Radial velocity motion compensation and dealiasing method in weather radar spiral scan mode

Abstract

The application provides a weather radar spiral scanning mode radial velocity motion compensation and deblurring method, and relates to the technical field of weather radar signal processing, and the method comprises the following steps: calculating a line-of-sight unit vector and predicting a platform radial projection based on an attitude and scanning geometry; establishing a folding observation relationship according to the Nyquist velocity, constructing a finite candidate folding integer set by using the platform prediction projection, determining the folding integer in the candidate set by matching the consistency constraint, realizing the velocity blurring correction; applying the meteorological small velocity physical prior and the spatial continuity constraint to the blurring correction result to suppress the false correction; and adopting the sequence of deblurring first and then deducting the platform projection to obtain the compensated radial velocity. The application reduces the velocity folding jump under the spiral scanning condition, reduces the systematic deviation related to the scanning phase, improves the radial velocity consistency, and provides reliable input for the cloud and rain radial velocity product and subsequent cloud wind field inversion.

Description

Technical Field

[0001] This invention relates to the field of meteorological Doppler radar signal processing technology, specifically to a method for radial velocity motion compensation and de-ambiguity in the spiral scanning mode of meteorological radar. Background Technology

[0002] Helical scanning meteorological Doppler radar uses a continuously rotating antenna to form a helical trajectory for observation, enabling the acquisition of key observations such as reflectivity and radial velocity over a large spatial range. For spaceborne or airborne platforms, due to the platform's high translational velocity and accompanying attitude changes, the original radial Doppler velocity will be superimposed with a projection component of the platform velocity along the line of sight. This platform projection component is usually significantly larger than the radial velocity component of cloud / rain particles or airflow, and varies periodically with the scanning phase. This can easily introduce systematic biases related to the scanning phase, reducing the consistency of radial velocity across the scanning phase dimension and affecting the quality of radial velocity products and the stability of subsequent wind field inversion.

[0003] Meanwhile, pulse Doppler velocimetry has a maximum unambiguous velocity limit, characterized by the Nyquist velocity. When the absolute value of the platform's radial projection component approaches or exceeds the Nyquist velocity, the observed original radial velocity may fold, manifesting as velocity folding jumps and a decrease in consistency related to the scanning phase. Since the folding operator and subtraction are not commutative, directly subtracting the platform projection from the folded velocity may lead to incorrect compensation, thereby introducing systematic biases related to the scanning phase and propagating errors in subsequent wind field inversion. Existing techniques often handle platform motion compensation and velocity unambiguation separately, lacking a unified method for helical scanning systems that incorporates the platform's predicted projection prior into unambiguation while ensuring the correct processing order. Summary of the Invention

[0004] This invention provides a radial velocity motion compensation and deambiguation method in the spiral scanning mode of a weather radar to solve the above-mentioned problems. The radial velocity motion compensation and deambiguation method provided by this invention constructs a candidate folded integer set through platform prediction projection prior and applies matching consistency constraints for ambiguity correction. Then, it performs deambiguation followed by platform subtraction and uses stationary clutter or steady-state samples for scanning phase correlation residual self-calibration. This reduces velocity fold jumps and lowers systematic deviations related to the scanning phase, improves radial velocity consistency, and provides reliable input for cloud and rain radial velocity products and subsequent cloud wind field inversion.

[0005] To achieve the above objectives, the present invention adopts the following technical solution:

[0006] (1) Line-of-sight geometric modeling and platform radial projection prediction;

[0007] (2) Determine the Nyquist velocity and establish the folding observation relationship based on the equivalent pulse repetition period;

[0008] (3) Construct a finite set of candidate folded integers based on platform prediction projection;

[0009] (4) Within the candidate set, folded integers are selected based on matching consistency constraints, and spatial continuity constraints and meteorological small velocity priors are combined to suppress miscorrection;

[0010] (5) The radial velocity is output in the order of first defuzzification and then platform subtraction;

[0011] (6) Estimate the scanning phase correlation residuals through static clutter or steady-state samples, and further self-calibrate the output of the final compensation speed.

[0012] The specific steps are as follows:

[0013] S1: Acquire per-pulse observation data and platform navigation data, wherein the observation data includes at least the raw radial Doppler velocity. , Scan azimuth Angle of incidence Distance gate index and reflectivity or quality control label The platform navigation data includes at least the platform speed. With attitude parameters;

[0014] S2: Construct the rotation matrix from the platform's body coordinate system to the Earth's fixed coordinate system based on attitude parameters. Based on the scanning geometry parameters, a unit vector of the antenna line-of-sight direction in the platform volume coordinate system is constructed. To obtain the line-of-sight unit vector in the Earth's fixed coordinate system. ;

[0015] S3: Predicting the radial projection of the platform based on the line-of-sight unit vector and platform velocity. And determined by the radar operating wavelength Equivalent pulse repetition period Determine the Nyquist velocity Establish velocity folding relationships and construct a candidate folding integer set using the platform's predicted radial projection. ;

[0016] S4: In the candidate folded integer set Internally, the folded integer is determined through matching consistency constraints. The unfuzzy radial velocity is obtained. Furthermore, the unfuzzy results are screened or corrected by combining spatial continuity constraints and prior knowledge of the radial velocity amplitude of meteorological targets.

[0017] S5: The initial compensated radial velocity is obtained by first defuzzifying and then subtracting the radial projection of the predicted platform. ;

[0018] S6: Estimate the scan phase correlation residual based on stationary clutter samples or steady-state statistical samples and then... Self-calibration, outputting the final compensated radial velocity. It is used for generating cloud and rain radial velocity products and inputting wind field inversion within clouds.

[0019] Furthermore, in step S2:

[0020] ,

[0021] and

[0022] .

[0023] Furthermore, the Nyquist velocity used for velocity fuzz correction in step S3 Radar operating wavelength Equivalent pulse repetition period Confirmed, satisfied:

[0024] ,

[0025] in The equivalent pulse repetition period corresponds to the pulse sequence used for current velocity estimation and fuzzy correction; when a fixed pulse repetition period is used, .

[0026] Furthermore, the platform predicts the radial projection to satisfy:

[0027] .

[0028] Furthermore, the original radial Doppler velocity satisfies the folding relation:

[0029] ,

[0030] in To fold the velocity into the interval The operator, For the radial velocity component of the meteorological target, For system residuals, For measuring noise.

[0031] Furthermore, in step S3, the central folded integer is first constructed:

[0032]

[0033] And construct a set of candidate folded integers:

[0034]

[0035] Where candidate width .

[0036] Furthermore, candidate width The adaptive setting satisfies:

[0037]

[0038] in For preset coefficients, This indicates rounding up to the nearest integer.

[0039] Furthermore, in step S4, in the candidate set Select the folded integer:

[0040]

[0041] Consistency cost It must include at least two of the following: platform consistency, continuity, and weather-related low-speed penalty:

[0042]

[0043] and

[0044]

[0045]

[0046]

[0047] in As the initial value for velocity bias, The reference deblurring velocity is constructed from adjacent pulses or adjacent distance gates. As a priori, the upper limit of the radial velocity amplitude of meteorological targets, This is the robust loss function.

[0048] Furthermore, the unfuzzy radial velocity satisfies:

[0049]

[0050] Furthermore, step S5 adopts the order of first defuzzifying and then subtracting the predicted radial projection of the platform:

[0051]

[0052] Furthermore, the spatial continuity constraint in step S4 is referenced to the defuzzification speed. This is reflected in:

[0053]

[0054] or

[0055]

[0056] Furthermore, in step S4, the upper limit of the radial velocity amplitude of the meteorological target is a priori. Adaptive settings based on quality control indicators and reflectivity gating, satisfying:

[0057]

[0058] in , This is a preset threshold.

[0059] Furthermore, the scanning phase is defined in step S6. Construct a scan phase-correlation residual model:

[0060]

[0061] In the sample set Seeking a solution:

[0062]

[0063] Output final compensation speed:

[0064]

[0065] The sample set Includes a set of static clutter samples Sum or steady-state statistical sample set .

[0066] Furthermore, the steady-state statistical sample set Constructed by at least one of the following:

[0067] (1) Within the cloud echo area that meets the quality control criteria, select samples with a spectral width less than the threshold and a reflectance within the preset range;

[0068] (2) Select samples with stable reflectivity and a radial velocity statistical mean close to zero within the sea surface or land surface echo area;

[0069] (3) Select the scanning phase dimension that makes The sample with the smallest mean phase drift.

[0070] Compared with the prior art, the beneficial effects of the present invention are:

[0071] 1. In cases where platform projection may trigger velocity folding, this invention significantly improves the reliability of velocity fuzziness correction by utilizing platform prediction projection priors; it employs a finite candidate set and matching consistency constraints to couple defuzzification with platform motion compensation, thereby reducing velocity folding jumps and lowering systematic biases related to the scanning phase.

[0072] 2. This invention explicitly adopts a processing order of first unfuzzing and then platform subtraction to avoid erroneous compensation caused by direct subtraction of the folded domain; it introduces scanning phase correlation residual self-calibration to improve the radial velocity consistency and stability under the spiral scanning system, providing a more reliable input for subsequent cloud wind field inversion. Attached Figure Description

[0073] Figure 1 This is a flowchart of the method of the present invention. Detailed Implementation

[0074] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0075] Example 1:

[0076] This embodiment provides a method for radial velocity motion compensation and de-ambiguity in the helical scanning mode of a weather radar, including the following steps:

[0077] Establish platform volume coordinate system Earth fixed coordinate system Construct a rotation matrix based on the platform's attitude parameters. The first in the spiral scanning system The line-of-sight unit vector corresponding to the pulse can be expressed in volume coordinates as:

[0078]

[0079] Rotate it to a fixed Earth coordinate system:

[0080]

[0081] Platform speed The predicted radial projection of the platform in the line-of-sight direction is obtained:

[0082]

[0083] Velocity blur correction uses Nyquist velocity Considering that different radars may use fixed or non-fixed pulse repetition periods, an equivalent pulse repetition period is defined. ,but:

[0084]

[0085] in This refers to the radar's operating wavelength. When a fixed pulse repetition period is used... .

[0086] The original radial velocity satisfies the folding observation relationship:

[0087]

[0088] in Fold speed to .when May exceed At this time, folding is mainly triggered by platform projection, causing velocity folding jumps in the original velocity and potentially introducing a decrease in consistency related to the scanning phase. Since directly subtracting the platform projection in the folding domain will produce error compensation and further introduce systematic deviations related to the scanning phase, this invention adopts a processing order of first deblurring and then subtracting the platform projection.

[0089] Define folded integers The speed of fuzzy resolution satisfies:

[0090]

[0091] To avoid searching the entire integer field, this invention utilizes platform predictive projection to construct the central integer:

[0092]

[0093] Constructing adaptive candidate width:

[0094]

[0095] And obtain the set of candidate folded integers:

[0096]

[0097] For any candidate in the candidate set Define the fuzzy velocity of candidate solutions:

[0098]

[0099] Construct the matching consistency cost function:

[0100]

[0101] in

[0102]

[0103]

[0104]

[0105] in This is the initial value for the velocity offset; For the reference unambiguity velocity constructed from the neighborhood, we can take:

[0106]

[0107] or

[0108]

[0109] The upper limit of the radial velocity amplitude of meteorological targets is a priori and can be adaptively set according to reflectivity gating:

[0110]

[0111] in , .

[0112] The folded integer selection is as follows:

[0113]

[0114] And the final unambiguity resolution speed is obtained:

[0115]

[0116] Equations (11) to (19) jointly constrain the selection of folded integers by platform consistency, spatial continuity and meteorological small velocity prior, thereby improving the consistency of radial velocity in the scanning phase dimension and suppressing miscorrection.

[0117] The method involves first defuzzifying and then subtracting from the platform:

[0118]

[0119] Define scan phase (can be (obtained by wrap-around normalization), construct the scan phase correlation residual model:

[0120]

[0121] In the sample set Estimated parameters:

[0122]

[0123] The sample set Can be derived from a set of static clutter samples AND or steady-state statistical sample set Composition. Output final compensated radial velocity:

[0124]

[0125] After compensation It can be used to generate cloud and rain radial velocity products and as a radial velocity input for cloud wind field inversion, improving inversion stability.

[0126] This invention addresses the problem that high-speed platform motion in spaceborne or airborne helical scanning systems leads to the superposition of a significant platform radial projection component in the radial Doppler velocity. This projection component may exceed the Nyquist velocity, triggering velocity folding. Directly subtracting the platform projection from the folded domain results in erroneous compensation, introduces systematic biases related to the scanning phase, and affects the stability of cloud and rain radial velocity products and subsequent cloud wind field inversion. To address this issue, a velocity ambiguity correction and platform motion compensation method based on "platform projection prior consistency" is proposed. This method includes: calculating the line-of-sight unit vector based on attitude and scanning geometry and predicting the platform radial projection; establishing folded observation relationships based on the Nyquist velocity, constructing a finite set of candidate folded integers using the predicted platform projection, and determining the folded integer within the candidate set through matching consistency constraints to achieve velocity ambiguity correction; applying meteorological small-velocity physical priors and spatial continuity constraints to the ambiguity correction result to suppress erroneous correction; subsequently obtaining the compensated radial velocity by first defuzzifying and then subtracting the platform projection, and performing self-calibration by estimating the scanning phase-related residuals using stationary clutter or steady-state statistical samples to output the final compensated velocity. This invention can significantly reduce velocity folding jumps under helical scanning conditions and reduce systematic deviations related to the scanning phase, improve radial velocity consistency, and provide reliable input for cloud and rain radial velocity products and subsequent cloud wind field inversion.

[0127] In addition to the embodiments described above, the present invention may have other implementations. All technical solutions formed by equivalent substitution or equivalent transformation fall within the protection scope claimed by the present invention.

Claims

1. A method for radial velocity motion compensation and deambiguation in the spiral scanning mode of a weather radar, characterized in that, Includes the following steps: S1: Acquire per-pulse observation data and platform navigation data, wherein the observation data includes at least the raw radial Doppler velocity. , Scan azimuth Angle of incidence Distance gate index and reflectivity or quality control label The platform navigation data includes at least the platform speed. With attitude parameters; S2: Construct the rotation matrix from the platform's body coordinate system to the Earth's fixed coordinate system based on attitude parameters. Based on the scanning geometry parameters, a unit vector of the antenna line-of-sight direction in the platform volume coordinate system is constructed. To obtain the line-of-sight unit vector in the Earth's fixed coordinate system. ; S3: Predicting the radial projection of the platform based on the line-of-sight unit vector and platform velocity. And determined by the radar operating wavelength Equivalent pulse repetition period Determine the Nyquist velocity Establish velocity folding relationships and construct a candidate folding integer set using the platform's predicted radial projection. ; S4: In the candidate folded integer set Internally, the folded integer is determined through matching consistency constraints. The unfuzzy radial velocity is obtained. Furthermore, the unfuzzy results are screened or corrected by combining spatial continuity constraints and prior knowledge of the radial velocity amplitude of meteorological targets. S5: The initial compensated radial velocity is obtained by first defuzzifying and then subtracting the radial projection of the predicted platform. ; S6: Estimate the scan phase correlation residual based on stationary clutter samples or steady-state statistical samples and then... Self-calibration, outputting the final compensated radial velocity. It is used for generating cloud and rain radial velocity products and inputting wind field inversion within clouds.

2. The radial velocity motion compensation and de-ambiguity method for weather radar in spiral scanning mode according to claim 1, characterized in that, The rotation matrix in step S2 Unit vector and line-of-sight unit vector The following relationship exists: ， and .

3. The radial velocity motion compensation and de-ambiguity method in the helical scanning mode of a weather radar according to claim 2, characterized in that, The Nyquist velocity used for velocity fuzz correction in step S3 Radar operating wavelength Equivalent pulse repetition period It is determined that the following relationship is satisfied: ， in The equivalent pulse repetition period corresponds to the pulse sequence used in the current velocity estimation and fuzzy correction; when a fixed pulse repetition period is used, .

4. The radial velocity motion compensation and de-ambiguity method for weather radar in spiral scanning mode according to claim 3, characterized in that, The platform's predicted radial projection satisfies the following relationship: 。 5. The radial velocity motion compensation and de-ambiguity method for weather radar in spiral scanning mode according to claim 4, characterized in that, The original radial Doppler velocity satisfies the folding relation: ， in To fold the velocity into the interval The operator, For the radial velocity component of the meteorological target, For system residuals, For measuring noise.

6. The radial velocity motion compensation and de-ambiguity method for weather radar in spiral scanning mode according to claim 5, characterized in that, In step S3, the central folded integer is first constructed: And construct a set of candidate folded integers: Where candidate width .

7. The radial velocity motion compensation and de-ambiguity method for weather radar in spiral scanning mode according to claim 6, characterized in that, The candidate width The adaptive setting satisfies: in For preset coefficients, This indicates rounding up to the nearest integer.

8. The radial velocity motion compensation and de-ambiguity method for weather radar in spiral scanning mode according to claim 7, characterized in that, In step S4, the candidate set Select the folded integer: Consistency cost It must include at least two of the following: platform consistency, continuity, and weather-related low-speed penalty: and in As the initial value for velocity bias, The reference deblurring velocity is constructed from adjacent pulses or adjacent distance gates. As a priori, the upper limit of the radial velocity amplitude of meteorological targets, This is the robust loss function.

9. The radial velocity motion compensation and de-ambiguity method for weather radar in spiral scanning mode according to claim 8, characterized in that, The unfuzzy radial velocity satisfies: Furthermore, step S5 adopts the order of first defuzzifying and then subtracting the predicted radial projection of the platform: 。 10. The radial velocity motion compensation and de-ambiguity method for weather radar in spiral scanning mode according to claim 9, characterized in that, The spatial continuity constraint in step S4 is referenced to the defuzzification speed. This is reflected in: or .

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