A method and system for denoising complex conjugate product signals from interferometric / polarimetric imaging radar based on the coefficient of variation.
Patent Information
- Application Number
- CN202511874060.0
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- Filing Date
- 2025-12-12
- Publication Date
- 2026-09-01
- Estimated Expiration
- 2045-12-12
AI Technical Summary
然而，该乘积信号易受噪声污染
Smart Images
Figure CN121831769B_ABST
Abstract
Description
Technical Field
[0001] This application belongs to the field of interferometric / polarimetric imaging radar data processing, specifically relating to a method and system for noise reduction processing of complex conjugate product signals of interferometric / polarimetric imaging radar based on the coefficient of variation. Background Technology
[0002] Traditional imaging radar, by emitting electromagnetic waves and receiving echoes, can achieve high-resolution imaging of the observed scene in all weather conditions and at all times, thus enjoying widespread application in both civilian and military fields. Interferometric / polarimetric imaging radar, developed from this foundation, significantly expands the target information acquisition capabilities and application scope of imaging radar. Interferometric imaging radar, by configuring spatially separated dual antennas, receives complex signal pairs from the same scene and then extracts the interferometric phase to obtain the target's elevation information, demonstrating unique advantages in topographic mapping and surface deformation monitoring. Polarimetric imaging radar, by emitting and receiving orthogonally polarized electromagnetic waves, obtains the target's fully polarimetric scattering matrix, thus playing an important role in target identification and classification. The data processing of both types of radar involves extracting the phase from the complex conjugate product of two complex signals (the dual-antenna signals of interferometric imaging radar, or the signals from different channels of polarimetric imaging radar). However, this product signal is susceptible to noise contamination. Therefore, effectively suppressing noise in the complex conjugate product is crucial for improving the accuracy of phase extraction. Summary of the Invention
[0003] The purpose of this application is to achieve effective noise reduction processing of complex conjugate product signals from interferometric / polarimetric imaging radar, thereby improving the accuracy of phase information extracted from the complex signal.
[0004] To achieve the above objectives, this application proposes a noise reduction method for complex conjugate product signals of interferometric / polarimetric imaging radar based on the coefficient of variation, the method comprising: Step 1) Read in the complex signal corresponding to the radar interferometer pair or the two polarization channels. and ; Step 2) Calculation and complex conjugate product signal The superscript * indicates complex conjugation; Step 3) According to the set window size and overlap size, Divide into overlapping sliding windows; Step 4) Calculate the coefficient of variation (CV) for each sliding window; Step 5) According to the set adjustable factor And the coefficient of variation (CV), calculate the filtering parameters corresponding to the effective pixels within each sliding window. ; Step 6) Perform a two-dimensional Fourier transform to... Transformation from the spatial domain to the frequency domain; Step 7) Based on filter parameters For each sliding window The spectrum is subjected to Goldstein filtering; Step 8) Use a two-dimensional inverse Fourier transform to convert the values of each sliding window after filtering. The spectrum is transformed back into the spatial domain.
[0005] As an improvement to the above method, the formula for calculating the coefficient of variation (CV) is as follows:
[0006] Among them, STD and These represent the standard deviation and mean of the data, respectively.
[0007] As an improvement to the above method, the adjustable factor The value range is (6.5, 40).
[0008] As an improvement to the above method, the adjustable factor The value is 20.
[0009] As an improvement to the above method, the filtering parameters The calculation formula is as follows:
[0010] Where, min( ) indicates taking the minimum value of all sliding windows.
[0011] As an improvement to the above method, the step of assigning each sliding window to... The spectrum, after Goldstein filtering, is as follows:
[0012] in, For smoothing operators; and These are the complex conjugate product signals within the sliding window before and after filtering. The spectrum; and This refers to the spatial frequency.
[0013] This application also provides a noise reduction system for complex conjugate product signals of interferometric / polarimetric imaging radar based on the coefficient of variation, implemented using the above method. The system includes: The signal reading module is used to read in the complex signals corresponding to the radar interferometer pairs or the two polarization channels. and ; The module for calculating complex conjugate products is used to calculate... and complex conjugate product signal The superscript * indicates complex conjugation; The split sliding window module is used to split windows according to a set window size and overlap size. Divide into overlapping sliding windows; The coefficient of variation (CV) calculation module is used to calculate the coefficient of variation (CV) for each sliding window. The filter parameter calculation module is used to calculate the filter parameters based on the set adjustable factors. And the coefficient of variation (CV), calculate the filtering parameters corresponding to the effective pixels within each sliding window. ; The Fourier transform module is used to transform the two-dimensional Fourier transform into the Fourier transform. Transformation from the spatial domain to the frequency domain; The Goldstein filtering module is used for filtering parameters. For each sliding window The spectrum is subjected to Goldstein filtering; The inverse Fourier transform module is used to transform the filtered sliding windows corresponding to each other using a two-dimensional inverse Fourier transform. The spectrum is transformed back into the spatial domain.
[0014] Compared with existing technologies, the advantages of this application are: The method and system of this invention introduce a parameter CV that reflects the relative variability of the amplitude of the complex conjugate product signal, and present a new adaptive noise reduction method for complex conjugate product signals in interferometric / polarimetric imaging radar, providing a new approach to noise suppression in the field of imaging radar. Attached Figure Description
[0015] Figure 1 The flowchart shown is a method for noise reduction of complex conjugate product signals from interferometric / polarimetric imaging radar based on the coefficient of variation. Figure 2 The image shown is an amplitude map of the main and secondary InSAR images of the X-band repeating orbit near the Enta crater in Italy, acquired by NASA / JPLSIR-C / X-SAR. Figure 3 The image shows the unpaired complex conjugate product signal. Amplitude diagram during filtering; Figure 4 The figure shows the complex conjugate product signal. The amplitude diagram obtained after filtering; Figure 5 The image shows the unpaired complex conjugate product signal. The interferometric phase diagram obtained after filtering; Figure 6 The figure shows the complex conjugate product signal. Interference phase diagram obtained after filtering. Detailed Implementation
[0016] The technical solution of this application will be described in detail below with reference to the accompanying drawings.
[0017] This invention provides a method for denoising complex conjugate product signals from interferometric / polarimetric imaging radar based on the coefficient of variation, the method comprising: Step 1) Read in the complex signal corresponding to the radar interferometer pair or the two polarization channels. and ; Step 2) Calculation and complex conjugate product signal The superscript * indicates complex conjugation; Step 3) According to the set window size and overlap size, Divide into overlapping sliding windows; The window size is set to 32. 32 pixels, with an overlap size of 14 pixels.
[0018] Step 4) Calculate the coefficient of variation (CV) for each sliding window; specifically including: In the formula, STD and These represent the standard deviation and mean of the data, respectively.
[0019] Step 5) According to the set adjustable factor Using the CV obtained in step 4), calculate the filtering parameters corresponding to the effective pixels within each sliding window. Specifically, it includes: Step 5-1) After multiple iterative experiments, recommend an adjustable factor. The value range is (6.5, 40). This experiment is set up as follows: The value is 20; Step 5-2) Calculate the effective pixels within each sliding window. :
[0020] In the formula, This indicates taking the maximum value from all sliding windows. This indicates taking the minimum value from all sliding windows; Step 6) Transform the spatial domain obtained in Step 2) using a two-dimensional Fourier transform. Transform to the frequency domain; Step 7) Based on the results obtained in Step 5) For each sliding window The spectrum is subjected to Goldstein filtering; specifically including:
[0021] In the formula, For smoothing operators, and The complex conjugate product signal within the sliding window before and after filtering. The spectrum, and This refers to the spatial frequency.
[0022] Step 8) Use a two-dimensional inverse Fourier transform to convert the values of each sliding window after filtering. The spectrum is transformed back into the spatial domain.
[0023] Example 1 refer to Figure 1 Embodiment 1 of the present invention proposes a noise reduction method for complex conjugate product signals of interferometric / polarimetric imaging radar based on the coefficient of variation, comprising the following steps: Step 1) Read in the complex signal corresponding to the radar interferometer pair or the two polarization channels. and ; In this embodiment, the data read in is a pair of primary and secondary images of the Enta crater region in Italy, obtained by NASA / JPL SIR-C / X-SAR using X-band repeating orbits. and The data matrix size is 1000 1000.
[0024] Step 2) Calculate and generate the complex conjugate product signal ; Based on the primary and secondary image pairs input in step 1) and In step 2), the generation is calculated. and complex conjugate product signal .
[0025] Step 3) According to the set window size and overlap size, Divide into overlapping sliding windows; Based on the complex conjugate product signal corresponding to the embodiment obtained in step 2), In step 3), Divide into overlapping sub-blocks, where each sub-block is set to a size of 32. 32 pixels, with an overlap size set to 14 pixels.
[0026] Step 4) Calculate the coefficient of variation (CV) for each effective pixel within the sliding window; In step 4), the coefficient of variation (CV) corresponding to the effective pixels within each sliding window is calculated using the following formula:
[0027] STD and These represent the standard deviation and mean of the data, respectively.
[0028] Step 5) According to the set adjustable factor Using the CV obtained in step 4), calculate the filtering parameters corresponding to the effective pixels within each sliding window. ; Based on the coefficient of variation (CV) calculated in step 4), in step 5), the set adjustable factor is combined. Calculate the filtering parameters corresponding to the effective pixels within each sliding window using the following formula. :
[0029] in This indicates taking the maximum value from all sliding windows. This indicates taking the minimum value from all sliding windows.
[0030] Step 6) Transform the spatial domain obtained in Step 2) using a two-dimensional Fourier transform. Transform to the frequency domain; In step 6), the complex conjugate product signal calculated in step 2) is transformed by a two-dimensional Fourier transform. Transformation from the spatial domain to the frequency domain.
[0031] Step 7) Based on the results obtained in Step 5) For each sliding window The spectrum is subjected to Goldstein filtering; In step 7), the complex conjugate product signals corresponding to each sliding window obtained in step 6) are transformed according to the following formula. Goldstein filtering is applied to the spectrum:
[0032] in For smoothing operators, and This represents the complex conjugate product signal within the sliding window before and after filtering. The spectrum, and This refers to the spatial frequency.
[0033] Step 8) Use a two-dimensional inverse Fourier transform to convert the values of each sliding window after filtering. The spectrum is transformed back into the spatial domain.
[0034] In step 8), the corresponding sliding windows obtained after filtering in step 7) are transformed by a two-dimensional inverse Fourier transform. Spectrum Transform back into the spatial domain to obtain the filtered spatial domain complex conjugate product signal.
[0035] Figure 2 This is an amplitude map of the main and secondary complex InSAR images of the SIR-C / X-SAR X-band repeating orbit near the Enta crater in Italy. The image size is 1000. 1000; Figure 3 The complex conjugate product signal is given. Amplitude graph at time. Figure 4 It is a complex conjugate product signal The amplitude diagram after filtering by the method of this invention. Compared to Figure 3 , Figure 4 The overall transition is smoother, and the contrast of details is further enhanced (mountains and craters stand out more). Figure 5 This is the interferometric phase diagram obtained without filtering. Figure 6 This is the interferometric phase diagram obtained using the filtering process described in this invention. Compared to... Figure 5 , Figure 6 Visually, it is also smoother, and the distribution of interference fringes is clearer, thus verifying the feasibility and effectiveness of the method of the present invention. Figures 3-6 Together, they verified the feasibility and effectiveness of the invented method.
[0036] Example 2 Embodiment 2 of the present invention provides a noise reduction processing system for complex conjugate product signals of interferometric / polarimetric imaging radar based on the coefficient of variation, implemented based on the above method, including: The signal reading module is used to read in the complex signals corresponding to the radar interferometer pairs or the two polarization channels. and ; The module for calculating complex conjugate products is used to calculate... and complex conjugate product signal The superscript * indicates complex conjugation; The split sliding window module is used to split windows according to a set window size and overlap size. Divide into overlapping sliding windows; The coefficient of variation (CV) calculation module is used to calculate the coefficient of variation (CV) for each sliding window. The filter parameter calculation module is used to calculate the filter parameters based on the set adjustable factors. And the coefficient of variation (CV), calculate the filtering parameters corresponding to the effective pixels within each sliding window. ; The Fourier transform module is used to transform the two-dimensional Fourier transform into the Fourier transform. Transformation from the spatial domain to the frequency domain; The Goldstein filtering module is used for filtering parameters. For each sliding window The spectrum is subjected to Goldstein filtering; The inverse Fourier transform module is used to transform the filtered sliding windows corresponding to each other using a two-dimensional inverse Fourier transform. The spectrum is transformed back into the spatial domain.
[0037] This application may also provide a computer device, including: at least one processor, memory, at least one network interface, and a user interface. The various components in this device are coupled together via a bus system. It is understood that the bus system is used to implement communication between these components. In addition to a data bus, the bus system also includes a power bus, a control bus, and a status signal bus.
[0038] The user interface can include a display, keyboard, or clicking device. Examples include a mouse, trackball, touchpad, or touchscreen.
[0039] It is understood that the memory in the embodiments disclosed in this application may be volatile memory or non-volatile memory, or may include both volatile and non-volatile memory. The non-volatile memory may be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. The volatile memory may be random access memory (RAM), which is used as an external cache. By way of example, but not limitation, many forms of RAM are available, such as Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), Synchronous DRAM (SDRAM), Double Data Rate SDRAM (DDRSDRAM), Enhanced Synchronous DRAM (ESDRAM), Synchlink DRAM (SLDRAM), and Direct Rambus RAM (DRRAM). The memories described herein are intended to include, but are not limited to, these and any other suitable types of memory.
[0040] In some implementations, the memory stores elements such as executable modules or data structures, or subsets thereof, or extended sets thereof: operating systems and applications.
[0041] The operating system includes various system programs, such as the framework layer, core library layer, and driver layer, used to implement various basic business functions and handle hardware-based tasks. The application programs include various applications, such as media players and browsers, used to implement various application functions. Programs implementing the methods of the embodiments of this disclosure can be included in the application programs.
[0042] In the above embodiments, the processor can also invoke programs or instructions stored in memory, specifically programs or instructions stored in an application program, for the following purposes: Follow the steps described above.
[0043] The above methods can be applied to or implemented by a processor. The processor may be an integrated circuit chip with signal processing capabilities. During implementation, each step of the above methods can be completed by integrated logic circuits in the processor's hardware or by software instructions. The processor can be a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. It can implement or execute the methods, steps, and logic diagrams disclosed above. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the disclosed methods can be directly implemented by a hardware decoding processor, or by a combination of hardware and software modules in the decoding processor. The software modules can reside in random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, or other mature storage media in the art. This storage medium is located in memory; the processor reads information from the memory and, in conjunction with its hardware, completes the steps of the above methods.
[0044] It is understood that the embodiments described in this application can be implemented using hardware, software, firmware, middleware, microcode, or a combination thereof. For hardware implementation, the processing unit can be implemented in one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), general-purpose processors, controllers, microcontrollers, microprocessors, other electronic units for performing the functions described in this application, or combinations thereof.
[0045] For software implementation, the technology of this application can be implemented by executing the functional modules (e.g., procedures, functions, etc.) of this application. The software code can be stored in memory and executed by a processor. The memory can be implemented in the processor or outside the processor.
[0046] This application may also provide a non-volatile storage medium for storing a computer program. When the computer program is executed by a processor, it can implement the steps in the above method embodiments.
[0047] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application and are not intended to limit it. Although this application has been described in detail with reference to the embodiments, those skilled in the art should understand that modifications or equivalent substitutions to the technical solutions of this application do not depart from the spirit and scope of the technical solutions of this application, and should all be covered within the scope of the claims of this application.
Claims
1. A method for denoising complex conjugate product signals from interferometric / polarimetric imaging radar based on the coefficient of variation, the method comprising: Step 1) Read in the complex signal corresponding to the radar interferometer pair or the two polarization channels. and ; Step 2) Calculation and complex conjugate product signal The superscript * indicates complex conjugation; Step 3) According to the set window size and overlap size, Divide into overlapping sliding windows; Step 4) Calculate the coefficient of variation (CV) for each sliding window; Step 5) According to the set adjustable factor And the coefficient of variation (CV), calculate the filtering parameters corresponding to the effective pixels within each sliding window. ; Step 6) Perform a two-dimensional Fourier transform to... Transformation from the spatial domain to the frequency domain; Step 7) Based on filter parameters For each sliding window The spectrum is subjected to Goldstein filtering; Step 8) Use a two-dimensional inverse Fourier transform to convert the values of each sliding window after filtering. Spectrum transformation back to the spatial domain; The filter parameters The calculation formula is as follows: ； in, This indicates taking the minimum value of all sliding windows.
2. The noise reduction method for complex conjugate product signals of interferometric / polarimetric imaging radar based on the coefficient of variation as described in claim 1, characterized in that, The formula for calculating the coefficient of variation (CV) is as follows: ； Among them, STD and These represent the standard deviation and mean of the data, respectively.
3. The noise reduction method for complex conjugate product signals of interferometric / polarimetric imaging radar based on the coefficient of variation as described in claim 1, characterized in that, The adjustable factor The value range is (6.5, 40).
4. The noise reduction method for complex conjugate product signals of interferometric / polarimetric imaging radar based on the coefficient of variation as described in claim 3, characterized in that, The adjustable factor The value is 20.
5. The noise reduction method for complex conjugate product signals of interferometric / polarimetric imaging radar based on the coefficient of variation as described in claim 1, characterized in that, The corresponding to each sliding window The spectrum, after Goldstein filtering, is as follows: ； in, For smoothing operators; and These are the complex conjugate product signals within the sliding window before and after filtering. The spectrum; and This refers to the spatial frequency.
6. A noise reduction system for complex conjugate product signals of interferometric / polarimetric imaging radar based on the coefficient of variation, implemented according to the method described in any one of claims 1-5, characterized in that, The system includes: The signal reading module is used to read in the complex signals corresponding to the radar interferometer pairs or the two polarization channels. and ; The module for calculating complex conjugate products is used to calculate... and complex conjugate product signal The superscript * indicates complex conjugation; The split sliding window module is used to split windows according to a set window size and overlap size. Divide into overlapping sliding windows; The coefficient of variation (CV) calculation module is used to calculate the coefficient of variation (CV) for each sliding window. The filter parameter calculation module is used to calculate the filter parameters based on the set adjustable factors. And the coefficient of variation (CV), calculate the filtering parameters corresponding to the effective pixels within each sliding window. ; The Fourier transform module is used to transform the two-dimensional Fourier transform into the Fourier transform. Transformation from the spatial domain to the frequency domain; The Goldstein filtering module is used for filtering parameters. For each sliding window The spectrum was subjected to Goldstein filtering; and The inverse Fourier transform module is used to transform the filtered sliding windows corresponding to each other using a two-dimensional inverse Fourier transform. The spectrum is transformed back into the spatial domain.