CN111664834A - Method/system for estimating elevation position of indoor moving body, storage medium, and apparatus - Google Patents

Method/system for estimating elevation position of indoor moving body, storage medium, and apparatus Download PDF

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CN111664834A
CN111664834A CN201910172168.3A CN201910172168A CN111664834A CN 111664834 A CN111664834 A CN 111664834A CN 201910172168 A CN201910172168 A CN 201910172168A CN 111664834 A CN111664834 A CN 111664834A
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data
moving body
motion state
noise covariance
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张榜
徐正蓺
魏建明
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Shanghai Advanced Research Institute of CAS
University of Chinese Academy of Sciences
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University of Chinese Academy of Sciences
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
    • G01C5/00—Measuring height; Measuring distances transverse to line of sight; Levelling between separated points; Surveyors' levels
    • G01C5/06—Measuring height; Measuring distances transverse to line of sight; Levelling between separated points; Surveyors' levels by using barometric means
    • 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
    • G01C21/005—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 with correlation of navigation data from several sources, e.g. map or contour matching
    • 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
    • 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
    • G01C21/20—Instruments for performing navigational calculations
    • G01C21/206—Instruments for performing navigational calculations specially adapted for indoor navigation

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Abstract

The invention discloses an elevation estimation method/system, a storage medium and equipment of an indoor moving body, which are used for judging and identifying the state of a measured moving body by utilizing acquired state data, estimating the elevation position of the measured moving body according to the judgment and identification result and facilitating the improvement of estimation precision; and when the measured moving body is identified and judged to be in non-planar motion, various data in the state data are combined with the extended Kalman algorithm to carry out fusion estimation on the elevation position of the measured moving body, so that the estimation precision of the elevation position of the indoor measured moving body can be further improved.

Description

Method/system for estimating elevation position of indoor moving body, storage medium, and apparatus
Technical Field
The invention relates to an elevation position estimation method, in particular to an elevation position estimation method of an indoor mobile unit.
Background
In the field of indoor positioning, height position information in the vertical direction is an important means for realizing three-dimensional space positioning. Currently, the estimation method of indoor height mainly uses an air pressure altimetry method and an inertia integration method, which are both estimated by collecting data in a sensor of an indoor mobile unit (such as a person). The method is simple in technology and high in operability, but large errors can be caused in the conversion process due to the fact that the relation between air pressure and height is quite complex, the air pressure can be influenced by environmental factors, and the estimated elevation position errors are large; while the inertial integration method calculates the vertical height change by using the vertical component of the acceleration, although the estimation accuracy is improved, the accumulated error is large after long-term use, which results in that the estimated elevation position accuracy is significantly reduced with the increase of the operation time.
Disclosure of Invention
In view of the above-mentioned shortcomings of the prior art, it is a primary object of the present invention to provide a method for estimating an elevation position of an indoor person to improve accuracy of estimating the elevation position of an indoor moving body.
In order to achieve the above objects and other related objects, the technical solution of the present invention is as follows:
an elevation position calculation method for an indoor mobile unit, comprising:
acquiring state data of a tested moving body in real time at a specified frequency, wherein the state data at least comprises air pressure data and acceleration data in the vertical direction;
equally dividing acceleration component data of the acceleration data in the vertical direction and the rest data in the state data according to time sequence to form a plurality of first subdata sets with specified duration;
inputting the first sub data set of the tested moving body in the current specified time into a pre-generated motion state recognition classifier, and judging whether the motion state of the tested moving body is in a flat motion state or a non-flat motion state according to the first sub data set,
if the tested moving body is in the flat ground motion state, the current output height corresponding to the current specified duration is equal to the previous output height corresponding to the previous specified duration,
and if the measured moving body is in the non-flat motion state, fusing all the first subdata by an extended Kalman filtering method to estimate the current output height.
Optionally, the method for identifying the motion state of the mobile object to be detected by the motion state identification classifier includes:
extracting classification features of the first sub data set,
and identifying the motion state of the tested moving body according to each type of the classification features.
Optionally, the training method of the motion state recognition classifier includes:
collecting the state data of the tested moving body in the flat ground motion state and the non-flat ground motion state as training data at the specified frequency,
evenly dividing acceleration component data of the acceleration data in the vertical component in the training data and the rest data in the training data according to a time sequence to form a plurality of second subdata sets with the specified duration;
extracting the classification features of each second sub data set to form a training sample;
inputting the training sample into a support vector machine model for training, optimizing parameters of the support vector machine model, and enabling the support vector machine model subjected to parameter optimization to form the motion state recognition classifier.
Optionally, the classification features include a mean vertical acceleration, a variance vertical acceleration, a barometric pressure difference, and a barometric pressure variance.
Optionally, the state of flat ground movement includes still, walking and jogging, and the state of non-flat ground movement includes going upstairs and downstairs.
Optionally, the method for estimating the current output height value by fusing the first sub data set through an extended kalman filter method includes:
constructing a system state equation through the acceleration component data in the vertical direction, and calculating the current prior height;
constructing a prior noise covariance matrix calculation formula according to the acceleration component data in the vertical direction and the previous posterior noise covariance matrix, and calculating a current prior noise covariance matrix;
constructing a system measurement equation through the air pressure data, and calculating the current measurement height;
constructing a Kalman gain calculation formula according to the prior noise covariance matrix, and calculating the current Kalman gain;
constructing a current output height fusion calculation equation according to the current prior height, the current measurement height and the current Kalman gain, and calculating the current output height;
and updating the current prior noise covariance matrix according to the current Kalman gain to obtain a current posterior noise covariance matrix.
Optionally, the system state equation is:
Figure BDA0001988427790000021
wherein i is the current specified duration, i-1 is the previous specified duration,
Figure BDA0001988427790000022
is the current a priori altitude or altitude,
Figure BDA0001988427790000023
is the previous output height corresponding to the previous specified duration,
Figure BDA0001988427790000031
is a relative altitude calculation function, the variable sa in the altitude calculation functioni＝[a1,a2,…,an]T，a1,a2,…,anMeans that the first subdata currently within the specified durationConcentrating the acceleration data in the vertical direction arranged according to the time sequence, wherein n represents the total times of acquiring the acceleration data in the vertical direction in the current specified duration, a1Representing acceleration data in a first one of the vertical directions currently within the specified time period; a isnRepresenting the last acceleration data in the vertical direction within the specified time period;
the prior noise covariance matrix calculation formula is as follows:
Figure BDA0001988427790000032
wherein the content of the first and second substances,
Figure BDA0001988427790000033
is the current prior noise covariance matrix corresponding to the current specified duration,
Figure BDA0001988427790000034
is the posterior noise covariance matrix corresponding to the previous specified duration,
Figure BDA0001988427790000035
is a function of the relative altitude calculation in the system equation of state
Figure BDA0001988427790000036
The jacobian matrix of (a) is,
Figure BDA0001988427790000037
Figure BDA0001988427790000038
is that
Figure BDA0001988427790000039
Q is the process noise covariance;
the system measurement equation is as follows:
Figure BDA00019884277900000310
wherein the content of the first and second substances,
Figure BDA00019884277900000311
indicating the current measured height, PiIndicating the current air pressure value, P1Indicating an initial air pressure value, P0Represents standard atmospheric pressure;
the current kalman gain calculation equation is:
Figure BDA00019884277900000312
wherein, KiRepresenting the current Kalman gain, RQIt is indicated that the measured variance is,
Figure BDA00019884277900000313
Figure BDA00019884277900000314
and
Figure BDA00019884277900000315
acceleration and barometer noise variance, respectively;
the current output height fusion calculation equation is:
Figure BDA00019884277900000316
updating the current prior noise covariance matrix according to the current Kalman gain to obtain a formula of a current posterior noise covariance matrix, wherein the formula comprises the following steps:
Figure BDA00019884277900000317
an elevation position estimation system of an indoor moving body, comprising:
the state data acquisition module is used for acquiring state data of the tested moving body in real time, wherein the state data at least comprises air pressure data and acceleration data in the vertical direction;
the data preprocessing module is used for averagely dividing acceleration component data of the acceleration data in the vertical direction and the rest data in the state data according to time sequence to form a plurality of first subdata sets with specified duration;
a motion state identification classifier, configured to read the first sub data set of the detected moving body within the current specified duration, and determine to identify which one of a flat motion state and a non-flat motion state the motion state of the detected moving body is in according to the first sub data set;
an output height calculating module for calculating the current output height corresponding to the current specified duration according to the identification result of the motion state identification classifier,
if the moving body to be detected is in the flat ground motion state, the output height calculation module outputs a previous output height corresponding to the previous specified duration as a current output height;
and if the measured moving body is in the non-flat motion state, the output height calculation module integrates all the first subdata to estimate the current output height by an extended Kalman filtering method.
Optionally, the motion state identification classifier includes:
a feature processing unit for extracting classification features of the first sub data set;
a state determination unit for identifying a motion state of the mobile body under test based on each type of the classification feature.
Optionally, the classification features include a mean vertical acceleration, a variance vertical acceleration, a barometric pressure difference, and a barometric pressure variance.
Optionally, the state of flat ground movement includes still, walking and jogging, and the state of non-flat ground movement includes going upstairs and downstairs.
Optionally, the output height calculating module includes:
a current prior height calculation unit for calculating a current prior height from the acceleration component data in the vertical direction;
the current prior noise covariance matrix calculation unit is used for calculating a current prior noise covariance matrix according to the acceleration component data in the vertical direction and the previous posterior noise covariance matrix;
a current measurement height calculation unit for calculating a current measurement height from the air pressure data;
a current Kalman gain calculation unit for calculating a current Kalman gain according to the current noise covariance;
a current output altitude fusion calculation unit for calculating the current output altitude according to the current prior altitude, the current measurement altitude and the current kalman gain fusion;
and the posterior noise covariance updating unit is used for updating the current prior noise covariance matrix according to the current Kalman gain to obtain a current posterior noise covariance matrix.
Optionally, the status data acquisition module includes an accelerometer and a barometer.
A storage medium having stored thereon a computer program that, when executed by a processor, implements any of the above-described elevation position estimation methods for an indoor mobile unit.
An apparatus comprising a processor and a memory, the memory storing a computer program, the processor executing the computer program stored in the memory to cause the apparatus to perform any one of the above-described elevation position estimation methods for an indoor moving body.
According to the elevation estimation method/system, the storage medium and the equipment for the indoor moving body, the state of the moving body to be measured is judged and identified by utilizing the acquired state data, and the elevation position of the moving body to be measured is estimated according to the judgment and identification result, so that the estimation precision is improved; and when the measured moving body is identified and judged to be in non-planar motion, various data in the state data are combined with the extended Kalman algorithm to carry out fusion estimation on the elevation position of the measured moving body, so that the estimation precision of the elevation position of the indoor measured moving body can be further improved.
Drawings
FIG. 1 is a flowchart illustrating an elevation position estimation method of an indoor mobile unit according to the present invention;
FIG. 2 is a flow chart of the present invention for estimating the current output height value by extended Kalman algorithm fusion;
FIG. 3 is a network configuration diagram showing an elevation position estimation system of an indoor moving body according to the present invention;
fig. 4 is a network structure diagram of the output height calculating module according to the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, embodiments of the present invention are described in further detail below with reference to the accompanying drawings. It is to be understood that the specific embodiments described herein are merely illustrative of the invention and are not limiting of the invention.
It should be further noted that, for the convenience of description, only some but not all of the relevant aspects of the present invention are shown in the drawings. Before discussing exemplary embodiments in more detail, it should be noted that some exemplary embodiments are described as processes or methods depicted as flowcharts. Although a flowchart may describe the operations (or steps) as a sequential process, many of the operations can be performed in parallel, concurrently or simultaneously. In addition, the order of the operations may be re-arranged. The process may be terminated when its operations are completed, but may have additional steps not included in the figure. The processes may correspond to methods, functions, procedures, subroutines, and the like.
Referring to fig. 1, the method for calculating an elevation position of an indoor mobile unit according to the present invention includes:
step 120, acquiring state data of the tested moving body in real time at a specified frequency, wherein the state data at least comprises air pressure data and acceleration data in the vertical direction;
step 140, data preprocessing: equally dividing acceleration component data of the acceleration data in the vertical direction and the rest data in the state data according to time sequence to form a plurality of first subdata sets with specified duration;
step 160, inputting the first sub data set of the tested moving body in the current specified duration into a pre-generated motion state identification classifier, and determining whether the motion state of the tested moving body is in a flat motion state or a non-flat motion state according to the first sub data set,
step 180B, if the tested moving body is in the state of flat motion, the current output height corresponding to the current specified duration is equal to the previous output height corresponding to the previous specified duration,
and step 180A, if the measured moving body is in the non-flat ground motion state, fusing all the first subdata by an extended Kalman filtering method to estimate the current output height.
In practical implementation, the measured moving body can be a human being, an animal or even a robot, in this embodiment, the measured moving body is an indoor person, when the measured moving body is a human being or an animal, the states of flat ground motion include still, walking and jogging, and the states of non-flat ground motion include going upstairs and going downstairs. When the above or the following estimation method is performed, the measured moving object needs to be equipped with a data acquisition module for acquiring the above state data, for example: the data acquisition module can comprise a barometer for acquiring air pressure and an accelerometer for acquiring acceleration in the vertical direction, the acceleration component data in the vertical direction can be acquired by decomposing the total acceleration, when the sensor is used for acquiring state data, the sensor can be embedded into wearable equipment and can be embedded into a mobile terminal carried by indoor personnel, the acquired position can be any position on the measured moving body, for example, when the measured moving body is the indoor personnel, the wearable equipment embedded with the sensor can be worn on the ankle, wrist, waist, thigh, arm or any other position on the body of the indoor personnel.
In this embodiment, the specified duration may be selected as any length, and the specified frequency may be selected as any frequency, but the shorter the specified duration is, the better the estimation accuracy of the elevation position is, but the shorter the specified duration is, the larger the data processing amount is, and similarly, the higher the specified frequency is, the better the estimation accuracy of the elevation position is, but the higher the specified leveling rate is, the higher the data processing amount is also increased. For example, the specified time duration may be selected to be 3 seconds, the specified frequency may be selected to be 100Hz, and the number of times of acquiring the state data in the current 3 seconds is 300, that is, there are 300 barometric pressure data and 300 acceleration data.
In this embodiment, for convenience of understanding, the specified time duration is defined as t, the collection times is defined as n, and i is defined as the current time, in this case, one first sub data set includes an acceleration data sequence in the vertical direction within the specified time duration and an air pressure data sequence within the specified time duration, and then the acceleration data sequence in the vertical direction in the first sub data set may be represented as (a)1,a2,…,an) The sequence of pressure data may be represented as (p)1,p2,…,pn) Wherein a is1Representing the acceleration component data of the first vertical direction acquired in time series within the specified time, anRepresenting the last vertical acceleration component data, p, acquired in time series within said specified time1Representing the first pressure data, p, acquired in time sequence within said given timenRepresenting the last barometric pressure data collected in time series within the specified time.
According to the elevation estimation method of the indoor moving body, the state of the moving body to be measured is judged and recognized by utilizing the collected state data, and the elevation position of the moving body to be measured is estimated according to the judgment and recognition result, so that the estimation precision is improved; when the measured moving body is identified and judged to be in non-planar motion, various data in the state data are combined with the extended Kalman algorithm to carry out fusion estimation on the elevation position of the measured moving body, so that the estimation precision of the elevation position of the indoor measured moving body can be further improved.
In some embodiments, the method for identifying the motion state of the mobile object to be detected by the motion state identification classifier comprises:
extracting classification features of the first sub data set,
and identifying the motion state of the tested moving body according to each type of the classification features.
In some embodiments, the classification features include a vertical direction acceleration mean, a vertical direction acceleration variance, a barometric pressure difference, and a barometric pressure variance.
The calculation formula of the vertical direction acceleration mean value is as follows:
Figure BDA0001988427790000071
wherein the content of the first and second substances,
Figure BDA0001988427790000072
the mean value of the vertical direction acceleration of the measured moving body in a specified time period t is obtained;
the calculation formula of the vertical direction acceleration variance is as follows:
Figure BDA0001988427790000073
wherein the content of the first and second substances,
Figure BDA0001988427790000074
the variance of the vertical direction acceleration within a specified time period t is measured;
the calculation formula of the air pressure difference is as follows:
Δp＝p1-p2
wherein Δ p represents an air pressure difference of the measured moving body within a specified time period t;
the calculation formula of the air pressure variance is as follows:
Figure BDA0001988427790000075
wherein the content of the first and second substances,
Figure BDA0001988427790000076
representing the body to be measuredThe variance of the air pressure over a specified time period t,
Figure BDA0001988427790000077
which represents the average air pressure of the measured moving body within a specified time period t,
Figure BDA0001988427790000078
in some embodiments, the training method of the motion state recognition classifier includes:
collecting the state data of the tested moving body in the flat ground motion state and the non-flat ground motion state as training data at the specified frequency,
evenly dividing the acceleration component data of the acceleration data in the vertical component in the training data and the rest data in the training data into a plurality of second subdata sets with the specified duration;
extracting the classification features of each second sub data set to form a training sample;
inputting the training sample into a support vector machine model for training, optimizing parameters of the support vector machine model, and enabling the support vector machine model subjected to parameter optimization to form the motion state recognition classifier.
In some embodiments, the training sample is a feature matrix X formed by arranging the classification features, a class label vector Y corresponding to each classification feature is defined, and when the support vector machine model is optimized, the feature matrix X and the class label vector Y are input into the support vector machine model, and the optimal parameters are solved, where a solving equation of the optimal parameters is as follows:
Figure BDA0001988427790000081
Figure BDA0001988427790000082
where m denotes the number of samples of the training samples, xvAnd xwRespectively representThe v and w samples, yv,ywRespectively represent samples xvAnd xwCorresponding label, βvAnd βwIs the parameter vector to be estimated.
Referring to FIG. 2, in some embodiments, a method of estimating the current output height value by fusing the first sub data set via extended Kalman filtering comprises:
step 181, constructing a system state equation through the acceleration component data in the vertical direction, and calculating the current prior height;
step 182, constructing a prior noise covariance matrix calculation formula according to the acceleration component data in the vertical direction and the previous-posterior noise covariance matrix, and calculating a current prior noise covariance matrix;
183, constructing a system measurement equation according to the air pressure data, and calculating the current measurement height;
184, constructing a Kalman gain calculation formula according to the prior noise covariance matrix, and calculating the current Kalman gain;
step 185, constructing a current output altitude fusion calculation equation according to the current prior altitude, the current measurement altitude and the current kalman gain, and calculating the current output altitude;
and 186, updating the current prior noise covariance matrix according to the current Kalman gain to obtain a current posterior noise covariance matrix.
In some embodiments, the system state equation is:
Figure BDA0001988427790000083
wherein i is the current specified duration, i-1 is the previous specified duration,
Figure BDA0001988427790000084
is the current a priori altitude or altitude,
Figure BDA0001988427790000085
is the previous output height corresponding to the previous specified duration,
Figure BDA0001988427790000091
is a relative altitude calculation function, the variable sa in the altitude calculation functioni＝[a1,a2,…,an]T，a1,a2,…,anThe acceleration data in the vertical direction are arranged in the first subdata set according to time sequence in the current specified duration, n represents the total times of acquiring the acceleration data in the vertical direction in the current specified duration, a1Representing acceleration data in a first one of the vertical directions currently within the specified time period; a isnRepresenting the last acceleration data in the vertical direction within the specified time period;
the prior noise covariance matrix calculation formula is as follows:
Figure BDA0001988427790000092
wherein the content of the first and second substances,
Figure BDA0001988427790000093
is the current prior noise covariance matrix corresponding to the current specified duration,
Figure BDA0001988427790000094
is the posterior noise covariance matrix corresponding to the previous specified duration,
Figure BDA0001988427790000095
is a function of the relative altitude calculation in the system equation of state
Figure BDA0001988427790000096
The jacobian matrix of (a) is,
Figure BDA0001988427790000097
Figure BDA0001988427790000098
is that
Figure BDA0001988427790000099
Q is the process noise covariance, which can be obtained by observation;
the system measurement equation is as follows:
Figure BDA00019884277900000910
wherein the content of the first and second substances,
Figure BDA00019884277900000911
indicating the current measured height, PiIndicating the current air pressure value, PiIs equal to p in the current first sub data setn，P1Indicating an initial air pressure value, P0Represents standard atmospheric pressure;
the current kalman gain calculation equation is:
Figure BDA00019884277900000912
wherein, KiRepresenting the current Kalman gain, RQIt is indicated that the measured variance is,
Figure BDA00019884277900000913
Figure BDA00019884277900000914
and
Figure BDA00019884277900000915
acceleration and barometer noise variance, respectively;
the current output height fusion calculation equation is:
Figure BDA00019884277900000916
updating the current prior noise covariance matrix according to the current Kalman gain to obtain a formula of a current posterior noise covariance matrix, wherein the formula comprises the following steps:
Figure BDA00019884277900000917
by using the method, the fusion height can be estimated by combining the air pressure data and the acceleration data with an extended Kalman gain algorithm, and the precision is high.
The present embodiment also discloses a storage medium having stored thereon a computer program corresponding to the above-described method, which when executed by a processor, implements any of the above-described elevation position estimation methods for an indoor mobile unit.
The storage medium in this embodiment can be understood by those skilled in the art as follows: all or a portion of the steps for implementing the method embodiments of the present description may be performed by computer program related hardware. The aforementioned computer program may be stored in a computer readable storage medium. When executed, performs steps comprising method embodiments of the present specification; and the aforementioned storage medium includes: various media that can store program codes, such as ROM, RAM, magnetic or optical disks.
Referring to fig. 3, in response to the elevation position estimation algorithm for the indoor mobile unit, the present embodiment further provides an elevation position estimation system for an indoor mobile unit, including:
the state data acquisition module 2 is used for acquiring state data of the tested moving body in real time, wherein the state data at least comprises air pressure data and acceleration data in the vertical direction;
the data preprocessing module 4 is used for averagely dividing the acceleration component data of the acceleration data in the vertical direction and the rest data in the state data according to time sequence to form a plurality of first subdata sets with specified duration;
a motion state recognition classifier 6, configured to read the first sub data set of the detected moving body within the current specified duration, and determine, according to the first sub data set, which one of a flat motion state and a non-flat motion state the motion state of the detected moving body is recognized;
an output height calculating module 8, configured to calculate a current output height corresponding to the current specified duration according to the recognition result of the motion state recognition classifier,
if the moving body to be detected is in the flat ground motion state, the output height calculation module outputs a previous output height corresponding to the previous specified duration as a current output height;
and if the measured moving body is in the non-flat motion state, the output height calculation module integrates all the first subdata to estimate the current output height by an extended Kalman filtering method.
In some embodiments, the motion state identification classifier may include:
a feature processing unit for extracting classification features of the first sub data set;
a state determination unit for identifying a motion state of the mobile body under test based on each type of the classification feature.
In other embodiments, the feature processing unit may also be arranged at the data preprocessing module, so that the formed first sub-data set collectively contains the classification features.
In some embodiments, the classification features include a vertical direction acceleration mean, a vertical direction acceleration variance, a barometric pressure difference, and a barometric pressure variance.
In some embodiments, the states of flat ground motion include stationary, walking, and jogging, and the states of non-flat ground motion include going upstairs and downstairs.
Referring to fig. 4, in some embodiments, the output height calculation module includes:
a current prior height calculation unit 81 for calculating a current prior height from the acceleration component data in the vertical direction;
a current prior noise covariance matrix calculation unit 82, configured to calculate a current prior noise covariance matrix according to the acceleration component data in the vertical direction and a previous posterior noise covariance matrix;
a current measurement height calculation unit 83 for calculating a current measurement height from the air pressure data;
a current kalman gain calculation unit 84 for calculating a current kalman gain from the current noise covariance;
a current output altitude fusion calculation unit 85 for calculating the current output altitude according to the current prior altitude, the current measurement altitude, and the current kalman gain fusion;
and an a posteriori noise covariance update unit 86, configured to update the current prior noise covariance matrix according to the current kalman gain to obtain a current a posteriori noise covariance matrix.
In some embodiments, the status data acquisition module includes an accelerometer and a barometer.
The present invention also provides an apparatus including a processor and a memory, the memory storing a computer program, the processor executing the computer program stored in the memory to cause the apparatus to perform any one of the above-described elevation position estimation methods for an indoor moving body.
The present embodiment provides an apparatus including a processor, a memory, a transceiver, and a communication interface, the memory and the communication interface being connected to the processor and the transceiver and performing communication therebetween, the memory being configured to store a computer program, the communication interface being configured to perform communication, the processor and the transceiver being configured to run the computer program, so that the apparatus performs the steps of the method for estimating an elevation position of an indoor movable body as described above.
In this embodiment, the Memory may include a Random Access Memory (RAM), and may also include a non-volatile Memory (non-volatile Memory), such as at least one disk Memory.
The Processor may be a general-purpose Processor, and includes a Central Processing Unit (CPU), a Network Processor (NP), and the like; the device can also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other Programmable logic device, a discrete Gate or transistor logic device, or a discrete hardware component.
The foregoing embodiments are merely illustrative of the principles and utilities of the present invention and are not intended to limit the invention. Any person skilled in the art can modify or change the above-mentioned embodiments without departing from the spirit and scope of the present invention. Accordingly, it is intended that all equivalent modifications or changes which can be made by those skilled in the art without departing from the spirit and technical spirit of the present invention be covered by the claims of the present invention.

Claims (15)

1. An elevation position estimation method for an indoor mobile unit, comprising:
acquiring state data of a tested moving body in real time at a specified frequency, wherein the state data at least comprises air pressure data and acceleration data;
equally dividing acceleration component data of the acceleration data in the vertical direction and the rest data in the state data according to time sequence to form a plurality of first subdata sets with specified duration;
inputting the first sub data set of the tested moving body in the current specified time into a pre-generated motion state recognition classifier, and judging whether the motion state of the tested moving body is in a flat motion state or a non-flat motion state according to the first sub data set,
if the tested moving body is in the flat ground motion state, the current output height corresponding to the current specified duration is equal to the previous output height corresponding to the previous specified duration,
and if the measured moving body is in the non-flat motion state, fusing all the first sub data sets by an extended Kalman filtering method to estimate the current output height.
2. The elevation position estimation method of an indoor mobile unit according to claim 1, wherein the method of the motion state recognition classifier for the motion state of the mobile unit under test comprises:
extracting classification features of the first sub data set,
and identifying the motion state of the tested moving body according to the classification characteristic.
3. The elevation position estimation method of an indoor mobile unit according to claim 2, characterized in that: the training method of the motion state recognition classifier comprises the following steps:
collecting the state data of the tested moving body in the flat ground motion state and the non-flat ground motion state as training data at the specified frequency,
evenly dividing acceleration component data of the acceleration data in the vertical component in the training data and the rest data in the training data according to a time sequence to form a plurality of second subdata sets with the specified duration;
extracting the classification features of each second sub data set to form a training sample;
inputting the training sample into a support vector machine model for training, optimizing parameters of the support vector machine model, and enabling the support vector machine model subjected to parameter optimization to form the motion state recognition classifier.
4. The elevation position estimation method of an indoor mobile unit according to claim 3, characterized in that: the classification features include a vertical direction acceleration mean, a vertical direction acceleration variance, a barometric pressure difference, and a barometric pressure variance.
5. The elevation position estimation method of an indoor mobile unit according to claim 1, characterized in that: the states of flat ground movement include still, walking and jogging, and the states of non-flat ground movement include going upstairs and downstairs.
6. The elevation position estimation method of an indoor mobile unit according to claim 1, characterized in that: the method for estimating the current output height value by fusing the first sub data set through an extended Kalman filtering method comprises the following steps:
constructing a system state equation through the acceleration component data in the vertical direction, and calculating the current prior height;
constructing a prior noise covariance matrix calculation formula according to the acceleration component data in the vertical direction and the previous posterior noise covariance matrix, and calculating a current prior noise covariance matrix;
constructing a system measurement equation through the air pressure data, and calculating the current measurement height;
constructing a Kalman gain calculation formula according to the prior noise covariance matrix, and calculating the current Kalman gain;
constructing a current output height fusion calculation equation according to the current prior height, the current measurement height and the current Kalman gain, and calculating the current output height;
and updating the current prior noise covariance matrix according to the current Kalman gain to obtain a current posterior noise covariance matrix.
7. The elevation position estimation method of an indoor mobile unit according to claim 6, characterized in that:
the system state equation is as follows:
Figure FDA0001988427780000021
wherein i is the current specified duration, i-1 is the previous specified duration,
Figure FDA0001988427780000022
is the current a priori altitude or altitude,
Figure FDA0001988427780000023
is the previous output height corresponding to the previous specified duration,
Figure FDA0001988427780000024
is a relative altitude calculation function, the variable sa in the altitude calculation functioni＝[a1,a2,…,an]T，a1,a2,…,anThe acceleration data in the vertical direction are arranged in the first subdata set according to time sequence in the current specified duration, n represents the total times of acquiring the acceleration data in the vertical direction in the current specified duration, a1Representing acceleration data in a first one of the vertical directions currently within the specified time period; a isnRepresenting the last acceleration data in the vertical direction within the specified time period;
the prior noise covariance matrix calculation formula is as follows:
Figure FDA0001988427780000025
wherein the content of the first and second substances,
Figure FDA0001988427780000026
is the current prior noise covariance matrix corresponding to the current specified duration,
Figure FDA0001988427780000027
is the posterior noise covariance matrix corresponding to the previous specified duration,
Figure FDA0001988427780000031
is a function of the relative altitude calculation in the system equation of state
Figure FDA0001988427780000032
The jacobian matrix of (a) is,
Figure FDA0001988427780000033
Figure FDA0001988427780000034
is that
Figure FDA0001988427780000035
Q is the process noise covariance;
the system measurement equation is as follows:
Figure FDA0001988427780000036
wherein the content of the first and second substances,
Figure FDA0001988427780000037
indicating the current measured height, PiIndicating the current air pressure value, P1Indicating an initial air pressure value, P0Represents standard atmospheric pressure; the current kalman gain calculation equation is:
Figure FDA0001988427780000038
wherein, KiRepresenting the current Kalman gain, RQIt is indicated that the measured variance is,
Figure FDA0001988427780000039
Figure FDA00019884277800000310
and
Figure FDA00019884277800000311
acceleration and barometer noise variance, respectively;
the current output height fusion calculation equation is:
Figure FDA00019884277800000312
updating the current prior noise covariance matrix according to the current Kalman gain to obtain a formula of a current posterior noise covariance matrix, wherein the formula comprises the following steps:
Figure FDA00019884277800000313
8. an elevation position estimation system for an indoor mobile unit, comprising:
the state data acquisition module is used for acquiring state data of the tested moving body in real time, wherein the state data at least comprises air pressure data and acceleration data;
the data preprocessing module is used for averagely dividing acceleration component data of the acceleration data in the vertical direction and the rest data in the state data according to time sequence to form a plurality of first subdata sets with specified duration;
a motion state identification classifier, configured to read the first sub data set of the detected moving body within the current specified duration, and determine to identify which one of a flat motion state and a non-flat motion state the motion state of the detected moving body is in according to the first sub data set;
an output height calculating module for calculating the current output height corresponding to the current specified duration according to the identification result of the motion state identification classifier,
if the moving body to be detected is in the flat ground motion state, the output height calculation module outputs a previous output height corresponding to the previous specified duration as a current output height;
and if the measured moving body is in the non-flat motion state, the output height calculation module integrates all the first subdata to estimate the current output height by an extended Kalman filtering method.
9. The elevation position estimation system for an indoor mobile unit according to claim 8, wherein the motion state recognition classifier includes:
a feature processing unit for extracting classification features of the first sub data set;
a state determination unit for identifying a motion state of the mobile body under test based on each type of the classification feature.
10. The elevation position estimation system for an indoor mobile unit according to claim 9, characterized in that: the classification features include a vertical direction acceleration mean, a vertical direction acceleration variance, a barometric pressure difference, and a barometric pressure variance.
11. The elevation position estimation system for an indoor mobile unit according to claim 8, characterized in that: the states of flat ground movement include still, walking and jogging, and the states of non-flat ground movement include going upstairs and downstairs.
12. The elevation position estimation system for an indoor mobile unit according to claim 8, wherein the output height calculation module includes:
a current prior height calculation unit for calculating a current prior height from the acceleration component data in the vertical direction;
the current prior noise covariance matrix calculation unit is used for calculating a current prior noise covariance matrix according to the acceleration component data in the vertical direction and the previous posterior noise covariance matrix;
a current measurement height calculation unit for calculating a current measurement height from the air pressure data;
a current Kalman gain calculation unit for calculating a current Kalman gain according to the current noise covariance;
a current output altitude fusion calculation unit for calculating the current output altitude according to the current prior altitude, the current measurement altitude and the current kalman gain fusion;
and the posterior noise covariance updating unit is used for updating the current prior noise covariance matrix according to the current Kalman gain to obtain a current posterior noise covariance matrix.
13. The elevation position estimation system for an indoor mobile unit according to claim 8, characterized in that: the state data acquisition module comprises an accelerometer and a barometer.
14. A storage medium having a computer program stored thereon, characterized in that: the program is executed by a processor to implement the method for estimating an elevation position of an indoor mobile unit according to any one of claims 1 to 7.
15. An apparatus, characterized by: comprising a processor for storing a computer program and a memory for executing the computer program stored in the memory to cause the apparatus to execute the elevation position estimation method for an indoor moving body according to any one of claims 1 to 7.
CN201910172168.3A 2019-03-07 2019-03-07 Method/system for estimating elevation position of indoor moving body, storage medium, and apparatus Pending CN111664834A (en)

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