slant-path visibility based on lidar, characterized in that, The specific steps are as follows: Step S1: Determine the laser emission path based on the measurement area, and acquire the scattered echo signal data, slant path visibility data, and wind data at the same distance along the laser emission path using micropulse lidar and coherent Doppler wind lidar, respectively. Step S2: Store and process the data obtained in step S1; Step S3: Input the processed data into the vector autoregressive exogenous variable prediction model after introducing the distance gate spatial correlation term and dynamically adjusted exogenous variable weights to obtain the predicted slant visibility value for the future time.

2. The omnidirectional slant path visibility prediction method based on lidar according to claim 1, characterized in that: In step S1, the micropulse lidar and the coherent Doppler wind lidar are powered on and start working simultaneously, and the spatiotemporal resolution is set uniformly. Micropulse lidar measures the backscattered echo signal at each distance gate along the laser emission path and calculates the slant path visibility in real time based on the backscattered echo signal; The wind data acquired by the coherent Doppler wind lidar includes wind speed and wind direction.

3. The omnidirectional slant path visibility prediction method based on lidar according to claim 1, characterized in that: In step S2, the data processing of the scattered echo signal includes photodetector nonlinearity correction, geometric factor correction, distance squared correction, and normalization processing; The processed scattered echo signal and wind data were subjected to spatiotemporal resolution unification.

4. The omnidirectional slant path visibility prediction method based on lidar according to claim 1, characterized in that: In step S3, the scattered echo signal with unified spatiotemporal resolution and wind data are input into the vector autoregressive exogenous variable prediction model; The endogenous variable is the scattered echo signal at each range gate of the micropulse lidar, and the exogenous variable is the wind data at each range gate of the coherent Doppler wind lidar; the stationarity of the input time series is tested using the sample autocorrelation function test.

5. The omnidirectional slant path visibility prediction method based on lidar according to claim 4, characterized in that: Vector of endogenous variables in a vector autoregressive model of exogenous variables as follows: ； in, For micropulse lidar at time Distance Gate The distance squared correction signal obtained at the location, ; The dimension of the endogenous variable vector represents the distance squared correction signal corresponding to each distance gate; Exogenous variable vector as follows: ； in, This indicates that the coherent Doppler wind lidar is at time... Distance Gate The eastward wind speed and air volume obtained at the location; This indicates that the coherent Doppler wind lidar is at time... Distance Gate Northward wind speed and volume obtained at [location], exogenous variable vector The dimension is 2 The corresponding eastward and northward wind speed components for each distance gate; The basic vector autoregressive exogenous variable prediction model expression is as follows: ； in, The lag order of the model is determined using the Akaike information criterion. for dimensional endogenous variable coefficient matrix, This characterizes the effect of lagged terms of endogenous variables on current terms; for The exogenous variable coefficient matrix represents the influence of exogenous variables and their lagged terms on endogenous variables. for A dimensional random error vector, satisfying , Here is the error covariance matrix; Visibility and scattered echo signal exhibit a monotonically decreasing relationship. The mapping model between visibility and scattered echo signal is as follows: ； in, For at any time Distance Gate Visibility, This is the mapping function between visibility and the scattered echo signal; Predicting endogenous variables as follows: ； in, For micropulse lidar at time Distance Gate The predicted distance squared correction signal obtained at the location, To predict the step size; The predicted visibility is as follows: ； in, For at any time Distance Gate Predicted visibility.

6. The omnidirectional slant path visibility prediction method based on lidar according to claim 7, characterized in that: Endogenous variable vector modified by introducing spatial correlation terms as follows: ； in, The spatial correlation coefficient is determined through cross-validation. For The dimensional weight matrix is ​​obtained using distance decay weights, and the expression for distance decay weights is as follows: ； in, For the first The distance between the gates is the first The spatial influence weight of each distance gate For spatial correlation threshold, Represented as distance gate and distance door The distance between them.

7. The omnidirectional slant path visibility prediction method based on lidar according to claim 6, characterized in that: Introducing a dynamic weight matrix The exogenous variables at each time point are weighted and adjusted, and the adjusted exogenous variable vector is as follows: ； in, for 3D diagonal matrix, diagonal elements The expression is as follows: ； in, The first exogenous variable vector Each element corresponds to a wind component at distance from the door. The weights are obtained by normalizing the absolute values ​​of the covariances of endogenous and exogenous variables in real time, thereby automatically weighting and enhancing exogenous variables with significant influence. Let covariance function be used. For traversal The maximum absolute value of the covariance obtained from each exogenous variable; The covariance of endogenous variables and exogenous variables is normalized by real-time calculation, thereby achieving automatic weighting and enhancement of exogenous variables.

8. The omnidirectional slant path visibility prediction method based on lidar according to claim 7, characterized in that: The expression for the vector autoregressive exogenous variable prediction model, after introducing the distance gate spatial correlation term and dynamically adjusted exogenous variable weights, is as follows: ； in, For lag Time-dependent endogenous variables are adjusted. For lag Time-dependent correction of exogenous variables.

9. An apparatus for performing the omnidirectional slant-path visibility prediction method based on lidar as described in claim 8, characterized in that: include: Micropulse lidar is used to acquire scattered echo signal data and calculate slant range visibility data; Coherent Doppler wind lidar is used to acquire wind data; The data acquisition and processing module is used for the acquisition and processing of scattered echo signal data, slant path visibility data, and wind data. The prediction module based on the vector autoregressive exogenous variable model is used to predict the slant visibility value at future times based on the scattered echo signal data, slant visibility data and wind data. Both the micropulse lidar and the coherent Doppler wind lidar are connected to the data acquisition and processing module, which in turn is connected to the prediction module based on a vector autoregression exogenous variable model.

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