A method for calibrating distance measurement results of a distance measurement sensor

Abstract

The invention relates to the field of distance measurement calibration, and discloses a distance measurement result calibration method of a distance measurement sensor, comprising the following steps: step 1: arranging sensors in a distance measurement area to collect distance measurement data, and collecting environmental factor data at the same time; step 2: obtaining the collected environmental data, converting the collected environmental data into a machine-readable language after preprocessing, constructing an interference factor model, and describing the variation trend of the distance measurement data and the environmental factors; step 3: analyzing the parameters of the interference factor model, and determining the influence of each environmental factor on the distance measurement result through machine learning; by establishing the interference factor model and a calibration strategy, by evaluating the influence of different environmental factors, and taking corresponding calibration measures according to the size of the interference factor model, the negative impact of the environmental factors on the distance measurement result can be effectively reduced, so that the distance measurement sensor can still provide more accurate distance measurement results under different environmental conditions, and is helpful to accurately measure the target distance.

Description

Technical Field

[0001] The invention relates to the technical field of distance measurement calibration, and in particular to a distance measurement result calibration method of a distance measurement sensor. Background Art

[0002] The distance measurement calibration of the distance measurement sensor is used to ensure the accuracy and reliability of the sensor's distance measurement. Calibration is a very important process because the performance of the sensor may vary in different environments and applications, and calibration can help eliminate or correct these changes to ensure that the sensor provides accurate distance measurement, which is crucial in the fields of navigation, obstacle avoidance, automated control, and scientific research.

[0003] However, the existing distance measurement sensor distance measurement result calibration method still has certain shortcomings, such as:

[0004] 1. Traditional distance measurement technology is often interfered and affected by a variety of environmental factors, which may have an adverse effect on the accuracy of the distance measurement results, resulting in deviations, distortions or even complete errors in the results. However, when facing these influencing factors, traditional distance measurement technology often lacks effective means and methods to judge and identify the degree of influence of these factors on the distance measurement results, and to identify which results are inaccurate. This makes traditional distance measurement technology incapable of coping with complex and changing environmental conditions, and it is difficult to ensure the accuracy and reliability of its measurement results;

[0005] 2. The quality of sensor signals often fluctuates or is interfered with in different transmission environments. This phenomenon is very common in many real-world scenarios, especially in complex environments where signal quality is more difficult to guarantee. These fluctuations and interferences may cause signal delays, data jitter, or even complete inability to measure distance. Summary of the invention

[0006] In view of the above shortcomings of the prior art, the present invention provides a distance measurement sensor ranging result calibration method, which can effectively solve the problem that the prior art is easily disturbed by environmental factors, resulting in result deviation, distortion or even error, but it is difficult to judge and identify the degree of influence and identify inaccurate results, making it difficult to ensure the measurement accuracy and reliability under complex environmental conditions. The sensor signal quality fluctuates or is disturbed in different transmission environments, resulting in signal delay, data jump or even inability to measure distance.

[0007] To achieve the above objectives, the present invention is implemented by the following technical solutions. The present invention discloses a method for calibrating the ranging result of a ranging sensor, comprising the following steps:

[0008] Step 1: Arrange sensors in the area to be measured to collect distance measurement data and environmental factor data;

[0009] Step 2: Obtain the collected environmental data, convert it into machine-readable language after preprocessing, build an interference factor model, and describe the changing trends of ranging data and environmental factors;

[0010] Step 3: Analyze the parameters of the interference factor model and determine the influence of each environmental factor on the ranging result through machine learning;

[0011] Step 4: Based on the established model and the influence of interference factors, evaluate the influence of current environmental factors on ranging;

[0012] Step 5: Obtain the influence coefficient and determine whether the interference factor can be corrected;

[0013] Step 6: If it is determined that the correction process can be performed, then determine whether the current ranging signal environment is too different from the standard value, otherwise ignore it;

[0014] Step 7: If the environment difference is large, the signal enhancement strategy is executed, and the ranging data is processed accordingly according to the selected strategy;

[0015] Step 8: In actual ranging, monitor and obtain current environmental data, substitute the current environmental data into the established interference factor model, calculate the predicted ranging result, calculate the residual between the actual ranging value and the predicted value, perform linear correction according to the size of the residual, and use the corrected ranging value as the final ranging result.

[0016] Furthermore, the environmental factor data in step 1 include: temperature, humidity, interference signal and light intensity.

[0017] Furthermore, the interference factor model in step 2 is expressed as:

[0018] ;

[0019] Among them, R represents the predicted ranging value, , ... represents several environmental factors, a, b and c represent parameters to be determined, and represent the linear influence of each environmental factor on the ranging result.

[0020] Furthermore, in step 4, the influence of the current environmental factors on the ranging is evaluated by estimating the parameters of the selected model to obtain the model weight coefficient.

[0021] Furthermore, in step 6, whether the current ranging signal environment is too different from the standard value is determined, and the judgment indicators include: signal strength, noise level and the number of interference sources, and a threshold is set for the indicator. When the signal environment indicator exceeds this threshold, it is considered that the environment difference is large.

[0022] Furthermore, the signal enhancement strategy in step 7 includes increasing the number of sampling times, filtering, changing the operating frequency, multipath reflection and increasing the transmission power.

[0023] Furthermore, after the signal enhancement strategy in step 7 is applied, the ranging result is monitored in real time to determine whether there is a change in the ranging result. If it is, it continues to run according to the predetermined settings. Otherwise, the problem information is reported to the management.

[0024] Furthermore, the calculation formula for the linear correction in step 8 is:

[0025] W = Gk*M;

[0026] Among them, W represents the corrected distance measurement value, G represents the actual distance measurement value, k represents the calibration coefficient, and M represents the residual.

[0027] Furthermore, during the actual distance measurement process of step 8, the accuracy of the actual interval measurement is calculated in real time, and the calculation formula is:

[0028] ;

[0029] In the formula, P represents the accuracy coefficient, G is the noise amplitude, and A is the measured signal amplitude. is the signal rising or falling edge time, is the sampling time slot uncertainty, TBA is the time base accuracy, and MI is the measurement time interval.

[0030] Compared with the known public technology, the technical solution provided by the present invention has the following beneficial effects:

[0031] 1. The present invention can effectively reduce the negative impact of environmental factors on the ranging results by establishing an interference factor model and a calibration strategy, evaluating the influence of different environmental factors, and taking corresponding calibration measures according to their magnitudes, so that the ranging sensor can still provide more accurate ranging results under different environmental conditions, which is helpful to accurately measure the target distance.

[0032] 2. The present invention uses a signal enhancement strategy to cope with signal quality fluctuations and interference in different transmission environments. This means that even in complex or noisy environments, the sensor can maintain stable ranging performance, reduce signal jitter and fluctuations, and adjust the ranging strategy according to changes in the actual ranging environment, which enables the sensor system to adapt to different application scenarios and environmental conditions without manual recalibration or parameter adjustment. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the prior art descriptions. Obviously, the drawings described below are only some embodiments of the present invention, and for ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0034] Figure 1 The present invention is a flow chart of a method for calibrating the ranging result of a ranging sensor. DETAILED DESCRIPTION

[0035] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0036] The present invention will be further described below in conjunction with the embodiments.

[0037] Embodiment 1: A distance measurement result calibration method of a distance measurement sensor in this embodiment, such as Figure 1 As shown, the following steps are included:

[0038] Step 1: Arrange sensors in the area to be measured to collect distance measurement data and environmental factor data, including temperature, humidity, interference signals and light intensity;

[0039] Step 2: Obtain the collected environmental data, convert it into machine-readable language after preprocessing, and construct an interference factor model to describe the changing trend of ranging data and environmental factors. The interference factor model is expressed as:

[0040] ;

[0041] Among them, R represents the predicted ranging value, , ... represents several environmental factors, a, b and c represent parameters to be determined, and represent the linear influence of each environmental factor on the ranging result;

[0042] Step 3: Analyze the parameters of the interference factor model and determine the influence of each environmental factor on the ranging result through machine learning;

[0043] Step 4: According to the established model and the influence of interference factors, the influence of current environmental factors on ranging is evaluated, and the influence of current environmental factors on ranging is evaluated by estimating the parameters of the selected model to obtain the model weight coefficient;

[0044] Step 5: Obtain the influence coefficient and determine whether the interference factor can be corrected;

[0045] Step 6: If it is determined that the correction process can be performed, then determine whether the current ranging signal environment is too different from the standard value, otherwise ignore it. The judgment indicators include: signal strength, noise level and number of interference sources, and set a threshold for the indicator. When the signal environment indicator exceeds this threshold, it is considered that the environment difference is large;

[0046] Step 7: If the environmental difference is large, the signal enhancement strategy is executed. According to the selected strategy, the ranging data is processed accordingly. The signal enhancement strategy includes increasing the number of sampling times, filtering, changing the operating frequency, multipath reflection and increasing the transmission power;

[0047] Step 8: In actual ranging, monitor and obtain current environmental data, substitute the current environmental data into the established interference factor model, calculate the predicted ranging result, calculate the residual between the actual ranging value and the predicted value, perform linear correction according to the size of the residual, and use the corrected ranging value as the final ranging result.

[0048] In the specific implementation of this embodiment, the traditional distance measurement technology is improved, the distance measurement error caused by environmental factors and signal quality fluctuations is reduced, and the performance and reliability of the distance measurement sensor are improved. Due to the improvement of the performance and reliability of the distance measurement sensor, these methods expand the application field of the distance measurement technology. They can be more reliably applied to many fields such as automatic navigation, intelligent manufacturing, remote sensing mapping, and intelligent transportation systems.

[0049] Embodiment 2: In this embodiment, the calculation formula of the linear correction in step 8 is:

[0050] W = Gk*M;

[0051] Among them, W represents the corrected distance measurement value, G represents the actual distance measurement value, k represents the calibration coefficient, and M represents the residual.

[0052] The choice of calibration coefficient k depends on the size of the residual and the system performance requirements. If the residual is positive, that is, the actual distance value is higher than the predicted value, then k will be a positive number; if the residual is negative, that is, the actual distance value is lower than the predicted value, then k will be a negative number. The absolute value of the calibration coefficient should be adjusted as needed to achieve the desired calibration effect.

[0053] Embodiment 3: In this embodiment, a method for calculating the accuracy of actual interval measurement is provided. In the actual distance measurement process of step 8, the accuracy of actual interval measurement is calculated in real time, and the calculation formula is:

[0054] ;

[0055] In the formula, P represents the accuracy coefficient, G is the noise amplitude, and A is the measured signal amplitude. is the signal rising or falling edge time, is the sampling time slot uncertainty, TBA is the time base accuracy, and MI is the measurement time interval.

[0056] A digital signal generator is used to generate a standard distance signal with a fixed delay with a ground-based special distance measurement calibration device. The signal enters the ground-based special distance measurement calibration device and a digital oscilloscope respectively through a directional coupler and a circulator. The standard delay time measured by the digital oscilloscope is used to calibrate the ground-based special distance measurement calibration device to measure the fixed delay and obtain the distance.

[0057] In summary, by establishing an interference model and a calibration strategy, the present invention can evaluate the influence of different environmental factors on the ranging results and take corresponding calibration measures to reduce them. The ranging sensor can provide more accurate ranging results under different environmental conditions, providing favorable conditions for accurately measuring the target distance.

[0058] At the same time, by applying signal enhancement strategies, the present invention can cope with fluctuations and interference in signal quality under different transmission environments. Even in complex or noisy environments, the sensor can maintain stable ranging performance and reduce signal jitter and fluctuations, so that the sensor system can adapt to different application scenarios and various environmental conditions without manual recalibration or adjustment of parameters.

[0059] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, a person skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements will not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for calibrating the ranging result of a ranging sensor, characterized in that: The following steps are involved: Step 1: Arrange sensors in the area to be measured to collect distance measurement data and environmental factor data; Step 2: Obtain the collected environmental data, convert it into machine-readable language after preprocessing, build an interference factor model, and describe the changing trends of ranging data and environmental factors; Step 3: Analyze the parameters of the interference factor model and determine the influence of each environmental factor on the ranging results through machine learning; Step 4: Based on the established model and the influence of interference factors, evaluate the influence of current environmental factors on ranging; Step 5: Obtain the influence coefficient and determine whether to correct the interference factor; Step 6: If it is determined that the correction process is to be performed, then determine whether the current ranging signal environment is too different from the standard value, otherwise ignore it; Step 7: If the environmental difference is judged to be greater than the standard value, the signal enhancement strategy is executed, and the ranging data is processed accordingly according to the selected strategy; Step 8: In actual ranging, monitor and obtain current environmental data, substitute the current environmental data into the established interference factor model, calculate the predicted ranging result, calculate the residual between the actual ranging value and the predicted value, perform linear correction according to the size of the residual, and use the corrected ranging value as the final ranging result; In step 6, whether the current ranging signal environment is too different from the standard value is determined, and the judgment indicators include: signal strength, noise level and the number of interference sources, and a threshold is set for the indicator. When the signal environment indicator exceeds the threshold, it is considered that the environment difference is large; The signal enhancement strategy in step 7 includes increasing the number of sampling times, filtering, changing the operating frequency, multipath reflection and increasing the transmission power; After the signal enhancement strategy in step 7 is applied, the ranging result is monitored in real time to determine whether a change value occurs. If so, the system continues to operate according to the preset settings. Otherwise, the problem information is reported to the management. The calculation formula of the linear correction in step 8 is: W = Dk*M; Where W represents the corrected distance measurement value, D represents the actual distance measurement value, k represents the calibration coefficient, and M represents the residual; During the actual distance measurement process of step 8, the accuracy of the actual interval measurement is calculated in real time, and the calculation formula is: Where P represents the accuracy coefficient, G is the noise amplitude, A is the measured signal amplitude, t m is the signal rising or falling edge time, t j is the sampling time slot uncertainty, TBA is the time base accuracy, and MI is the measurement time interval.

2. A distance measurement result calibration method for a distance measurement sensor according to claim 1, characterized in that: The environmental factor data in step 1 include: temperature, humidity, interference signal and light intensity.

3. A distance measurement sensor distance measurement result calibration method according to claim 1, characterized in that: The interference factor model in step 2 is expressed as: R=a*ht1+b*ht2+...+c*ht n ; Among them, R represents the predicted distance value, ht1, ht2...ht n represents several environmental factors, a, b and c represent parameters to be determined, and represent the linear influence of each environmental factor on the ranging result.

4. A distance measurement sensor distance measurement result calibration method according to claim 1, characterized in that: In step 4, the influence of the current environmental factors on the ranging is evaluated by estimating the parameters of the selected model to obtain the model weight coefficient.

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