Radar ranging method, apparatus and radar level gauge
By superimposing echo signals and combining Rife interpolation and cubic spline interpolation, the hardware cost and efficiency problems caused by the fence effect in traditional radar ranging are solved, and efficient and accurate radar ranging is achieved.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- ZHEJIANG MEIYI INTELLIGENT SENSING TECH CO LTD
- Filing Date
- 2026-01-22
- Publication Date
- 2026-05-08
AI Technical Summary
In traditional radar ranging methods, increasing the number of Fourier transform points to improve spectral resolution leads to increased hardware costs, increased design complexity, and longer signal processing time, resulting in reduced ranging efficiency.
By superimposing multiple echo signals reflected from the ranging target, a dual interpolation method of local fine Rife interpolation and global optimized cubic spline interpolation is adopted to significantly suppress noise, reduce picket fence effect error, and achieve efficient and accurate radar ranging.
It achieves efficient and accurate radar ranging, reduces computing power consumption and hardware costs, and effectively eliminates noise fluctuations and system errors.
Smart Images
Figure CN121559534B_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of radar ranging technology, specifically to radar ranging methods, devices, and radar level gauges. Background Technology
[0002] Radar level gauges measure distance by transmitting signals and receiving reflected signals from targets, utilizing the frequency difference between the two (i.e., beat frequencies). In traditional radar ranging procedures, the Fast Fourier Transform (FFT) discretizes the continuous beat spectrum into fixed-interval, fence-like spectral lines. The ranging result is accurate only when the true beat frequency of the target falls precisely on one of the fence lines. If the true beat frequency lies between two fence lines, a fence-like error occurs.
[0003] To alleviate this problem, a conventional approach is to increase the number of Fourier transform points to improve spectral resolution. However, this method significantly increases the demands on computation and storage capacity, leading not only to higher hardware costs and increased design complexity but also to longer signal processing time and reduced radar ranging efficiency. Summary of the Invention
[0004] This invention provides a radar ranging method, device, and radar level gauge to address the problems of traditional radar ranging, such as picket fence error, the need to increase the number of Fourier transform points to significantly increase computational and storage requirements, leading to higher hardware costs, increased design difficulty, longer signal processing time, and reduced radar ranging efficiency.
[0005] In a first aspect, the present invention provides a radar ranging method, the method comprising:
[0006] Receive multiple echo signals reflected from the ranging target, and superimpose the multiple echo signals to obtain a superimposed echo signal;
[0007] Select multiple target spectral lines from the spectrum of the superimposed echo signal;
[0008] Rife interpolation is performed based on multiple target spectral lines to obtain multiple interpolation points, and the initial distance to the ranging target is calculated.
[0009] Multiple target interpolation points are selected from multiple interpolation points based on the initial distance;
[0010] Cubic spline interpolation is performed based on multiple target interpolation points to obtain a cubic spline interpolation function. The initial distance is then substituted into the cubic spline interpolation function to obtain the target distance.
[0011] This invention significantly suppresses noise and improves the signal-to-noise ratio by superimposing multiple echo signals reflected from the ranging target. Multiple target spectral lines are selected from the spectrum of the superimposed echo signals for Rife interpolation, effectively mitigating the picket fence error of the Fast Fourier Transform and initially calculating the distance to the ranging target. Target interpolation points are selected from the interpolation points obtained from Rife interpolation based on the initial distance, ensuring that subsequent cubic spline interpolation only fits local points strongly correlated with the initial distance, reducing computational overhead. Global smoothing fitting of multiple target interpolation points is performed using cubic spline interpolation, eliminating system errors introduced by noise fluctuations and hardware nonlinearity. Substituting the initial distance into the cubic spline interpolation function outputs an accurate target distance. Through dual interpolation of locally refined Rife interpolation and globally optimized cubic spline interpolation, efficient and accurate radar ranging is achieved.
[0012] In one alternative implementation, the target spectral line includes a peak spectral line, a left adjacent spectral line, and a right adjacent spectral line.
[0013] When the amplitude of the left adjacent spectral line is greater than that of the right adjacent spectral line, the initial distance is calculated using the following formula:
[0014]
[0015] When the amplitude of the left adjacent spectral line is less than that of the right adjacent spectral line, the initial distance is calculated using the following formula:
[0016]
[0017] In the formula, distance Indicates the initial distance; Indicates the frequency of the peak spectral line; △R Represents distance grid; This represents the larger of the frequencies of the left and right adjacent spectral lines. This represents the smaller of the frequencies of the left and right adjacent spectral lines. RES It represents the frequency difference between any two adjacent interpolation points.
[0018] In this embodiment, the distance offset calculated by Rife interpolation is used to correct the coarse distance to an initial distance that is closer to the true value, so as to effectively reduce the local error caused by the fence effect.
[0019] In one alternative implementation, selecting multiple target interpolation points from multiple interpolation points based on an initial distance includes:
[0020] Obtain the distance corresponding to each interpolation point, and sort the multiple interpolation points in descending order according to their distances;
[0021] Traverse each sorted interpolation point and determine multiple related interpolation points of the interpolation point;
[0022] Based on the initial distance, the distance corresponding to the interpolation point, and the distance corresponding to each relevant interpolation point, determine whether the interpolation point satisfies the first condition;
[0023] If the interpolation point does not meet the first condition, traverse the next interpolation point until the interpolation point meets the first condition, and determine the interpolation point and its corresponding multiple related interpolation points as multiple target interpolation points.
[0024] This embodiment can filter out target interpolation points with high matching degree with the initial distance and reasonable local distance distribution by using the first condition, and only select a few interpolation points. While ensuring the fitting accuracy, it avoids the increase in computing power consumption due to too many interpolation points.
[0025] In one alternative implementation, the first condition is expressed by the following formula:
[0026]
[0027] In the formula, i Indicates the index of the interpolation point; i -1, i +1, i +2 represents the index of the relevant interpolation point; This represents the distance corresponding to the interpolation point; These represent the distances corresponding to the relevant interpolation points; distance Indicates the initial distance; N This represents the total number of interpolation points.
[0028] This embodiment can select four target interpolation points with high matching degree with the initial distance and reasonable local distance distribution by using the first condition. While ensuring the fitting accuracy, it avoids the increase in computing power consumption due to too many interpolation points.
[0029] In one alternative implementation, the method further includes:
[0030] If multiple interpolation points do not meet the first condition, multiple interpolation points that meet any of the second conditions are determined as multiple target interpolation points.
[0031] This embodiment filters all interpolation points based on a second condition to ensure that even if local point group adaptation fails, valid points that meet the requirements of cubic spline interpolation can still be obtained.
[0032] In one alternative implementation, the second condition is expressed by the following formula:
[0033]
[0034]
[0035] In the formula, distance Indicates the initial distance; N This represents the total number of interpolation points.
[0036] This embodiment filters all interpolation points based on the second condition to ensure that even if the local point group adaptation fails, four valid points that meet the requirements of cubic spline interpolation can still be obtained. At the same time, only four target interpolation points are obtained, which ensures fitting accuracy while avoiding increased computing power consumption due to too many interpolation points.
[0037] In one optional implementation, cubic spline interpolation is performed based on multiple target interpolation points to obtain a cubic spline interpolation function. The initial distance is then substituted into the cubic spline interpolation function to obtain the target distance, including:
[0038] Construct a cubic polynomial for each interpolation interval formed by multiple target interpolation points;
[0039] A matrix equation is constructed based on multiple cubic polynomials using free boundary conditions, and the order of the matrix equation is consistent with the number of target interpolation points.
[0040] The matrix equation is solved by the chasing method to obtain the cubic spline interpolation function corresponding to each interpolation interval;
[0041] Substitute the initial distance into the cubic spline interpolation function of the interpolation interval containing the initial distance to obtain the target distance.
[0042] This embodiment uses cubic spline interpolation for global smooth fitting, effectively offsetting the noise fluctuations and hardware nonlinearity errors remaining from Rife interpolation.
[0043] In one optional implementation, multiple echo signals are superimposed to obtain a superimposed echo signal, including:
[0044] Align the pulse phases of multiple echo signals;
[0045] After aligning multiple echo signals according to their sampling positions, sum them point by point, and then normalize the amplitude to obtain the superimposed echo signal.
[0046] This embodiment significantly suppresses noise and improves the signal-to-noise ratio by superimposing signals, effectively highlighting the signals of distant targets.
[0047] In a second aspect, the present invention provides a radar ranging device, the device comprising:
[0048] The superposition module is used to receive multiple echo signals reflected from the ranging target, and superimpose the multiple echo signals to obtain a superimposed echo signal;
[0049] The first screening module is used to screen multiple target spectral lines from the spectrum of the superimposed echo signal;
[0050] The first interpolation module is used to perform Rife interpolation based on multiple target spectral lines to obtain multiple interpolation points and calculate the initial distance to the ranging target;
[0051] The second filtering module is used to filter multiple target interpolation points from multiple interpolation points based on the initial distance;
[0052] The second interpolation module is used to perform cubic spline interpolation based on multiple target interpolation points to obtain a cubic spline interpolation function. The initial distance is then substituted into the cubic spline interpolation function to obtain the target distance.
[0053] Thirdly, the present invention provides a radar level gauge, comprising: a memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the computer instructions to perform the radar ranging method of the first aspect or any corresponding embodiment described above.
[0054] Fourthly, the present invention provides a computer-readable storage medium storing computer instructions for causing a computer to perform the radar ranging method of the first aspect or any corresponding embodiment described above. Attached Figure Description
[0055] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.
[0056] Figure 1 This is a schematic diagram of a radar ranging method according to an embodiment of the present invention;
[0057] Figure 2 This is a structural block diagram of a radar ranging device according to an embodiment of the present invention;
[0058] Figure 3 This is a schematic diagram of the hardware structure of the radar level gauge according to an embodiment of the present invention. Detailed Implementation
[0059] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.
[0060] It is understood that before using the technical solutions disclosed in the various embodiments of the present invention, users should be informed of the types, scope of use, and usage scenarios of the personal information involved in the present invention and their authorization should be obtained in accordance with relevant laws and regulations through appropriate means.
[0061] The terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.
[0062] In traditional radar ranging processes, the Fast Fourier Transform (FFT) introduces picket fence error. To mitigate this, a conventional approach is to increase the number of Fourier transform points to improve spectral resolution. However, this method significantly increases the computational and storage requirements, leading to higher hardware costs, increased design complexity, longer signal processing time, and reduced radar ranging efficiency. This invention, by superimposing multiple echo signals reflected from the ranging target, significantly suppresses noise and improves the signal-to-noise ratio. It employs a dual interpolation method—locally refined Rife interpolation and globally optimized cubic spline interpolation—to achieve efficient and accurate radar ranging.
[0063] According to an embodiment of the present invention, a radar ranging method embodiment is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.
[0064] This embodiment provides a radar ranging method that can be used with radar level gauges. Figure 1 This is a flowchart of a radar ranging method according to an embodiment of the present invention, such as... Figure 1 As shown, the process includes the following steps:
[0065] Step S101: Receive multiple echo signals reflected from the ranging target, and superimpose the multiple echo signals to obtain a superimposed echo signal.
[0066] Specifically, the radar level gauge generates a frequency-modulated continuous wave signal via a voltage-controlled oscillator, which is amplified by a power amplifier and then transmitted to the ranging target via an antenna. A receiving antenna then captures multiple consecutive echo signals reflected from the ranging target. These echo signals are weak and have an extremely low signal-to-noise ratio due to attenuation over long distances, environmental clutter interference, and RF front-end noise. Therefore, the echo signals are first amplified by a low-noise amplifier and down-converted to an intermediate frequency signal by a mixer. Then, the signals are superimposed using an analog-to-digital converter, significantly suppressing noise and improving the signal-to-noise ratio. This effectively highlights the signal of distant targets, laying the foundation for subsequent spectrum analysis.
[0067] Step S102: Select multiple target spectral lines from the spectrum of the superimposed echo signal.
[0068] Specifically, a Fast Fourier Transform (FFT) is performed on the superimposed echo signal to convert the time-domain discrete signal into a frequency-domain discrete spectrum. Since the discrete spectrum also contains invalid spectral lines corresponding to environmental clutter and hardware residual noise, the CA-CFAR (Cell-Averaging Constant False Alarm Rate) algorithm is used to filter target spectral lines: the peak spectral line, the left adjacent spectral line, and the right adjacent spectral line. The peak spectral line has the highest amplitude, i.e., the largest ordinate; the left adjacent spectral line is the second-to-left peak spectral line adjacent to the peak spectral line; and the right adjacent spectral line is the second-to-right peak spectral line adjacent to the peak spectral line.
[0069] Step S103: Perform Rife interpolation based on multiple target spectral lines to obtain multiple interpolation points, and calculate the initial distance to the ranging target.
[0070] Specifically, by inserting multiple frequency interpolation points at equal frequency intervals between the three target spectral lines using Rife interpolation, the picket fence effect error of the Fast Fourier Transform can be effectively reduced. After interpolation, the distance between the radar level gauge and the ranging target is initially calculated.
[0071] Step S104: Select multiple target interpolation points from multiple interpolation points based on the initial distance.
[0072] Specifically, target interpolation points are selected from the interpolation points obtained by Rife interpolation using the initial distance. This ensures that subsequent cubic spline interpolation only fits local points that are strongly correlated with the initial distance, reducing computational cost and laying a data foundation for subsequent interpolation.
[0073] Step S105: Perform cubic spline interpolation based on multiple target interpolation points to obtain a cubic spline interpolation function. Substitute the initial distance into the cubic spline interpolation function to obtain the target distance.
[0074] Specifically, while Rife interpolation reduces the fence effect at individual points, random fluctuations still exist at local interpolation points due to hardware channel nonlinearity and residual environmental clutter. Global smoothing fitting using cubic spline interpolation is necessary to effectively offset noise fluctuations and system errors. Substituting the initial distance into this function outputs the smoothed target distance, achieving efficient and accurate radar ranging.
[0075] This invention significantly suppresses noise and improves the signal-to-noise ratio by superimposing multiple echo signals reflected from the ranging target. Multiple target spectral lines are selected from the spectrum of the superimposed echo signals for Rife interpolation, effectively mitigating the picket fence error of the Fast Fourier Transform and providing a preliminary calculation of the distance to the ranging target. Target interpolation points are selected from the interpolation points obtained from Rife interpolation based on the initial distance, ensuring that subsequent cubic spline interpolation only fits local points strongly correlated with the initial distance, reducing computational overhead. Global smoothing fitting of multiple target interpolation points is performed using cubic spline interpolation, eliminating system errors introduced by noise fluctuations and hardware nonlinearity. Substituting the initial distance into the cubic spline interpolation function outputs an accurate target distance. Through dual interpolation—locally refined Rife interpolation and globally optimized cubic spline interpolation—efficient and accurate radar ranging is achieved.
[0076] This embodiment provides a radar ranging method that can be used for the aforementioned radar level gauge. The method specifically includes the following steps:
[0077] Step S201: Receive multiple echo signals reflected from the ranging target, superimpose the multiple echo signals to obtain a superimposed echo signal.
[0078] Specifically, in step S201 above, multiple echo signals are superimposed to obtain a superimposed echo signal, including:
[0079] Step S2011: Align the pulse phases of multiple echo signals.
[0080] Specifically, a key challenge for radar level gauges in long-range ranging is overcoming signal attenuation. The farther the transmitted signal travels, the more spatial interference it encounters, and the smaller the relatively effective radar cross-section of the target. This results in a weak echo signal amplitude captured by the receiving antenna. Furthermore, interference from surrounding clutter and noise from the RF front-end system further amplify this, significantly reducing the signal-to-noise ratio (SNR). If the SNR falls below the radar level gauge's target detection threshold, the true target signal cannot be identified from the noise. To address this, coherent accumulation of multiple consecutive echo signals can be performed. The effective target signal gradually increases with the number of accumulations. However, the noise is Gaussian white noise, with a random distribution, and its amplitude does not increase proportionally with the number of pulses accumulated. Therefore, coherent accumulation can significantly suppress noise, increase the signal amplitude, and thus improve the SNR.
[0081] Since coherent accumulation relies on pulse coherence, meaning that the phases of each pulse signal must be strictly consistent, the pulse phases of multiple echo signals must first be aligned. The phase references of the transmitted and received pulses are calibrated using a clock synchronization module, and a phase compensation circuit is used to offset the phase deviation introduced by the RF channel, ensuring that the phase error of each pulse is less than a preset threshold.
[0082] In step S2012, multiple echo signals are aligned according to their sampling positions and summed point by point. After amplitude normalization, the superimposed echo signals are obtained.
[0083] Specifically, multiple phase-aligned echo signals are matched one-to-one according to their sampling positions. For example, the k-th sampling point of the first pulse is matched with the k-th sampling point of the second to M pulses. A point-by-point summation is performed, and then amplitude normalization is used to avoid signal amplitude overflow, finally obtaining the superimposed echo signal. If the signal-to-noise ratio of a single echo signal is SNR, after coherently accumulating M echo signals, the signal-to-noise ratio of the superimposed echo signal can be improved to M×SNR.
[0084] Step S202 involves selecting multiple target spectral lines from the spectrum of the superimposed echo signal. The target spectral lines include the peak spectral line, the left adjacent spectral line, and the right adjacent spectral line. For details, please refer to [link to relevant documentation]. Figure 1 Step S102 of the illustrated embodiment will not be described again here.
[0085] Step S203: Perform Rife interpolation based on multiple target spectral lines to obtain multiple interpolation points, and calculate the initial distance to the ranging target.
[0086] Specifically, when the amplitude of the left adjacent spectral line is greater than the amplitude of the right adjacent spectral line, it indicates that the true difference frequency is biased to the left of the peak spectral line, and the initial distance is calculated by the following formula (1):
[0087] (1)
[0088] When the amplitude of the left adjacent spectral line is less than the amplitude of the right adjacent spectral line, it indicates that the true difference frequency is biased to the right of the peak spectral line. The initial distance is calculated by the following formula (2):
[0089] (2)
[0090] In the formula, distance Indicates the initial distance; Indicates the frequency of the peak spectral line; △R Represents distance grid; This represents the larger of the frequencies of the left and right adjacent spectral lines. This represents the smaller of the frequencies of the left and right adjacent spectral lines. RES It represents the frequency difference between any two adjacent interpolation points.
[0091] In equations (1) and (2) above, the product of the peak spectral line frequency and the distance grid is the coarse distance obtained after Fast Fourier Transform discretization. The distance grid is a fixed parameter, referring to the proportional relationship between the horizontal axis of the spectrum and the actual distance. The right side of the minus or plus sign is the distance offset calculated by Rife interpolation. Since the coarse distance is calculated based on the ideal assumption that the true difference frequency falls exactly on the peak spectral line, it deviates from the true distance due to the picket fence effect and needs to be corrected according to the offset direction of the true difference frequency. When the true difference frequency is shifted to the left, the corresponding true distance will be smaller than the coarse distance, and the distance offset needs to be subtracted from the coarse distance; when the true difference frequency is shifted to the right, the corresponding true distance will be larger than the coarse distance, and the distance offset needs to be added to the coarse distance. The distance offset calculated by Rife interpolation corrects the coarse distance to an initial distance closer to the true value, so as to effectively reduce the local error caused by the picket fence effect.
[0092] Step S204: Select multiple target interpolation points from multiple interpolation points based on the initial distance.
[0093] Specifically, step S204 includes:
[0094] Step S2041: Obtain the distance corresponding to each interpolation point, and sort the multiple interpolation points in descending order according to their distances.
[0095] Specifically, Rife interpolation generates frequency interpolation points. Using a fixed frequency-distance conversion formula, each frequency interpolation point is converted into a corresponding distance value, and multiple interpolation points are sorted in descending order according to distance.
[0096] Step S2042: Traverse each sorted interpolation point and determine multiple related interpolation points of the interpolation point.
[0097] Specifically, each interpolation point is traversed sequentially (denoted as the index of the currently traversed interpolation point).i For each index... i The interpolation point is selected, and its adjacent specific index interpolation point is selected as the relevant interpolation point, that is, the index is i 1. i +1、 i +2 interpolation points.
[0098] Step S2043: Based on the initial distance, the distance corresponding to the interpolation point, and the distance corresponding to each relevant interpolation point, determine whether the interpolation point satisfies the first condition.
[0099] Specifically, the first condition is expressed by the following formula (3):
[0100] (3)
[0101] In the formula, i Indicates the index of the interpolation point; i -1, i +1, i +2 represents the index of the relevant interpolation point; This represents the distance corresponding to the interpolation point; These represent the distances corresponding to the relevant interpolation points; distance Indicates the initial distance; N This represents the total number of interpolation points.
[0102] The first condition allows us to select a group of interpolation points that have a high degree of matching with the initial distance and a reasonable local distance distribution.
[0103] Step S2044: If the interpolation point does not meet the first condition, traverse the next interpolation point until the interpolation point meets the first condition, and determine the interpolation point and its corresponding multiple related interpolation points as multiple target interpolation points.
[0104] Specifically, if the current interpolation point and its related interpolation points satisfy the above formula (3), it means that the local point group formed by these 4 interpolation points has a high degree of matching between its distance distribution and the initial distance, and there is no obvious abnormal jump. These 4 interpolation points can be directly determined as target interpolation points for subsequent cubic spline interpolation. In addition, only 4 target interpolation points are determined, which ensures the fitting accuracy while avoiding the increase in computing power consumption due to too many interpolation points. If formula (3) is not satisfied, return to step S2042 and traverse the next interpolation point in index order until a point group that meets the conditions is found.
[0105] Step S2045: When multiple interpolation points do not meet the first condition, multiple interpolation points that meet any of the second conditions are determined as multiple target interpolation points.
[0106] Specifically, when a group of points satisfying the first condition is not found after traversing all interpolation points, i.e., there are fewer than two usable interpolation points on both sides of the initial distance, the target interpolation points need to be selected through the second condition. The second condition is expressed by the following formulas (4) and (5):
[0107] (4)
[0108] (5)
[0109] In the formula, distance Indicates the initial distance; N This represents the total number of interpolation points.
[0110] If formula (4) is satisfied, then the four interpolation points that satisfy formula (4) are taken as target interpolation points; if formula (5) is satisfied, then the four interpolation points that satisfy formula (5) are taken as target interpolation points. By screening all interpolation points based on the second condition, it is ensured that even if the local point group adaptation fails, four valid points that meet the requirements of cubic spline interpolation can still be obtained. Furthermore, determining only four target interpolation points ensures fitting accuracy while avoiding increased computational power consumption due to an excessive number of interpolation points.
[0111] Step S2046: If multiple interpolation points do not meet any of the second conditions, return to traversing each of the multiple interpolation points, traversing the next interpolation point, until the interpolation point meets the first condition or multiple interpolation points meet any of the second conditions, and then determine the interpolation point and multiple related interpolation points as multiple target interpolation points.
[0112] Step S205: Perform cubic spline interpolation based on multiple target interpolation points to obtain a cubic spline interpolation function. Substitute the initial distance into the cubic spline interpolation function to obtain the target distance.
[0113] Specifically, step S205 includes:
[0114] Step S2051: Construct a cubic polynomial for each interpolation interval formed by multiple target interpolation points.
[0115] Specifically, the four target interpolation points can form three interpolation intervals. The core of cubic spline interpolation is to construct an independent cubic polynomial for each interpolation interval, so that the curves of all intervals are spliced together to form a piecewise continuous and smooth fitting curve. The curve corresponding to each interpolation interval can be represented by the cubic polynomial shown in the following equation (6):
[0116] (6)
[0117] To ensure curve smoothness, the function values of the polynomials corresponding to two adjacent interpolation intervals are equal at the target interpolation point (e.g., the distance values of the first and second segments at the second target interpolation point are consistent), and the derivatives are continuous, including continuous first derivatives (smooth slope changes of adjacent segments at the target interpolation point) and continuous second derivatives (natural curvature transition of adjacent segments at the target interpolation point).
[0118] Step S2052: A matrix equation is constructed based on multiple cubic polynomials using free boundary conditions. The order of the matrix equation is consistent with the number of target interpolation points.
[0119] Specifically, the free boundary condition means that the second derivative at both ends of the interpolation interval (i.e., the first and fourth target interpolation points) is 0. Based on the above free boundary condition and the smoothing constraint of the cubic polynomial, a system of linear equations with the second derivative of each target interpolation point as unknowns can be derived. Since there are 4 target interpolation points, the final matrix equation is a 4th-order tridiagonal matrix equation, in which only the main diagonal and the two adjacent subdiagonals have non-zero elements, and all other positions are 0.
[0120] Step S2053: Solve the matrix equation using the chasing method to obtain the cubic spline interpolation function corresponding to each interpolation interval.
[0121] Specifically, the pursuit method is used to solve the equation of the fourth-order tridiagonal matrix, and the coefficients of each segment of the cubic polynomial are obtained. a , b , c , d Once the coefficients of the polynomial are determined, equation (6) above is the cubic spline interpolation function with known coefficients.
[0122] Step S2054: Substitute the initial distance into the cubic spline interpolation function of the interpolation interval where the initial distance is located to obtain the target distance.
[0123] Specifically, based on the interpolation interval in which the initial distance lies, it is substituted as an independent variable into the corresponding cubic spline interpolation function. The calculated function value is the target distance after smoothing optimization. This distance can effectively offset the noise fluctuations and hardware nonlinear errors remaining from Rife interpolation, achieving high-precision ranging.
[0124] Assume the distances between the four target interpolation points are 10, 20, 30, and 40, forming interpolation intervals of 10–20, 20–30, and 30–40, respectively. Each interpolation interval corresponds to a cubic polynomial. If the initial distance is 25m, falling within the 20–30 interpolation interval, then the cubic polynomial for that interval is substituted into the calculation.
[0125] This invention significantly suppresses noise and improves the signal-to-noise ratio by superimposing multiple echo signals reflected from the ranging target. Multiple target spectral lines are selected from the spectrum of the superimposed echo signals for Rife interpolation, effectively mitigating the picket fence error of the Fast Fourier Transform and providing a preliminary calculation of the distance to the ranging target. Target interpolation points are selected from the interpolation points obtained from Rife interpolation based on the initial distance, ensuring that subsequent cubic spline interpolation only fits local points strongly correlated with the initial distance, reducing computational overhead. Global smoothing fitting of multiple target interpolation points is performed using cubic spline interpolation, eliminating system errors introduced by noise fluctuations and hardware nonlinearity. Substituting the initial distance into the cubic spline interpolation function outputs an accurate target distance. Through dual interpolation—locally refined Rife interpolation and globally optimized cubic spline interpolation—efficient and accurate radar ranging is achieved.
[0126] This embodiment also provides a radar ranging device for implementing the above embodiments and preferred embodiments; details already described will not be repeated. As used below, the term "module" can refer to a combination of software and / or hardware that performs a predetermined function. Although the device described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.
[0127] This embodiment provides a radar ranging device, such as... Figure 2 As shown, it includes:
[0128] The superposition module 201 is used to receive multiple echo signals reflected by the ranging target, superimpose the multiple echo signals to obtain a superimposed echo signal.
[0129] The first screening module 202 is used to screen multiple target spectral lines from the spectrum of the superimposed echo signal.
[0130] The first interpolation module 203 is used to perform Rife interpolation based on multiple target spectral lines to obtain multiple interpolation points and calculate the initial distance to the ranging target.
[0131] The second filtering module 204 is used to filter multiple target interpolation points from multiple interpolation points based on the initial distance.
[0132] The second interpolation module 205 is used to perform cubic spline interpolation based on multiple target interpolation points to obtain a cubic spline interpolation function, and to substitute the initial distance into the cubic spline interpolation function to obtain the target distance.
[0133] In some alternative implementations, the target spectral line includes a peak spectral line, a left adjacent spectral line, and a right adjacent spectral line.
[0134] When the amplitude of the left adjacent spectral line is greater than that of the right adjacent spectral line, the initial distance is calculated using the following formula:
[0135]
[0136] When the amplitude of the left adjacent spectral line is less than that of the right adjacent spectral line, the initial distance is calculated using the following formula:
[0137]
[0138] In the formula, distance Indicates the initial distance; Indicates the frequency of the peak spectral line; △R Represents distance grid; This represents the larger of the frequencies of the left and right adjacent spectral lines. This represents the smaller of the frequencies of the left and right adjacent spectral lines. RES It represents the frequency difference between any two adjacent interpolation points.
[0139] In some alternative implementations, the second screening module 204 includes:
[0140] The sorting unit is used to obtain the distance corresponding to each interpolation point and sort multiple interpolation points in descending order according to their distance.
[0141] The first determining unit is used to traverse each sorted interpolation point and determine multiple related interpolation points of the interpolation point.
[0142] The judgment unit is used to determine whether the interpolation point satisfies the first condition based on the initial distance, the distance corresponding to the interpolation point, and the distance corresponding to each relevant interpolation point.
[0143] The second determining unit is used to traverse the next interpolation point until the interpolation point satisfies the first condition when the interpolation point does not meet the first condition, and determine the interpolation point and its corresponding multiple related interpolation points as multiple target interpolation points.
[0144] In some alternative implementations, the first condition is expressed by the following formula:
[0145]
[0146] In the formula, i Indicates the index of the interpolation point; i -1, i +1, i +2 represents the index of the relevant interpolation point; This represents the distance corresponding to the interpolation point; These represent the distances corresponding to the relevant interpolation points; distance Indicates the initial distance; N This represents the total number of interpolation points.
[0147] In some alternative embodiments, the device further includes:
[0148] The determination module is used to determine multiple interpolation points that satisfy any second condition as multiple target interpolation points when none of the multiple interpolation points satisfy the first condition.
[0149] In some alternative implementations, the second condition is expressed by the following formula:
[0150]
[0151]
[0152] In the formula, distance Indicates the initial distance; N This represents the total number of interpolation points.
[0153] In some alternative implementations, the second interpolation module 205 includes:
[0154] The first building unit is used to build a cubic polynomial for each interpolation interval formed by multiple target interpolation points.
[0155] The second building unit is used to construct a matrix equation based on multiple cubic polynomials using free boundary conditions. The order of the matrix equation is consistent with the number of target interpolation points.
[0156] The solving unit is used to solve the matrix equation using the chasing method to obtain the cubic spline interpolation function corresponding to each interpolation interval.
[0157] The third determining unit is used to substitute the initial distance into the cubic spline interpolation function of the interpolation interval where the initial distance is located to obtain the target distance.
[0158] In some alternative implementations, the overlay module 201 includes:
[0159] Alignment unit, used to align the pulse phases of multiple echo signals.
[0160] The superposition unit is used to align multiple echo signals according to their sampling positions and sum them point by point. After amplitude normalization, the superimposed echo signal is obtained.
[0161] The radar ranging device provided in this embodiment of the invention can execute the radar ranging method provided in any embodiment of the invention, and has the corresponding functional modules and beneficial effects for executing the method. Further functional descriptions of the various modules and units described above are the same as in the corresponding embodiments described above, and will not be repeated here.
[0162] Figure 3 This is a schematic diagram of the structure of a radar level gauge provided in an embodiment of the present invention.
[0163] The following is a detailed reference. Figure 3 The diagram illustrates a suitable structural schematic for implementing a radar level gauge in an embodiment of the present invention. The radar level gauge may include a processor (e.g., a central processing unit, graphics processor, etc.) 301, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 302 or a program loaded from memory 308 into random access memory (RAM) 303. The RAM 303 also stores various programs and data required for the operation of the radar level gauge. The processor 301, ROM 302, and RAM 303 are interconnected via a bus 304. An input / output (I / O) interface 305 is also connected to the bus 304.
[0164] Typically, the following devices can be connected to I / O interface 305: input devices 306 including, for example, touchscreens, touchpads, keyboards, mice, cameras, microphones, accelerometers, gyroscopes, etc.; output devices 307 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; memory devices 308 including, for example, magnetic tapes, hard disks, etc.; and communication devices 309. Communication device 309 allows the radar level gauge to communicate wirelessly or wiredly with other devices to exchange data. Although Figure 3 A radar level gauge with various devices is shown, but it should be understood that it is not required to implement or have all of the devices shown, and more or fewer devices may be implemented or have instead.
[0165] In particular, according to embodiments of the present invention, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of the present invention include a computer program product comprising a computer program carried on a non-transitory computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device 309, or installed from a memory 308, or installed from a ROM 302. When the computer program is executed by the processor 301, it performs the functions defined in the radar ranging method of the embodiments of the present invention.
[0166] Figure 3 The radar level gauge shown is merely an example and should not be construed as limiting the functionality and scope of the embodiments of the present invention.
[0167] This invention also provides a computer-readable storage medium. The methods described above according to embodiments of the invention can be implemented in hardware or firmware, or implemented as computer code that can be recorded on a storage medium, or implemented as computer code downloaded via a network and originally stored on a remote storage medium or a non-transitory machine-readable storage medium and then stored on a local storage medium. Thus, the methods described herein can be processed by software stored on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. The storage medium can be a magnetic disk, optical disk, read-only memory, random access memory, flash memory, hard disk, or solid-state drive, etc.; further, the storage medium can also include combinations of the above types of memory. It is understood that computers, processors, microprocessor controllers, or programmable hardware include storage components capable of storing or receiving software or computer code. When the software or computer code is accessed and executed by the computer, processor, or hardware, the radar ranging method shown in the above embodiments is implemented.
[0168] A portion of this invention can be applied as a computer program product, such as computer program instructions, which, when executed by a computer, can invoke or provide the methods and / or technical solutions according to the invention through the operation of the computer. Those skilled in the art will understand that the forms in which computer program instructions exist in a computer-readable medium include, but are not limited to, source files, executable files, installation package files, etc. Correspondingly, the ways in which computer program instructions are executed by a computer include, but are not limited to: the computer directly executing the instructions, or the computer compiling the instructions and then executing the corresponding compiled program, or the computer reading and executing the instructions, or the computer reading and installing the instructions and then executing the corresponding installed program. Here, the computer-readable medium can be any available computer-readable storage medium or communication medium accessible to a computer.
[0169] Although embodiments of the invention have been described in conjunction with the accompanying drawings, those skilled in the art can make various modifications and variations without departing from the spirit and scope of the invention, and such modifications and variations all fall within the scope defined by the appended claims.
Claims
1. A radar ranging method, characterized in that, The method includes: Receive multiple echo signals reflected from the ranging target, and superimpose the multiple echo signals to obtain a superimposed echo signal; Multiple target spectral lines are selected from the spectrum of the superimposed echo signal; Based on the multiple target spectral lines, Rife interpolation is performed to obtain multiple interpolation points, and the initial distance to the ranging target is calculated. Multiple target interpolation points are selected from the plurality of interpolation points based on the initial distance; Based on the multiple target interpolation points, cubic spline interpolation is performed to obtain a cubic spline interpolation function. The initial distance is then substituted into the cubic spline interpolation function to obtain the target distance.
2. The method according to claim 1, characterized in that, The target spectral line includes a peak spectral line, a left adjacent spectral line, and a right adjacent spectral line. When the amplitude of the left adjacent spectral line is greater than the amplitude of the right adjacent spectral line, the initial distance is calculated using the following formula: When the amplitude of the left adjacent spectral line is less than the amplitude of the right adjacent spectral line, the initial distance is calculated using the following formula: In the formula, distance Indicates the initial distance; Indicates the frequency of the peak spectral line; △R Represents distance grid; This represents the larger of the frequencies of the left and right adjacent spectral lines. This represents the smaller of the frequencies of the left and right adjacent spectral lines. RES It represents the frequency difference between any two adjacent interpolation points.
3. The method according to claim 1, characterized in that, The step of selecting multiple target interpolation points from the multiple interpolation points based on the initial distance includes: Obtain the distance corresponding to each interpolation point, and sort the multiple interpolation points in descending order according to their distances; Traverse each sorted interpolation point and determine multiple related interpolation points of the interpolation point; Based on the initial distance, the distance corresponding to the interpolation point, and the distance corresponding to each relevant interpolation point, determine whether the interpolation point satisfies the first condition; If the interpolation point does not meet the first condition, the next interpolation point is traversed until the interpolation point meets the first condition. The interpolation point and its corresponding multiple related interpolation points are then determined as the multiple target interpolation points.
4. The method according to claim 3, characterized in that, The first condition is expressed by the following formula: In the formula, i Indicates the index of the interpolation point; i -1, i +1, i +2 represents the index of the relevant interpolation point; This represents the distance corresponding to the interpolation point; These represent the distances corresponding to the relevant interpolation points; distance Indicates the initial distance; N This represents the total number of interpolation points.
5. The method according to claim 4, characterized in that, The method further includes: If none of the multiple interpolation points satisfy the first condition, the multiple interpolation points that satisfy any of the second conditions are determined as the multiple target interpolation points.
6. The method according to claim 5, characterized in that, The second condition is expressed by the following formula: In the formula, distance Indicates the initial distance; N This represents the total number of interpolation points.
7. The method according to claim 1, characterized in that, The process of obtaining a cubic spline interpolation function by performing cubic spline interpolation based on the multiple target interpolation points, and substituting the initial distance into the cubic spline interpolation function to obtain the target distance includes: Construct a cubic polynomial for each interpolation interval formed by the plurality of target interpolation points; A matrix equation is constructed based on multiple cubic polynomials using free boundary conditions, and the order of the matrix equation is consistent with the number of target interpolation points. The matrix equation is solved by the chasing method to obtain the cubic spline interpolation function corresponding to each interpolation interval; The target distance is obtained by substituting the initial distance into the cubic spline interpolation function of the interpolation interval in which the initial distance lies.
8. The method according to claim 1, characterized in that, The superposition of the multiple echo signals to obtain the superimposed echo signal includes: Align the pulse phases of the plurality of echo signals; The multiple echo signals are aligned according to their sampling positions and then summed point by point. The superimposed echo signal is obtained by normalizing the amplitude.
9. A radar ranging device, characterized in that, The device includes: The superposition module is used to receive multiple echo signals reflected by the ranging target and superimpose the multiple echo signals to obtain a superimposed echo signal. The first screening module is used to screen multiple target spectral lines from the spectrum of the superimposed echo signal; The first interpolation module is used to perform Rife interpolation based on the multiple target spectral lines to obtain multiple interpolation points, and to calculate the initial distance to the ranging target; The second filtering module is used to filter multiple target interpolation points from the multiple interpolation points based on the initial distance; The second interpolation module is used to perform cubic spline interpolation based on the multiple target interpolation points to obtain a cubic spline interpolation function, and to substitute the initial distance into the cubic spline interpolation function to obtain the target distance.
10. A radar level gauge, characterized in that, include: A memory and a processor are communicatively connected, the memory stores computer instructions, and the processor executes the radar ranging method according to any one of claims 1 to 8 by executing the computer instructions.
Citation Information
Patent Citations
Laser radar angle resolution enhancement method and system
CN120908776A
Forward-looking synthetic aperture radar processing
US8305253B1