CN107289951B - Indoor mobile robot positioning method based on inertial navigation - Google Patents

Indoor mobile robot positioning method based on inertial navigation Download PDF

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CN107289951B
CN107289951B CN201710636739.5A CN201710636739A CN107289951B CN 107289951 B CN107289951 B CN 107289951B CN 201710636739 A CN201710636739 A CN 201710636739A CN 107289951 B CN107289951 B CN 107289951B
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acceleration data
acceleration
frequency domain
frame
inertial sensor
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CN107289951A (en
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郭振昊
万虎
凌丹
邰圣辉
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University of Electronic Science and Technology of China
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    • GPHYSICS
    • G01MEASURING; TESTING
    • G01CMEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
    • G01C21/00Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
    • G01C21/20Instruments for performing navigational calculations
    • G01C21/206Instruments for performing navigational calculations specially adapted for indoor navigation
    • GPHYSICS
    • G01MEASURING; TESTING
    • G01CMEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
    • G01C21/00Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
    • G01C21/10Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration
    • G01C21/12Navigation; 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
    • G01C21/16Navigation; 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

Abstract

The invention discloses an indoor mobile robot positioning method based on inertial navigation, which is applied to the field of mobile robots; firstly, classifying the states of the sensors by receiving original acceleration data output by the inertial navigation sensor; correcting the acceleration data in the motion state by taking the average value of the acceleration data of each frame in the set time in the static state as a zero drift amount; secondly, selecting a Simpson integration method to carry out integration processing on the acceleration data to obtain displacement; moreover, frequency domain analysis is added on the basis of time domain analysis as a second error reduction method, the result precision is greatly improved by processing and filtering in the frequency domain, and the problem that the actual use is influenced due to overlarge accumulated error of the traditional inertial navigation integration algorithm is fundamentally solved.

Description

Indoor mobile robot positioning method based on inertial navigation
Technical Field
The invention belongs to the field of mobile robots, and particularly relates to a positioning technology of an inertial navigation indoor mobile robot.
Background
Inertial navigation is a process of finding position by integrating velocity and velocity by integrating total acceleration. The total acceleration refers to the sum of the acceleration due to gravity and the applied non-gravity (i.e., the specific acceleration). An Inertial Navigation System (INS) includes: a navigation computer for integrating; the precise clock is used for timing the integral operation, and the accelerometer group platform is used for measuring the specific force acceleration; gravity model software left in the navigation computer for gravitational acceleration calculations as a function of calculated position, and attitude references for defining the angular orientation of the accelerometer triples as part of the velocity calculations. In modern INS, the attitude reference is provided by a software integration function resident in the INS computer, the input of which is from an inertial angular velocity sensor having three axes. One arrangement in which the triads of angular rate sensors and accelerometers are mounted on a common, rigid frame that is mounted to the chassis of the INS to ensure accurate alignment between each inertial sensor is known as a strapdown INS. Because the inertial sensor is firmly fixed within the chassis, it is also firmly fixed to the mobile robot on which the INS is mounted.
According to inertial navigation theory, the basic functions in an INS computer are the integral function that transforms angular velocity into attitude (referred to as attitude integral), the measured acceleration values are transformed into an appropriate navigation coordinate system using attitude data and then integrated into a function of vector velocity (referred to as vector velocity integral), and the navigation system vector velocity is integrated into a function of position (referred to as position integral). Therefore, three integral functions, namely an attitude function, a vector velocity function and a position function, are provided, and the precision requirement of each function is very high so as to ensure that the function error is extremely small and meet the requirement of the precision of the inertial sensor. In practical application, the current pose of the mobile robot can be obtained by giving the pose of the initial robot through an inertial navigation theory.
In order to achieve the function of integration in the time domain, numerical integration is required. The purpose of numerical integration is to approximate f (x) over the interval [ a, b ] by calculating the value of f (x) over a finite number of sample points]The above constant integral. Let a be x0<x1<…<xMB is shaped as
And has properties ofThe formula (b) is numerical integration or area formula. Term E [ f ]]Truncation error, value, called integralReferred to as the area node(s),referred to as a weight. A unique polynomial P with the degree less than or equal to M exists through M +1 equidistant pointsM(x) In that respect When the polynomial is used to approximate [ a, b ]]When f (x) above, PM(x) The integral of (a) is approximately equal to the integral of (f), (x), and this type of equation is called the newton-cotts equation. When using sample point x0A and xMWhen b, it is called a closed newton-ketes formula.
Let xk=x0+ kh is an equidistant node, and fk=f(xk) The simpson integral formula is:the accuracy of the Simpson formula is n-3, if f is equal to C4[a,b]Then, then
For inertial navigation, the following disadvantages are present: when the acceleration information is collected from the inertial sensor, the interference of noise is inevitably received, and the noise is not only from the quantization operation of the sensor and the circuit noise but also from the interference of external conditions in the transmission process; if the noise is not controlled or the inertia module has zero drift characteristics, the accuracy of the next integration operation is influenced certainly, at the moment, the noise becomes a trend term, the trend term shows a speed error increasing along with time when the speed information is integrated for one time, and the error is amplified again after the second integration, namely the accumulated error. For inertial navigation, the accumulated error is the most dominant factor affecting its accuracy.
Appropriate correction and processing becomes necessary for these disturbances. To eliminate the noise signal, the characteristics of the analysis signal need to be observed first, and the characteristics of the noise signal cannot be observed in the time domain, which involves analyzing the acceleration signal in the frequency domain. Fast Fourier Transform (Fast Fourier Transform) is one of the most important algorithms in the field of signal processing and data analysis. The FFT (fast Fourier transform) is a fast algorithm of Discrete Fourier transform (Discrete Fourier transform), which changes the complexity of the algorithm from original O (N2) to O (NlogN), and the Discrete Fourier transform DFT is like the more familiar continuous Fourier transform, and has the following positive and inverse definition forms:
Forward Discrete Fourier Transform(DFT):
Inverse Discrete Fourier Transform(IDFT):
xnto XkThe transformation of (a) is a time-to-frequency domain transformation, which helps to study the power spectrum of the signal and makes the computation of some problems more efficient. After the signal is subjected to fast fourier transform, the frequency of the approximate distribution of noise and interference can be obtained. Meanwhile, the frequency of the effective signal is determined by combining the actual use occasion of the inertial sensor, and the pass band width of the low-pass filter is determined according to the Nyquist sampling theorem that the sampling frequency is more than two times of the frequency band, so that the effective noise elimination processing of the original signal can be completed.
Disclosure of Invention
The invention provides an indoor mobile robot positioning method based on inertial navigation to solve the technical problems, wherein a Simpson integral method is selected to realize remarkable precision improvement on the premise of not increasing the calculated amount; and the problem that the accumulated error of the traditional inertial navigation integration algorithm is too large to influence the practical use is solved by processing and filtering on the frequency domain.
The technical scheme adopted by the invention is as follows: an indoor mobile robot positioning method based on inertial navigation comprises the following steps:
s1, receiving acceleration data and Euler angles of each frame acquired by the inertial sensor;
s2, correcting the acceleration data of each frame obtained in the step S1;
s3, converting the corresponding acceleration data obtained in the step S2 into an acceleration variable relative to the ground according to each Euler angle;
s4, performing discrete integration on the acceleration variable corresponding to the motion state of the inertial sensor to obtain speed information;
s5, carrying out filtering processing on the speed information obtained in the step S4 on a frequency domain through fast Fourier transform; obtaining a speed frequency domain signal;
s6, performing inverse fast Fourier transform on the speed frequency domain signal obtained in the step S5 to obtain a speed time domain signal;
s7, performing discrete integration on the velocity time domain signal obtained in the step S6 to obtain an initial displacement signal;
s8, carrying out filtering processing on the obtained speed information on the frequency domain through fast Fourier transform on the initial displacement signal obtained in the step S7 to obtain a frequency domain signal of the initial displacement;
and S9, performing inverse fast Fourier transform on the frequency domain signal of the initial displacement to obtain a time domain signal of the initial displacement.
Further, the step S1 specifically includes the following sub-steps:
s11, setting a serial port protocol as follows: setting the arrangement sequence of the original acceleration data and the Euler angles, wherein each frame is a fixed length; and initializing a frame header;
s12, if the frame header of the received data does not accord with the preset value, discarding the received whole frame data;
and S13, performing segmentation operation on the data received in the step S12 according to the arrangement sequence to obtain a group of acceleration data and Euler angles.
Further, the step S2 is specifically:
judging the state of the inertial sensor by analyzing each frame of acceleration data received in step S1; the states include: a stationary state and a moving state;
and taking the average value of a plurality of frames of acceleration data acquired at set time when the inertial sensor is in a static state as the zero drift amount of the acceleration data corresponding to the inertial sensor in a motion state, and subtracting the zero drift amount value from the original value of the acceleration data to finish zero correction.
Further, the process of determining the static state or the motion state is as follows: if the differential value of the acceleration data received among the frames in the set time meets the fixed probability density, judging that the inertial sensor is in a static state; otherwise the inertial sensor is in motion.
Further, step S3 is specifically: transforming the acceleration data obtained in the step S2 into a ground reference coordinate system according to the rotation matrix by the following formula:
wherein, axAnd ayThe coordinates of the acceleration data relative to the vehicle body coordinate system; a isEAnd aNCoordinates of the acceleration data relative to a ground coordinate system; euler is Euler angle; E. and N is two axial directions of the ground coordinate system.
Further, the discrete integration adopts a Simpson integration.
Further, the step S4 further includes: and if the inertial sensor is in a static state, setting the corresponding speed to be zero.
The invention has the beneficial effects that: the invention relates to an indoor mobile robot positioning method based on inertial navigation, which comprises the following steps of firstly, carrying out state classification on received original acceleration data output by an inertial navigation sensor; the acceleration data in the motion state are corrected according to the average value of the acceleration data of each frame in the set time in the static state as the zero drift amount, so that the influence of the static drift and the zero drift caused by the characteristics of the accelerometer on the accuracy of the integration algorithm is effectively reduced, and the accumulated error is obviously reduced; secondly, a method which is a Simpson integration method and takes accuracy and calculated amount into consideration is selected, and compared with a widely used trapezoidal integration method, the accuracy is obviously improved on the premise of not increasing calculated amount; moreover, frequency domain analysis is added on the basis of time domain analysis as a second error reduction method, the result precision is greatly improved by processing and filtering in the frequency domain, and the problem that the actual use is influenced due to overlarge accumulated error of the traditional inertial navigation integration algorithm is fundamentally solved.
Drawings
Fig. 1 is a main flow diagram of the method of the present invention.
Fig. 2 is a schematic diagram of coordinate transformation according to the present invention.
Detailed Description
In order to facilitate the understanding of the technical contents of the present invention by those skilled in the art, the present invention will be further explained with reference to the accompanying drawings.
As shown in fig. 1, a scheme flow chart of the present invention is provided, and the technical scheme of the present invention is as follows: an indoor mobile robot positioning method based on inertial navigation comprises the following steps:
s1, receiving acceleration data and Euler angles acquired by the inertial sensor;
the acceleration data collected by the inertial sensor comprises the following specific processes:
1) and a data output format is set at the sensor end, so that each frame of acceleration data can be equal in length through the following setting, and the following data segmentation and processing are facilitated.
The first bit: initial bit, frame header, set to symbol! "
Second to seventh positions: the original acceleration in the X direction is compensated for 6 bits if no 6 bits exist;
eighth bit: spaces for spacing;
ninth to fourteenth positions: the original acceleration in the Y direction is compensated for 6 bits if no 6 bits exist;
the fifteenth bit: spaces for spacing;
sixteenth bit to twenty-first bit: euler angle, less than 6 bits complement 6 bits;
twenty-two and twenty-three positions: and (5) changing to the enter key.
The application adopts the serial port protocol with 23 bits per frame and fixed length, and the protocol is easier to maintain the integrity of data and is convenient to process. However, in the actual processing process, the fixed requirement of the serial port protocol is that each frame of data has a fixed length, and the specific length of each frame is determined according to the actual situation.
2) And the serial port receives data: and the serial port data is received by presetting serial port attributes such as baud rate and the like. The frame header and the frame length of each frame are judged according to the serial port protocol set in the step 1), so that data loss and errors caused by single transmission data incompleteness of the serial port are prevented. And if the frame header does not accord with the preset value, discarding the received whole frame data. For example, the length of each frame set in the present application is 23 bits, and if the received data is less than 23 bits, the method waits until the received data is greater than 23 bits, and extracts the first 23 bits from the received data as a complete data frame.
Specifically, each frame set in this application is 23 bits in length, the first bit is the frame header, set to the symbol "! "; the second to seventh bits are the original acceleration in the X direction, and if there are no 6 bits, 6 bits are complemented; the eighth bit is a space for spacing; ninth to fourteenth bits are the original acceleration in the Y direction, and 6 bits are complemented if 6 bits are not present; the fifteenth bit is a space for spacing; the sixteenth bit to the twenty-first bit are Euler angles, and less than 6 bits complement 6 bits; twenty-two and twenty-three positions: and (5) changing to the enter key.
3) And data segmentation: and (3) carrying out segmentation operation on the whole frame data received in the step (2), and extracting the information of X-direction linear acceleration, Y-direction linear acceleration and Euler angle (Euler angle) required by the patent.
The method specifically comprises the following steps: after each frame of original data is received by the serial port; all the spaces in the frame data are replaced by commas (namely, "), and the commas are taken as the segmented effective data, namely, the X-direction linear acceleration, the Y-direction linear acceleration and the Euler angle.
S2, correcting the acceleration data of each frame obtained in the step S1; the method specifically comprises the following steps: and carrying out primary processing on the received original data.
First, state partitioning is performed; performing state judgment on each frame of acceleration data received in step S1; the states include: a stationary state and a moving state;
then, probability analysis is performed on each frame of acceleration data received in step S1, and it is found through experiments that the difference value between the acceleration frame and the frame satisfies a fixed probability density whether the acceleration frame is initially stationary or stationary after moving: i.e., 95% of the data collected over time were less than 0.035. According to the findings, the sensor module, namely the robot platform, is judged to be in a static state at the moment by analyzing 100 data collected in 1S if the conditions are met; recording the motion state at the moment as a static state for later use; otherwise, recording as a motion state.
Secondly, considering that the inertia module has zero drift, when the sensor is judged to be in a static state at present, recording the average value of acceleration data acquired within a certain time under the static state as the zero drift of the acceleration of the next motion state;
and finally, when the acceleration in the motion state is output, subtracting the obtained zero drift value from the original value of the acceleration to finish zero correction.
S3, converting the acceleration data obtained in the step S2 into an acceleration variable relative to the ground according to the Euler angle; as shown in fig. 2. For two-dimensional navigation systems; n and E are two axes of a ground reference system,for the clockwise deflection angle of the vehicle body with respect to the N-axis, the present application is made by aligning the initial position of the vehicle body with the N-axis and then replacing the Euler angle (Euler angle) obtained in step S1 with the sameThe angle is used for the next operation.
A with respect to the vehicle body coordinate system can be converted from the rotation matrix byxAnd ayTransforming into a ground reference coordinate system, and facilitating further positioning and navigation operations:
the E, N direction acceleration m relative to the ground reference system is obtained according to the formulaEAnd mNOn the basis, the E and N direction speeds of the mobile robot relative to the initial position of the ground reference system can be obtained by performing superposition operation at the upper computer end.
S4, performing discrete integration on the acceleration variable in the motion state to obtain speed information; if the speed is in a static state, setting the corresponding speed to be zero; the calculation of the module is carried out when the current motion state is judged, and the accumulated error caused by the integration of the sensor error is reduced as much as possible. After comparing the trapezoidal integration, the simpson integration is selected as the integration method of the patent. The advantage is that the calculation precision is higher by two stages on the premise that the calculated amount is not too much compared with the trapezoidal integral.
And performing primary integration on the acceleration information in the motion state to obtain a speed signal, and storing the speed signal as a global variable for later use in a track tracking and motion control link. Meanwhile, the motion state recorded in the previous data processing step needs to be judged, if the motion state is recorded as a static state, the speed is directly output to be zero, and the integral operation is skipped.
S5, filtering the speed information obtained in the step S4 in a frequency domain through Fast Fourier Transform (FFT); obtaining a speed frequency domain signal;
s6, performing inverse fast Fourier transform on the speed frequency domain signal obtained in the step S5 to obtain a speed time domain signal;
and performing fast Fourier transform on the speed information after the primary integration. The algorithm used is a radix-2 FFT algorithm with one-dimensional frequency decimation. The fast Fourier method is selected to remarkably reduce the operation times and improve the operation speed.
Firstly, the obtained speed frequency domain signal is subjected to fast Fourier transform, and the frequency of an effective signal is about 10Hz according to the running state of an actual robot platform, so that the passband of frequency domain analysis is determined to be 20Hz according to the Nyquist sampling law, a proper filter type is selected, the signal is filtered on the frequency domain, and high-frequency noise is eliminated.
And then, performing inverse Fourier transform on the obtained frequency domain signal to obtain a signal on a time domain again.
S7, performing discrete integration on the velocity time domain signal obtained in the step S6 to obtain an initial displacement signal;
and performing time domain integration again on the basis of the speed information obtained by the primary integration and subjected to filtering processing to obtain displacement information. Meanwhile, under the condition that the initial coordinate is known, the displacement information can be equivalent to the current coordinate value of the robot platform.
S8, filtering the obtained speed information on the frequency domain through Fast Fourier Transform (FFT) on the initial displacement signal obtained in the step S7 to obtain a frequency domain signal of the initial displacement;
and carrying out fast Fourier transform on the displacement information after the secondary integration. The algorithm used is a radix-2 FFT algorithm with one-dimensional frequency decimation. Similar to the previous processing of the velocity information, the frequency domain filtering processing is performed.
And S9, performing inverse fast Fourier transform on the frequency domain signal of the initial displacement to obtain a time domain signal of the initial displacement.
It will be appreciated by those of ordinary skill in the art that the embodiments described herein are intended to assist the reader in understanding the principles of the invention and are to be construed as being without limitation to such specifically recited embodiments and examples. Various modifications and alterations to this invention will become apparent to those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the scope of the claims of the present invention.

Claims (4)

1. An indoor mobile robot positioning method based on inertial navigation is characterized by comprising the following steps:
s1, receiving acceleration data and Euler angles of each frame acquired by the inertial sensor;
s2, correcting the acceleration data of each frame obtained in the step S1; the step S2 specifically includes:
judging the state of the inertial sensor by analyzing each frame of acceleration data received in step S1; the states include: a stationary state and a moving state;
taking the average value of a plurality of frames of acceleration data acquired at set time when the inertial sensor is in a static state as the zero drift amount of the acceleration data corresponding to the inertial sensor in a motion state, and subtracting the zero drift amount value from the original value of the acceleration data to finish zero correction;
s3, converting the corresponding acceleration data obtained in the step S2 into an acceleration variable relative to the ground according to each Euler angle; step S3 specifically includes: transforming the acceleration data obtained in the step S2 into a ground reference coordinate system according to the rotation matrix by the following formula:
wherein, axAnd ayThe coordinates of the acceleration data relative to the vehicle body coordinate system; a isEAnd aNCoordinates of the acceleration data relative to a ground coordinate system; euler is Euler angle; E. n is two axial directions of a ground coordinate system;
s4, performing discrete integration on the acceleration variable corresponding to the motion state of the inertial sensor to obtain speed information;
s5, carrying out filtering processing on the speed information obtained in the step S4 on a frequency domain through fast Fourier transform; obtaining a speed frequency domain signal;
s6, performing inverse fast Fourier transform on the speed frequency domain signal obtained in the step S5 to obtain a speed time domain signal;
s7, performing discrete integration on the velocity time domain signal obtained in the step S6 to obtain an initial displacement signal;
s8, carrying out filtering processing on the obtained speed information on the frequency domain through fast Fourier transform on the initial displacement signal obtained in the step S7 to obtain a frequency domain signal of the initial displacement;
s9, performing inverse fast Fourier transform on the frequency domain signal of the initial displacement to obtain a time domain signal of the initial displacement;
and the discrete integration adopts Simpson integration.
2. The method as claimed in claim 1, wherein the step S1 comprises the following sub-steps:
s11, setting a serial port protocol as follows: setting the arrangement sequence of the original acceleration data and the Euler angles, wherein each frame is a fixed length; and initializing a frame header;
s12, if the frame header of the received data does not accord with the preset value, discarding the received whole frame data;
and S13, performing segmentation operation on the data received in the step S12 according to the arrangement sequence to obtain a group of acceleration data and Euler angles.
3. The method of claim 1, wherein the static state or the motion state is determined by: if the differential value of the acceleration data received among the frames in the set time meets the fixed probability density, judging that the inertial sensor is in a static state; otherwise the inertial sensor is in motion.
4. The inertial navigation-based indoor mobile robot positioning method according to claim 1, wherein the step S4 further includes: and if the inertial sensor is in a static state, setting the corresponding speed to be zero.
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Families Citing this family (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN108519105A (en) * 2018-03-09 2018-09-11 天津大学 A kind of zero-speed correction localization method based on difference statistics
CN109521226B (en) * 2018-11-29 2021-02-02 歌尔科技有限公司 Speed calculation method, system, electronic equipment and readable storage medium
CN109682382B (en) * 2019-02-28 2020-09-08 电子科技大学 Global fusion positioning method based on self-adaptive Monte Carlo and feature matching
CN112050829A (en) * 2019-06-06 2020-12-08 华为技术有限公司 Motion state determination method and device
CN112146678A (en) * 2019-06-27 2020-12-29 华为技术有限公司 Method for determining calibration parameters and electronic equipment

Citations (9)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102948150A (en) * 2010-06-07 2013-02-27 索尼公司 Image decoder apparatus, image encoder apparatus and methods and programs thereof
KR101514790B1 (en) * 2013-10-30 2015-04-23 건국대학교 산학협력단 freezing of gait discriminator using fuzzy theory and frequency band and freezing of gait discriminating Method using The Same
CN104730498A (en) * 2015-04-01 2015-06-24 西安电子科技大学 Target detection method based on Keystone and weighting rotating FFT
CN105758404A (en) * 2016-01-26 2016-07-13 广州市香港科大霍英东研究院 Real-time positioning method and system of intelligent equipment
CN106123897A (en) * 2016-06-14 2016-11-16 中山大学 Indoor fusion and positioning method based on multiple features
AU2015215845B2 (en) * 2014-08-22 2017-02-02 Apple Inc. Heart rate path optimizer
CN106443614A (en) * 2016-08-23 2017-02-22 西安电子科技大学 Hypersonic velocity target acceleration testing method
CN106940805A (en) * 2017-03-06 2017-07-11 江南大学 A kind of group behavior analysis method based on mobile phone sensor
CN106970371A (en) * 2017-04-28 2017-07-21 电子科技大学 A kind of object detection method based on Keystone and matched filtering

Family Cites Families (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US10001386B2 (en) * 2014-04-03 2018-06-19 Apple Inc. Automatic track selection for calibration of pedometer devices

Patent Citations (9)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102948150A (en) * 2010-06-07 2013-02-27 索尼公司 Image decoder apparatus, image encoder apparatus and methods and programs thereof
KR101514790B1 (en) * 2013-10-30 2015-04-23 건국대학교 산학협력단 freezing of gait discriminator using fuzzy theory and frequency band and freezing of gait discriminating Method using The Same
AU2015215845B2 (en) * 2014-08-22 2017-02-02 Apple Inc. Heart rate path optimizer
CN104730498A (en) * 2015-04-01 2015-06-24 西安电子科技大学 Target detection method based on Keystone and weighting rotating FFT
CN105758404A (en) * 2016-01-26 2016-07-13 广州市香港科大霍英东研究院 Real-time positioning method and system of intelligent equipment
CN106123897A (en) * 2016-06-14 2016-11-16 中山大学 Indoor fusion and positioning method based on multiple features
CN106443614A (en) * 2016-08-23 2017-02-22 西安电子科技大学 Hypersonic velocity target acceleration testing method
CN106940805A (en) * 2017-03-06 2017-07-11 江南大学 A kind of group behavior analysis method based on mobile phone sensor
CN106970371A (en) * 2017-04-28 2017-07-21 电子科技大学 A kind of object detection method based on Keystone and matched filtering

Non-Patent Citations (3)

* Cited by examiner, † Cited by third party
Title
"Modeling laser-driven electron acceleration using WARP with Fourier decomposition(Article)";Lee, P等;《Nuclear Instruments and Methods in Physics Research, Section A: Accelerators, Spectrometers, Detectors and Associated Equipment》;20161231;第829卷;358-362 *
"基于傅里叶级数分解的示功图位移测量算法";刘箭言等;《北京理工大学学报》;20150531;第35卷(第5期);506-511 *
"基于智能积分的改进增量式PID算法";邵伟等;《机电工程技术》;20101231;第39卷(第11期);46-48,79 *

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