A physical parameter estimation method and device based on sensor

Abstract

An embodiment of the present invention provides a sensor-based physical parameter estimation method and device. This relates to the field of data processing technology. The specific scheme is as follows: obtaining a first observation value of a first sensor based on collected data and a second observation value based on data collected by a second sensor; calculating the difference between the first observation value and the second observation value; obtaining a normalized error based on the difference and the observation noise of a target sensor, where the target sensor is either the first sensor or the second sensor; adjusting the first observation noise of the first sensor and the second observation noise of the second sensor based on the normalized error; and estimating a first predicted value of the target physical parameter based on the first observation noise and the second observation noise. Application of the scheme provided by the embodiment of the present invention can improve the accuracy of the determined physical parameters.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular to a sensor-based physical parameter estimation method and device. Background Art

[0002] In fields such as aviation, aerospace, autonomous driving, and robotics, data collected by sensors onboard devices must be processed to determine the device's physical parameters. The device then uses these physical parameters to perform control functions such as positioning, path planning, attitude control, and movement control. For example, a drone can process data collected by its gyroscope to determine its heading angle, which serves as a physical parameter. This heading angle is then used to control the drone's heading. The accuracy of the heading angle affects the accuracy of the drone's heading control. Therefore, the accuracy of the physical parameters determined based on sensor data is crucial.

[0003] In the existing technology, the device processes data collected by a single sensor to obtain physical parameters. However, a single sensor often has problems such as limited accuracy and noise interference. Therefore, the accuracy of the data collected by the sensor will affect the accuracy of the physical parameters of the device, resulting in low accuracy of the determined physical parameters. Summary of the Invention

[0004] The purpose of the embodiments of the present invention is to provide a sensor-based physical parameter estimation method and apparatus to improve the accuracy of the determined physical parameters. The specific technical solution is as follows:

[0005] According to one aspect of an embodiment of the present invention, a sensor-based physical parameter estimation method is provided, the method comprising:

[0006] Obtaining a first observation value of a first sensor and a second observation value of a second sensor provided on the mobile device; wherein the first observation value is an observation value of a target physical parameter obtained based on data collected by the first sensor, and the second observation value is an observation value of the target physical parameter obtained based on data collected by the second sensor;

[0007] calculating a difference between the first observation and the second observation;

[0008] Obtaining a normalized error according to the difference and observation noise of a target sensor, wherein the target sensor is: the first sensor or the second sensor;

[0009] adjusting the first observation noise and the second observation noise according to the normalized error;

[0010] A first predicted value of a target physical parameter is estimated based on the first observation noise and the second observation noise.

[0011] In one embodiment of the present invention, adjusting the first observation noise and the second observation noise according to the normalized error includes:

[0012] If the normalized error is greater than a preset error threshold, adding the first observation noise and the second observation noise to obtain a first adjusted noise of the first sensor and a second adjusted noise of the second sensor;

[0013] If the normalized error is less than or equal to a preset error threshold, the first observation noise and the second observation noise are reduced to obtain a third adjusted noise of the first sensor and a fourth adjusted noise of the second sensor.

[0014] In one embodiment of the present invention, the adding the first observation noise and the second observation noise to obtain a first adjusted noise of the first sensor and a second adjusted noise of the second sensor includes:

[0015] Increasing the first observed noise by a preset noise growth step to obtain an increased first observed noise; and obtaining a first adjusted noise based on the increased first observed noise;

[0016] The second observation noise is increased by a preset noise growth step to obtain an increased second observation noise; and a second adjusted noise is obtained based on the increased second observation noise.

[0017] In one embodiment of the present invention, obtaining the first adjusted noise based on the increased first observed noise includes:

[0018] If the increased first observed noise is greater than or equal to the preset noise upper limit threshold, the preset noise upper limit threshold is used as the first adjusted noise; if the increased first observed noise is less than the preset noise upper limit threshold, the increased first observed noise is used as the first adjusted noise.

[0019] In one embodiment of the present invention, the preset error threshold is determined in the following manner: the preset error threshold is determined from a preset chi-square distribution table according to preset degrees of freedom and a preset significance level value.

[0020] In one embodiment of the present invention, the observation noise of the sensor is represented by an observation noise matrix, wherein the observation noise matrix is a square matrix, the order of the observation noise matrix is the same as the number of sensors provided on the mobile device, and each diagonal element in the observation noise matrix corresponds to the observation noise of each sensor;

[0021] The adding of the first observation noise and the second observation noise comprises:

[0022] Increasing the values of the diagonal elements corresponding to the first sensor in the observation noise matrix, and increasing the values of the diagonal elements corresponding to the second sensor in the observation noise matrix;

[0023] The reducing the first observation noise and the second observation noise includes:

[0024] The values of the diagonal elements corresponding to the first sensor in the observation noise matrix are reduced, and the values of the diagonal elements corresponding to the second sensor in the observation noise matrix are reduced.

[0025] In one embodiment of the present invention, obtaining a normalized error based on the difference and the observation noise of the target sensor includes:

[0026] Determining a difference representation value between the first observation value and the second observation value according to the difference;

[0027] Determine a normalization basis based on the observation noise of the target sensor;

[0028] A normalized error is obtained based on the gap representation value and a normalized basis.

[0029] In one embodiment of the present invention, the method further comprises:

[0030] Predicting a target physical parameter of the mobile device based on data collected by a sensor disposed on the mobile device to obtain a second predicted value of the target physical parameter;

[0031] Determine the prior state covariance based on the posterior state covariance and the process noise covariance;

[0032] After adjusting the first observation noise and the second observation noise according to the normalized error, the method further includes:

[0033] For each sensor, determine a gain coefficient of the sensor based on the adjusted observation noise of the sensor and the prior state covariance, wherein the gain coefficient of the sensor represents a degree of change in noise uncertainty of an observation value obtained based on data collected by the sensor;

[0034] Determining an updated a posteriori state covariance based on the a priori state covariance and the determined gain coefficients of the sensors;

[0035] The estimating a first predicted value of a target physical parameter according to the first observation noise and the second observation noise includes:

[0036] A first predicted value of the target physical parameter is estimated based on the determined gain coefficient of each sensor and the measurement residual between each observation value and the second predicted value.

[0037] In one embodiment of the present invention, the target physical parameter is: heading angle;

[0038] The step of predicting a target physical parameter of the mobile device based on data collected by a sensor provided on the mobile device to obtain a second predicted value of the target physical parameter includes:

[0039] Predicting the heading angle change based on the roll angle, pitch angle, and the angular velocity of the roll angle and the angular velocity of the pitch angle collected by sensors mounted on the mobile device;

[0040] Based on the predicted heading angle change, a second predicted value of the heading angle is obtained.

[0041] According to another aspect of an embodiment of the present invention, a sensor-based physical parameter estimation device is provided, the device comprising:

[0042] An observation value acquisition module is configured to acquire a first observation value of a first sensor and a second observation value of a second sensor provided on the mobile device; wherein the first observation value is an observation value of a target physical parameter obtained based on data collected by the first sensor, and the second observation value is an observation value of the target physical parameter obtained based on data collected by the second sensor;

[0043] a difference calculation module, configured to calculate the difference between the first observation value and the second observation value;

[0044] a normalized error acquisition module, configured to acquire a normalized error based on the difference and observation noise of a target sensor, wherein the target sensor is: the first sensor or the second sensor;

[0045] an observation noise adjustment module, configured to adjust the first observation noise and the second observation noise according to the normalized error;

[0046] A numerical estimation module is used to estimate a first predicted value of a target physical parameter based on the first observation noise and the second observation noise.

[0047] In one embodiment of the present invention, the observation noise adjustment module is specifically used to: if the normalized error is greater than a preset error threshold, increase the first observation noise and the second observation noise to obtain a first adjusted noise of the first sensor and a second adjusted noise of the second sensor; if the normalized error is less than or equal to the preset error threshold, reduce the first observation noise and the second observation noise to obtain a third adjusted noise of the first sensor and a fourth adjusted noise of the second sensor.

[0048] In one embodiment of the present invention, the observation noise adjustment module is specifically used to: increase the first observation noise by a preset noise growth step to obtain an increased first observation noise; obtain a first adjustment noise based on the increased first observation noise; increase the second observation noise by a preset noise growth step to obtain an increased second observation noise; and obtain a second adjustment noise based on the increased second observation noise.

[0049] In one embodiment of the present invention, the observation noise adjustment module is specifically used to: if the increased first observation noise is greater than or equal to a preset noise upper limit threshold, then use the preset noise upper limit threshold as the first adjustment noise; if the increased first observation noise is less than the preset noise upper limit threshold, then use the increased first observation noise as the first adjustment noise.

[0050] In one embodiment of the present invention, the preset error threshold is determined in the following manner: the preset error threshold is determined from a preset chi-square distribution table according to preset degrees of freedom and a preset significance level value.

[0051] In one embodiment of the present invention, the observation noise of the sensor is represented by an observation noise matrix, wherein the observation noise matrix is a square matrix, the order of the observation noise matrix is the same as the number of sensors provided on the mobile device, and each diagonal element in the observation noise matrix corresponds to the observation noise of each sensor;

[0052] The observation noise adjustment module is specifically used to: increase the values of the diagonal elements corresponding to the first sensor in the observation noise matrix, and increase the values of the diagonal elements corresponding to the second sensor in the observation noise matrix; reduce the values of the diagonal elements corresponding to the first sensor in the observation noise matrix, and reduce the values of the diagonal elements corresponding to the second sensor in the observation noise matrix.

[0053] In one embodiment of the present invention, the observation noise adjustment module is specifically used to determine a gap representation value between the first observation value and the second observation value based on the difference; determine a normalization basis based on the observation noise of the target sensor; and obtain a normalized error based on the gap representation value and the normalization basis.

[0054] In one embodiment of the present invention, the device further comprises:

[0055] a parameter prediction module, configured to predict a target physical parameter of the mobile device based on data collected by a sensor provided on the mobile device, and obtain a second predicted value of the target physical parameter;

[0056] a covariance determination module, configured to determine a priori state covariance based on a posterior state covariance and a process noise covariance;

[0057] a gain coefficient determination module, configured to determine, for each sensor, a gain coefficient of the sensor based on the adjusted observation noise of the sensor and the prior state covariance, wherein the gain coefficient of the sensor represents a degree of change in noise uncertainty of an observation value obtained based on data collected by the sensor;

[0058] a covariance updating module, configured to determine an updated a posteriori state covariance based on the a priori state covariance and the determined gain coefficients of the sensors;

[0059] The observation noise adjustment module is specifically configured to estimate a first predicted value of a target physical parameter based on the determined gain coefficients of the sensors and the measurement residuals between the observation values and the second predicted values.

[0060] In one embodiment of the present invention, the target physical parameter is: heading angle;

[0061] The parameter prediction module is specifically used to: predict the heading angle change based on the roll angle, pitch angle, angular velocity of the roll angle and angular velocity of the pitch angle collected by sensors loaded on the mobile device; and obtain a second predicted value of the heading angle based on the predicted heading angle change.

[0062] According to another aspect of an embodiment of the present invention, there is provided an electronic device, comprising a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other via the communication bus;

[0063] Memory for storing computer programs;

[0064] The processor is configured to implement any of the above-mentioned sensor-based physical parameter estimation methods when executing a program stored in the memory.

[0065] According to another aspect of an embodiment of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the computer program implements any of the above-mentioned sensor-based physical parameter estimation methods.

[0066] According to yet another aspect of an embodiment of the present invention, a computer program product comprising instructions is provided, which, when executed on a computer, enables the computer to execute any of the above-mentioned sensor-based physical parameter estimation methods.

[0067] Beneficial effects of the embodiments of the present invention:

[0068] In the sensor-based physical parameter estimation method provided by an embodiment of the present invention, the observation noise of each sensor can characterize the noise with uncertainty contained in the data collected by the sensor. By adjusting the observation noise of each sensor, the gain coefficient of each sensor can be adjusted, and the weight of the observation value of each sensor in the physical parameter estimation can be indirectly adjusted. According to the normalized error obtained by the difference between the first observation value and the second observation value and the observation noise of the target sensor, the uncertainty of the data collected by the first sensor and the second sensor can be determined. Therefore, adjusting the observation noise according to the above-mentioned normalized error can be regarded as adjusting the observation noise according to the uncertainty of the data collected by the sensor. In this way, the weight of the observation value of each sensor in the physical parameter estimation can be flexibly adjusted according to the uncertainty of the data collected by the sensor, thereby reducing the impact of the data collected by the high-uncertainty sensor on the accuracy of the parameter estimation, improving the impact of the data collected by the low-uncertainty sensor on the accuracy of the parameter estimation, and improving the accuracy of the first predicted value of the estimated target physical parameter.

[0069] Of course, it is not necessary to achieve all of the advantages described above simultaneously in order to implement any product or method of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0070] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other embodiments can also be obtained based on these drawings.

[0071] Figure 1 A schematic flow chart of a sensor-based physical parameter estimation method provided in an embodiment of the present invention;

[0072] Figure 2 A schematic diagram of a flow chart of an observation noise adjustment method provided by an embodiment of the present invention;

[0073] Figure 3A schematic flow chart of another sensor-based physical parameter estimation method provided in an embodiment of the present invention;

[0074] Figure 4 A schematic flow chart of another sensor-based physical parameter estimation method provided in an embodiment of the present invention;

[0075] Figure 5 A schematic structural diagram of a sensor-based physical parameter estimation device provided in an embodiment of the present invention;

[0076] Figure 6 A schematic structural diagram of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0077] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field based on the present invention are within the scope of protection of the present invention.

[0078] The following describes the execution entities and application scenarios of the embodiments of the present invention.

[0079] The solutions provided by the embodiments of the present invention can be applied to various types of autonomous mobile devices, such as aircraft, vehicles, and robots. Specifically, they can be applied to devices such as drones and autonomous vehicles. These autonomous mobile devices can be widely used to perform various navigation tasks, such as unmanned driving, aerospace, and robotic navigation. For ease of description, the execution entities provided by the embodiments of the present invention are collectively referred to as mobile devices.

[0080] The following describes the frames.

[0081] The solution provided by the embodiment of the present invention can be executed periodically. For example, if the first physical parameter estimation is performed and the first predicted value of the target physical parameter is obtained, the obtained first predicted value can be referred to as: the first predicted value of the target physical parameter of the first frame; if the physical parameter estimation is performed again, that is, the second physical parameter estimation is performed, and the first predicted value of the target physical parameter is obtained again, the first predicted value obtained again can be referred to as: the first predicted value of the target physical parameter of the second frame; in this case, in the process of performing the second physical parameter estimation, the first predicted value of the target physical parameter of the first frame can be referred to as: the first predicted value of the target physical parameter of the previous frame; similarly, the data obtained in the process of performing the first physical parameter estimation can also become the data of the previous frame, for example, the second predicted value of the previous frame, the observation value of the previous frame, the observation noise of the previous frame, and the gain coefficient of the previous frame, etc. In other words, the data of the previous frame represents the data obtained when the physical parameter estimation was performed last time. Similarly, when performing the second physical parameter estimation, the first predicted value of the target physical parameter of the second frame can be referred to as the first predicted value of the target physical parameter of the current frame. Similarly, the data obtained during the second physical parameter estimation process can also be referred to as the data of the current frame, for example, the second predicted value of the current frame, the observed value of the current frame, the observed noise of the current frame, the gain coefficient of the current frame, and the fusion value of the current frame. For example, the following steps S101-S106 can be executed in a loop, and each execution of steps S101-S106 can obtain the first predicted value of the target physical parameter of this frame.

[0082] The solution provided by the embodiment of the present invention is described in detail below.

[0083] In one embodiment of the present invention, see Figure 1 A flow chart of a sensor-based physical parameter estimation method is provided, wherein the method includes the following steps S101-S106.

[0084] Step S101: Acquire a first observation value of a first sensor and a second observation value of a second sensor provided on a mobile device.

[0085] The first observation value is an observation value of a target physical parameter obtained based on data collected by the first sensor, and the second observation value is an observation value of a target physical parameter obtained based on data collected by the second sensor.

[0086] The target physical parameters are physical parameters of the mobile device. For example, the target physical parameters may include navigation and positioning parameters such as heading angle, roll angle, pitch angle, angular velocity, speed, acceleration, mileage, and position. The number of sensors mounted on the mobile device is at least two. The sensors mounted on the mobile device may include sensors of the same type or sensors of at least two different sensor types. The number of sensors of each sensor type may be one or more. The use of sensors of different sensor types is described in the embodiments below and will not be described in detail here.

[0087] Sensors of different sensor types can collect different types of data, and the methods for obtaining the observed values of the target physical parameters of the sensors for different types of data are also different. In addition, the methods for obtaining the observed values of the target physical parameters of the sensors for different types of target physical parameters are also different.

[0088] The following uses different types of target physical parameters as examples to illustrate how to obtain the observed values of the target physical parameters.

[0089] 1. The target physical parameters are: speed

[0090] The following describes a method for obtaining the observed value of the target physical parameter by taking the target physical parameter as an example.

[0091] If the sensor is an accelerometer, the mobile device may integrate the acceleration collected by the accelerometer to obtain an observed value of the speed of the mobile device.

[0092] If the sensor is a visual sensor, then the mobile device can use the optical flow method to obtain the observed value of the speed of the mobile device for the image data collected by the visual sensor.

[0093] 2. The target physical parameters are: heading angle

[0094] If the sensor is a gyroscope, the mobile device may integrate the angular velocity of the heading angle collected by the gyroscope to obtain an observed value of the heading angle of the mobile device.

[0095] If the sensor is a visual sensor, then the mobile device may use an optical flow method to obtain an observation value of the heading angle of the mobile device based on the image data collected by the visual sensor.

[0096] For each sensor, an observation value of the target physical parameter can be obtained in the above manner. That is, based on the data collected by the first sensor, a first observation value of the target physical parameter can be obtained. Similarly, based on the data collected by the second sensor, a second observation value of the target physical parameter can be obtained.

[0097] Step S102: Calculate the difference between the first observation value and the second observation value.

[0098] Specifically, the absolute value of the difference between the first observation value and the second observation value may be used as the difference between the first observation value and the second observation value.

[0099] Step S103: Obtain a normalized error based on the difference and the observation noise of the target sensor.

[0100] The target sensor is the first sensor or the second sensor. Each sensor is provided with an observation noise corresponding to the sensor.

[0101] In one implementation, the mobile device may obtain a normalized error between the first observation value and the second observation value based on the difference and the observation noise of the first sensor.

[0102] In another implementation, the mobile device may obtain a normalized error between the first observation value and the second observation value based on the difference and the observation noise of the second sensor.

[0103] In another implementation, the mobile device may obtain the normalized error between the first observation value and the second observation value based on the above-mentioned "one implementation" and "another implementation." In this case, the number of normalized errors between the first observation value and the second observation value obtained is two. Therefore, observation noise adjustment may be performed separately for each normalized error obtained. For example, assuming that the normalized errors between the two first observation values and the second observation value obtained are normalized error A and normalized error B, step S104 may be performed for normalized error A to perform observation noise adjustment for the observation noise of the first sensor and the observation noise of the second sensor. Then, step S104 may be performed for normalized error B to perform observation noise adjustment for the observation noise of the first sensor and the observation noise of the second sensor.

[0104] Specifically, in the above implementation, the mobile device may obtain the normalized error between the first observation value and the second observation value according to the following steps A to C:

[0105] Step A: Determine a difference representation value between the first observation value and the second observation value based on the difference.

[0106] In one implementation, the mobile device may calculate the square of the difference as a representation of the gap between the first observation value and the second observation value.

[0107] Step B: Determine the normalization basis based on the observation noise of the target sensor.

[0108] In one implementation, the mobile device may calculate the square value of the observation noise of the target sensor as the normalization basis.

[0109] Step C: Obtain a normalized error based on the gap representation value and the normalized basis.

[0110] In one implementation, the mobile device may calculate the quotient of the disparity representation value and the normalized basis as the normalized error. For example, the normalized error may be obtained using the following expression:

[0111] ;

[0112] in, is the normalized error, is the above difference, is the observation noise of the target sensor, is the gap characterization value, The normalized basis is used. This allows for more accurate calculation of the normalized error between the first and second observation values, more accurately determining whether there is a significant difference between the first observation value of the first sensor and the second observation value of the second sensor. This allows for more accurate adjustment of the sensor's observation noise, improving the accuracy of the target physical parameter estimation.

[0113] Step S104: adjusting the first observation noise of the first sensor and the second observation noise of the second sensor according to the normalized error.

[0114] The specific implementation of step S104 of the observation noise adjustment will be described in the following embodiments and will not be described in detail here.

[0115] Furthermore, when the number of sensors provided on the mobile device is three or more, the mobile device may perform steps S101-S104 for each sensor group to adjust the observation noise of the sensor group until the observation noise adjustment is performed for each sensor group. A sensor group includes two different sensors from among the sensors provided on the mobile device. The mobile device may loop through each sensor group. For example, looping through each sensor group may be implemented using a double loop, such as (for (int i = 0; ...; i++) and for (int j = i + 1; ...; j++)).

[0116] Step S105: Estimate a first predicted value of the target physical parameter according to the first observation noise and the second observation noise.

[0117] The specific implementation of the first prediction value estimation in step S105 is described in the following embodiments and will not be described in detail here.

[0118] In the sensor-based physical parameter estimation method provided by an embodiment of the present invention, the observation noise of each sensor can characterize the noise with uncertainty contained in the data collected by the sensor. By adjusting the observation noise of each sensor, the gain coefficient of each sensor can be adjusted, and the weight of the observation value of each sensor in the physical parameter estimation can be indirectly adjusted. According to the normalized error obtained by the difference between the first observation value and the second observation value and the observation noise of the target sensor, the uncertainty of the data collected by the first sensor and the second sensor can be determined. Therefore, adjusting the observation noise according to the above-mentioned normalized error can be regarded as adjusting the observation noise according to the uncertainty of the data collected by the sensor. In this way, the weight of the observation value of each sensor in the physical parameter estimation can be flexibly adjusted according to the uncertainty of the data collected by the sensor, thereby reducing the impact of the data collected by the high-uncertainty sensor on the accuracy of the parameter estimation, improving the impact of the data collected by the low-uncertainty sensor on the accuracy of the parameter estimation, and improving the accuracy of the first predicted value of the estimated target physical parameter.

[0119] In one embodiment of the present invention, the sensors installed on the mobile device include sensors of at least two different sensor types.

[0120] Different sensor types can be used for different target physical parameters. For example, if the target physical parameter is heading angle, sensor types may include: magnetometer, RTK (Real-time Kinematic) antenna, vision sensor, gyroscope, and ESKF (Error State Kalman Filter). If the target physical parameter is mileage, sensor types may include: accelerometer, odometer, and GNSS (Global Navigation Satellite System).

[0121] A single type of sensor may have limitations depending on the working environment of different mobile devices.

[0122] The limitations of different types of sensors are explained below.

[0123] Magnetometers are significantly affected by changes in the ambient magnetic field, especially in complex environments such as cities and underground. For example, the presence of various facilities in cities that can easily cause large changes in the magnetic field can easily interfere with the magnetometer, leading to large errors in the heading angle obtained from the data collected by the magnetometer.

[0124] RTK antennas offer the advantage of high precision, but they impose strict requirements on the operating environment of the mobile device. For example, if the mobile device's operating environment requires signal obstruction, the accuracy of the data collected by the RTK antenna will be significantly affected. Furthermore, the accuracy of the data collected by the RTK antenna can be affected by factors such as satellite line of sight, making it difficult for the RTK antenna to provide accurate data in harsh operating environments.

[0125] Visual sensors rely on the clarity of visual features. In low-light and complex working environments, it is difficult for visual sensors to obtain clear visual data. If the clarity of visual features is low, it will be difficult to obtain accurate data based on the data collected by the visual sensors.

[0126] The gyroscope can provide continuous angular velocity data, but over time, due to its characteristics, a drift effect will occur, which will cause the errors in the data collected by the gyroscope to gradually accumulate, making it difficult to obtain accurate data.

[0127] The use of sensors of multiple different sensor types can adjust the observation noise of sensors with lower uncertainty in the data collected by the sensors in the current working environment in order to deal with the noise changes of sensors in a dynamically changing and complex working environment, thereby increasing the weight of the observation values of the above sensors in the estimation of physical parameters, and reducing the weight of the observation values of sensors with lower uncertainty in the data collected by the sensors in the estimation of physical parameters, thereby improving the accuracy of the first predicted value of the target physical parameter.

[0128] Using multiple sensors of different sensor types can effectively reduce the interference and noise issues that a single sensor type may be subject to from the operating environment. While one sensor type is affected by interference and noise from the operating environment, sensors of other sensor types may be less affected. For sensors of other sensor types, the observed values of the target physical parameters obtained based on data collected by the other sensor types remain relatively accurate, thereby improving the accuracy of the first predicted values of the target physical parameters. This allows for automatic adjustment of observation noise and gain factors based on the uncertainty of data from different sensors, improving the stability and accuracy of the mobile device in different operating environments, and enhancing the robustness and reliability of the mobile device. By combining the advantages of multiple sensor types, the accuracy of target physical parameter estimation is significantly improved, reducing reliance on high-precision sensors. The sensor-based physical parameter estimation method provided by embodiments of the present invention can provide more stable and accurate target physical parameter estimation in the face of varying environmental interference, such as electromagnetic interference, lighting conditions, multipath effects in urban environments, and signal obstruction in mountainous areas.

[0129] The specific implementation of the observation noise adjustment in step S104 is described below.

[0130] In one embodiment of the present invention, see Figure 2 A flow chart of an observation noise adjustment method is provided, wherein the method includes the following steps S201-S203 for adjusting the observation noise.

[0131] Step S201: Determine whether the normalized error is greater than a preset error threshold.

[0132] If the judgment is yes, that is, the normalized error is greater than the preset error threshold, step S202 is executed; if the judgment is no, that is, the normalized error is less than or equal to the preset error threshold, step S203 is executed.

[0133] Step S202: adding the first observation noise and the second observation noise to obtain a first adjusted noise of the first sensor and a second adjusted noise of the second sensor.

[0134] The following describes a method of adding the first observation noise to obtain the first adjustment noise.

[0135] The mobile device may increase the first observed noise by a preset noise growth step to obtain an increased first observed noise, obtain a first adjusted noise based on the increased first observed noise, and use the obtained first adjusted noise as the adjusted first observed noise.

[0136] For example, the noise growth step can be set to 0.1f, where f is the value of the observation noise of the first sensor before adjustment. Then, the value of the observation noise of the first sensor after increase is: f+0.1f, and f+0.1f is used as the adjusted first observation noise.

[0137] Furthermore, a noise upper threshold value may be set to set the maximum value of the observed noise. The mobile device may obtain the first adjusted noise based on the increased first observed noise in the following manner:

[0138] If the increased first observed noise is greater than or equal to the preset noise upper threshold, the preset noise upper threshold is used as the first adjusted noise. If the increased first observed noise is less than the preset noise upper threshold, the increased first observed noise is used as the first adjusted noise.

[0139] Similarly, the mobile device can add the second observation noise to obtain the second adjustment noise in the same manner as described above to obtain the first adjustment noise:

[0140] The mobile device may increase the second observed noise by a preset noise growth step to obtain an increased second observed noise, obtain a second adjusted noise based on the increased second observed noise, and use the obtained second adjusted noise as the adjusted second observed noise.

[0141] For example, the noise growth step size can be set to 0.1f, where f is the value of the second sensor's observation noise before adjustment. The value of the second sensor's observation noise after adjustment is f + 0.1f, with f + 0.1f being the adjusted second observation noise. The noise growth step size represents the step size for each increase in the observation noise. By setting different noise growth step sizes, the sensitivity of the observation noise increase can be adjusted. Different noise growth step sizes can be selected based on different actual conditions, increasing the flexibility of noise adjustment.

[0142] Furthermore, a noise upper threshold value may be set to set the maximum value of the observed noise. The mobile device may obtain the second adjusted noise based on the increased second observed noise in the following manner:

[0143] If the increased second observed noise is greater than or equal to the preset noise upper threshold, the preset noise upper threshold is used as the second adjusted noise. If the increased second observed noise is less than the preset noise upper threshold, the increased second observed noise is used as the second adjusted noise.

[0144] Specifically, the mobile device can obtain the first adjusted noise according to the following expression: R = min(MAX_R, f + 0.1f), where R is the first observed noise and MAX_R is the preset upper noise threshold. Similarly, the second adjusted noise can also be obtained according to the above expression, where R is the second observed noise.

[0145] In this way, by setting the upper noise threshold of the observation noise, the adjustment range of the observation noise is limited so that the observation noise will not be too large, thereby reducing the situation that causes instability in the physical parameter estimation method and improving the stability of physical parameter estimation in mobile devices.

[0146] Step S203: reducing the first observation noise and the second observation noise to obtain a third adjusted noise of the first sensor and a fourth adjusted noise of the second sensor.

[0147] The following describes a method for reducing the first observation noise to obtain the third adjustment noise.

[0148] The mobile device may reduce the first observed noise by a preset noise reduction step size to obtain a reduced first observed noise, obtain a third adjusted noise based on the reduced first observed noise, and use the obtained third adjusted noise as the adjusted first observed noise.

[0149] For example, the noise reduction step size can be set to 0.05f, where f is the value of the observation noise of the first sensor before adjustment. The value of the observation noise of the first sensor after adjustment is then: f - 0.05f, with f - 0.05f being the adjusted first observation noise. The noise reduction step size represents the step size for each increase in the observation noise. By setting different noise reduction step sizes, the sensitivity of the observation noise reduction can be adjusted. Different noise reduction step sizes can be selected according to different actual situations, which increases the flexibility of noise adjustment.

[0150] Furthermore, a noise lower limit threshold may be set to set the minimum value of the observed noise. The mobile device may obtain the third adjusted noise based on the reduced first observed noise in the following manner:

[0151] If the reduced first observation noise is greater than or equal to the preset noise upper threshold, the preset noise upper threshold is used as the third adjusted noise. If the reduced first observation noise is less than the preset noise upper threshold, the reduced first observation noise is used as the third adjusted noise.

[0152] Similarly, the mobile device can reduce the second observation noise to obtain the fourth adjusted noise in the same manner as described above, by reducing the first observation noise to obtain the third adjusted noise:

[0153] The mobile device may reduce the second observed noise by a preset noise reduction step size to obtain a reduced second observed noise, obtain a fourth adjusted noise based on the reduced second observed noise, and use the obtained fourth adjusted noise as the adjusted second observed noise.

[0154] For example, the noise reduction step size can be set to 0.05f, where f is the value of the observation noise of the second sensor before adjustment. Then, the value of the observation noise of the second sensor after reduction is: f-0.05f, and f-0.05f is used as the adjusted second observation noise.

[0155] Furthermore, a noise lower limit threshold may be set to set the minimum value of the observed noise. The mobile device may obtain the fourth adjusted noise based on the reduced second observed noise in the following manner:

[0156] If the reduced second observed noise is greater than or equal to the preset noise floor threshold, the reduced second observed noise is used as the fourth adjusted noise. If the reduced second observed noise is less than the preset noise floor threshold, the preset noise floor threshold is used as the fourth adjusted noise.

[0157] The mobile device can obtain the third adjusted noise using the following expression: R = max(MIN_R, f - 0.05f), where R is the first observed noise and MIN_R is the preset noise floor threshold. Similarly, the fourth adjusted noise can also be obtained using the above expression, where R is the second observed noise.

[0158] In this way, by setting the noise lower limit threshold of the observation noise, the adjustment range of the observation noise is limited so that the observation noise will not be too small, thereby reducing the situation that causes instability of the physical parameter estimation method and improving the stability of physical parameter estimation of mobile devices.

[0159] As can be seen above, based on the consistency of the readings of the first and second sensors, that is, based on the difference between the first and second observations, the uncertainty of the data collected by the first and second sensors can be determined using the obtained normalized error. When the difference between the readings of the first and second sensors is too large, it indicates that the first and second sensors may be faulty, interfered with, or have low accuracy. In other words, the uncertainty of the data collected by the first and second sensors is high and the reliability is low. The observation noise of the first and second sensors can be increased, thereby reducing the weight of the first and second observations in the estimation of physical parameters. Conversely, when the difference between the readings of the first and second sensors is small, it indicates that the credibility of the data collected by the first and second sensors is high. In other words, the uncertainty of the data collected by the first and second sensors is low and the reliability is high. The observation noise of the first and second sensors can be reduced, thereby increasing the weight of the first and second observations in the estimation of physical parameters. By comparing the normalized error with a preset error threshold, the uncertainty and reliability of the data from the first and second sensors are determined. This adaptively and dynamically adjusts the observation noise based on real-time changes in the sensor data. This can increase the influence of observations from sensors with low uncertainty, reduce the influence of observations from sensors with high uncertainty, and improve the accuracy of the first predicted value of the target physical parameter. This physical parameter estimation method allows mobile devices to adapt to diverse operating environments and conditions.

[0160] In one embodiment of the present invention, the preset error threshold is: the preset error threshold is determined in the following manner: according to the preset degrees of freedom and the preset significance level value, the preset error threshold is determined from a preset chi-square distribution table.

[0161] The preset error threshold is a statistical threshold that represents the probability of a significant difference between the observed values of the target physical parameter obtained from data collected by different sensors at a preset confidence level. For example, for the assumption that "when the normalized error between the observed values of the target parameter obtained from data collected by two different sensors is greater than or equal to the preset error threshold, there is a significant difference between the two observed values," the degree of freedom can be determined to be 1. For another example, the preset confidence level can be 95%. For a chi-square distribution with 1 degree of freedom, the corresponding preset error threshold for a 95% confidence level can be found in a preset chi-square distribution table as 3.8415.

[0162] In this way, it is possible to accurately determine whether there are significant differences between multiple sensor readings, that is, to determine whether there is a significant difference in the error between the first observation value and the second observation value. The observation noise of each sensor can be adjusted more accurately to improve the accuracy of the first predicted value of the target physical parameter.

[0163] In one embodiment of the present invention, the observation noise of each sensor is represented by an observation noise matrix.

[0164] The observation noise matrix is a square matrix, the order of the observation noise matrix is the same as the number of sensors, and each diagonal element in the observation noise matrix corresponds to the observation noise of each sensor.

[0165] The above step S104 can adjust the observation noise of the first sensor and the second sensor in the following manner: adjust the values of the diagonal elements corresponding to the first sensor in the observation noise matrix, and adjust the values of the diagonal elements corresponding to the second sensor in the observation noise matrix.

[0166] In one implementation, when increasing the observation noise of the first sensor and the observation noise of the second sensor, the mobile device may increase the values of the diagonal elements corresponding to the first sensor in the observation noise matrix and increase the values of the diagonal elements corresponding to the second sensor in the observation noise matrix. When decreasing the observation noise of the first sensor and the observation noise of the second sensor, the mobile device may decrease the values of the diagonal elements corresponding to the first sensor in the observation noise matrix and decrease the values of the diagonal elements corresponding to the second sensor in the observation noise matrix.

[0167] For example, if the number of sensors is 3, then the observation noise matrix is a 3×3 square matrix. Assuming that the observation noise A of sensor A is 0.1, the observation noise B of sensor B is 0.5, and the observation noise C of sensor C is 0.3, the observation noise matrix can be expressed as follows: For example, if the first sensor and the second sensor are sensor A and sensor B respectively, when adjusting the first observation noise of the first sensor, the elements in the above observation noise matrix are When adjusting the second observation noise of the second sensor, the elements in the above observation noise matrix are adjusted. Make adjustments.

[0168] In addition, if there is correlation between the observation noises of various sensors, then when adjusting the observation noise, elements other than the diagonal elements in the above-mentioned observation noise matrix may also be adjusted.

[0169] By using the observation noise matrix to represent the noise of each sensor, the observation noise corresponding to each sensor can be adjusted more conveniently by adjusting the diagonal elements in the observation noise matrix, thereby improving the flexibility of adjusting the observation noise.

[0170] In one embodiment of the present invention, see Figure 3 A flow chart of another sensor-based physical parameter estimation method is provided. The method includes the following steps S301-S307. The above step S104 can be implemented by the following step S302:

[0171] Step S301: Based on data collected by a sensor provided on the mobile device, a value of a target physical parameter of the mobile device is predicted to obtain a second predicted value of the target physical parameter.

[0172] For different types of target physical parameters, different prediction methods can be used to predict the second predicted value of the target physical parameter. The following description will be made using the example of the target physical parameter being mileage or heading angle.

[0173] Case 1: The target physical parameter is mileage

[0174] The mobile device can predict the mileage value of the mobile device based on the first predicted value of the mileage of the previous frame obtained by fusing data collected by sensors mounted on the mobile device of the previous frame, and obtain a second predicted value of the mileage.

[0175] Specifically, the mobile device may calculate the product of a preset state transition matrix and the first predicted value of the mileage of the previous frame as the second predicted value of the mileage.

[0176] Case 2: The target physical parameter is the heading angle

[0177] The mobile device predicts a heading angle change based on the roll angle, the pitch angle, and the angular velocity of the roll angle and the angular velocity of the pitch angle collected by sensors mounted on the mobile device. The historical heading angle is updated based on the predicted heading angle change to obtain a second predicted heading angle value.

[0178] The mobile device can obtain a roll angle based on a roll angle acquisition system of the mobile device, and can obtain a pitch angle based on a roll angle acquisition system of the mobile device. The angular velocity of the roll angle and the angular velocity of the pitch angle can be obtained using a gyroscope mounted on the mobile device. The historical heading angle can include a first predicted value of the heading angle of the previous frame.

[0179] In this way, the heading angle prediction can be performed without directly using the heading angle and the angular velocity of the heading angle. A different method than that of obtaining the heading angle using sensor data can be used to predict the heading angle, thereby reducing the impact of the sensor data on the second predicted value. This can improve the accuracy of the obtained second predicted value, and thereby improve the accuracy of the obtained first predicted value of the heading angle.

[0180] Specifically, the mobile device predicts the heading angle change according to the following expression:

[0181] ;

[0182] in, is the heading angle change, is the roll angle, is the pitch angle, is the angular velocity of the pitch angle, is the angular velocity of the roll angle.

[0183] Then, after obtaining the heading angle change, the mobile device may add the obtained heading angle change to the historical heading angle to obtain a second predicted value of the heading angle.

[0184] In this way, using the above expression to obtain the heading angle change and then predicting the second predicted value of the heading angle can further improve the accuracy of the obtained second predicted value.

[0185] Furthermore, after obtaining the second predicted value of the heading angle, the second predicted value may be normalized to limit the second predicted value to the range of [-π, π]. For example, a preset normalization function may be used to implement the normalization of the second predicted value.

[0186] Furthermore, the data collected by the sensors on the mobile device used to obtain the second predicted value may be different from the data used to obtain the observed value of the target physical parameter. For example, when obtaining the observed value of the heading angle, the mobile device may obtain the observed value of the heading angle of the mobile device using the angular velocity of the heading angle collected by a gyroscope. However, when obtaining the second predicted value of the heading angle of the mobile device, the data used may include: the roll angle, the pitch angle, the angular velocity of the roll angle, and the angular velocity of the pitch angle collected by the sensors on the mobile device, but does not include the angular velocity of the heading angle.

[0187] Step S302: Determine the priori state covariance based on the posterior state covariance and the process noise covariance.

[0188] Specifically, the mobile device can determine the prior state covariance of the current frame based on the posterior state covariance of the previous frame obtained by the last physical parameter estimation and the preset process noise covariance. The mobile device can determine the prior state covariance of the current frame according to the following expression:

[0189] ;

[0190] in, The current frame is the prior estimated covariance, is the preset state covariance matrix, is the posterior estimated covariance of the previous frame, is the transposed matrix of the preset state covariance matrix, is the preset process noise covariance matrix.

[0191] Step S303: Acquire a first observation value of a first sensor and a second observation value of a second sensor provided on the mobile device.

[0192] Step S304: Calculate the difference between the first observation value and the second observation value; obtain a normalized error based on the difference and the observation noise of the target sensor; and adjust the first observation noise and the second observation noise based on the normalized error.

[0193] The target sensor is: the first sensor or the second sensor.

[0194] Steps S303-S304 are the same as the above steps S101-S104 and will not be described in detail here.

[0195] Step S305: for each sensor, determine the gain coefficient of the sensor based on the adjusted observation noise of the sensor and the priori state covariance.

[0196] The sensor's gain coefficient represents the degree of change in the noise uncertainty of the observations obtained based on the data collected by the sensor. A larger value for the sensor's gain coefficient indicates a smaller degree of change in the noise uncertainty of the observations obtained based on the data collected by the sensor. Consequently, the credibility of the observations obtained based on the data collected by the sensor is higher, and the credibility of the second predicted value is lower. Conversely, a larger value for the sensor's gain coefficient indicates a larger degree of change in the noise uncertainty of the observations obtained based on the data collected by the sensor. Consequently, the credibility of the observations obtained based on the data collected by the sensor is lower, and the credibility of the second predicted value is higher.

[0197] Specifically, the mobile device may determine the gain coefficient of the current frame based on the adjusted observation noises and the prior state covariance of the current frame in the following manner:

[0198] ;

[0199] in is the gain coefficient of the current frame, The current frame is the prior estimated covariance, is the identity matrix, is the transposed matrix of the identity matrix, is the adjusted observation noise of the current frame.

[0200] Step S306: Determine an updated a posteriori state covariance based on the a priori state covariance and the determined gain coefficients of the sensors.

[0201] Among them, the updated posterior state covariance can be the posterior state covariance of the current frame

[0202] ;

[0203] in, is the posterior state covariance of the current frame, is the identity matrix.

[0204] Step S307: Estimate a first predicted value of the target physical parameter based on the determined gain coefficients of the sensors and the measurement residuals between the observed values and the second predicted values.

[0205] Specifically, the mobile device may subtract the second predicted value from the observed value of each sensor to obtain the measurement residual between the observed value of the sensor and the second predicted value, and then multiply the gain coefficient of each sensor in matrix form by the measurement residual of each sensor in matrix form to obtain the first predicted value

[0206] In step S307, a first predicted value of the target physical parameter of the mobile device can be obtained according to the following expression:

[0207] ;

[0208] in, is the second predicted value of the target physical parameter of the current frame, is the observed value of the target physical parameter of each sensor in the current frame, is the measurement residual between each observation and the second predicted value.

[0209] Step S307 is the same as the above-mentioned step S105 and will not be described in detail here.

[0210] In addition, the first predicted value, the second predicted value, and the observed value can be the actual value of the target physical parameter or the value change of the target physical parameter. For example, when the target physical parameter is the heading angle, the first predicted value calculated by the mobile device can be: the heading angle change. In this case, the mobile device can update the actual value of the heading angle obtained in the previous frame based on the first predicted value of the current frame to obtain the actual value of the heading angle in the current frame. For example, the update can be performed according to the following expression:

[0211] ;

[0212] in, is the first predicted value of the current frame, is the actual value of the heading angle obtained in the previous frame. The actual value of the heading angle of the current frame.

[0213] In summary, combining the influence of the uncertainty of the second predicted value and the uncertainty of the observed value, in order to reduce the direction of the uncertainty of the second predicted value and the uncertainty of the observed value, comprehensively considering the second predicted value and the observed value, and re-determining the posterior state covariance can reduce the uncertainty of the state estimation, and improve the accuracy of the subsequent calculation of the gain coefficient that characterizes the degree of change of the noise uncertainty of the observed value, thereby improving the accuracy of the first predicted value of the target physical parameter.

[0214] In this way, the use of adaptive Kalman filtering technology can dynamically adjust the sensor's observation noise, adapt to different environmental changes and different sensor qualities, reduce the error accumulation of a single sensor, and improve the accuracy of the first predicted value of the estimated target physical parameter. Furthermore, by using adaptive Kalman filtering technology to fuse data collected by multiple sensors, it can effectively combine the data collected by multiple sensors to estimate physical parameters, reducing the computational bottlenecks that may be encountered in traditional methods, and supporting more efficient and faster estimation of target physical parameters, thereby improving the efficiency of target physical parameter estimation.

[0215] In addition, before step S101, if the initialization setting has not been performed, the initialization setting needs to be performed. For example, when the physical parameter estimation is performed for the first time, the initialization setting needs to be performed. If the initialization setting has been performed when the physical parameter estimation is performed subsequently, the above step S101 can be executed directly. For example, for the case one of the above step S101: when the target physical parameter is mileage, the initialization setting may include setting the initial mileage and setting other parameters. For the case two of the above step S101: when the target physical parameter is the heading angle, the initialization setting may include setting the initial heading angle and setting other parameters. Among them, setting other parameters may include setting the initial posterior estimation covariance, the unit matrix H, the initial observation noise of each sensor, and the process noise covariance, etc.

[0216] For example, see Figure 4 A flow chart of another sensor-based physical parameter estimation method is provided, wherein the method includes the following steps S401-S408:

[0217] Step S401: Determine whether initialization settings have been performed.

[0218] If the judgment is yes, execute step S403; if the judgment is no, execute step S402.

[0219] Step S402: Perform initialization settings.

[0220] After executing step S402, execute step S403.

[0221] Step S403: Acquire a first observation value of a first sensor and a second observation value of a second sensor provided on the mobile device.

[0222] Step S404: Calculate the difference between the first observation value and the second observation value.

[0223] Step S405: Obtain a normalized error based on the difference and the observation noise of the target sensor.

[0224] Step S406: adjusting the first observation noise of the first sensor and the second observation noise of the second sensor according to the normalized error.

[0225] Step S407: Estimate a first predicted value of the target physical parameter according to the first observation noise and the second observation noise.

[0226] The above steps S403-S407 are the same as steps S101-S105 and will not be described in detail here.

[0227] Corresponding to the above-mentioned sensor-based physical parameter estimation method, an embodiment of the present invention further provides a sensor-based physical parameter estimation device. Figure 5 A schematic diagram of the structure of a sensor-based physical parameter estimation device is provided, the device comprising:

[0228] An observation value obtaining module 501 is configured to obtain a first observation value of a first sensor and a second observation value of a second sensor provided on the mobile device; wherein the first observation value is an observation value of a target physical parameter obtained based on data collected by the first sensor, and the second observation value is an observation value of the target physical parameter obtained based on data collected by the second sensor;

[0229] A difference calculation module 502 is configured to calculate the difference between the first observation value and the second observation value;

[0230] a normalized error acquisition module 503, configured to acquire a normalized error based on the difference and observation noise of a target sensor, wherein the target sensor is: the first sensor or the second sensor;

[0231] an observation noise adjustment module 504, configured to adjust the first observation noise and the second observation noise according to the normalized error;

[0232] The numerical estimation module 505 is configured to estimate a first predicted value of a target physical parameter according to the first observation noise and the second observation noise.

[0233] In the sensor-based physical parameter estimation method provided by an embodiment of the present invention, the observation noise of each sensor can characterize the noise with uncertainty contained in the data collected by the sensor. By adjusting the observation noise of each sensor, the gain coefficient of each sensor can be adjusted, and the weight of the observation value of each sensor in the physical parameter estimation can be indirectly adjusted. According to the normalized error obtained by the difference between the first observation value and the second observation value and the observation noise of the target sensor, the uncertainty of the data collected by the first sensor and the second sensor can be determined. Therefore, adjusting the observation noise according to the above-mentioned normalized error can be regarded as adjusting the observation noise according to the uncertainty of the data collected by the sensor. In this way, the weight of the observation value of each sensor in the physical parameter estimation can be flexibly adjusted according to the uncertainty of the data collected by the sensor, thereby reducing the impact of the data collected by the high-uncertainty sensor on the accuracy of the parameter estimation, improving the impact of the data collected by the low-uncertainty sensor on the accuracy of the parameter estimation, and improving the accuracy of the first predicted value of the estimated target physical parameter.

[0234] In one embodiment of the present invention, the observation noise adjustment module is specifically used to: if the normalized error is greater than a preset error threshold, increase the first observation noise and the second observation noise to obtain a first adjusted noise of the first sensor and a second adjusted noise of the second sensor; if the normalized error is less than or equal to the preset error threshold, reduce the first observation noise and the second observation noise to obtain a third adjusted noise of the first sensor and a fourth adjusted noise of the second sensor.

[0235] As can be seen above, based on the consistency of the readings of the first and second sensors, that is, based on the difference between the first and second observations, the uncertainty of the data collected by the first and second sensors can be determined using the obtained normalized error. When the difference between the readings of the first and second sensors is too large, it indicates that the first and second sensors may be faulty, interfered with, or have low accuracy. In other words, the uncertainty of the data collected by the first and second sensors is high and the reliability is low. The observation noise of the first and second sensors can be increased, thereby reducing the weight of the first and second observations in the estimation of physical parameters. Conversely, when the difference between the readings of the first and second sensors is small, it indicates that the credibility of the data collected by the first and second sensors is high. In other words, the uncertainty of the data collected by the first and second sensors is low and the reliability is high. The observation noise of the first and second sensors can be reduced, thereby increasing the weight of the first and second observations in the estimation of physical parameters. By comparing the normalized error with a preset error threshold, the uncertainty and reliability of the data from the first and second sensors are determined. This adaptively and dynamically adjusts the observation noise based on real-time changes in the sensor data. This can increase the influence of observations from sensors with low uncertainty, reduce the influence of observations from sensors with high uncertainty, and improve the accuracy of the first predicted value of the target physical parameter. This physical parameter estimation method allows mobile devices to adapt to diverse operating environments and conditions.

[0236] In one embodiment of the present invention, the observation noise adjustment module is specifically used to: increase the first observation noise by a preset noise growth step to obtain an increased first observation noise; obtain a first adjustment noise based on the increased first observation noise; increase the second observation noise by a preset noise growth step to obtain an increased second observation noise; and obtain a second adjustment noise based on the increased second observation noise.

[0237] The noise growth step size represents the step size for increasing the observation noise each time. By setting different noise growth step sizes, the sensitivity of increasing the observation noise can be adjusted. Different noise growth step sizes can be selected according to different actual conditions, which can improve the flexibility of noise adjustment.

[0238] In one embodiment of the present invention, the observation noise adjustment module is specifically used to: if the increased first observation noise is greater than or equal to a preset noise upper limit threshold, then use the preset noise upper limit threshold as the first adjustment noise; if the increased first observation noise is less than the preset noise upper limit threshold, then use the increased first observation noise as the first adjustment noise.

[0239] In this way, by setting the upper noise threshold of the observation noise, the adjustment range of the observation noise is limited so that the observation noise will not be too large, thereby reducing the situation that causes instability in the physical parameter estimation method and improving the stability of physical parameter estimation in mobile devices.

[0240] In one embodiment of the present invention, the preset error threshold is determined in the following manner: the preset error threshold is determined from a preset chi-square distribution table according to preset degrees of freedom and a preset significance level value.

[0241] In this way, it is possible to accurately determine whether there are significant differences between multiple sensor readings, that is, to determine whether there is a significant difference in the error between the first observation value and the second observation value. The observation noise of each sensor can be adjusted more accurately to improve the accuracy of the first predicted value of the target physical parameter.

[0242] In one embodiment of the present invention, the observation noise of the sensor is represented by an observation noise matrix, wherein the observation noise matrix is a square matrix, the order of the observation noise matrix is the same as the number of sensors provided on the mobile device, and each diagonal element in the observation noise matrix corresponds to the observation noise of each sensor;

[0243] The observation noise adjustment module is specifically used to: increase the values of the diagonal elements corresponding to the first sensor in the observation noise matrix, and increase the values of the diagonal elements corresponding to the second sensor in the observation noise matrix; reduce the values of the diagonal elements corresponding to the first sensor in the observation noise matrix, and reduce the values of the diagonal elements corresponding to the second sensor in the observation noise matrix.

[0244] By using the observation noise matrix to represent the noise of each sensor, the observation noise corresponding to each sensor can be adjusted more conveniently by adjusting the diagonal elements in the observation noise matrix, thereby improving the flexibility of adjusting the observation noise.

[0245] In one embodiment of the present invention, the observation noise adjustment module is specifically used to determine a gap representation value between the first observation value and the second observation value based on the difference; determine a normalization basis based on the observation noise of the target sensor; and obtain a normalized error based on the gap representation value and the normalization basis.

[0246] In this way, the normalized error between the first observation value and the second observation value can be calculated more accurately to more accurately determine whether there is a significant difference between the first observation value of the first sensor and the second observation value of the second sensor, and then more accurately adjust the observation noise of the sensor to improve the accuracy of the target physical parameter estimation.

[0247] In one embodiment of the present invention, the device further comprises:

[0248] a parameter prediction module, configured to predict a target physical parameter of the mobile device based on data collected by a sensor provided on the mobile device, and obtain a second predicted value of the target physical parameter;

[0249] a covariance determination module, configured to determine a priori state covariance based on a posterior state covariance and a process noise covariance;

[0250] a gain coefficient determination module, configured to determine, for each sensor, a gain coefficient of the sensor based on the adjusted observation noise of the sensor and the prior state covariance, wherein the gain coefficient of the sensor represents a degree of change in noise uncertainty of an observation value obtained based on data collected by the sensor;

[0251] a covariance updating module, configured to determine an updated a posteriori state covariance based on the a priori state covariance and the determined gain coefficients of the sensors;

[0252] The observation noise adjustment module is specifically configured to estimate a first predicted value of a target physical parameter based on the determined gain coefficients of the sensors and the measurement residuals between the observation values and the second predicted values.

[0253] In summary, combining the influence of the uncertainty of the second predicted value and the uncertainty of the observed value, in order to reduce the direction of the uncertainty of the second predicted value and the uncertainty of the observed value, comprehensively considering the second predicted value and the observed value, and re-determining the posterior state covariance can reduce the uncertainty of the state estimation, and improve the accuracy of the subsequent calculation of the gain coefficient that characterizes the degree of change of the noise uncertainty of the observed value, thereby improving the accuracy of the first predicted value of the target physical parameter.

[0254] In one embodiment of the present invention, the target physical parameter is: heading angle;

[0255] The parameter prediction module is specifically used to: predict the heading angle change based on the roll angle, pitch angle, angular velocity of the roll angle and angular velocity of the pitch angle collected by sensors loaded on the mobile device; and obtain a second predicted value of the heading angle based on the predicted heading angle change.

[0256] In this way, the heading angle prediction can be performed without directly using the heading angle and the angular velocity of the heading angle. A different method than that of obtaining the heading angle using sensor data can be used to predict the heading angle, thereby reducing the impact of the sensor data on the second predicted value. This can improve the accuracy of the obtained second predicted value, and thereby improve the accuracy of the obtained first predicted value of the heading angle.

[0257] The embodiment of the present invention further provides an electronic device, such as Figure 6 As shown, it includes a processor 601 , a communication interface 602 , a memory 603 and a communication bus 604 , wherein the processor 601 , the communication interface 602 , and the memory 603 communicate with each other via the communication bus 604 .

[0258] Memory 603, used for storing computer programs;

[0259] The processor 601 is configured to implement any of the above-mentioned sensor-based physical parameter estimation methods when executing the program stored in the memory 603 .

[0260] The communication bus mentioned in the electronic devices mentioned above can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus. This communication bus can be divided into address buses, data buses, control buses, etc. For ease of illustration, only a single thick line is used in the figure, but this does not mean that there is only one bus or only one type of bus.

[0261] The communication interface is used for communication between the above electronic device and other devices.

[0262] The memory may include random access memory (RAM) or non-volatile memory (NVM), such as at least one disk storage. Alternatively, the memory may be at least one storage device located away from the processor.

[0263] The above-mentioned processor can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, and discrete hardware components.

[0264] In another embodiment of the present invention, a computer-readable storage medium is provided, in which a computer program is stored. When the computer program is executed by a processor, any of the above-mentioned sensor-based physical parameter estimation methods is implemented.

[0265] In another embodiment of the present invention, a computer program product including instructions is provided, which, when executed on a computer, enables the computer to execute any of the above-mentioned sensor-based physical parameter estimation methods.

[0266] In the above embodiments, all or part of the embodiments can be implemented using software, hardware, firmware, or any combination thereof. When implemented using software, all or part of the embodiments can be implemented in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the processes or functions described in accordance with the embodiments of the present invention are generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that can be accessed by a computer, or a data storage device such as a server or data center that integrates one or more available media. The available medium can be magnetic media (e.g., floppy disk, hard disk, tape), optical media (e.g., DVD), or semiconductor media (e.g., solid-state disk (SSD)).

[0267] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply the existence of any such actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article, or device comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, article, or device. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of other identical elements in the process, method, article, or device comprising the element.

[0268] Each embodiment in this specification is described in a related manner. Similar portions between the various embodiments can be referenced to each other. Each embodiment focuses on the differences between the other embodiments. In particular, the device, electronic device, storage medium, and computer program product embodiments are generally similar to the method embodiments, so their descriptions are relatively simple. For related portions, reference can be made to the descriptions of the method embodiments.

[0269] The above description is only a preferred embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present invention are included in the scope of protection of the present invention.

Claims

1. A method for estimating physical parameters based on sensors, characterized in that: The method comprises: Obtaining a first observation value of a first sensor and a second observation value of a second sensor provided on a mobile device; wherein the first observation value is an observation value of a target physical parameter obtained based on data collected by the first sensor, and the second observation value is an observation value of the target physical parameter obtained based on data collected by the second sensor, and the number of sensors on the mobile device is three or more; calculating a difference between the first observation and the second observation; Obtaining a normalized error according to the difference and observation noise of a target sensor, wherein the target sensor is: the first sensor or the second sensor; adjusting a first observation noise of the first sensor and a second observation noise of the second sensor according to the normalized error; estimating a first predicted value of a target physical parameter based on the first observation noise and the second observation noise; The adjusting, according to the normalized error, a first observation noise of the first sensor and a second observation noise of the second sensor includes: If the normalized error is greater than a preset error threshold, adding the first observation noise and the second observation noise to obtain a first adjusted noise of the first sensor and a second adjusted noise of the second sensor; If the normalized error is less than or equal to a preset error threshold, reducing the first observation noise and the second observation noise to obtain a third adjusted noise of the first sensor and a fourth adjusted noise of the second sensor; The adding the first observation noise and the second observation noise to obtain a first adjusted noise of the first sensor and a second adjusted noise of the second sensor includes: Increasing the first observed noise by a preset noise growth step to obtain an increased first observed noise; and obtaining a first adjusted noise based on the increased first observed noise; Increasing the second observed noise by a preset noise growth step to obtain an increased second observed noise; and obtaining a second adjusted noise based on the increased second observed noise; The obtaining of a first adjusted noise based on the increased first observed noise includes: If the increased first observed noise is greater than or equal to the preset noise upper limit threshold, the preset noise upper limit threshold is used as the first adjusted noise; if the increased first observed noise is less than the preset noise upper limit threshold, the increased first observed noise is used as the first adjusted noise.

2. The method according to claim 1, characterized in that The preset error threshold is determined in the following manner: the preset error threshold is determined from a preset chi-square distribution table according to the preset degrees of freedom and the preset significance level value.

3. The method according to claim 1, characterized in that The observation noise of the sensor is represented by an observation noise matrix, wherein the observation noise matrix is a square matrix, the order of the observation noise matrix is the same as the number of sensors provided on the mobile device, and each diagonal element in the observation noise matrix corresponds to the observation noise of each sensor; The adding of the first observation noise and the second observation noise further includes: Increasing the values of the diagonal elements corresponding to the first sensor in the observation noise matrix, and increasing the values of the diagonal elements corresponding to the second sensor in the observation noise matrix; The reducing the first observation noise and the second observation noise includes: The values of the diagonal elements corresponding to the first sensor in the observation noise matrix are reduced, and the values of the diagonal elements corresponding to the second sensor in the observation noise matrix are reduced.

4. The method according to claim 1, wherein The obtaining of a normalized error according to the difference and the observation noise of the target sensor includes: Determining a difference representation value between the first observation value and the second observation value according to the difference; Determine a normalization basis based on the observation noise of the target sensor; A normalized error is obtained based on the gap representation value and a normalized basis.

5. The method according to any one of claims 1 to 4, characterized in that The method further comprises: Predicting a target physical parameter of the mobile device based on data collected by a sensor disposed on the mobile device to obtain a second predicted value of the target physical parameter; Determine the prior state covariance based on the posterior state covariance and the process noise covariance; After adjusting the first observation noise of the first sensor and the second observation noise of the second sensor according to the normalized error, the method further includes: For each sensor, determine a gain coefficient of the sensor based on the adjusted observation noise of the sensor and the prior state covariance, wherein the gain coefficient of the sensor represents a degree of change in noise uncertainty of an observation value obtained based on data collected by the sensor; Determining an updated a posteriori state covariance based on the a priori state covariance and the determined gain coefficients of the sensors; The estimating a first predicted value of a target physical parameter according to the first observation noise and the second observation noise includes: A first predicted value of the target physical parameter is estimated based on the determined gain coefficient of each sensor and the measurement residual between each observation value and the second predicted value.

6. The method according to claim 5, characterized in that The target physical parameters are: heading angle; The step of predicting a target physical parameter of the mobile device based on data collected by a sensor provided on the mobile device to obtain a second predicted value of the target physical parameter includes: Predicting the heading angle change based on the roll angle, pitch angle, and the angular velocity of the roll angle and the angular velocity of the pitch angle collected by sensors mounted on the mobile device; Based on the predicted heading angle change, a second predicted value of the heading angle is obtained.

7. A physical parameter estimation device based on a sensor, characterized in that: The device comprises: an observation value obtaining module, configured to obtain a first observation value of a first sensor and a second observation value of a second sensor provided on the mobile device; wherein the first observation value is an observation value of a target physical parameter obtained based on data collected by the first sensor, and the second observation value is an observation value of the target physical parameter obtained based on data collected by the second sensor, and the number of sensors on the mobile device is three or more; a difference calculation module, configured to calculate the difference between the first observation value and the second observation value; a normalized error acquisition module, configured to acquire a normalized error based on the difference and observation noise of a target sensor, wherein the target sensor is: the first sensor or the second sensor; an observation noise adjustment module, configured to adjust a first observation noise of the first sensor and a second observation noise of the second sensor according to the normalized error; a numerical estimation module, configured to estimate a first predicted value of a target physical parameter based on the first observation noise and the second observation noise; The observation noise adjustment module is specifically configured to: if the normalized error is greater than a preset error threshold, increase the first observation noise and the second observation noise to obtain a first adjusted noise of the first sensor and a second adjusted noise of the second sensor; if the normalized error is less than or equal to the preset error threshold, reduce the first observation noise and the second observation noise to obtain a third adjusted noise of the first sensor and a fourth adjusted noise of the second sensor; The observation noise adjustment module is specifically configured to: increase the first observation noise by a preset noise growth step to obtain an increased first observation noise; obtain a first adjusted noise based on the increased first observation noise; increase the second observation noise by a preset noise growth step to obtain an increased second observation noise; and obtain a second adjusted noise based on the increased second observation noise; The observation noise adjustment module is specifically used to: if the increased first observation noise is greater than or equal to the preset noise upper limit threshold, then use the preset noise upper limit threshold as the first adjustment noise; if the increased first observation noise is less than the preset noise upper limit threshold, then use the increased first observation noise as the first adjustment noise.

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