Consistency detection method of observation results, program product, equipment and medium

Abstract

The invention provides a consistency detection method for observation results, a program product, equipment and a medium, and the method comprises the steps: obtaining a plurality of redundant observation results of a movable platform for the same observed quantity in the operation process of the movable platform; determining test statistics and a rejection domain of each observation result in a hypothesis test theory; the original hypothesis in the hypothesis testing theory is that all observation results are normal and obey preset distribution; the standby assumption is that an abnormal observation result deviating from preset distribution exists; and for each observation result, determining that the observation result of which the test statistics fall into the rejection domain is an abnormal observation result, and determining that the observation result of which the test statistics do not fall into the rejection domain passes consistency detection. Therefore, an abnormal observation result and a normal observation result are distinguished in real time. And through automatic data acquisition and processing, the working efficiency is improved, and personal errors are reduced. And the detection requirements of various sensors in different environments are met, and long-term stable operation of the movable platform is ensured.

Description

Technical Field

[0001] The present application relates to the field of mobile platform technology, and in particular to a method, program product, device and medium for detecting consistency of observation results. Background Art

[0002] The precise positioning and navigation of mobile platforms rely on the various sensors they carry to sense the surrounding environment and rigorous algorithm processing. At present, in order to improve the reliability of observation results, mobile platforms can use multiple sensors and / or multiple processing algorithms to perform redundant observations on the same observation quantity, obtain multiple redundant observation results, and perform consistency detection on these redundant observation results. In related technologies, the consistency detection of multiple observation results mostly relies on offline manual comparison and basic data analysis, which has problems of low efficiency and poor accuracy. It is also difficult to perform consistency detection on the redundant observation results of multiple observation quantities at the same time, and it is impossible to effectively deal with the impact of environmental changes on sensor performance. Summary of the invention

[0003] The purpose of the embodiments of the present application is to provide a consistency detection method, a program product, a device and a medium to solve at least one of the above technical problems.

[0004] A first aspect of an embodiment of the present application provides a method for detecting consistency of an observation result, the method being used to detect consistency of multiple redundant observation results of the same observation; the method comprising: During the operation of the movable platform, obtaining a plurality of redundant observation results of the movable platform on the same observation quantity; Determine the test statistic and rejection region of each observation result in the hypothesis test theory; wherein the null hypothesis in the hypothesis test theory is: all observation results are normal and obey the preset distribution; the alternative hypothesis in the hypothesis test theory is: there are abnormal observation results that deviate from the preset distribution; For each of the observation results, it is determined that the observation result whose test statistic falls into the rejection region is an abnormal observation result, and it is determined that the observation result whose test statistic does not fall into the rejection region passes the consistency test.

[0005] In the above implementation process, hypothesis testing theory is used to perform consistency detection on multiple observation results obtained in real time by the mobile platform during operation, so as to distinguish abnormal observation results from normal observation results in real time, which can effectively improve the reliability and stability of the observation results. Through the automated data collection and processing process, manual intervention is greatly reduced, work efficiency is improved, human errors are reduced, and the accuracy of consistency detection is improved. And the consistency detection of redundant observation results of multiple observation quantities can be realized simultaneously through automatic algorithms. In addition, since this method is applicable to the operation process of the mobile platform, it can meet the detection needs of various types of sensors and different environmental conditions, and can timely identify sensors with poor environmental robustness, timely discover problems and correct them, to ensure the long-term stable operation of the mobile platform.

[0006] Furthermore, before determining the test statistic and the rejection region of each of the observation results in the hypothesis testing theory, the method further comprises: Obtaining an observation difference between every two of the observation results in the plurality of observation results, to obtain a plurality of the observation differences; It is determined that at least one of the observed differences is less than a preset difference threshold.

[0007] In the above implementation process, when it is determined that at least one observation difference is less than the difference threshold, the hypothesis test theory is used to perform consistency test, which can ensure that the consistency test result is effective and reliable.

[0008] Furthermore, the method further comprises: If it is determined that all the observed differences are greater than the difference threshold, it is determined that all the observed results are invalid.

[0009] In the above implementation process, by comparing the observation differences between the observation results pairwise, and using hypothesis testing theory to perform consistency detection when at least one observation difference is less than the difference threshold, and determining that all observation results are invalid when all observation differences are greater than the difference threshold, the validity of the consistency detection results can be ensured.

[0010] Further, the observed quantity includes the attitude of the movable platform; the observation result is an attitude angle; the multiple attitude angles include any multiple of an attitude angle obtained based on dual antenna direction finding carried by the movable platform, an attitude angle obtained based on visual positioning, an attitude angle measured based on a magnetic compass carried by the movable platform, and an attitude angle obtained based on speed observation and solution; the step of obtaining the observation difference between each two of the multiple observation results includes: Convert each posture angle into a two-dimensional unit vector; For every two posture angles, the dot product of the corresponding two two-dimensional unit vectors is calculated, and the angle between the two two-dimensional unit vectors is determined based on the dot product; wherein the angle is the observed difference between the two posture angles.

[0011] In the above implementation process, by converting the posture angle into a two-dimensional unit vector, the observed difference between the two posture angles is obtained by using the relationship between the dot product between the vectors and the angle, and by comparing the angle differences between the posture angles pairwise, and then using the hypothesis testing theory to perform consistency detection on all posture angles when at least one angle difference is less than the angle threshold, the validity of the consistency detection result can be ensured.

[0012] Furthermore, the movable platform is also equipped with a gyroscope; the method further comprises: If it is determined that all of the observed differences are greater than the difference threshold, all of the attitude angles are determined to be invalid, and the gyroscope is used to determine the target attitude angle of the drone.

[0013] In the above implementation process, if it is determined that the angle differences between all attitude angles are greater than the angle threshold, then all attitude angles are determined to be invalid, and the angular velocity sensed by the gyroscope is integrated over time to obtain the target attitude angle, thereby ensuring the observation accuracy of the movable platform attitude observation quantity and providing a reliable target attitude angle for subsequent positioning and navigation.

[0014] Furthermore, the determination of the test statistic and the rejection region of each of the observation results in the hypothesis testing theory includes: determining an expected value based on a plurality of said observations; For each of the observation results, a test statistic of the observation result is calculated based on the value of the observation result, the standard deviation, the number of observations, and the expected value; The critical value of the rejection region is determined based on a preset significance level and the redundant number of the observation results to obtain the rejection region.

[0015] In the above implementation process, multiple observations of the same observation are used to determine the expected value, and the rejection region is determined based on the significance level and the number of redundancies. Then, whether the observation deviates from the expected value is evaluated based on whether the test statistic of each observation falls into the rejection region, so as to find out abnormal observations.

[0016] Furthermore, the movable platform is equipped with a sensor for observing the observed quantity; the plurality of observation results include sensor observation results obtained by observing with the sensor; and the method further comprises: If the sensor is a first type of sensor, determining a preset standard deviation of the sensor as the standard deviation of the sensor observation result; If the sensor is a second type of sensor, the real-time sample standard deviation of the sensor is determined to be the standard deviation of the sensor observation result; wherein the environmental robustness of the first type of sensor is better than the environmental robustness of the second type of sensor.

[0017] In the above implementation process, different standard deviations of sensor observation results are determined based on the environmental robustness of the sensor, which balances the efficiency and accuracy of consistency detection.

[0018] A second aspect of an embodiment of the present application provides a computer program product, wherein the computer program product includes a computer program, and when the computer program is executed by a processor, any method described in the first aspect is implemented.

[0019] According to a third aspect of the present application, an electronic device is provided, the electronic device comprising: processor; a memory for storing processor-executable instructions; Wherein, when the processor calls the executable instruction, it implements the operation of any method described in the first aspect.

[0020] A fourth aspect of an embodiment of the present application provides a computer-readable storage medium having computer instructions stored thereon, which, when executed by a processor, implement the steps of any method described in the first aspect. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the embodiments of the present application will be briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0022] Figure 1 A schematic diagram of a flow chart of a method for detecting consistency of observation results provided in an embodiment of the present application; Figure 2 A schematic diagram of a flow chart of another method for detecting consistency of observation results provided in an embodiment of the present application; Figure 3 A schematic diagram of a flow chart of another method for detecting consistency of observation results provided in an embodiment of the present application; Figure 4 A schematic diagram of a flow chart of another method for detecting consistency of observation results provided in an embodiment of the present application; Figure 5 A hardware structure diagram of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0023] The technical solutions in the embodiments of the present application will be described below in conjunction with the drawings in the embodiments of the present application.

[0024] It should be noted that similar reference numerals and letters represent similar items in the following drawings, so once an item is defined in one drawing, it does not need to be further defined and explained in the subsequent drawings. At the same time, in the description of this application, the terms "first", "second", etc. are only used to distinguish the description and cannot be understood as indicating or implying relative importance.

[0025] A mobile platform refers to any device that can move, including but not limited to land vehicles, water vehicles, air vehicles, and other types of mobile vehicles. As an example, a mobile platform can be an unmanned aerial vehicle (UAV), an unmanned vehicle, a robot, etc. A mobile platform is usually equipped with a variety of sensors to sense the surrounding environment and obtain observation results. The mobile platform can perform redundant observations on the same observation to improve the reliability of the observation results.

[0026] Specifically, multiple redundant observation results of the same observation quantity need to pass consistency detection to ensure the credibility of the observation results. In the related art, the consistency detection of multiple redundant observation results in the mobile platform mostly relies on offline manual comparison and basic data analysis. Specifically, multiple redundant observation results of the same observation quantity are obtained when the mobile platform is stationary, and then the multiple observation results are manually compared to see whether they meet the consistency requirements. However, the detection method of manual comparison has the problems of low efficiency and poor accuracy. Furthermore, since some observation results are obtained by sensing by sensors carried by the mobile platform, the sensors may be affected by complex environments, resulting in fluctuations in the observation results. The observation results sensed by the sensor pass the consistency test under the condition that the mobile platform is stationary, which does not mean that it can also pass the consistency test in a complex and fluctuating environment. Therefore, offline manual comparison is difficult to meet the detection requirements under different usage conditions. Moreover, manual comparison can only detect the consistency of a single sensor or a single observation quantity, and cannot simultaneously perform consistency detection of redundant observation results of multiple observation quantities.

[0027] In order to solve at least one of the above technical problems, the present application provides a method for consistency detection of observation results. The method is used to realize online consistency detection of multiple redundant observation results of the same observation quantity. The method can be executed by a mobile platform, or by other computing devices such as a server or a control device that is connected to the mobile platform. The present application does not limit the execution subject of the method. See Figure 1 , the consistency detection method includes steps 110 to 130.

[0028] Step 110: During the operation of the movable platform, a plurality of redundant observation results of the movable platform on the same observation quantity are obtained.

[0029] Different from the above-mentioned consistency detection scheme when the movable platform is stationary, the consistency detection method provided in this embodiment is executed when the movable platform is in operation. For example, if the movable platform is an aircraft, the method can be triggered to execute when the aircraft is powered on, taking off, or flying. If the movable platform is a robot, the method can be triggered to execute when the robot starts to move.

[0030] An observation refers to a specific physical quantity, state or feature that can be obtained and recorded. An observation result refers to a quantified result of an observation. The observation may refer to the state quantity of the movable platform itself during operation, such as speed, acceleration, and posture, etc.; or it may refer to the characteristic quantity of the environment in which the movable platform is operating, such as temperature, humidity, obstacle distance, etc. The observation result may be obtained in different ways. As an example, the observation result may be obtained based on the sensing of the sensor mounted on the movable platform. For example, speed data sensed by a speed sensor, attitude data sensed by a magnetic compass, depth data sensed by a laser radar, etc. As another example, the observation result may be obtained by processing and solving the operation data of the movable platform through an algorithm. For example, the heading data calculated by the moving speed of the movable platform, etc. This application does not limit the source of the observation result. Exemplarily, the consistency detection method provided in this embodiment may be executed periodically. The execution cycle may match the sensing cycle of the sensor and / or the solving cycle of the algorithm. Then the multiple redundant observation results obtained in step 110 refer to the observation results obtained in real time by the movable platform during operation.

[0031] Exemplarily, multiple observation results obtained for the same observation quantity can be stored in the form of an array. Specifically, each array corresponds to an observation quantity. The array includes multiple elements, each element being an observation result of the observation quantity corresponding to the array. If there are observation results of multiple observation quantities on the movable platform that need to be checked for consistency at the same time, then an array can be set for each observation quantity, and each array stores multiple observation results of the corresponding observation quantity. Subsequently, the observation results in multiple arrays can be read through multiple parallel threads or processes, and the observation results of multiple observation quantities can be checked for consistency at the same time.

[0032] As an example, the array stores the latest observation result of the same observation value in the current execution cycle. As another example, each observation result stored in the array is an observation result after noise reduction processing to reduce the interference of environmental noise on consistency detection.

[0033] Step 120: Determine the test statistic and rejection region of each observation result in the hypothesis testing theory; wherein the null hypothesis in the hypothesis testing theory is: all observation results are normal and obey the preset distribution; the alternative hypothesis in the hypothesis testing theory is: there are abnormal observation results that deviate from the preset distribution.

[0034] Step 130: For each observation result, determine that the observation result whose test statistic falls into the rejection region is an abnormal observation result, and determine that the observation result whose test statistic does not fall into the rejection region passes the consistency test.

[0035] Hypothesis Testing Theory is a statistical method used to determine whether sample data supports the null hypothesis H0. It can be used to determine whether the differences between samples and samples, or between samples and the population, are caused by sampling errors or essential differences. It compares the deviation between sample data and expected values ​​to assess whether the deviation is large enough, thereby rejecting the null hypothesis H0 and accepting the alternative hypothesis H1, or reaching the conclusion that the null hypothesis H0 cannot be rejected.

[0036] This embodiment uses hypothesis testing theory to verify the consistency of multiple observation results of the same observation. Then first set the null hypothesis H0 in the hypothesis testing theory as: all observation results of the same observation are normal and obey the preset distribution. The preset distribution may include but is not limited to the normal distribution. And the alternative hypothesis H1 in the hypothesis testing theory can be set as: there are abnormal observations that deviate from the preset distribution. Then compare the deviation between each observation and the expected value. If the deviation corresponding to one or some observations is large enough, the null hypothesis H0 can be rejected and the alternative hypothesis H1 can be accepted. That is, it is determined that the observations with sufficiently large deviations deviate from the preset distribution and are abnormal observations. The abnormal observations do not pass the consistency test. If the deviation corresponding to one or some observations is small, the null hypothesis H0 cannot be rejected. That is, it is determined that the observations with smaller deviations obey the preset distribution and are normal observations. The normal observations pass the consistency test.

[0037] Specifically, when comparing the deviation between each observation result and the expected value and evaluating whether the deviation is large enough, the test statistic and the corresponding rejection domain of each observation result can be determined. Then, for each observation result, it can be determined whether its corresponding test statistic falls into the corresponding rejection domain. If the test statistic of the observation result falls into the rejection domain, it means that the observation result does not support the establishment of the original hypothesis H0, so it can be determined that the observation result is an abnormal observation result that deviates from the preset distribution. If the test statistic of the observation result does not fall into the rejection domain, that is, it falls into the acceptance domain, it means that the observation result supports the establishment of the original hypothesis H0, so it can be determined that the observation result passes the consistency test and is a credible normal observation result that obeys the preset distribution.

[0038] Optionally, after determining abnormal observations and normal observations from all observations, the abnormal observations that fail the consistency test can be excluded, and the target observation of the observation quantity can be determined based on the observations that pass the consistency test. For example, the average or weighted average of all observations that pass the consistency test can be determined as the target observation of the observation quantity.

[0039] It can be seen that the consistency detection method of an observation result provided by the present application utilizes hypothesis testing theory to perform consistency detection on multiple observation results obtained in real time by a movable platform during operation, so as to distinguish abnormal observation results from normal observation results in real time, which can effectively improve the reliability and stability of the observation results. Through the automated data acquisition and processing process, manual intervention is greatly reduced, work efficiency is improved, human errors are reduced, and the accuracy of consistency detection is improved. And the consistency detection of redundant observation results of multiple observation quantities can be realized simultaneously through an automatic algorithm. In addition, since this method is applicable to the operation process of a movable platform, it can meet the detection needs of various types of sensors and under different environmental conditions, and can timely identify sensors with poor environmental robustness, timely discover problems and make corrections, to ensure the long-term stable operation of the movable platform.

[0040] The following is a detailed description of steps 110 to 130.

[0041] According to some embodiments of the present application, after executing step 110 and before executing step 120, the method may further include: Figure 2 Steps 210 to 220 are shown.

[0042] Step 210: Obtain an observation difference between every two of the observation results in the plurality of observation results to obtain a plurality of the observation differences; Step 220: Determine whether at least one of the observed differences is less than a preset difference threshold.

[0043] For example, if there are N observations of the same observation, and the observation difference between every two observations is compared, then a total of Among all the obtained observed differences, if at least one observed difference is less than a preset difference threshold, then continue to perform steps 120 to 130.

[0044] It can be understood that the existence of at least one observation difference less than the difference threshold indicates that there are at least two observations that are relatively close among all the observations. This is a judgment to determine whether the consistency test using hypothesis testing theory is effective, and it is also a preliminary judgment on the consistency of all observations. When it is determined that at least one observation difference is less than the difference threshold, the consistency test is performed using hypothesis testing theory to ensure that the consistency test result is effective and reliable. Optionally, the difference threshold can be a chi-square distribution critical value.

[0045] It is worth noting that among all the observation differences obtained, even if some observation differences are greater than the difference threshold, in the subsequent consistency test, all observation results will still be tested for consistency together, and the two observation results corresponding to the observation differences greater than the difference threshold will not be excluded.

[0046] Furthermore, if it is determined that all observation differences are greater than the difference threshold, it means that there are large differences between all observations. It can be understood that the role of hypothesis testing theory in this scheme is to test whether multiple observations of the same observation quantity obey the preset distribution, or whether there are abnormal data that deviate from the preset distribution. However, if there are large differences between all observations, these observations do not obey the preset distribution. At this time, even if the hypothesis testing theory is used to perform consistency testing to identify normal observations, it is unreliable. Therefore, if it is determined that all observation differences are greater than the difference threshold, it can be determined that all observations are invalid. At this time, invalid observations should not be used to determine the target observations of the observation quantity.

[0047] It can be seen that by comparing the observation differences between the observation results pairwise, and then using the hypothesis testing theory to perform consistency detection when at least one observation difference is less than the difference threshold, all observation results are determined to be invalid when all observation differences are greater than the difference threshold, the validity of the consistency detection results can be ensured.

[0048] The following is an example of taking the attitude of a movable platform as an observation quantity. The attitude of a movable platform can be represented by any one of pitch, roll and yaw angles or a combination of multiple attitude angles. If the observation quantity includes the attitude of the movable platform, then the observation result may be one or more of the above attitude angles. Each attitude angle can be measured in a variety of ways to obtain redundant attitude angles. For example, multiple redundant heading angles, multiple redundant pitch angles, etc. can be obtained. Multiple redundant attitude angles include attitude angles obtained based on dual-antenna direction finding carried by the movable platform, attitude angles obtained based on visual positioning, attitude angles measured based on a magnetic compass carried by the movable platform, and attitude angles obtained based on velocity observation and solution. Based on this, in step 210, the observation difference between each two observation results is obtained, which may specifically include the following: Figure 3 Steps 310 to 320 are shown.

[0049] Step 310: Convert each posture angle into a two-dimensional unit vector.

[0050] The so-called two-dimensional unit vector refers to a two-dimensional vector with a modulus of 1. The process of converting the posture angle into a two-dimensional unit vector can be referred to in the related art, and this embodiment will not be expanded here.

[0051] Step 320: For every two posture angles, calculate the dot product of the corresponding two two-dimensional unit vectors, and determine the angle between the two two-dimensional unit vectors based on the dot product; wherein the angle is the observed difference between the two posture angles.

[0052] Exemplarily, for each two posture angles, the two-dimensional unit vectors corresponding to the two posture angles are obtained, and the dot product of the two corresponding two-dimensional unit vectors is calculated. For example, the two-dimensional unit vector corresponding to the posture angle γ is , the two-dimensional unit vector corresponding to the attitude angle β , then the dot product of two two-dimensional unit vectors can be expressed as:

[0053] in, is a two-dimensional unit vector The modulus of is 1; is a two-dimensional unit vector The modulus of is 1; angle is a two-dimensional unit vector With the two-dimensional unit vector The angle between them, that is, the observed difference between the attitude angle γ and the attitude angle β, is also the angle difference between the attitude angle γ and the attitude angle β. It can be seen that the angle δ angle It can be expressed as:

[0054] The LIMIT function is used to limit the input value range to [-1, 1] to avoid illegal input caused by numerical errors. The final output angle δ angle The value range is [0, π] and is applicable to a variety of geometric computing scenarios.

[0055] It can be seen that this embodiment converts the posture angle into a two-dimensional unit vector, uses the relationship between the dot product between the vectors and the angle to obtain the observed difference between the two posture angles, compares the angle differences between the posture angles pairwise, and uses the hypothesis testing theory to perform consistency detection on all posture angles when at least one angle difference is less than the angle threshold, thereby ensuring the validity of the consistency detection result.

[0056] After obtaining the angle difference between each two posture angles, it can be determined whether each angle difference is greater than the angle threshold. angle If the difference is less than the preset angle threshold, the hypothesis test theory is used to perform consistency test on all posture angles. According to some embodiments of the present application, the movable platform is also equipped with a gyroscope, which can sense the angular velocity of the movable platform. Thus, based on the above embodiment, if it is determined that all observed differences are greater than the difference threshold, that is, all angle differences δ angle If all of them are greater than the preset angle threshold, it is determined that all attitude angles are invalid. The angular velocity sensed by the gyroscope is integrated over time to obtain the target attitude angle.

[0057] It can be seen that in this embodiment, if it is determined that the angle differences between all attitude angles are greater than the angle threshold, then all attitude angles are determined to be invalid, and the angular velocity sensed by the gyroscope is integrated over time to obtain the target attitude angle, thereby ensuring the observation accuracy of the movable platform attitude observation quantity and providing a reliable target attitude angle for subsequent positioning and navigation.

[0058] Based on any of the above embodiments, the test statistic and the rejection region for each observation result determined in step 120 may specifically include: Figure 4 Steps 121 to 123 are shown.

[0059] Step 121: Determine an expected value based on a plurality of said observation results.

[0060] The expected value refers to the true value of the observed quantity. However, in actual situations, since the true value of the observed quantity cannot be known, the average or weighted average of multiple observations can be determined as the expected value.

[0061] Step 122: For each of the observation results, calculate the test statistic of the observation result based on the value of the observation result, the standard deviation, the number of observations, and the expected value.

[0062] Exemplarily, the test statistic types include multiple types, such as but not limited to t-test, z-test, etc. Those skilled in the art can select the required test statistic type according to actual needs and calculate the corresponding test statistic. For different test statistic types, the test statistic of the observation result can be calculated based on the value of the observation result, the standard deviation of the observation result, the number of observations, and the expected value. Among them, the number of observations refers to the number of sampling times of the observation quantity, and the number of observations of different observation results can be the same or different.

[0063] Taking the z test as an example, the test statistic Zi for each observation can be expressed as:

[0064] in, is the value of the i-th observation result, μ is the expected value, is the standard deviation of the ith observation, and k is the number of observations.

[0065] Step 123: Determine the critical value of the rejection region based on the preset significance level and the redundant number of the observation results to obtain the rejection region.

[0066] Exemplarily, the significance level can be preset, for example, including but not limited to 0.05 or 0.01. The redundant number of observations refers to how many observations there are for a certain observation. The redundant number of observations can be used as degrees of freedom, and the significance level and degrees of freedom are used to find the critical value of the rejection region from the chi-square test table to obtain the rejection region.

[0067] It can be seen that this embodiment uses multiple observation results of the same observation to determine the expected value, and determines the rejection region based on the significance level and the number of redundancies, and then evaluates whether the observation result deviates from the expected value based on whether the test statistic of each observation result falls into the rejection region, thereby finding abnormal observation results.

[0068] In addition, as described above, the observation results can be obtained in different ways, including sensing by sensors, or by using the operating data of the movable platform through algorithm solution. In some embodiments, for multiple observation results of the same observation quantity, there are at least some observation results obtained by observing the observation quantity using the sensor carried by the movable platform, and these observation results can be called sensor observation results. At this time, the standard deviation of the sensor observation results is related to the environmental robustness of the sensor. The environmental robustness of the sensor refers to whether the sensor can maintain performance, stability and reliability in complex and changeable environments such as weather changes, terrain differences, and electromagnetic interference. In this embodiment, sensors with better environmental robustness are called first-class sensors, and sensors with poor environmental robustness are called second-class sensors. It can be seen that the first-class sensors and the second-class sensors are distinguished only based on the environmental robustness of the sensor, and do not specifically refer to a specific sensor.

[0069] Specifically, each sensor is calibrated with its own standard deviation when it leaves the factory. If the sensor is a first-class sensor with better environmental robustness, the sensor observation results sensed by it are less affected by the environment, and the sensor observation results are relatively stable even in a complex and changing environment. Therefore, when the movable platform is operating, the standard deviation of the sensor observation results will not differ too much from the standard deviation calibrated at the factory. In this way, when executing step 122, it can be determined that the preset standard deviation of the sensor at the factory is the standard deviation of the sensor observation results. . And when executing step 122, the preset standard deviation can be directly read from the storage space, thereby improving the consistency detection efficiency. If the sensor is a second type sensor with poor environmental robustness, it means that the sensor observation result sensed by it will be affected by the environment. At this time, the standard deviation of the sensor observation result during the operation of the movable platform may be significantly different from the standard deviation calibrated when the sensor leaves the factory. Therefore, when executing step 122, it can be determined that the real-time sample standard deviation of the sensor is the standard deviation of the sensor observation result , thereby improving the accuracy of consistency checks.

[0070] It can be seen that this embodiment determines different standard deviations of sensor observation results based on the environmental robustness of the sensor, thereby balancing the efficiency and accuracy of consistency detection.

[0071] In order to better understand this scheme, the heading attitude of the UAV is taken as an example for explanation.

[0072] During the operation of the UAV, real-time heading attitude is required for positioning and navigation. The methods of obtaining heading attitude usually include but are not limited to the following: ① Using the RTK (Real-Time Kinematic, real-time dynamic carrier phase difference technology) dual antenna direction finding carried by the UAV to obtain the heading attitude of the UAV; ② Using the visual positioning sensor system carried by the UAV to measure the heading attitude; ③ Using the magnetic compass carried by the UAV to measure the heading attitude; ④ Based on the flight speed observation of the UAV, the heading attitude is calculated. Using the above four acquisition methods, four observation results (four heading angles) about the heading attitude of the UAV at a certain moment can be obtained, that is, the number of redundant observation results is 4.

[0073] First, for each heading attitude acquisition method, the obtained heading angle can be subjected to noise reduction. Taking the heading angle measured by the sensor as an example, k observation values ​​of the heading attitude of the i-th sensor in a continuous period of time can be obtained, and the average of the k observation values ​​is determined to be the heading angle of the i-th sensor for the heading attitude. , specifically expressed as:

[0074] Among them, x ij is the measurement value of the i-th sensor at the j-th time.

[0075] The obtained heading angle It can be stored in an array corresponding to the heading attitude. In this embodiment, the heading attitude observation results include 4, which are correspondingly stored in 4-bit elements of the data.

[0076] Subsequently, the observed difference between each two heading angles of the four heading angles can be evaluated, that is, whether the angle difference between each two heading angles is greater than the preset angle threshold. The specific process can be found in the above embodiment. If the angle difference between each two heading angles is greater than the angle threshold, it means that the heading angles obtained by the four methods are quite different. At this time, it is possible that there are problems with the four acquisition methods, and the normal heading angle cannot be determined through consistency detection. Therefore, it is determined that the above four heading angles are invalid, and the angular velocity sensed by the gyroscope on the drone is integrated over time to obtain the target heading angle.

[0077] If there are at least two heading angles whose angle difference is less than the angle threshold, the hypothesis test theory is used to perform consistency test on the four heading angles. Specifically, for the sensor observation results, if the sensor is a first-class sensor with better environmental robustness, the preset standard deviation of the sensor is determined to be the standard deviation of the sensor observation results. If the sensor is a second type sensor with poor environmental robustness, the real-time sample standard deviation of the sensor is calculated, and the real-time sample standard deviation is determined as the standard deviation of the sensor observation result. . Real-time sample standard deviation S i The calculation formula is:

[0078] Then, the test statistic of each observation result is calculated. The calculation process can be referred to in the above embodiment. And according to the preset significance level α and the number of redundant observation results (4 in this embodiment), the critical value Z of the rejection region is found from the chi-square test table. α / 2 , and get the rejection region.

[0079] For each observation, if the test statistic of the observation falls into the rejection region, that is , determine that the observation result is an abnormal observation result. If the observation result is sensed by a sensor, it can be determined that the sensor is a faulty sensor. If the test statistic of the observation result does not fall into the rejection region, it is determined that the observation result passes the consistency test.

[0080] It can be seen that in this embodiment, for multiple redundant observation results obtained by different methods for the same observation value, the observation differences between the observation values ​​are first compared to see if they are within the difference threshold. If all observation differences exceed the difference threshold, all the obtained observation results are considered invalid, which is a preliminary judgment on the consistency of the observation results.

[0081] If there is at least one observation difference that does not exceed the difference threshold, the hypothesis test theory is used to perform consistency detection on each observation result, thereby distinguishing abnormal observation results from normal observation results. If the abnormal observation result is sensed by a sensor, it can also be determined that the sensor is faulty, thereby screening out the faulty sensor.

[0082] It can be seen that this embodiment realizes real-time consistency detection of multiple redundant observation results in the operation of the movable platform, so as to distinguish abnormal observation results from normal observation results in real time, which can effectively improve the reliability and stability of the observation results. Through the automated data collection and processing process, manual intervention is greatly reduced, work efficiency is improved, human errors are reduced, and the accuracy of consistency detection is improved. And the consistency detection of redundant observation results of multiple observation quantities can be realized simultaneously through automatic algorithms. In addition, since this method is applicable to the operation process of the movable platform, it can meet the detection needs of various types of sensors and different environmental conditions, and can timely identify sensors with poor environmental robustness, timely discover problems and correct them, to ensure the long-term stable operation of the movable platform.

[0083] Based on the consistency detection method of an observation result described in any of the above embodiments, the present application also provides a computer program product, which includes one or more computer programs or instructions. The computer program or instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. When the computer program is executed by a processor, the consistency detection method of an observation result described in any of the above embodiments is implemented.

[0084] Based on a consistency detection method of an observation result described in any of the above embodiments, the present application also provides the following Figure 5 A schematic diagram of the structure of an electronic device is shown in FIG. Figure 5 At the hardware level, the electronic device includes a processor, an internal bus, a network interface, a memory, and a non-volatile memory, and may also include hardware required for other services. The processor reads the corresponding computer program from the non-volatile memory into the memory and then runs it to implement a consistency detection method for an observation result described in any of the above embodiments.

[0085] As an example, the electronic device may be a mobile platform, such as but not limited to a drone, an unmanned vehicle, a robot, etc. As another example, the electronic device may be a computing device communicatively connected to the mobile platform, such as but not limited to a server, a central control device, a remote controller, etc.

[0086] The present application also provides a computer storage medium, which stores a computer program. When the computer program is executed by a processor, it can be used to execute a consistency detection method for observation results described in any of the above embodiments.

[0087] In several embodiments provided in the present application, it should be understood that the disclosed devices and methods can also be implemented in other ways. The device embodiments described above are merely schematic. For example, the flowcharts and block diagrams in the accompanying drawings show the possible architecture, functions and operations of the devices, methods and computer program products according to multiple embodiments of the present application. In this regard, each box in the flowchart or block diagram can represent a module, a program segment or a part of a code, and the module, a program segment or a part of a code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two consecutive boxes can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flowchart, and the combination of boxes in the block diagram and / or flowchart can be implemented with a dedicated hardware-based system that performs a specified function or action, or can be implemented with a combination of dedicated hardware and computer instructions.

[0088] In addition, the functional modules in the various embodiments of the present application may be integrated together to form an independent part, or each module may exist separately, or two or more modules may be integrated to form an independent part.

[0089] If the function is implemented in the form of a software function module and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, and other media that can store program codes.

[0090] The above description is only an embodiment of the present application and is not intended to limit the scope of protection of the present application. For those skilled in the art, the present application may have various changes and variations. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application should be included in the scope of protection of the present application. It should be noted that similar reference numerals and letters represent similar items in the following drawings, so once an item is defined in one drawing, it does not need to be further defined and explained in the subsequent drawings.

[0091] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0092] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

Claims

1. A method for detecting consistency of observation results, characterized in that: The method is used to perform consistency detection on multiple redundant observation results of the same observation; the method comprises: During the operation of the movable platform, obtaining a plurality of redundant observation results of the movable platform on the same observation quantity; Determine the test statistic and rejection region of each observation result in the hypothesis test theory; wherein the null hypothesis in the hypothesis test theory is: all observation results are normal and obey the preset distribution; the alternative hypothesis in the hypothesis test theory is: there are abnormal observation results that deviate from the preset distribution; For each of the observation results, it is determined that the observation result whose test statistic falls into the rejection region is an abnormal observation result, and it is determined that the observation result whose test statistic does not fall into the rejection region passes the consistency test.

2. The method according to claim 1, characterized in that Before determining the test statistic and the rejection region of each of the observation results in the hypothesis testing theory, the method further comprises: Obtaining an observation difference between every two of the observation results in the plurality of observation results, to obtain a plurality of the observation differences; It is determined that at least one of the observed differences is less than a preset difference threshold.

3. The method according to claim 2, characterized in that The method further comprises: If it is determined that all the observed differences are greater than the difference threshold, it is determined that all the observed results are invalid.

4. The method according to claim 2 or 3, characterized in that: The observed quantity includes the attitude of the movable platform; the observation result is an attitude angle; the multiple attitude angles include any multiple of an attitude angle obtained based on dual antenna direction finding carried by the movable platform, an attitude angle obtained based on visual positioning, an attitude angle measured based on a magnetic compass carried by the movable platform, and an attitude angle obtained based on velocity observation and solution; the obtaining of the observation difference between each two of the multiple observation results includes: Convert each posture angle into a two-dimensional unit vector; For every two posture angles, the dot product of the corresponding two two-dimensional unit vectors is calculated, and the angle between the two two-dimensional unit vectors is determined based on the dot product; wherein the angle is the observed difference between the two posture angles.

5. The method according to claim 4, characterized in that The movable platform is also equipped with a gyroscope; the method further comprises: If it is determined that all of the observed differences are greater than the difference threshold, all of the attitude angles are determined to be invalid, and the gyroscope is used to determine the target attitude angle of the movable platform.

6. The method according to any one of claims 1 to 3, characterized in that: Determining the test statistic and the rejection region of each of the observation results in the hypothesis testing theory includes: determining an expected value based on a plurality of said observations; For each of the observation results, a test statistic of the observation result is calculated based on the value of the observation result, the standard deviation, the number of observations, and the expected value; The critical value of the rejection region is determined based on a preset significance level and the redundant number of the observation results to obtain the rejection region.

7. The method according to claim 6, characterized in that The movable platform is equipped with a sensor for observing the observed quantity; the plurality of observation results include sensor observation results obtained by observing with the sensor; the method further includes: If the sensor is a first type of sensor, determining a preset standard deviation of the sensor as the standard deviation of the sensor observation result; If the sensor is a second type of sensor, the real-time sample standard deviation of the sensor is determined to be the standard deviation of the sensor observation result; wherein the environmental robustness of the first type of sensor is better than the environmental robustness of the second type of sensor.

8. A computer program product, characterized in that The computer program product comprises a computer program, and when the computer program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

9. An electronic device, characterized in that: The electronic device comprises: processor; a memory for storing processor-executable instructions; Wherein, when the processor calls the executable instructions, the operation of any method described in claims 1-7 is implemented.

10. A computer-readable storage medium, characterized in that: Computer instructions are stored thereon, and when the computer instructions are executed by a processor, the steps of any method described in claims 1-7 are implemented.

Images