Microsystem processor performance evaluation method and system based on multi-sensor fusion

Abstract

The application relates to the technical field of data processing, in particular to a microsystem processor performance evaluation method and system based on multi-sensor fusion. The method comprises the following steps: determining the result deviation degree of a processor based on the position difference and the attitude angle difference of an aircraft obtained by different measurement units; dividing the navigation data of the aircraft into flight stages to obtain a plurality of flight state intervals; determining the error influence coefficient of an inertial measurement unit on the data processing result; obtaining the optimal deviation level in the flight state interval; taking the sum of the optimal deviation levels corresponding to each flight state interval as a first index; taking the time difference of the time spent by the processor in processing data of different data measurement units as a second index to construct a processor evaluation index, and evaluating the processor performance. The application has the effects of facilitating the evaluation of the processor performance and improving the final navigation accuracy.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of data processing, and in particular to a microsystem processor performance evaluation method and system based on multi-sensor fusion. BACKGROUND

[0002] Intelligent unmanned aircraft has applications in navigation fields such as aerospace and intelligent logistics. Aircraft often need to perform tasks in complex and dynamically changing environments, which puts high requirements on the precise navigation capabilities of the aircraft itself. Different types of navigation methods usually have different advantages and disadvantages. For example, in the optical tracking method, the state of the aircraft is determined based on external data, which has good accuracy but is easily affected by the external environment. The inertial measurement method is based on sensors such as accelerometers and gyroscopes, which are less affected by the external environment but are greatly affected by installation errors or measurement errors. In order to improve the accuracy of the final navigation, some navigation systems based on multiple sensors are gradually applied in navigation tasks. The signals collected by multiple sensors are processed to obtain multiple sets of navigation information, and the multiple navigation information is integrated to improve the accuracy of navigation and facilitate the aircraft to perform different tasks. In general, to ensure the accuracy of navigation, the staff will evaluate and calibrate each measurement unit to ensure that each unit can meet the precision requirements of the aircraft within a certain period of time. For example, the patent document with the authorization announcement number CN111351508B discloses a MEMS inertial measurement unit system-level batch calibration method, which mainly estimates the error parameters of the inertial measurement unit through Kalman filtering technology to complete the calibration of the measurement unit and improve the navigation accuracy.

[0003] However, the aircraft is continuously performing actual flight tasks, and in addition to the influence of the measurement unit itself on the navigation accuracy, the processor in the navigation microsystem for processing the data collected by each measurement unit also has a certain influence on the final navigation accuracy. The performance of the processor in processing data largely determines the navigation accuracy. Once the processor performance is poor, it may cause deviations in data processing, thereby affecting the navigation accuracy of the aircraft, and even possibly causing the task to fail. The traditional evaluation method ignores the influence of the performance of the microsystem processor on the final navigation accuracy, which ultimately leads to a large navigation deviation. SUMMARY

[0004] In order to solve the problem of large navigation deviation caused by the inability to evaluate the performance of the processor in the related art, the present application provides a microsystem processor performance evaluation method and system based on multi-sensor fusion.

[0005] In a first aspect, the present application provides a microsystem processor performance evaluation method based on multi-sensor fusion, which adopts the following technical solution:

[0006] The method for evaluating performance of a microsystem processor based on multi-sensor fusion comprises the following steps: acquiring aircraft position and aircraft attitude angle data collected by a photoelectric measurement unit and an inertial measurement unit; determining a result deviation degree of the processor based on position differences and attitude angle differences of the aircraft acquired by different measurement units;

[0007] Flight phase division is performed on navigation data of the aircraft to obtain a plurality of flight state intervals; a correlation index of a deviation sequence constituted by the result deviation degrees and a time sequence in the flight state intervals is acquired to determine an error influence coefficient of the data processing result of the inertial measurement unit; the result deviation degrees in the flight state intervals are corrected using the corresponding error influence coefficients to obtain optimal deviation levels in the flight state intervals; a sum of the optimal deviation levels corresponding to the flight state intervals is taken as a first index, and a time difference of time spent by the processor in processing data of different data measurement units is taken as a second index to construct a processor evaluation index to evaluate the performance of the processor.

[0008] The beneficial effects are as follows: in the microsystem based on multi-sensor fusion, the photoelectric measurement unit and the inertial measurement unit acquire signals using sensors, and the processor processes the signals acquired by the sensors to convert them into navigation data. In the case that the external environment is good and the installation precision of the inertial measurement unit is high, the navigation information acquired by the photoelectric measurement unit and the inertial measurement unit is relatively accurate, so the navigation information obtained by processing the signals by the processor should be similar or identical. If the navigation information obtained by the two measurement units has a large deviation, it indicates that the processor has different abilities in processing signals acquired by different sensors, resulting in a deviation in the obtained navigation information, which further reflects the performance of the processor.

[0009] In the navigation information, mainly includes the aircraft position and the aircraft attitude angle, so the result deviation degree is determined in combination with the position differences and the attitude angle differences. In order to reduce the influence of the error of the inertial measurement itself on the calculation of the result deviation degree, the error influence coefficient of the result error degree of the inertial measurement unit is calculated according to different flight states. The result error degree is corrected according to the influence of the inertial measurement on the result error degree to obtain the optimal error degree, and then the processor evaluation index is constructed in combination with the time of processing data by the processor. The performance of the processor is evaluated to improve the accuracy of the evaluation of the performance of the processor. The performance of the processor in processing the two measurement units is evaluated to determine the direct influence of the processor on the navigation accuracy, and then the processor can be optimized or adjusted to improve the navigation accuracy.

[0010] Optionally, the step of determining the result deviation degree of the processor based on the position difference and the attitude angle difference of the aircraft obtained by different measurement units comprises: taking the Euclidean distance of the position information of the aircraft obtained by different measurement units as the position difference; taking the absolute difference of the attitude angle of the aircraft obtained by different measurement units as the attitude angle difference; and taking the sum of the normalized result of the position difference and the normalized result of the attitude angle difference as the result deviation degree.

[0011] The beneficial effect is that the Euclidean distance of the position information in the navigation information corresponding to the two measurement units represents the position difference of the signals collected by different measurement units after being processed by the processor, and the difference between the processed attitude angle information is combined, so that the ability of the processor to process two different signals can be reflected to a certain extent.

[0012] Optionally, the step of dividing the navigation data of the aircraft into flight stages to obtain a plurality of flight state intervals comprises: clustering the data points at each time based on the navigation data collected by the optoelectronic measurement unit and the sampling time using a clustering method to form a plurality of clustering clusters, obtaining the maximum value and the minimum value of the time stamp corresponding to the data points in the clustering cluster, and taking the time region between the maximum value and the minimum value as a flight state interval.

[0013] The beneficial effect is that during the aircraft test process, the aircraft needs to simulate multiple flight states, such as different directions, different speeds, different accelerations, etc. Under different flight states, the influence of the inertial measurement unit on the result deviation degree may not be the same, so here the data in the aircraft test process is clustered based on the clustering method to form a plurality of flight state intervals, each flight state interval corresponds to a flight state, and the influence of the inertial measurement unit itself error on the result deviation degree is based on the flight state.

[0014] Optionally, the Pearson correlation coefficient between the deviation sequence corresponding to the flight state interval and the time sequence is taken as the correlation index between the deviation sequence and the time sequence.

[0015] The beneficial effect is that the Pearson correlation coefficient is used to measure the correlation between two variables, and when applied in the present application, it refers to the correlation index of the result deviation degree with respect to time.

[0016] Optionally, the iterative self-organizing clustering method is used to cluster the data points at each time until each clustering cluster has a stable clustering center.

[0017] Optionally, the step of calculating the error influence coefficient of the data processing result of the inertial measurement unit comprises: obtaining a mean value of the result deviation degree in the flight state interval, and taking the mean value as the deviation degree; and determining the influence degree in combination with the deviation degree and the correlation index, and taking a normalized result of the influence degree as the error influence coefficient.

[0018] The mean value of the result deviation degree in the flight state reflects the overall result deviation degree in the flight state interval. The influence degree of the inertial measurement unit on the result deviation degree is determined in combination with the result deviation degree and the correlation index. The greater the correlation index, the more the change of the result deviation degree conforms to the error accumulation of the inertial measurement unit, and thus the greater the influence degree of the error of the inertial measurement unit itself on the result deviation degree.

[0019] Optionally, the step of obtaining the optimal deviation level in the flight state interval by correcting the result deviation degree using the corresponding error influence coefficient in the flight state interval comprises: for each data point in each flight state interval, determining a compensation weight of each time point based on the time stamp corresponding to each data point, taking the product of the compensation weight and the error influence coefficient as a correction coefficient corresponding to each data point, taking the product of the correction coefficient of each time point and the result deviation degree as the optimal deviation degree at the time point, and taking the mean value of the optimal deviation degree corresponding to each data point in the flight state interval as the optimal deviation level.

[0020] In the above steps, the error influence coefficient of the influence of the inertial measurement unit on the result deviation degree in the flight state interval is obtained. However, in the flight state interval, the error of the inertial measurement unit is continuously accumulated, so the error influence coefficients at different time points should be different. Then, in the method, the compensation weight is determined based on the time stamp corresponding to each data point at each time point, the error influence coefficient is first adjusted according to the compensation weight to obtain the correction coefficient, and finally the result deviation degree at each time point is adjusted through the correction coefficient, further improving the accuracy of the performance evaluation of the processor.

[0021] In a second aspect, the present application provides a microsystem processor performance evaluation system based on multi-sensor fusion, which adopts the following technical solution:

[0022] The microsystem processor performance evaluation system based on multi-sensor fusion comprises a processor and a memory, and the memory stores computer program instructions. When the computer program instructions are executed by the processor, the microsystem processor performance evaluation system based on multi-sensor fusion is realized.

[0023] The computer program based on the above-mentioned microsystem processor performance evaluation method based on multi-sensor fusion is generated and stored in the memory to be loaded and executed by the processor, so that the system is convenient to use according to the memory and the processor.

[0024] The present application has the following technical effects:

[0025] The ability of the processor to process signals collected by multiple measurement units is directly evaluated in the present application. Based on the index, the processor can be optimized or adjusted by the researchers, thereby further improving the accuracy of the final navigation of the aircraft. Compared with the traditional technology of adjusting the measurement unit by separately measuring the accuracy of the two measurement units, the evaluation index provided in the present application can find more factors affecting the accuracy in the navigation process, thereby making more comprehensive adjustments and further improving the accuracy of the final navigation. BRIEF DESCRIPTION OF DRAWINGS

[0026] Figure 1 is a method flowchart of a microsystem processor performance evaluation method based on multi-sensor fusion according to an embodiment of the present application. DETAILED DESCRIPTION

[0027] The embodiment of the present application discloses a microsystem processor performance evaluation method based on multi-sensor fusion, which combines the difference between the data obtained by two measurement units to calculate the result deviation degree of the processor processing result; the collected navigation data is divided to obtain different flight state intervals. The error influence coefficient of the inertial measurement unit on the data processing result in each flight state interval is obtained, and the result deviation degree is corrected based on the error influence coefficient to obtain the optimal deviation level. In the entire data of the flight test, the processor evaluation index for evaluating the processor performance is constructed based on the optimal deviation level and the processing time of the processor processing data, and the processor performance is evaluated.

[0028] REFERENCE Figure 1 The microsystem processor performance evaluation method based on multi-sensor fusion includes steps S1-S4.

[0029] S1: Obtain the aircraft position and aircraft attitude angle data collected by the photoelectric measurement unit and the inertial measurement unit; determine the result deviation degree of the processor based on the position difference and the attitude angle difference of the aircraft obtained by different measurement units.

[0030] The microsystem includes a photoelectric measurement unit and an inertial measurement unit. The photoelectric measurement unit determines the navigation information of the aircraft based on the external information of the aircraft. The inertial measurement unit determines the navigation information based on the characteristics of the aircraft itself. The way in which the photoelectric measurement unit and the inertial measurement unit obtain the navigation information is a conventional technical means in the art, which will not be described here. During the test of the aircraft; a variety of signals of the outside world or the aircraft itself are collected by the photoelectric measurement unit and the inertial measurement unit, and the corresponding navigation information of each measurement unit is obtained after being processed by the processor. The navigation information includes: aircraft position, aircraft attitude angle.

[0031] During actual flight, the aircraft's state is unique. Under ideal conditions where the optoelectronic measurement unit (OMU) and inertial measurement unit (INS) are sufficiently accurate, the navigation information obtained by the OMU and INS should be consistent. Therefore, the more consistent the navigation information obtained by the two units, the higher the reliability of the information after subsequent data integration and processing, and thus the better the processor performance; conversely, the less consistent the data, the worse the processor performance.

[0032] Specifically, the Euclidean distance between the aircraft's position information obtained by different measurement units is taken as the position difference; the absolute difference between the aircraft's attitude angles obtained by different measurement units is taken as the attitude angle difference; the sum of the position difference and the attitude angle difference is taken as the total difference; and the normalized value of the total difference is taken as the result deviation.

[0033] The formula for calculating the result deviation can be expressed as:

[0034] In the formula, No. The degree of deviation in the processor's processing results at any given moment; Indicates the first The unit and the first The unit in the first The Euclidean distance between the location information corresponding to each moment in this embodiment is the first... The first unit refers to the photoelectric measurement unit, the... Each unit refers to an inertial measurement unit; Indicates the first The navigation information corresponding to the unit is the first Attitude angle at any moment; Indicates the first The navigation information corresponding to the unit is the first Attitude angle at any moment; This represents the standard normalization function.

[0035] The Euclidean distance represents the spatial distance between the aircraft's position information measured by two measurement units. The calculation of the Euclidean distance is a standard technique in this field and will not be elaborated upon here. The Euclidean distance represents the spatial distance between the aircraft's position information measured by the two measurement units; the larger this distance, the greater the positional deviation between the two measurement units. Similarly, This indicates that the two measurement units are in the first... The absolute difference between the measured navigation attitude angles at any given moment is considered the indicator. A larger value indicates a significant deviation in the UAV's orientation measured by the two measurement units. A large deviation suggests that the microsystem integration results at that corresponding moment are unreliable, and consequently, that the processor performance is poor.

[0036] S2: flight phase division is performed on navigation data of the aircraft to obtain a plurality of flight state intervals; a correlation index of a deviation sequence constituted by result deviation degrees in the flight state intervals and a time sequence is obtained to determine an error influence coefficient of the inertial measurement unit on data processing results.

[0037] The inertial measurement unit is an autonomous navigation system independent of external information. However, the inertial measurement unit has a higher requirement for installation accuracy, and when an error occurs in installation, deviation will occur in the navigation information finally obtained based on the inertial measurement unit, and the deviation will gradually accumulate over time. Meanwhile, the error accumulation speed and the influence on navigation information are different in different flight states. For example, in the process of acceleration and deceleration of the aircraft, the accelerometer error of the inertial measurement unit will have a greater influence on the measurement results, while in the stable flight stage, the error grows relatively slowly. The deviation of the navigation information caused by this kind of situation is not a processor performance problem, so the result deviation degree of this kind of situation needs to be adjusted.

[0038] Therefore, in the present application, the navigation data obtained in the flight test process of the aircraft is segmented to divide different flight state intervals. The optical measurement unit determines the navigation information of the aircraft based on external information, and the information obtained in a good external environment is more accurate, so the present application clusters the multiple dimension information of each data point of the navigation information obtained based on the optical measurement unit. The multiple dimension information includes: aircraft position, aircraft flight speed, aircraft attitude angle, and time stamp of data point collection time, etc. In the present embodiment, an iterative self-organizing clustering algorithm is used for clustering, and the clustering result is obtained when the clustering center of each clustering cluster is stable or the preset maximum iteration number is reached.

[0039] In the flight process of the aircraft, taking the aircraft flying to the southeast direction as an example, in a certain period of time, the flight speed corresponding to each data point is the same, the aircraft position corresponding to the data points is similar, and the time stamp of the collection time corresponding to the data points is similar, so the data points can be clustered into one clustering cluster. When the flight direction or the flight speed changes, the corresponding information in the data points changes, and then the data points after the flight state change are clustered into another clustering cluster.

[0040] For any one clustering cluster, it includes a plurality of data points, the time stamps of the collection time corresponding to the plurality of data points are obtained, and the time region between the value of the largest time stamp and the value of the smallest time stamp is taken as the flight state interval corresponding to the clustering cluster.

[0041] Based on the above step S1 and the description of the accumulation of inertial measurement unit error, when the result difference degree in one flight state interval changes with time and becomes larger, it means that the result difference degree accumulates with time. This feature is consistent with the accumulation of inertial measurement unit error, so the result difference degree in this cluster is likely to be caused by the error of the inertial measurement unit itself, not the performance problem of the processor.

[0042] Therefore, the correlation between the deviation sequence constituted by the result deviation degree in the flight state interval and the time sequence can be obtained to determine the error influence coefficient of the inertial measurement unit on the data processing result.

[0043] The step of obtaining the correlation index between the result deviation degree and the time sequence includes: the plurality of result deviation degrees in the flight state interval form a deviation sequence based on the time sequence, and the corresponding time stamps form a time sequence. The Pearson correlation coefficient between the two sequences is obtained, and the Pearson correlation coefficient between the two sequences is taken as the correlation index between the deviation sequence and the time sequence.

[0044] After obtaining the correlation index between the deviation sequence and the time sequence, the error influence coefficient of the inertial measurement unit on the data processing result is obtained based on the correlation index.

[0045] Specifically, the mean of the result deviation degree in the flight state interval is obtained, and the mean is taken as the deviation degree; the influence degree is determined in combination with the deviation degree and the correlation index; and the normalized result of the influence degree is taken as the error influence coefficient.

[0046] S3: The result deviation degree corresponding to the error influence coefficient in the flight state interval is corrected to obtain the optimal deviation level in the flight state interval.

[0047] The data point with the largest time stamp in the flight state interval corresponds to the last data point in the flight state interval. At the time of this data point, the error accumulated by the inertial measurement unit is the largest. Therefore, the compensation weight corresponding to each data point can be determined according to the time interval between other data points and this data point. The result deviation degree is weighted and averaged based on the compensation weight, so that the influence of the error of the inertial measurement unit itself on the result deviation degree is eliminated to a certain extent. The accuracy of the processor performance evaluation is improved.

[0048] S4: The sum of the optimal deviation levels corresponding to each flight state interval is taken as the first index; the time difference of the time taken by the processor to process data of different data measurement units is taken as the second index to construct a processor evaluation index to evaluate the performance of the processor.

[0049] The sum of the optimal deviation levels corresponding to each flight state interval is a first index for evaluating the performance of the processor. If the first index is larger, it means that the processor itself has a larger problem except for the error of the inertial measurement unit, that is, the performance of the processor is poorer.

[0050] The time difference between the time spent by the processor in processing the signals obtained by the photoelectric tracking measurement unit and the time spent by the processor in processing the signals obtained by the inertial measurement unit at the same moment reflects the performance of the processor in synchronously processing data, and the time difference is taken as a second index for evaluating the performance of the processor, so as to further improve the accuracy of the evaluation of the performance of the processor.

[0051] The performance threshold is set, and when the processor evaluation index is less than the performance threshold, it is considered that the performance of the processor is poorer, and the processor needs to be optimized to ensure the navigation accuracy in the task process of the aircraft. The performance threshold can be set according to actual requirements, for example, the performance threshold can be set to 0.7.

[0052] The embodiment of the application further discloses a microsystem processor performance evaluation system based on multi-sensor fusion, comprising a processor and a memory, and the memory stores computer program instructions, and when the computer program instructions are executed by the processor, the microsystem processor performance evaluation method based on multi-sensor fusion according to the application is realized.

[0053] The above system further comprises other components such as a communication bus and a communication interface which are well known to those skilled in the art, and the settings and functions thereof are known in the art, so they will not be described here.

[0054] The above are preferred embodiments of the application, and do not limit the protection scope of the application, so: any equivalent changes made on the basis of the structure, shape, principle of the application should be covered within the protection scope of the application.

Claims

1. A microsystem processor performance evaluation method based on multi-sensor fusion, characterized in that, The steps include: acquiring aircraft position and attitude angle data collected by the photoelectric measurement unit and the inertial measurement unit; determining the processor's result deviation based on the differences in aircraft position and attitude angles acquired by different measurement units; The navigation data of the aircraft is divided into flight phases to obtain multiple flight state intervals. The correlation index between the deviation sequence and the time series formed by the deviation degree of the results in the flight state interval is obtained to determine the error influence coefficient of the inertial measurement unit on the data processing results. The result deviation degree is corrected using the corresponding error influence coefficient in the flight state interval to obtain the optimal deviation level in the flight state interval. The sum of the optimal deviation levels corresponding to each flight state interval is used as the first indicator, and the sum of the time difference of the time spent by the processor to process the data of different data measurement units is used as the second indicator to construct a processor evaluation index to evaluate the processor performance.

2. The microsystem processor performance evaluation method based on multi-sensor fusion according to claim 1, characterized in that, The steps for determining the processor's result deviation based on the position and attitude angle differences of the aircraft obtained from different measurement units include: taking the Euclidean distance of the aircraft's position information obtained from different measurement units as the position difference; taking the absolute difference of the aircraft's attitude angles obtained from different measurement units as the attitude angle difference; and taking the sum of the normalized results of the position difference and the normalized results of the attitude angle difference as the result deviation.

3. The microsystem processor performance evaluation method based on multi-sensor fusion according to claim 1, characterized in that, The steps for dividing the aircraft's navigation data into flight phases and obtaining multiple flight state intervals include: using a clustering method to cluster the data points at each time point based on the navigation data collected by the photoelectric measurement unit and the sampling time, forming multiple clusters, obtaining the maximum and minimum timestamps corresponding to the data points in the clusters, and taking the time range between the maximum and minimum values ​​as a flight state interval.

4. The microsystem processor performance evaluation method based on multi-sensor fusion according to claim 1, characterized in that, The Pearson correlation coefficient between the deviation sequence and the time series corresponding to the flight state interval is used as the correlation index between the deviation sequence and the time series.

5. The microsystem processor performance evaluation method based on multi-sensor fusion according to claim 1, characterized in that, The iterative self-organizing clustering method is used to cluster the data points at each time point until each cluster has a stable cluster center.

6. The microsystem processor performance evaluation method based on multi-sensor fusion according to claim 1, characterized in that, The steps for calculating the error influence coefficient of the data processing results by the inertial measurement unit include: obtaining the mean value of the result deviation in the flight state interval, and using this mean value as the degree of deviation; determining the influence degree by combining the degree of deviation with the correlation index; and using the normalized result of the influence degree as the error influence coefficient.

7. The microsystem processor performance evaluation method based on multi-sensor fusion according to claim 1, characterized in that, The steps for obtaining the optimal deviation level in a flight state interval by correcting the deviation using the corresponding error influence coefficient in the flight state interval include: for each data point in each flight state interval, determining the compensation weight at each time point based on the timestamp corresponding to each data point, using the product of the compensation weight and the error influence coefficient as the correction coefficient corresponding to each data point, using the product of the correction coefficient at each time point and the result deviation as the optimal deviation at that time point, and using the average of the optimal deviations corresponding to each data point in the flight state interval as the optimal deviation level.

8. A microsystem processor performance evaluation system based on multi-sensor fusion, characterized in that, include: A processor and a memory, wherein the memory stores computer program instructions that, when executed by the processor, implement the microsystem processor performance evaluation method based on multi-sensor fusion according to any one of claims 1-7.

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