WO2018014449A1 - 改善相对位置传感器性能的方法、装置及计算机存储介质 - Google Patents
改善相对位置传感器性能的方法、装置及计算机存储介质 Download PDFInfo
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C25/00—Manufacturing, calibrating, cleaning, or repairing instruments or devices referred to in the other groups of this subclass
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
- G01C21/10—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration
- G01C21/12—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning
- G01C21/16—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning by integrating acceleration or speed, i.e. inertial navigation
- G01C21/165—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning by integrating acceleration or speed, i.e. inertial navigation combined with non-inertial navigation instruments
- G01C21/1654—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning by integrating acceleration or speed, i.e. inertial navigation combined with non-inertial navigation instruments with electromagnetic compass
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C25/00—Manufacturing, calibrating, cleaning, or repairing instruments or devices referred to in the other groups of this subclass
- G01C25/005—Manufacturing, calibrating, cleaning, or repairing instruments or devices referred to in the other groups of this subclass initial alignment, calibration or starting-up of inertial devices
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
- G06F17/11—Complex mathematical operations for solving equations, e.g. nonlinear equations, general mathematical optimization problems
- G06F17/13—Differential equations
Definitions
- the present invention relates to the field of electronic technologies, and in particular, to a method, apparatus, and computer storage medium for improving the performance of a relative position sensor.
- Relative position sensor used to measure the relative position between two objects (for example: relative angle and relative distance), is widely used in the field of robotics and can be used to track the object under test.
- the current relative position sensor generally has poor performance, and the measurement data is susceptible to interference, resulting in jitter, resulting in inaccurate measurement data and poor dynamic performance, thus seriously affecting the application of the relative position measurement sensor.
- the embodiment of the invention provides a method, a device and a computer storage medium for improving the performance of a relative position sensor, which solves the problem that the relative position sensor in the prior art has measurement data that is susceptible to interference, which causes jitter, resulting in inaccurate measurement data, and dynamic performance. Poor technical issues.
- the present invention provides the following technical solutions through an embodiment:
- a method for improving the performance of a relative position sensor is applied to a measuring device for measuring a relative position between a measured object and the measuring device, the measuring device being provided with a relative position sensor and a first
- the auxiliary sensor is provided with a second auxiliary sensor, and the method includes:
- the first measurement data is corrected using the extended Kalman filter.
- the first measurement data includes:
- the second measurement data includes:
- the method before the constructing the extended Kalman filter based on the first measurement data and the second measurement data, the method further includes:
- Mathematical modeling is performed on the system consisting of the measuring device and the measured object to obtain a mathematical model:
- the magnitude of the projection of the forward velocity vector of the measuring device toward the radial direction of the measuring device and the object to be measured a magnitude of a projection of a forward velocity vector of the measuring device toward a normal of a radial direction of the measuring device and the object to be measured;
- a magnitude of a projection of a velocity vector of the object to be measured in a radial direction connecting the measuring device and the object to be measured a magnitude of a projection of a velocity vector of the object to be measured to a normal direction connecting the measuring device and the radial direction of the object to be measured
- T( ⁇ ) is a rotation transformation matrix in a two-dimensional space, which is used to indicate that the vector to which it is multiplied is rotated counterclockwise ( ⁇ ).
- the constructing the extended Kalman filter based on the first measurement data and the second measurement data includes:
- v is the measurement noise vector, obeys a Gaussian distribution with a mean of 0, a covariance of R, and ti is time.
- h is the measurement matrix.
- the modifying, by using the extended Kalman filter, the first measurement data includes:
- P(0) is the system state transition probability matrix
- E[ ⁇ ] represents the expectation of .
- the first measurement data is modified by performing a recursive algorithm based on the following equation:
- K is continuously updated according to the following equation:
- K is the corrected magnitude of the state of the simulated system based on the error of the simulated system and the actual system
- y m is the measurement result containing the noise, including the relative distance of the output of the relative position sensor Relative angle Is an intermediate variable
- P is a propagation probability matrix
- a measuring device for measuring a relative position between a measured object and the measuring device wherein the measuring device is provided with a relative position sensor and a first auxiliary sensor, and the measured object is provided with a second auxiliary sensor, the measuring device comprising:
- An acquiring unit configured to acquire first measurement data measured by the relative position sensor, and acquire second measurement data measured by the first auxiliary sensor and the second auxiliary sensor;
- a constructing unit configured to construct an extended Kalman filter based on the first measurement data and the second measurement data
- a correction unit configured to correct the first measurement data by using the extended Kalman filter.
- the first measurement data includes:
- the method before the constructing the extended Kalman filter based on the first measurement data and the second measurement data, the method further includes:
- Mathematical modeling is performed on the system consisting of the measuring device and the measured object to obtain a mathematical model:
- the magnitude of the projection of the forward velocity vector of the measuring device toward the radial direction of the measuring device and the object to be measured a magnitude of a projection of a forward velocity vector of the measuring device toward a normal of a radial direction of the measuring device and the object to be measured;
- a magnitude of a projection of a velocity vector of the object to be measured in a radial direction connecting the measuring device and the object to be measured a magnitude of a projection of a velocity vector of the object to be measured to a normal direction connecting the measuring device and the radial direction of the object to be measured
- T( ⁇ ) is a rotation transformation matrix in a two-dimensional space, which is used to indicate that the vector to which it is multiplied is counterclockwise turn( ⁇ ).
- the structural unit is specifically configured as:
- v is the measurement noise vector, obeys a Gaussian distribution with a mean of 0, a covariance of R, and ti is time.
- h is the measurement matrix.
- the modifying unit is specifically configured as:
- the modifying unit is specifically configured to initialize the extended Kalman filter based on the following equation:
- P(0) is the system state transition probability matrix
- E[ ⁇ ] represents the expectation of .
- the modifying unit is specifically configured to perform a recursive algorithm to modify the first measurement data based on the following equation:
- K is continuously updated according to the following equation:
- K is the corrected amplitude of the state of the simulated system based on the error of the simulated system and the actual system
- y m is the measurement result containing the noise, including the relative distance of the output of the relative position sensor Relative angle Is an intermediate variable
- P is a propagation probability matrix
- the present invention by another embodiment, provides a computer storage medium having computer executable instructions stored therein, the computer executable instructions being configured to perform the following processing:
- the first measurement data is corrected using the extended Kalman filter.
- a method, apparatus, and computer storage medium for improving performance of a relative position sensor are disclosed, which are applied to a measurement device, acquire first measurement data measured by a relative position sensor, and acquire a first auxiliary a second measurement data measured by the sensor and the second auxiliary sensor; constructing an extended Kalman filter based on the first measurement data and the second measurement data; and correcting the first measurement data by using an extended Kalman filter. Since the first measurement data measured by the relative position sensor is corrected by using the extended Kalman filter, the relative position sensor in the prior art is effectively solved, and the measurement data is easily interfered, causing jitter, resulting in inaccurate measurement data. , technical problems with poor dynamic performance; and thus improve the accuracy of the measurement data of the relative position sensor, The technical effect of improving the dynamic performance of the relative position sensor.
- FIG. 1 is a flow chart of a method for improving performance of a relative position sensor according to an embodiment of the present invention
- FIG. 3 are schematic diagrams showing a model of a system composed of a measuring device and an object to be measured according to an embodiment of the present invention
- FIG. 4 is a schematic structural view of a measuring device according to an embodiment of the present invention.
- the embodiment of the invention provides a method and a device for improving the performance of a relative position sensor, which solves the problem that the relative position sensor in the prior art is susceptible to interference, resulting in jitter, resulting in inaccurate measurement data and poor dynamic performance. problem.
- a method for improving the performance of a relative position sensor is applied to a measuring device for measuring a relative position between a measured object and a measuring device, wherein the measuring device is provided with a relative position sensor and a first auxiliary sensor, and the measured object Providing a second auxiliary sensor, the method comprising: acquiring first measurement data measured by the relative position sensor, and acquiring second measurement data measured by the first auxiliary sensor and the second auxiliary sensor; based on the first measurement The data and the second measurement data construct an extended Kalman filter; the first measurement data is corrected using an extended Kalman filter.
- the term "and/or" appearing in this article is merely an association relationship describing an associated object, indicating that there may be three relationships, for example, A and / or B, which may indicate that A exists separately, while There are three cases of A and B, and B alone.
- the character "/" in this article generally indicates that the contextual object is an "or" relationship.
- the present embodiment provides a method for improving the performance of a relative position sensor, which is used in a measuring device for measuring a relative position between a measured object and a measuring device, wherein a relative position sensor is disposed on the measuring device and The first auxiliary sensor is provided with a second auxiliary sensor on the object to be measured.
- the measuring device may be: a ground robot, or a self-balancing vehicle, or a drone, or an electric vehicle.
- the embodiment does not specifically limited.
- the measured object may be a stationary or moving person or object.
- a positioning device may be disposed on the object to be tested (or the object to be measured carries the positioning device), and a second auxiliary sensor is disposed in the positioning device, and the measuring device is actually a measuring positioning device. Relative position with the measuring device.
- the positioning device may be: a smart phone, or a tablet computer, or a remote control key, or a fitness device, or a personal digital assistant, or a game console, and the like.
- the inertial measurement unit (English full name: Inertial Measurement Unit, English abbreviation: IMU) is an electronic system consisting of single or multiple acceleration sensors and angular velocity sensors, microprocessors and peripheral circuits.
- the first auxiliary sensor or the second auxiliary sensor includes: an inertial measurement unit (which integrates a device such as a gyroscope, an accelerometer, and an electronic compass), and a speed sensor (for example, a code wheel or an optical flow) Sensor), and so on.
- an inertial measurement unit which integrates a device such as a gyroscope, an accelerometer, and an electronic compass
- a speed sensor for example, a code wheel or an optical flow
- gyroscopes, accelerometers, and electronic compasses can all be considered as inertial measurement units.
- the method for improving the performance of a relative position sensor includes:
- Step S101 Acquire first measurement data measured by the relative position sensor, and acquire by the first The second measurement data measured by the auxiliary sensor and the second auxiliary sensor.
- the first measurement data includes:
- the second measurement data includes:
- a second auxiliary sensor for example, a gyroscope and/or an accelerometer disposed on the object to be measured, which contains noise;
- the forward speed of the measuring device And the rotational speed of the measuring device Is data measured by a first auxiliary sensor (eg, a code wheel) disposed on the measuring device, which contains noise;
- a first auxiliary sensor eg, a code wheel
- ⁇ s between a positive direction of the measuring device and a magnetic north pole of the earth
- ⁇ t between a positive direction of the object to be measured and a magnetic north pole of the earth
- ⁇ s is the first set on the measuring device
- the data measured by the auxiliary sensor for example, an electronic compass
- ⁇ t is data measured by a second auxiliary sensor (for example, an electronic compass) provided on the object to be measured.
- the measuring device can communicate with the positioning device on the object to be tested by using a wireless communication technology, thereby acquiring data measured by the second auxiliary sensor.
- the line communication technology may be: UWB (Ultra Wideband) technology, or WiFi (Wireless Fidelity) technology, or Bluetooth technology, and the like.
- Step S102 Construct an extended Kalman filter based on the first measurement data and the second measurement data.
- Extended Kalman Filter (English full name: Extended Kalman Filter, English abbreviation: EKF) is a nonlinear system optimal state estimator that combines the measurement of multiple linear or nonlinear relationships with inaccurate sensors. Data, resulting in higher accuracy than a single sensor. Essentially recursive least squares.
- the relative position sensor between the object to be measured and the measuring device are measured by the relative position sensor
- the relative position of the first measurement data and the second measurement are combined by the extended Kalman filter to correct the first measurement data, thereby improving the accuracy, accuracy, and dynamic performance of the relative position sensor.
- the method further includes:
- a component of the measured object along the second auxiliary sensor for example, an inertial measurement unit disposed on the object to be measured
- the component of the measured object along the second auxiliary sensor (for example, the inertial measurement unit disposed on the object to be measured) in the direction of the velocity on the Y-axis; And in Figure 2 coincide, And in Figure 2 coincide);
- the magnitude of the projection of the forward velocity vector of the measuring device toward the radial direction of the measuring device and the object to be measured a magnitude of a projection of a forward velocity vector of the measuring device toward a normal of a radial direction of the measuring device and the object to be measured;
- a magnitude of a projection of a velocity vector of the object to be measured in a radial direction connecting the measuring device and the object to be measured a magnitude of a projection of a velocity vector of the object to be measured to a normal direction connecting the measuring device and the radial direction of the object to be measured
- T( ⁇ ) is a rotation transformation matrix in a two-dimensional space, which is used to indicate that the vector to which it is multiplied is rotated counterclockwise ( ⁇ ).
- the extended Kalman filter can be roughly divided into two steps:
- the first step is to predict.
- the established model based on system difference equation/differential equation is equivalent to a kind of “simulation” to the corresponding system in the real world.
- driving force or “driving force” of the simulation system
- the state of the system is simulated. Will change constantly. Since the simulation system has been fully modeled on the actual system, and the driving force is also “driving” the actual system operation while the state of the “drive” simulation system is being updated, the state changes of the model and the actual system are almost simultaneous. same.
- the system state can already be acquired from the simulation system, which is called "state prediction.”
- state prediction the system state equation is linear.
- the extended Kalman filter is applied to the state prediction of nonlinear systems, the first-order Taylor expansion of the nonlinear system is performed at the current state, and the approximate system state update differential equation is obtained.
- the second step is to fix it.
- the extended Kalman filter continually corrects the state of the analog system by the difference between the actual system output collected by the relative position sensor and the output of the analog system. Since there is an error in the data collected by the relative position sensor, the magnitude of the correction satisfies the optimal estimation criterion that must be calculated, that is, the variance of the noise caused by the correction amount noise in the output of the analog system is minimized.
- the extended Kalman filter is an algorithm that relies on system modeling to indirectly estimate the state of the system using the second measurement data. Due to the existence of the system model and the optimal estimation method, the extended Kalman filter directly compares the relative position sensor data with the FIR (Finite Impulse Response) filter and the IIR (Infinite Impulse Response) filter.
- the filtering method has the advantages of real-time response and high precision, and can further improve the accuracy by combining data of a plurality of sensors.
- the extended Kalman filter is constructed based on the first measurement data and the second measurement data, including:
- the true speed of the target being measured ie:
- the true relative position of the measured object the true relative angle s ⁇ between the measuring device and the measured object, the true relative distance s ⁇ between the measuring device and the measured object
- the inertial sensor zero offset the change in the geometry of the measuring device (eg, the change in the geometry of the body of the self-balancing vehicle) will be measured as a function of time. This complete modeling can greatly improve the accuracy of the measurement.
- v is the measurement noise vector, obeys a Gaussian distribution with a mean of 0 and a covariance of R, and ti is the time.
- h is the measurement matrix:
- Step S103 Correcting the first measurement data by using an extended Kalman filter.
- step S103 includes:
- the extended Kalman filter can be initialized based on the following equation:
- P(0) is the system state transition probability matrix
- E[ ⁇ ] represents the expectation of .
- the first measurement data may be modified by performing a recursive algorithm based on the following equation:
- the first item The part is “predictive”, that is, the state of the simulation system is updated by the sensor data of the "driver” part of the simulation system by equation (1), and the second term K[y m -h(x, v 0 , ti)] is "Correction".
- K represents the correction range of the state of the simulation system based on the error of the simulation system and the actual system. If this correction satisfies the optimal estimation criterion, then K needs to be continuously updated as follows:
- K is the corrected magnitude of the state of the simulated system based on the error of the simulated system and the actual system
- y m is the measurement result containing the noise, including the relative distance of the output of the relative position sensor Relative angle Is an intermediate variable
- P is a propagation probability matrix
- the above first-order partial derivative matrix is the matrix of the local linearization of the system in the current state, where A is the partial derivative matrix of the state transition function f to the state variable x, and C is the partial derivative matrix of the measurement matrix h to the state variable x , M is the partial derivative matrix of the measurement matrix h to the measurement noise v.
- y m is the measurement result containing noise, including the relative distance and relative angle of the relative position sensor output.
- the extended Kalman filter updates the state by the input quantity u of the state space equation, and continuously corrects the state variable of the extended Kalman filter by estimating the deviation between the output result and the actual output.
- the "rate" of the modified state is slow enough relative to the rapidly beating measurement noise, and is fast enough for the real state change, so the true can be extracted from the noise-contaminated measurement data with almost no lag. value.
- a method for improving the performance of a relative position sensor is disclosed, which is applied to a measuring device for measuring a relative position between a measured object and a measuring device, and a relative position sensor is disposed on the measuring device.
- the first auxiliary sensor, the second auxiliary sensor is disposed on the object to be measured, the method includes: acquiring first measurement data measured by the relative position sensor, and acquiring a second measured by the first auxiliary sensor and the second auxiliary sensor Measuring data; constructing an extended Kalman filter based on the first measurement data and the second measurement data; and correcting the first measurement data by using an extended Kalman filter.
- the measured position sensor A measurement data is corrected, so that the relative position sensor in the prior art has a technical problem that the measurement data is easily interfered, the jitter is generated, the measurement data is inaccurate, and the dynamic performance is poor. Further, the technical effect of improving the accuracy of the measurement data of the relative position sensor, thereby improving the dynamic performance of the relative position sensor, is achieved.
- another embodiment of the present invention provides a measuring apparatus for implementing a method for improving the performance of a relative position sensor in an embodiment of the present invention.
- a measuring device is used for measuring a relative position between a measured object and a measuring device.
- the measuring device is provided with a relative position sensor and a first auxiliary sensor, and the object to be tested is provided with a second Auxiliary sensors, measuring devices include:
- the acquiring unit 401 is configured to acquire first measurement data measured by the relative position sensor, and acquire second measurement data measured by the first auxiliary sensor and the second auxiliary sensor;
- the constructing unit 402 is configured to construct an extended Kalman filter based on the first measurement data and the second measurement data;
- the correcting unit 403 is configured to correct the first measurement data by using an extended Kalman filter.
- the first measurement data includes:
- the second measurement data includes:
- a second auxiliary sensor for example, a gyroscope and/or an accelerometer disposed on the object to be measured, which contains noise;
- the forward speed of the measuring device And the rotational speed of the measuring device Is data measured by a first auxiliary sensor (eg, a code wheel) disposed on the measuring device, which contains noise;
- a first auxiliary sensor eg, a code wheel
- ⁇ s between a positive direction of the measuring device and a magnetic north pole of the earth
- ⁇ t between a positive direction of the object to be measured and a magnetic north pole of the earth
- ⁇ s is the first set on the measuring device
- the data measured by the auxiliary sensor for example, an electronic compass
- ⁇ t is data measured by a second auxiliary sensor (for example, an electronic compass) provided on the object to be measured.
- the measuring device further includes:
- the modeling unit is configured to perform mathematical modeling on the system consisting of the measuring device and the measured object to obtain a mathematical model before constructing the extended Kalman filter based on the first measurement data and the second measurement data:
- a component of the measured object along the second auxiliary sensor for example, an inertial measurement unit disposed on the object to be measured
- the component of the measured object along the second auxiliary sensor (for example, the inertial measurement unit disposed on the object to be measured) in the direction of the velocity on the Y-axis; And in Figure 2 coincide, And in Figure 2 coincide);
- the magnitude of the projection of the forward velocity vector of the measuring device toward the radial direction of the measuring device and the object to be measured a magnitude of a projection of a forward velocity vector of the measuring device toward a normal of a radial direction of the measuring device and the object to be measured;
- a magnitude of a projection of a velocity vector of the object to be measured in a radial direction connecting the measuring device and the object to be measured a magnitude of a projection of a velocity vector of the object to be measured to a normal direction connecting the measuring device and the radial direction of the object to be measured
- T( ⁇ ) is a rotation transformation matrix in a two-dimensional space, which is used to indicate that the vector to which it is multiplied is rotated counterclockwise ( ⁇ ).
- the constructing unit 402 is specifically configured to:
- v is the measurement noise vector, obeys a Gaussian distribution with a mean of 0, a covariance of R, and ti is time.
- h is the measurement matrix:
- the modifying unit 403 is specifically configured to:
- the modifying unit 403 is specifically configured to initialize the extended Kalman filter based on the following equation:
- P(0) is the system state transition probability matrix
- E[ ⁇ ] represents the expectation of .
- the modifying unit 403 is specifically configured to perform a recursive algorithm to modify the first measurement data based on the following equation:
- the first item The part is “predictive”, that is, the state of the simulation system is updated by the sensor data of the "driver” part of the simulation system by equation (1), and the second term K[y m -h(x, v 0 , ti)] is "Correction".
- K represents the correction range of the state of the simulation system based on the error of the simulation system and the actual system. If this correction satisfies the optimal estimation criterion, then K needs to be continuously updated as follows:
- K is the corrected amplitude of the state of the simulated system based on the error of the simulated system and the actual system
- y m is the measurement result containing the noise, including the relative distance of the output of the relative position sensor Relative angle Is an intermediate variable
- P is a propagation probability matrix
- the above first-order partial derivative matrix is the matrix of the local linearization of the system in the current state, where A is the partial derivative matrix of the state transition function f to the state variable x, and C is the partial derivative matrix of the measurement matrix h to the state variable x , M is the partial derivative matrix of the measurement matrix h to the measurement noise v.
- the measuring device introduced in this embodiment is a device used in the method for improving the performance of the relative position sensor in the embodiment of the present invention
- the method for improving the performance of the relative position sensor according to the embodiment of the present invention is known in the art.
- a person skilled in the art can understand the specific embodiment of the measuring device of the present embodiment and various changes thereof, so that the method in the embodiment of the present invention is not described in detail herein.
- the apparatus employed by those skilled in the art to implement the method for improving the performance of the relative position sensor in the embodiments of the present invention is within the scope of the present invention.
- a measuring device configured to measure a relative position between the measured object and the measuring device.
- the measuring device is provided with a relative position sensor and a first auxiliary sensor, and the measured object is set.
- a second auxiliary sensor the measuring device, comprising: an acquiring unit configured to acquire first measurement data measured by the relative position sensor, and acquire second measurement data measured by the first auxiliary sensor and the second auxiliary sensor; And a unit configured to construct an extended Kalman filter based on the first measurement data and the second measurement data; and a correction unit configured to modify the first measurement data by using an extended Kalman filter.
- the relative position sensor in the prior art is effectively solved, and the measurement data is easily interfered, causing jitter and causing measurement data. Inaccurate technical problems with poor dynamic performance. Further, the technical effect of improving the accuracy of the measurement data of the relative position sensor, thereby improving the dynamic performance of the relative position sensor, is achieved.
- the present invention by another embodiment, provides a computer storage medium having computer executable instructions stored therein, the computer executable instructions being configured to perform the following processing:
- the first measurement data is corrected using the extended Kalman filter.
- embodiments of the present invention can be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or a combination of software and hardware. Moreover, the invention can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) including computer usable program code.
- computer-usable storage media including but not limited to disk storage, CD-ROM, optical storage, etc.
- the computer program instructions can also be stored in a computer readable memory that can direct a computer or other programmable data processing device to operate in a particular manner, such that the instructions stored in the computer readable memory produce an article of manufacture comprising the instruction device.
- the apparatus implements the functions specified in one or more blocks of a flow or a flow and/or block diagram of the flowchart.
- These computer program instructions can also be loaded onto a computer or other programmable data processing device such that a series of operational steps are performed on a computer or other programmable device to produce computer-implemented processing for execution on a computer or other programmable device.
- the instructions provide steps for implementing the functions specified in one or more of the flow or in a block or blocks of a flow diagram.
- Embodiments of the present invention provide a method for improving performance of a relative position sensor, the method comprising: acquiring first measurement data measured by a relative position sensor, and acquiring a first measurement by the first auxiliary sensor and the second auxiliary sensor Two measurement data; constructing an extended Kalman filter based on the first measurement data and the second measurement data; and correcting the first measurement data by using an extended Kalman filter. Since the first measurement data measured by the relative position sensor is corrected by using the extended Kalman filter, the relative position sensor in the prior art is effectively solved, and the measurement data is easily interfered, causing jitter, resulting in inaccurate measurement data. The technical problem of poor dynamic performance; further improving the accuracy of the measurement data of the relative position sensor, thereby improving the technical effect of the dynamic performance of the relative position sensor.
Abstract
一种改善相对位置传感器性能的方法、装置及计算机存储介质，应用于测量装置中，测量装置用于测量被测对象与测量装置之间的相对位置，测量装置上设置有相对位置传感器和第一辅助传感器，被测对象上设置有第二辅助传感器，所述方法包括：获取由相对位置传感器测得的第一测量数据，以及获取由第一辅助传感器和第二辅助传感器测得的第二测量数据(S101)；基于第一测量数据和第二测量数据，构造扩展卡尔曼滤波器(S102)；利用扩展卡尔曼滤波器，对第一测量数据进行修正(S103)。该方法实现了提高相对位置传感器的测量数据的准确性，从而达到改善相对位置传感器的动态性能的技术效果。
Description
本发明涉及电子技术领域，尤其涉一种改善相对位置传感器性能的方法、装置及计算机存储介质。
相对位置传感器，用于测量两个物体之间的相对位置(例如：相对夹角和相对距离)，在机器人领域应用广泛，可以用于对被测对象进行跟踪。
但是，目前的相对位置传感器普遍性能较差，测量数据容易受到干扰，产生跳动，导致测量数据不准确，动态性能较差，从而严重影响了相对位置测量传感器的应用。
综上所述，如何改善相对位置传感器性能，已成为现阶段亟待解决的问题。
发明内容
本发明实施例通过提供一种改善相对位置传感器性能的方法、装置及计算机存储介质，解决了现有技术中的相对位置传感器存在测量数据容易受到干扰，产生跳动，导致测量数据不准确，动态性能较差的技术问题。
一方面，本发明通过一实施例提供如下技术方案：
一种改善相对位置传感器性能的方法，应用于测量装置中，所述测量装置用于测量被测对象与所述测量装置之间的相对位置，所述测量装置上设置有相对位置传感器和第一辅助传感器，所述被测对象上设置有第二辅助传感器，所述方法包括：
获取由所述相对位置传感器测得的第一测量数据，以及获取由所述第一辅助传感器和所述第二辅助传感器测得的第二测量数据；
基于所述第一测量数据和所述第二测量数据，构造扩展卡尔曼滤波器；
利用所述扩展卡尔曼滤波器，对所述第一测量数据进行修正。
上述方案中，所述第一测量数据，包括：
所述测量装置与所述被测对象之间的相对夹角
以及所述测量装置与所述被测对象之间的相对距离
上述方案中，所述第二测量数据，包括：
所述被测对象的加速度在X轴上的分量
以及所述被测对象的加速度在Y轴上的分量
所述测量装置的前进速度
以及所述测量装置的旋转速度
所述测量装置的正方向与地球磁北极的夹角θs，以及所述被测对象的正方向与地球磁北极的夹角θt。
上述方案中，所述基于所述第一测量数据和所述第二测量数据，构造扩展卡尔曼滤波器之前，还包括：
对由所述测量装置和所述被测对象所组成的系统进行数学建模，获得数学模型：
其中，
为所述被测对象的加速度在X轴上的零偏，
为所述被测对象的加速度在Y轴上的零偏；
T(·)为二维空间中的旋转变换矩阵，用于表示将其所后乘的向量逆时针旋转(·)。
上述方案中，所述基于所述第一测量数据和所述第二测量数据，构造扩展卡尔曼滤波器，包括：
构造状态变量：
构造输入变量：
基于所述状态变量和所述输入变量，将所述数学模型调整为如下表达式，得到扩展卡尔曼滤波器：
其中，v为测量噪声向量，服从均值为0、协方差为R的高斯分布，ti为时间，
为系统微分方程模型，
为系统输出量，h为测量矩阵。
上述方案中，所述利用所述扩展卡尔曼滤波器，对所述第一测量数据进行修正，包括：
对所述扩展卡尔曼滤波器进行初始化；
执行递推算法对所述第一测量数据进行修正，从而获得到
和
的最优状态估计。
上述方案中，基于如下等式，对于所述扩展卡尔曼滤波器进行初始化：
其中，
为系统初始状态x(0)的估计值，P(0)为系统状态转移概率矩阵，E[·]代表对·的期望。
上述方案中，基于如下等式，执行递推算法对所述第一测量数据进行修正：
其中，K按照如下等式不断更新：
其中，
另一方面，本发明通过另一实施例，提供如下技术方案：
一种测量装置，所述测量装置用于测量被测对象与所述测量装置之间的相对位置，所述测量装置上设置有相对位置传感器和第一辅助传感器，所述被测对象上设置有第二辅助传感器，所述测量装置包括：
获取单元，配置为获取由所述相对位置传感器测得的第一测量数据，以及获取由所述第一辅助传感器和所述第二辅助传感器测得的第二测量数据；
构造单元，配置为基于所述第一测量数据和所述第二测量数据，构造扩展卡尔曼滤波器；
修正单元，配置为利用所述扩展卡尔曼滤波器，对所述第一测量数据进行修正。
上述方案中，所述第一测量数据，包括：
所述被测对象的加速度在X轴上的分量
以及所述被测对象的加速度在Y轴上的分量
所述测量装置的前进速度
以及所述测量装置的旋转速度
所述测量装置的正方向与地球磁北极的夹角θs，以及所述被测对象的正方向与地球磁北极的夹角θt。
上述方案中，所述基于所述第一测量数据和所述第二测量数据，构造扩展卡尔曼滤波器之前，还包括：
对由所述测量装置和所述被测对象所组成的系统进行数学建模，获得数学模型：
其中，
为所述被测对象的加速度在X轴上的零偏，
为所述被测对象的加速度在Y轴上的零偏；
T(·)为二维空间中的旋转变换矩阵，用于表示将其所后乘的向量逆时针旋
转(·)。
上述方案中，所述构造单元，具体配置为：
构造状态变量：
构造输入变量：
基于所述状态变量和所述输入变量，将所述数学模型调整为如下表达式，得到扩展卡尔曼滤波器：
其中，v为测量噪声向量，服从均值为0、协方差为R的高斯分布，ti为时间，
为系统微分方程模型，
为系统输出量，h为测量矩阵。
上述方案中，所述修正单元，具体配置为：
执行递推算法对所述第一测量数据进行修正，从而获得到
和
的最优状态估计。
上述方案中，所述修正单元，具体配置为基于如下等式，对于所述扩展卡尔曼滤波器进行初始化：
其中，
为系统初始状态x(0)的估计值，P(0)为系统状态转移概率矩阵，E[·]代表对·的期望。
上述方案中，所述修正单元，具体配置为基于如下等式，执行递推算法对所述第一测量数据进行修正：
其中，K按照如下等式不断更新：
其中，
本发明通过另一实施例，提供一种计算机存储介质，所述计算机存储介质中存储有计算机可执行指令，所述计算机可执行指令配置为执行以下处理：
获取由所述相对位置传感器测得的第一测量数据，以及获取由所述第一辅助传感器和所述第二辅助传感器测得的第二测量数据；
基于所述第一测量数据和所述第二测量数据，构造扩展卡尔曼滤波器；
利用所述扩展卡尔曼滤波器，对所述第一测量数据进行修正。
本发明实施例中提供的一个或多个技术方案，至少具有如下技术效果或优点：
在本发明实施例中，公开了一种改善相对位置传感器性能的方法、装置及计算机存储介质，应用于测量装置中，获取由相对位置传感器测得的第一测量数据，以及获取由第一辅助传感器和第二辅助传感器测得的第二测量数据；基于第一测量数据和第二测量数据，构造扩展卡尔曼滤波器；利用扩展卡尔曼滤波器，对第一测量数据进行修正。由于利用扩展卡尔曼滤波器，对相对位置传感器测得的第一测量数据进行修正，所以，有效解决了现有技术中的相对位置传感器存在测量数据容易受到干扰，产生跳动，导致测量数据不准确，动态性能较差的技术问题；进而实现了提高相对位置传感器的测量数据的准确性，从
而改善相对位置传感器的动态性能的技术效果。
为了更清楚地说明本发明实施例中的技术方案，下面将对实施例描述中所需要使用的附图作一简单地介绍，显而易见地，下面描述中的附图是本发明的一些实施例，对于本领域普通技术人员来讲，在不付出创造性劳动的前提下，还可以根据这些附图获得其他的附图。
图1为本发明实施例中一种改善相对位置传感器性能的方法的流程图；
图2-图3为本发明实施例中由测量装置和被测对象组成的系统的模型的示意图；
图4为本发明实施例中一种测量装置的结构示意图。
本发明实施例通过提供一种改善相对位置传感器性能的方法及装置，解决了现有技术中的相对位置传感器存在测量数据容易受到干扰，产生跳动，导致测量数据不准确，动态性能较差的技术问题。
本发明实施例的技术方案为解决上述技术问题，总体思路如下：
一种改善相对位置传感器性能的方法，应用于测量装置中，测量装置用于测量被测对象与测量装置之间的相对位置，测量装置上设置有相对位置传感器和第一辅助传感器，被测对象上设置有第二辅助传感器，所述方法包括：获取由相对位置传感器测得的第一测量数据，以及获取由第一辅助传感器和第二辅助传感器测得的第二测量数据；基于第一测量数据和第二测量数据，构造扩展卡尔曼滤波器；利用扩展卡尔曼滤波器，对第一测量数据进行修正。
为了更好的理解上述技术方案，下面将结合说明书附图以及具体的实施方式对上述技术方案进行详细的说明。
首先说明，本文中出现的术语“和/或”，仅仅是一种描述关联对象的关联关系，表示可以存在三种关系，例如，A和/或B，可以表示：单独存在A，同时
存在A和B，单独存在B这三种情况。另外，本文中字符“/”，一般表示前后关联对象是一种“或”的关系。
实施例一
本实施例提供了一种改善相对位置传感器性能的方法，应用于测量装置中，测量装置用于测量被测对象与测量装置之间的相对位置，其中，在测量装置上设置有相对位置传感器和第一辅助传感器，在被测对象上设置有第二辅助传感器。
在具体实施过程中，测量装置可以是：地面机器人、或自平衡车、或无人机、或电动汽车等设备，此处，对于所述测量装置具体是何种设备，本实施例不做具体限定。
在具体实施过程中，被测对象可以为静止或移动的人或物体。
在具体实施过程中，可以在被测对象上设置一定位装置(或被测对象携带有该定位装置)，第二辅助传感器设置在所述定位装置内，所述测量装置实际上是测量定位装置与测量装置之间的相对位置。其中，定位装置可以是：智能手机、或平板电脑、或遥控钥匙、或健身设备、或个人数字助理、或游戏控制台，等等。
在具体实施过程中，惯性测量单元(英文全称：Inertial Measurement Unit，英文缩写：IMU)，是由单个或多个加速度传感器和角速度传感器、微处理器和外围电路组成的电子系统。
在本发明实施例中，第一辅助传感器或第二辅助传感器，包括：惯性测量单元(其集成有陀螺仪、加速度计、以及电子罗盘等器件)，测速传感器(例如：码盘、或光流传感器)，等等。一般，陀螺仪、加速度计、以及电子罗盘都可以看作是惯性测量单元。
如图1所示，所述改善相对位置传感器性能的方法，包括：
步骤S101：获取由相对位置传感器测得的第一测量数据，以及获取由第一
辅助传感器和第二辅助传感器测得的第二测量数据。
在具体实施过程中，第一测量数据，包括：
所述测量装置与所述被测对象之间的相对夹角
以及所述测量装置与所述被测对象之间的相对距离
此处，
和
是由设置在测量装置上的相对位置传感器测得的数据，其中含有噪声；
在具体实施过程中，如图2-图3所示，第二测量数据，包括：
所述被测对象的加速度在X轴上的分量
以及所述被测对象的加速度在Y轴上的分量
此处，
和
是由设置在被测对象上的第二辅助传感器(例如：陀螺仪和/或加速度计)测量的数据，其中含有噪声；
所述测量装置的前进速度
以及所述测量装置的旋转速度
是由设置在测量装置上的第一辅助传感器(例如：码盘)测得的数据，其中含有噪声；
所述测量装置的正方向与地球磁北极的夹角θs，以及所述被测对象的正方向与地球磁北极的夹角θt，其中，θs是由设置在测量装置上的第一辅助传感器(例如：电子罗盘)测得的数据，θt是由设置在被测对象上的第二辅助传感器(例如：电子罗盘)测得的数据。
在具体实施过程中，测量装置可以通过无线通信技术与被测对象上的定位装置进行通信，从而获取第二辅助传感器测量的数据。其中，所述线通信技术，可以为：UWB(Ultra Wideband，超宽带)技术、或WiFi(Wireless Fidelity，无线保真)技术、或蓝牙(Bluetooth)技术，等等。
步骤S102：基于第一测量数据和第二测量数据，构造扩展卡尔曼滤波器。
扩展卡尔曼滤波器(英文全称：Extended Kalman Filter，英文缩写：EKF)，是一种非线性系统最优状态估计器，通过融合多个具有线性或非线性关系的、且不够准确的传感器的测量数据，得到比单一传感器精度更高的测量结果。本质上是递推最小二乘法。
在本发明实施例中，在通过相对位置传感器测量被测对象与测量装置之间
的相对位置时，利用扩展卡尔曼滤波器将第一测量数据和第二测量进行数据融合，以对第一测量数据进行修正，从而提高相对位置传感器的准确性、精确性、以及动态性能。
作为一种可选的实施方式，在步骤S102之前，还包括：
对由测量装置和被测对象组成的系统进行数学建模，获得数学模型，如下：
其中，
为所述被测对象的加速度在X轴上的零偏，
为所述被测对象的加速度在Y轴上的零偏；
T(·)为二维空间中的旋转变换矩阵，用于表示将其所后乘的向量逆时针旋转(·)。
在具体实施过程中，扩展卡尔曼滤波器大体上可以分为两步实现：
第一步，预测。
建立的基于系统差分方程/微分方程的模型相当于对现实世界中对应的系统的一种“模拟”，给定此模拟系统一系列输入量(或称“驱动力”)，则模拟系统的状态会不断变化。由于模拟系统已经对实际系统进行了较为完善的建模，且驱动力在“驱动”模拟系统的状态更新时也在同时“驱动”实际系统运行，因此模型和实际系统的状态变化几乎是同时、同样的。因此在通过各种传感器(即：相对位置传感器、第一辅助传感器和第二辅助传感器)采集实际系统的状态之前，已经可以从模拟系统中获取系统状态了，这称为“状态预测”。传统上，系统状态方程是线性的。而扩展卡尔曼滤波器应用于非线性系统状态预测时，将非线性系统在当前状态处进行一阶泰勒展开，得到近似的系统状态更新微分方程。
第二步，修正。
在理想情况下，理应可以直接使用第一步的预测值做状态反馈控制。然而，在现实中会存在两大问题：1、不确定初值的问题；2、建模不准确的问题。这
两个问题会使得模拟系统的状态逐渐偏离实际状态。为了解决这个问题，在本实施例中，扩展卡尔曼滤波器通过相对位置传感器采集到的实际系统输出和模拟系统的输出之间的差值不断修正模拟系统的状态。由于相对位置传感器采集到的数据存在误差，因此修正的幅度满足须经过计算使之最优估计准则，即使得模拟系统的输出中受修正量噪声引起的噪声的方差最小。
从以上两步算法可知，扩展卡尔曼滤波器是依赖于系统建模的、利用第二测量数据间接地估计系统状态的算法。由于系统模型的存在和最优估计方法，扩展卡尔曼滤波器相比FIR(Finite Impulse Response，有限脉冲响应)滤波器、IIR(Infinite Impulse Response，无限冲击响应)滤波器等直接对相对位置传感器数据滤波的方法，有响应实时、精度高的优点，而且可以通过融合多个传感器的数据进一步提高精度。
作为一种可选的实施方式，基于第一测量数据和第二测量数据，构造扩展卡尔曼滤波器，包括：
首先，构造状态变量：
在这里，不仅将被测目标真实速度(即：
)和被测目标真实相对位置(测量装置与被测对象之间的真实的相对夹角sα、测量装置与被测对象之间的真实的相对距离sρ)作为需要观测的状态变量，也将会随时间变化的惯性传感器零点偏移、测量装置几何尺寸变化(例如：自平衡车的车身几何尺寸的变化)作为观测量。这样完整的建模可以极大地提高测量的准确性。
然后，构造输入变量：
最后，基于状态变量和输入变量，对所述数学模型进行调整得到扩展卡尔曼滤波器：
其中，v为测量噪声向量，服从均值为0、协方差为R的高斯分布，ti为时
间，
为系统微分方程模型，(即：数学模型的等式1)，
为系统输出量，h为测量矩阵：
步骤S103：利用扩展卡尔曼滤波器，对第一测量数据进行修正。
作为一种可选的实施方式，步骤S103，包括：
首先，对扩展卡尔曼滤波器进行初始化；
然后，执行递推算法对第一测量数据进行修正，从而获得到
和
的最优状态估计。
作为一种可选的实施方式，可以基于如下等式，对于扩展卡尔曼滤波器进行初始化：
其中，
为系统初始状态x(0)的估计值，P(0)为系统状态转移概率矩阵，E[·]代表对·的期望。
作为一种可选的实施方式，可以基于如下等式，执行递推算法对第一测量数据进行修正：
上式中，第一项
部分是“预测”，即通过等式(1)作为模拟系统“驱动力”部分的传感器数据更新模拟系统的状态，而第二项K[ym-h(x,v0,ti)]为“修正”。K代表了根据模拟系统和实际系统的误差修正模拟系统状态的修正幅度，这一修正要满足最优估计准则，则K需要按照以下方式不断更新：
其中，
为系统状态变量估计值的导数；K为根据模拟系统和实际系统的
误差修正模拟系统状态的修正幅度；ym为含有噪声的测量结果，其中包含相对位置传感器输出的相对距离
和相对夹角
为中间变量；P为传播概率矩阵；
为传播概率矩阵的导数：
以上一阶偏导数矩阵即为系统在当前状态下进行局部线性化的矩阵，其中，A是状态转移函数f对状态变量x的偏导数矩阵，C是测量矩阵h对状态变量x的偏导数矩阵，M是测量矩阵h对测量噪声v的偏导数矩阵。
上式中，ym为含有噪声的测量结果，包含相对位置传感器输出的相对距离和相对角度。类似于传统的状态观测器，扩展卡尔曼滤波器通过状态空间方程的输入量u更新状态，并通过估计输出结果和实际输出的偏差不断修正扩展卡尔曼滤波器的状态变量。从直观上来讲，其修正状态的“速率”相对于迅速跳动的测量噪声来说足够缓慢，而对于真实的状态变化又足够迅速，因此可以从被噪声污染的测量数据中几乎无滞后地提取真实值。
上述本发明实施例中的技术方案，至少具有如下的技术效果或优点：
在本发明实施例中，公开了一种改善相对位置传感器性能的方法，应用于测量装置中，测量装置用于测量被测对象与测量装置之间的相对位置，测量装置上设置有相对位置传感器和第一辅助传感器，被测对象上设置有第二辅助传感器，方法包括：获取由相对位置传感器测得的第一测量数据，以及获取由第一辅助传感器和第二辅助传感器测得的第二测量数据；基于第一测量数据和第二测量数据，构造扩展卡尔曼滤波器；利用扩展卡尔曼滤波器，对第一测量数据进行修正。由于采用了利用扩展卡尔曼滤波器，对相对位置传感器测得的第
一测量数据进行修正，所以，有效解决了现有技术中的相对位置传感器存在测量数据容易受到干扰，产生跳动，导致测量数据不准确，动态性能较差的技术问题。进而实现了提高相对位置传感器的测量数据的准确性，从而改善相对位置传感器的动态性能的技术效果。
实施例二
基于同一发明构思，本发明另一实施例提供一种实施本发明实施例中改善相对位置传感器性能的方法的测量装置。
如图4所示，一种测量装置，测量装置用于测量被测对象与测量装置之间的相对位置，测量装置上设置有相对位置传感器和第一辅助传感器，被测对象上设置有第二辅助传感器，测量装置包括：
获取单元401，配置为获取由相对位置传感器测得的第一测量数据，以及获取由第一辅助传感器和第二辅助传感器测得的第二测量数据；
构造单元402，配置为基于第一测量数据和第二测量数据，构造扩展卡尔曼滤波器；
修正单元403，配置为利用扩展卡尔曼滤波器，对第一测量数据进行修正。
作为一种可选的实施方式，第一测量数据，包括：
所述测量装置与所述被测对象之间的相对夹角
以及所述测量装置与所述被测对象之间的相对距离
作为一种可选的实施方式，第二测量数据，包括：
所述被测对象的加速度在X轴上的分量
以及所述被测对象的加速度在Y轴上的分量
此处，
和
是由设置在被测对象上的第二辅助传感器(例如：陀螺仪和/或加速度计)测量的数据，其中含有噪声；
所述测量装置的前进速度
以及所述测量装置的旋转速度
是由设置在测量装置上的第一辅助传感器(例如：码盘)测得的数据，其中含有噪声；
所述测量装置的正方向与地球磁北极的夹角θs，以及所述被测对象的正方
向与地球磁北极的夹角θt，其中，θs是由设置在测量装置上的第一辅助传感器(例如：电子罗盘)测得的数据，θt是由设置在被测对象上的第二辅助传感器(例如：电子罗盘)测得的数据。
作为一种可选的实施方式，测量装置，还包括：
建模单元，配置为基于第一测量数据和第二测量数据，构造扩展卡尔曼滤波器之前，对由测量装置和被测对象所组成的系统进行数学建模，获得数学模型：
其中，
为所述被测对象的加速度在X轴上的零偏，
为所述被测对象的加速度在Y轴上的零偏；
T(·)为二维空间中的旋转变换矩阵，用于表示将其所后乘的向量逆时针旋转(·)。
作为一种可选的实施方式，构造单元402，具体配置为：
构造状态变量：
构造输入变量：
基于状态变量和输入变量，将数学模型调整为如下表达式，得到扩展卡尔曼滤波器：
其中，v为测量噪声向量，服从均值为0、协方差为R的高斯分布，ti为时间，
为系统微分方程模型(即：数学模型的等式1)，
为系统输出量，h为测量矩阵：
作为一种可选的实施方式，修正单元403，具体配置为：
对扩展卡尔曼滤波器进行初始化；执行递推算法对第一测量数据进行修正，从而获得到
和
的最优状态估计。
作为一种可选的实施方式，修正单元403，具体配置为基于如下等式，对于扩展卡尔曼滤波器进行初始化：
其中，
为系统初始状态x(0)的估计值，P(0)为系统状态转移概率矩阵，E[·]代表对·的期望。
作为一种可选的实施方式，修正单元403，具体配置为基于如下等式，执行递推算法对第一测量数据进行修正：
上式中，第一项
部分是“预测”，即通过等式(1)作为模拟系统“驱动力”部分的传感器数据更新模拟系统的状态，而第二项K[ym-h(x,v0,ti)]为“修正”。K代表了根据模拟系统和实际系统的误差修正模拟系统状态的修正幅度，这一修正要满足最优估计准则，则K需要按照以下方式不断更新：
其中，
为系统状态变量估计值的导数；K为根据模拟系统和实际系统的误差修正模拟系统状态的修正幅度；ym为含有噪声的测量结果，其中包含相对位置传感器输出的相对距离
和相对夹角
为中间变量；P为传播概率矩阵；
为传播概率矩阵的导数；
以上一阶偏导数矩阵即为系统在当前状态下进行局部线性化的矩阵，其中，A是状态转移函数f对状态变量x的偏导数矩阵，C是测量矩阵h对状态变量x的偏导数矩阵，M是测量矩阵h对测量噪声v的偏导数矩阵。
由于本实施例所介绍的测量装置为实施本发明实施例中改善相对位置传感器性能的方法所采用的装置，故而基于本发明实施例中所介绍的改善相对位置传感器性能的方法，本领域所属技术人员能够了解本实施例的测量装置的具体实施方式以及其各种变化形式，所以在此对于该测量装置如何实现本发明实施例中的方法不再详细介绍。只要本领域所属技术人员实施本发明实施例中改善相对位置传感器性能的方法所采用的装置，都属于本发明所欲保护的范围。
上述本发明实施例中的技术方案，至少具有如下的技术效果或优点：
在本发明实施例中，公开了一种测量装置，测量装置用于测量被测对象与测量装置之间的相对位置，测量装置上设置有相对位置传感器和第一辅助传感器，被测对象上设置有第二辅助传感器，测量装置，包括：获取单元，配置为获取由相对位置传感器测得的第一测量数据，以及获取由第一辅助传感器和第二辅助传感器测得的第二测量数据；构造单元，配置为基于第一测量数据和第二测量数据，构造扩展卡尔曼滤波器；修正单元，配置为利用扩展卡尔曼滤波器，对第一测量数据进行修正。由于采用了利用扩展卡尔曼滤波器，对相对位置传感器测得的第一测量数据进行修正，所以，有效解决了现有技术中的相对位置传感器存在测量数据容易受到干扰，产生跳动，导致测量数据不准确，动态性能较差的技术问题。进而实现了提高相对位置传感器的测量数据的准确性，从而改善相对位置传感器的动态性能的技术效果。
本发明通过另一实施例，提供一种计算机存储介质，所述计算机存储介质中存储有计算机可执行指令，所述计算机可执行指令配置为执行以下处理：
获取由所述相对位置传感器测得的第一测量数据，以及获取由所述第一辅
助传感器和所述第二辅助传感器测得的第二测量数据；
基于所述第一测量数据和所述第二测量数据，构造扩展卡尔曼滤波器；
利用所述扩展卡尔曼滤波器，对所述第一测量数据进行修正。
进一步地，上述计算机存储介质所执行的处理与上述实施例一相同，这里不再进行赘述。
本领域内的技术人员应明白，本发明的实施例可提供为方法、系统、或计算机程序产品。因此，本发明可采用完全硬件实施例、完全软件实施例、或结合软件和硬件方面的实施例的形式。而且，本发明可采用在一个或多个其中包含有计算机可用程序代码的计算机可用存储介质(包括但不限于磁盘存储器、CD-ROM、光学存储器等)上实施的计算机程序产品的形式。
本发明是参照根据本发明实施例的方法、设备(系统)、和计算机程序产品的流程图和/或方框图来描述的。应理解可由计算机程序指令实现流程图和/或方框图中的每一流程和/或方框、以及流程图和/或方框图中的流程和/或方框的结合。可提供这些计算机程序指令到通用计算机、专用计算机、嵌入式处理机或其他可编程数据处理设备的处理器以产生一个机器，使得通过计算机或其他可编程数据处理设备的处理器执行的指令产生用于实现在流程图一个流程或多个流程和/或方框图一个方框或多个方框中指定的功能的装置。
这些计算机程序指令也可存储在能引导计算机或其他可编程数据处理设备以特定方式工作的计算机可读存储器中，使得存储在该计算机可读存储器中的指令产生包括指令装置的制造品，该指令装置实现在流程图一个流程或多个流程和/或方框图一个方框或多个方框中指定的功能。
这些计算机程序指令也可装载到计算机或其他可编程数据处理设备上，使得在计算机或其他可编程设备上执行一系列操作步骤以产生计算机实现的处理，从而在计算机或其他可编程设备上执行的指令提供用于实现在流程图一个流程或多个流程和/或方框图一个方框或多个方框中指定的功能的步骤。
尽管已描述了本发明的优选实施例，但本领域内的技术人员一旦得知了基
本创造性概念，则可对这些实施例作出另外的变更和修改。所以，所附权利要求意欲解释为包括优选实施例以及落入本发明范围的所有变更和修改。
显然，本领域的技术人员可以对本发明进行各种改动和变型而不脱离本发明的精神和范围。这样，倘若本发明的这些修改和变型属于本发明权利要求及其等同技术的范围之内，则本发明也意图包含这些改动和变型在内。
本发明实施例提供了一种改善相对位置传感器性能的方法，所述方法包括：获取由相对位置传感器测得的第一测量数据，以及获取由第一辅助传感器和第二辅助传感器测得的第二测量数据；基于第一测量数据和第二测量数据，构造扩展卡尔曼滤波器；利用扩展卡尔曼滤波器，对第一测量数据进行修正。由于利用扩展卡尔曼滤波器，对相对位置传感器测得的第一测量数据进行修正，所以，有效解决了现有技术中的相对位置传感器存在测量数据容易受到干扰，产生跳动，导致测量数据不准确，动态性能较差的技术问题；进而实现了提高相对位置传感器的测量数据的准确性，从而改善相对位置传感器的动态性能的技术效果。
Claims (17)
- 一种改善相对位置传感器性能的方法，应用于测量装置中，所述测量装置用于测量被测对象与所述测量装置之间的相对位置，所述测量装置上设置有相对位置传感器和第一辅助传感器，所述被测对象上设置有第二辅助传感器，所述方法包括：获取由所述相对位置传感器测得的第一测量数据，以及获取由所述第一辅助传感器和所述第二辅助传感器测得的第二测量数据；基于所述第一测量数据和所述第二测量数据，构造扩展卡尔曼滤波器；利用所述扩展卡尔曼滤波器，对所述第一测量数据进行修正。
- 如权利要求1所述的改善相对位置传感器性能的方法，其中，所述第一测量数据，包括：所述测量装置与所述被测对象之间的相对夹角以及所述测量装置与所述被测对象之间的相对距离
- 如权利要求2所述的改善相对位置传感器性能的方法，其中，所述第二测量数据，包括：所述被测对象的加速度在X轴上的分量以及所述被测对象的加速度在Y轴上的分量
所述测量装置的前进速度以及所述测量装置的旋转速度
所述测量装置的正方向与地球磁北极的夹角θs，以及所述被测对象的正方向与地球磁北极的夹角θt。 - 如权利要求3所述的改善相对位置传感器性能的方法，其中，在所述基于所述第一测量数据和所述第二测量数据，构造扩展卡尔曼滤波器之前，还包括：对由所述测量装置和所述被测对象所组成的系统进行数学建模，获得数学模型：
其中，为所述被测对象的加速度在X轴上的零偏，
为所述被测对象的加速度在Y轴上的零偏；
为由所述第一辅助传感器测得的所述测量装置的前进速度与所述测量装置的真实前进速度的比值，
为由所述第一辅助传感器测得的所述测量装置的旋转速度与所述测量装置的真实旋转速度的比值；
为所述被测对象沿所述第二辅助传感器指向方向上的速度在X轴上的分量，
为所述被测对象沿所述第二辅助传感器指向方向上的速度在Y轴上的分量；
为所述测量装置的前进速度矢量向所述测量装置和所述被测对象的矢径方向的投影的大小，
为所述测量装置的前进速度矢量向所述测量装置和所述被测对象的矢径方向的法向的投影的大小；
为所述被测对象的速度矢量向连接所述测量装置和所述被测对象的矢径方向的投影的大小，
为所述被测对象的速度矢量向连接所述测量装置和所述被测对象的矢径方向的法向的投影的大小；
T(·)为二维空间中的旋转变换矩阵。 - 如权利要求4所述的改善相对位置传感器性能的方法，其中，所述基于所述第一测量数据和所述第二测量数据，构造扩展卡尔曼滤波器，包括：构造状态变量：
构造输入变量：
基于所述状态变量和所述输入变量，对所述数学模型进行调整得到扩展卡尔曼滤波器：
其中，v为测量噪声向量，服从均值为0、协方差为R的高斯分布，ti为时间，为系统微分方程模型，
为系统输出量，h为测量矩阵。
- 如权利要求5所述的改善相对位置传感器性能的方法，其中，所述利用所述扩展卡尔曼滤波器，对所述第一测量数据进行修正，包括：对所述扩展卡尔曼滤波器进行初始化；执行递推算法对所述第一测量数据进行修正，从而获得到和
的最优状态估计。
- 如权利要求6所述的改善相对位置传感器性能的方法，其中，对所述扩展卡尔曼滤波器进行初始化，包括：基于如下等式，对于所述扩展卡尔曼滤波器进行初始化：
其中，为系统初始状态x(0)的估计值，P(0)为系统状态转移概率矩阵，E[·]代表对·的期望。
- 如权利要求7所述的改善相对位置传感器性能的方法，其中，执行递推算法对所述第一测量数据进行修正，包括：基于如下等式，执行递推算法对所述第一测量数据进行修正：
其中，K按照如下等式不断更新：
其中，
为系统状态变量估计值的导数；K为根据模拟系统和实际系统的误差修正模拟系统状态的修正幅度；ym为含有噪声的测量结果，其中包含相对位置传感器输出的相对距离
和相对夹角
为中间变量；P为传播概率矩阵；
为传播概率矩阵的导数。
- 一种测量装置，所述测量装置用于测量被测对象与所述测量装置之间的相对位置，所述测量装置上设置有相对位置传感器和第一辅助传感器，所述被测对象上设置有第二辅助传感器，所述测量装置包括：获取单元，配置为获取由所述相对位置传感器测得的第一测量数据，以及获取由所述第一辅助传感器和所述第二辅助传感器测得的第二测量数据；构造单元，配置为基于所述第一测量数据和所述第二测量数据，构造扩展卡尔曼滤波器；修正单元，配置为利用所述扩展卡尔曼滤波器，对所述第一测量数据进行修正。
- 如权利要求9所述的测量装置，其中，所述第一测量数据，包括：所述测量装置与所述被测对象之间的相对夹角以及所述测量装置与所 述被测对象之间的相对距离
- 如权利要求10所述的测量装置，其中，所述第二测量数据，包括：所述被测对象的加速度在X轴上的分量以及所述被测对象的加速度在Y轴上的分量
所述测量装置的前进速度以及所述测量装置的旋转速度
所述测量装置的正方向与地球磁北极的夹角θs，以及所述被测对象的正方向与地球磁北极的夹角θt。 - 如权利要求11所述的测量装置，其中，所述测量装置还包括：建模单元，配置为所述基于所述第一测量数据和所述第二测量数据，构造扩展卡尔曼滤波器之前，对由所述测量装置和所述被测对象所组成的系统进行数学建模，获得数学模型：
其中，为所述被测对象的加速度在X轴上的零偏，
为所述被测对象的加速度在Y轴上的零偏；
为由所述第一辅助传感器测得的所述测量装置的前进速度与所述测量装 置的真实前进速度的比值，
为由所述第一辅助传感器测得的所述测量装置的旋转速度与所述测量装置的真实旋转速度的比值；
为所述被测对象沿所述第二辅助传感器指向方向上的速度在X轴上的分量，
为所述被测对象沿所述第二辅助传感器指向方向上的速度在Y轴上的分量；
为所述测量装置的前进速度矢量向所述测量装置和所述被测对象的矢径方向的投影的大小，
为所述测量装置的前进速度矢量向所述测量装置和所述被测对象的矢径方向的法向的投影的大小；
为所述被测对象的速度矢量向连接所述测量装置和所述被测对象的矢径方向的投影的大小，
为所述被测对象的速度矢量向连接所述测量装置和所述被测对象的矢径方向的法向的投影的大小；
T(·)为二维空间中的旋转变换矩阵。 - 如权利要求12所述的测量装置，其中，所述构造单元，配置为：构造状态变量：
构造输入变量：
基于所述状态变量和所述输入变量，将所述数学模型调整为如下表达式，得到扩展卡尔曼滤波器：
其中，v为测量噪声向量，服从均值为0、协方差为R的高斯分布，ti为时间，为系统微分方程模型，
为系统输出量，h为测量矩阵。
- 如权利要求13所述的测量装置，其中，所述修正单元，配置为：执行递推算法对所述第一测量数据进行修正，从而获得到和
的最优状态估计。
- 如权利要求14所述的测量装置，其中，所述修正单元，配置为基于如 下等式，对于所述扩展卡尔曼滤波器进行初始化：
其中，为系统初始状态x(0)的估计值，P(0)为系统状态转移概率矩阵，E[·]代表对·的期望。
- 如权利要求15所述的测量装置，其中，所述修正单元，配置为基于如下等式，执行递推算法对所述第一测量数据进行修正：
其中，K按照如下等式不断更新：
其中，
为系统状态变量估计值的导数；K为根据模拟系统和实际系统的误差修正模拟系统状态的修正幅度；ym为含有噪声的测量结果，其中包含相对位置传感器输出的相对距离
和相对夹角
为中间变量；P为传播概率矩阵；
为传播概率矩阵的导数。
- 一种计算机存储介质，所述计算机存储介质中存储有计算机可执行指令，所述计算机可执行指令配置为执行以下处理：获取由所述相对位置传感器测得的第一测量数据，以及获取由所述第一辅助传感器和所述第二辅助传感器测得的第二测量数据；基于所述第一测量数据和所述第二测量数据，构造扩展卡尔曼滤波器；利用所述扩展卡尔曼滤波器，对所述第一测量数据进行修正。
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