Narrowband radar super-resolution ranging method based on MTD data
By using a narrowband radar super-resolution ranging method based on MTD data, and employing a sparse Bayesian algorithm and dictionary matrix for super-resolution ranging, the high hardware cost and large ranging error of traditional radar ranging methods are solved, achieving high-precision ranging results.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- CNGC INST NO 206 OF CHINA ARMS IND GRP
- Filing Date
- 2026-03-24
- Publication Date
- 2026-05-08
AI Technical Summary
Traditional pulse compression radar ranging methods face challenges such as high hardware costs, sampling frequency limitations, and large ranging errors when improving range resolution, making it difficult to meet the high-precision measurement requirements in complex scenarios.
A narrowband radar super-resolution ranging method based on MTD data is adopted. Target detection is performed on the narrowband radar received data, and super-resolution ranging is performed using the sparse Bayesian algorithm and dictionary matrix. The range grid is divided and calculated by combining prior information of the target type.
It improves ranging accuracy, reduces computational load and processing time, and enhances the ranging accuracy and applicability of narrowband radar.
Smart Images
Figure CN121995358A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of radar ranging technology, and in particular to a narrowband radar super-resolution ranging method based on MTD data. Background Technology
[0002] High-precision target distance measurement is a core requirement in fields such as autonomous driving, early warning monitoring, and terrain mapping. The range resolution of radar directly determines the application effectiveness of a radar system. Traditional pulse Doppler radar ranging is based on the Nyquist sampling theorem for signal acquisition and uses pulse compression technology for distance measurement. The range resolution depends on the main lobe width of the pulse-compressed waveform in the time domain, i.e., the bandwidth of the transmitted signal. Improving range resolution requires increasing the transmitted signal bandwidth. Increasing the bandwidth will lead to a significant increase in data transmission and hardware costs for the radar system. Moreover, even with increased bandwidth, the range resolution is still limited by the sampling frequency, making it difficult to meet the demand for high-precision target measurement in complex scenarios. Therefore, it is necessary to find methods to improve range resolution from the perspective of signal processing.
[0003] In recent years, with the continuous development of super-resolution theory and technology, it has become possible to apply super-resolution theory to the field of high-precision range measurement. The idea is based on the sparse distribution characteristics of strong scattering points of a target in a one-dimensional range image, allowing the recovery of the original target's scattering characteristics from sparse samples. To explain its principle, starting from a traditional signal model, it is assumed that the radar transmits a linear frequency modulated signal. Under narrowband conditions, the target is modeled as a point target, but in reality, the target is composed of... The radar receives a target reflected signal composed of several scattering points, each with varying scattering intensities. for: (1) in, Indicates the first The scattering intensity at each scattering point Indicates the first The propagation delay corresponding to the distance between each scattering point This indicates the received noise signal. The echo signal after traditional pulse compression processing can be represented as: (2) in, , Indicates the bandwidth of the transmitted signal. Indicates the first The amplitude after pulse compression at each scattering point. From this expression, it can be seen that when the time delay of each scattering point... Traditional pulse compression processing cannot distinguish between different scattering points, as they are simultaneously compressed into the same range cell. When the sampling rate is fixed and the range cell corresponding to the received data is fixed, there is an inherent error in ranging. (The target distance is set to...) The distance between the sampling points corresponding to the pulse compression is ,So , This indicates the cell size corresponding to the current sampling rate. The lower the sampling rate, the smaller the corresponding cell size. The larger the value, the greater the inherent error in distance measurement.
[0004] To address the issue of large ranging errors in traditional pulse compression processing, the pulse compression echo signal is treated as a time-domain sparse signal. Using sparsity theory, the pulse compression signal can be expressed as: (3) Among them, the number of sampling points Furthermore, only a few scattering points within the target are strong scattering points, while the amplitudes of other scattering points are small or approach zero. The number of larger elements is much smaller than the total number of scattering points. .
[0005] By utilizing the sparsity properties of the target scattering points and combining them with sparsity theory, the solution for the range parameter can be transformed into a range super-resolution solution process: (4) Different sparse solution algorithms can be used to obtain super-resolution distance estimation results for targets. However, in practical applications, the problems of model adaptation and high algorithm complexity make engineering applications difficult.
[0006] Therefore, it is necessary to improve one or more of the problems existing in the above-mentioned related technical solutions.
[0007] It should be noted that this section is intended to provide background or context for the technical solutions of this disclosure as set forth in the claims. The description herein does not constitute an admission that it is prior art simply because it is included in this section. Summary of the Invention
[0008] The purpose of this invention is to provide a narrowband radar super-resolution ranging method based on MTD data, thereby overcoming, at least to some extent, one or more problems caused by the limitations and defects of related technologies.
[0009] This invention first provides a narrowband radar super-resolution ranging method based on MTD data, comprising: S1, perform target detection on the processed narrowband radar received data, and record the range cell and Doppler cell of the MTD plane where the target is located; S2, extract the one-dimensional range image data corresponding to the Doppler cell where the target is located; S3, using the distance cell where the target is located, extract the data of a preset number of distance cells before and after the distance cell as super-resolution ranging data; S4. Based on the prior information of the super-resolution ranging data and the target type, the super-resolution ranging data is divided into distance grids according to the super-resolution factor. S5. Based on the distance grid division and the number of distance cells extracted, determine the search range of the target distance and construct a dictionary matrix corresponding to the set of distance grid points within the search range. S6. Using super-resolution ranging data and a dictionary matrix, the amplitude vector corresponding to the super-resolution grid points is obtained by iterative calculation using a sparse Bayesian algorithm. The distance corresponding to the peak point in the amplitude vector is the super-resolution ranging result.
[0010] In this invention, S1 includes the following steps: S101, pulse compression and target detection preprocessing of narrowband radar received data to obtain MTD plane data; S102, perform target detection on MTD plane data, and after the target is detected, record the distance cell and Doppler cell where the target is located.
[0011] In this invention, the super-resolution ranging data in S3 is as follows:
[0012] in, Doppler unit number, for One-dimensional distance image, This represents the number of distance cells for forward or backward screenshots. This is the distance unit number.
[0013] In this invention, the search range in S5 is:
[0014] in, For distance grid resolution, Indicates distance resolution. This represents the super-resolution factor.
[0015] In this invention, the dictionary matrix in S5 The column vector representation is as follows:
[0016] Among them, dictionary matrix The column vectors are pulse compression vectors corresponding to different distance values. , j This refers to the grid point number.
[0017] In this invention, the target type in S4 refers to the target appearance size.
[0018] The present invention further provides a narrowband radar super-resolution ranging device based on MTD data, comprising: The super-resolution ranging data acquisition module is used to perform target detection on the processed narrowband radar received data, record the range cell and Doppler cell of the MTD plane where the target is located; extract the one-dimensional range image data corresponding to the Doppler cell where the target is located; and use the range cell where the target is located to extract the data of a preset number of range cells before and after the range cell as super-resolution ranging data. The super-resolution ranging module is used to divide the super-resolution ranging data into distance grids according to the super-resolution factor based on the super-resolution ranging data and prior information about the target type. Based on the distance grid division and the number of intercepted distance cells, the search range for the target distance is determined, and a dictionary matrix corresponding to the set of distance grid points within the search range is constructed. Using the super-resolution ranging data and the dictionary matrix, the amplitude vector corresponding to each super-resolution grid point is calculated iteratively using a sparse Bayesian algorithm. The distance corresponding to the peak point in this amplitude vector is the super-resolution ranging result.
[0019] The technical solution provided by this invention may include the following beneficial effects: This invention discloses a narrowband radar super-resolution ranging method based on MTD data. First, a processing flow based on MTD data is designed to extract data for super-resolution ranging. Second, based on a fast sparse Bayesian algorithm and combined with target type information, the computational load is significantly reduced, the computation time is shortened, and the ranging accuracy is improved. Attached Figure Description
[0020] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this disclosure and, together with the description, serve to explain the principles of this disclosure. It is obvious that the drawings described below are merely some embodiments of this disclosure, and those skilled in the art can obtain other drawings based on these drawings without any inventive effort.
[0021] Figure 1 A flowchart illustrating a narrowband radar super-resolution ranging method based on MTD data in an exemplary embodiment of this disclosure is shown. Figure 2 The simulation results of exemplary embodiments of this disclosure are shown. Detailed Implementation
[0022] Exemplary embodiments will now be described more fully with reference to the accompanying drawings. However, these exemplary embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, they are provided so that this disclosure will be more comprehensive and complete, and will fully convey the concept of the exemplary embodiments to those skilled in the art. The described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.
[0023] Furthermore, the accompanying drawings are merely illustrative diagrams of embodiments of this disclosure and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and therefore repeated descriptions of them will be omitted. Some block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities.
[0024] This example implementation first provides a narrowband radar super-resolution ranging method based on MTD data. Please refer to [link / reference]. Figure 1 This method may include: S1-S6, as follows: S1, perform target detection on the processed narrowband radar received data, and record the range cell and Doppler cell of the MTD plane where the target is located; S2, extract the one-dimensional range image data corresponding to the Doppler cell where the target is located; S3, using the distance cell where the target is located, extract the data of a preset number of distance cells before and after the distance cell as super-resolution ranging data; S4. Based on the prior information of the super-resolution ranging data and the target type, the super-resolution ranging data is divided into distance grids according to the super-resolution factor. S5. Based on the distance grid division and the number of distance cells extracted, determine the search range of the target distance and construct a dictionary matrix corresponding to the set of distance grid points within the search range. S6. Using super-resolution ranging data and a dictionary matrix, the amplitude vector corresponding to the super-resolution grid points is obtained by iterative calculation using a sparse Bayesian algorithm. The distance corresponding to the peak point in the amplitude vector is the super-resolution ranging result.
[0025] In this embodiment, a processing flow based on MTD data is first designed to extract data for super-resolution ranging. Secondly, based on the Fast Sparse Bayes algorithm and combined with target type information, the computational load is significantly reduced, the computation time is shortened, and the ranging accuracy is improved.
[0026] The narrowband radar super-resolution ranging method based on MTD data of this application will be further described below through specific embodiments.
[0027] 1. Narrowband radar super-resolution ranging data extraction (1) Perform pulse compression and moving target detection preprocessing on the radar received data to obtain two-dimensional data. This refers to MTD planar data, which is used for target detection. (2) When a target is detected, record the distance cell number of the MTD plane where the target is located. and Doppler unit number ; (3) Using the recorded Doppler unit number Extract the one-dimensional range image of the Doppler cell containing the target. ; (4) Set the distance cells that need to be processed by super-resolution ranging according to the recorded target distance cell number, and extract the target distance cells before and after. From a range cell of data, we obtain data for super-resolution ranging:
[0028] Generally, because the range cell of a narrowband radar is much larger than the target, It can be set to 1.
[0029] 2. Super-resolution ranging method based on fast sparse Bayes (1) Based on the recorded distance units and combined with prior information about the target type, the distance grid can be divided: for small targets, such as small drones, a denser distance grid can be divided; for large targets, such as large drones, a sparser distance grid can be divided.
[0030] Assuming the super-resolution factor is Then the distance to the grid corresponds to ,in This indicates the distance resolution; different settings can be configured for different target types. The value corresponds to different distance grids.
[0031] (2) Based on the distance grid points set above and the number of distance cells in the screenshot, the search range for the target distance can be determined as follows: Within this range Given a set of distance grid points, a dictionary matrix is constructed corresponding to the set of distance grid points within the search range. dictionary matrix The column vectors in the table are pulse compression vectors corresponding to different distance values, i.e. .
[0032] (3) Using super-resolution ranging data and dictionary matrix, the amplitude vector corresponding to the super-resolution grid points can be calculated through iterative calculation based on the idea of sparse Bayes algorithm. Searching at this time The result of super-resolution ranging is obtained by taking the mid-peak point and the corresponding distance. .
[0033] Simulation experiments were conducted using the above embodiments: Assume the radar transmits a linear frequency modulated signal with a bandwidth of 15MHz, corresponding to a range resolution of 10m. Assume the target is a small unmanned aerial vehicle (UAV), and the super-resolution multiplier is set to 5x, then the super-resolution ranging corresponds to a range grid of 2m. The target's actual position is 5004m. The traditional pulse compression results and the super-resolution ranging results are as follows... Figure 2 As shown. From Figure 2 It can be seen that traditional pulse compression results can only compress the target to the nearest sampling point, resulting in a fixed ranging error. However, the ranging results obtained by the super-resolution ranging method of this application are closer to the actual target distance, and the ranging accuracy is significantly improved.
[0034] This invention discloses a narrowband radar super-resolution ranging method based on MTD data, which improves upon the inherent errors of narrowband radar pulse compression ranging. This invention enhances the applicability of super-resolution ranging algorithms to narrowband radar. This invention designs a generalized radar super-resolution ranging process, which, combined with target information, enables super-resolution mesh generation for different types of targets, improving computational efficiency and providing support for engineering applications.
[0035] This disclosure also provides a narrowband radar super-resolution ranging device based on MTD data, comprising: The super-resolution ranging data acquisition module is used to perform target detection on the processed narrowband radar received data, record the range cell and Doppler cell of the MTD plane where the target is located; extract the one-dimensional range image data corresponding to the Doppler cell where the target is located; and use the range cell where the target is located to extract the data of a preset number of range cells before and after the range cell as super-resolution ranging data. The super-resolution ranging module is used to divide the super-resolution ranging data into distance grids according to the super-resolution factor based on the super-resolution ranging data and prior information about the target type. Based on the distance grid division and the number of intercepted distance cells, the search range for the target distance is determined, and a dictionary matrix corresponding to the set of distance grid points within the search range is constructed. Using the super-resolution ranging data and the dictionary matrix, the amplitude vector corresponding to each super-resolution grid point is calculated iteratively using a sparse Bayesian algorithm. The distance corresponding to the peak point in this amplitude vector is the super-resolution ranging result.
[0036] Regarding the apparatus in the above embodiments, the specific manner in which each module performs its operation has been described in detail in the embodiments related to the method, and will not be elaborated upon here.
[0037] It should be noted that although several modules of the system for executing actions are mentioned in the detailed description above, this division is not mandatory. In fact, according to embodiments of the present invention, the features and functions of two or more modules described above can be embodied in one module. Conversely, the features and functions of one module described above can be further divided into multiple modules for embodiment. Components shown as modules may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of the present invention according to actual needs. Those skilled in the art can understand and implement this without any inventive effort.
[0038] The embodiments of the present invention have been described above with reference to the accompanying drawings. However, the present invention is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of the present invention without departing from the spirit and scope of the claims. All of these forms are within the protection scope of the present invention.
[0039] It should be noted that the installation of image acquisition and personal identification equipment in public places involved in this application is necessary for maintaining public safety, complies with relevant national regulations, and is accompanied by prominent warning signs. The collected personal images and identification information can only be used for the purpose of maintaining public safety and not for other purposes; or the images, personal identification data, etc. in this application are all legally and compliantly obtained or collected with the individual's separate consent.
Claims
1. A narrowband radar super-resolution ranging method based on MTD data, characterized in that, include: S1, perform target detection on the processed narrowband radar received data, and record the range cell and Doppler cell of the MTD plane where the target is located; S2, extract the one-dimensional range image data corresponding to the Doppler cell where the target is located; S3, using the distance cell where the target is located, extract the data of a preset number of distance cells before and after the distance cell as super-resolution ranging data; S4. Based on the prior information of the super-resolution ranging data and the target type, the super-resolution ranging data is divided into distance grids according to the super-resolution factor. S5. Based on the distance grid division and the number of distance cells extracted, determine the search range of the target distance and construct a dictionary matrix corresponding to the set of distance grid points within the search range. S6. Using super-resolution ranging data and a dictionary matrix, the amplitude vector corresponding to the super-resolution grid points is obtained by iterative calculation using a sparse Bayesian algorithm. The distance corresponding to the peak point in the amplitude vector is the super-resolution ranging result.
2. The narrowband radar super-resolution ranging method based on MTD data according to claim 1, characterized in that, S1 includes the following steps: S101, pulse compression and target detection preprocessing of narrowband radar received data to obtain MTD plane data; S102, perform target detection on MTD plane data, and after the target is detected, record the distance cell and Doppler cell where the target is located.
3. The narrowband radar super-resolution ranging method based on MTD data according to claim 1, characterized in that, In S3, the super-resolution ranging data is as follows: in, Doppler unit number, for One-dimensional distance image, This represents the number of distance cells for forward or backward screenshots. This is the distance unit number.
4. The narrowband radar super-resolution ranging method based on MTD data according to claim 3, characterized in that, The search scope in S5 is: in, For distance grid resolution, Indicates distance resolution. This represents the super-resolution factor.
5. The narrowband radar super-resolution ranging method based on MTD data according to claim 4, characterized in that, dictionary matrix in S5 The column vector representation is as follows: Among them, dictionary matrix The column vectors are pulse compression vectors corresponding to different distance values. , j This refers to the grid point number.
6. The narrowband radar super-resolution ranging method based on MTD data according to claim 5, characterized in that, In S4, the target type refers to the target's appearance size.
7. A narrowband radar super-resolution ranging device based on MTD data, characterized in that, include: The super-resolution ranging data acquisition module is used to perform target detection on the processed narrowband radar received data, record the range cell and Doppler cell of the MTD plane where the target is located; extract the one-dimensional range image data corresponding to the Doppler cell where the target is located; and use the range cell where the target is located to extract the data of a preset number of range cells before and after the range cell as super-resolution ranging data. The super-resolution ranging module is used to divide the super-resolution ranging data into distance grids according to the super-resolution factor based on the super-resolution ranging data and prior information about the target type. Based on the distance grid division and the number of intercepted distance cells, the search range for the target distance is determined, and a dictionary matrix corresponding to the set of distance grid points within the search range is constructed. Using the super-resolution ranging data and the dictionary matrix, the amplitude vector corresponding to each super-resolution grid point is calculated iteratively using a sparse Bayesian algorithm. The distance corresponding to the peak point in this amplitude vector is the super-resolution ranging result.