CN113218388A - Mobile robot positioning method and system considering variable colored measurement noise - Google Patents

Mobile robot positioning method and system considering variable colored measurement noise Download PDF

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CN113218388A
CN113218388A CN202110228825.9A CN202110228825A CN113218388A CN 113218388 A CN113218388 A CN 113218388A CN 202110228825 A CN202110228825 A CN 202110228825A CN 113218388 A CN113218388 A CN 113218388A
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CN113218388B (en
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徐元
孙明旭
毕淑慧
孙斌
赵钦君
马荔瑶
曹靖
冯寄东
李明然
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JINAN LING SHENG INFORMATION TECHNOLOGY CO.,LTD.
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University of Jinan
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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
    • 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
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W64/00—Locating users or terminals or network equipment for network management purposes, e.g. mobility management

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Abstract

The invention discloses a mobile robot positioning method and a system considering variable colored measurement noise, which comprises the following steps: taking the east and north positions and speeds of the mobile robot at the moment k as state quantities, and taking the distance between the mobile robot and a UWB reference node obtained by UWB measurement as a system observed quantity to construct a filtering model; on the basis of expanding the finite impulse response filter, constructing m different sub-filters considering colored measurement noise according to different local filtering windows selected in an off-line stage; and fusing the constructed output of the sub-filters in an IMM mode to obtain the optimal position prediction of the mobile robot at the current moment, so as to realize the positioning of the mobile robot. The method constructs sub EFIR filters under different colored measurement noises, and fuses the outputs of the sub filters through an interactive multi-model algorithm to finally obtain the optimal position estimation of the mobile robot for the optimal UWB measurement.

Description

Mobile robot positioning method and system considering variable colored measurement noise
Technical Field
The invention relates to the technical field of combined positioning in a complex environment, in particular to a mobile robot positioning method and system considering variable colored measurement noise.
Background
The statements in this section merely provide background information related to the present disclosure and may not necessarily constitute prior art.
Mobile robot navigation and positioning, as a basis for home service robots to provide high quality services to humans, currently face many challenges. Especially in indoor environment, the indoor layout, building materials and even space size of a building can influence navigation signals, and positioning accuracy is seriously influenced. Meanwhile, a relatively small platform of the home service robot facing the indoor environment also puts severe requirements on the volume, the precision and the cost of the navigation instrument. In an indoor environment, how to utilize the acquired limited information to eliminate the influence of an indoor complex navigation environment on the accuracy and the real-time performance of the acquisition of the navigation information of the service robot, and ensure the continuous and stable navigation precision of the service robot in the indoor environment has important scientific theoretical significance and practical application value.
Among the existing positioning methods, Global Navigation Satellite System (GNSS) is the most commonly used method. Although the GNSS can continuously and stably obtain the position information with high precision, the application range of the GNSS is limited by the defect that the GNSS is easily influenced by external environments such as electromagnetic interference and shielding, and particularly in some closed and environment-complex scenes such as indoor and underground roadways, GNSS signals are seriously shielded, and effective work cannot be performed.
In recent years, uwb (ultra wideband) has shown great potential in the field of short-distance local positioning due to its high positioning accuracy in a complex environment. Researchers have proposed the use of UWB-based target tracking for pedestrian navigation in GNSS-disabled environments. Although indoor positioning can be realized by the method, because the indoor environment is complicated and changeable, UWB signals are easily interfered to cause the reduction of positioning accuracy and even the unlocking; meanwhile, because the communication technology adopted by the UWB is generally a short-distance wireless communication technology, if a large-range indoor target tracking and positioning is to be completed, a large number of network nodes are required to complete together, which inevitably introduces a series of problems such as network organization structure optimization design, multi-node multi-cluster network cooperative communication, and the like. Therefore, at present, the UWB-based target tracking still faces many challenges in the field of indoor navigation, and particularly in the process of steering the mobile robot, Colored Measurement Noise (CMN) affects the position estimation accuracy of the mobile robot, but the existing technology cannot accurately predict the Colored Measurement Noise.
Disclosure of Invention
In order to solve the problems, the invention provides a mobile robot positioning method and a mobile robot positioning system considering variable colored measurement noise, which improve a traditional Extended Finite Impulse Response (EFIR) filter, construct sub EFIR filters under different colored measurement noise according to different colored noise ratios selected in an off-line stage, and fuse the constructed sub EFIR filters by an interactive multi-model (IMM) method to finally obtain the optimal mobile robot position estimation at the current moment.
In some embodiments, the following technical scheme is adopted:
a mobile robot positioning method taking into account variable colored measurement noise, comprising:
taking the east and north positions and speeds of the mobile robot at the moment k as state quantities, and taking the distance between the mobile robot and a UWB reference node obtained by UWB measurement as a system observed quantity to construct a filtering model;
on the basis of expanding the finite impulse response filter, according to different local filtering windows selected in an off-line stage, m different sub-filters considering colored measurement noise are constructed, wherein m is a set value;
and fusing the constructed output of the sub-filters in an IMM mode to obtain the optimal position prediction of the mobile robot at the current moment, so as to realize the positioning of the mobile robot.
As a further improvement, the state equation of each sub-filter is specifically as follows:
Figure BDA0002958006910000031
wherein (x)k,yk)、(xk-1,yk-1) The positions of the east and north of the mobile robot at the moment k and the moment k-1 respectively;
Figure BDA0002958006910000032
the speed of the mobile robot in the east direction and the north direction at the moment k and k-1 respectively; tau is a sampling period; w is akIs the system noise at time k.
As a further improvement, the observation equation of each sub-filter is specifically as follows:
Figure BDA0002958006910000033
wherein (x)i,yi),i∈[1,4]The position of the ith UWB reference node; v. ofkThe observed noise at the system k moment;
Figure BDA0002958006910000034
and the observed distance between the mobile robot and the UWB reference node is measured by UWB at the time k.
As a further improvement, the number of different local filtering windows selected during the off-line phase is selected according to the requirements on the performance of the algorithm.
As a further improvement, the constructed sub-filters are fused in an IMM manner, and the specific process includes:
constructing a Markov transition probability matrix, and introducing a weight matrix on the basis;
calculating the weight of each sub-filter;
and obtaining the total output of the data fusion filter according to the weight of each sub-filter and the state vector prediction of the sub-filters.
As a further improvement, the weight of each sub-filter is calculated, and the specific process includes:
Figure BDA0002958006910000041
Figure BDA0002958006910000042
Figure BDA0002958006910000043
wherein the content of the first and second substances,
Figure BDA0002958006910000044
the weighted value of the k moment obtained from the k-1 moment; i is the number of the sub-filters;
Figure BDA0002958006910000045
is the weight value C of the current timek＝Hk-γiHk-1F-1，
Figure BDA0002958006910000046
γiIs the ratio of CMN in the ith sub-filter,
Figure BDA0002958006910000047
for the error matrix at time k derived from time k-1, R observes the noise covariance, φk＝QkγiHk-1F-1，QkAnd F is a system matrix.
As a further improvement, the total output of the data fusion filter is obtained, specifically:
Figure BDA0002958006910000048
wherein the content of the first and second substances,
Figure BDA0002958006910000049
is the output of the main filter at the time k,
Figure BDA00029580069100000410
The state of the sub-filter at time k is estimated.
In other embodiments, the following technical solutions are adopted:
a mobile robot positioning system that accounts for variable colored measurement noise, comprising:
the device is used for taking the east and north positions and speeds of the mobile robot k as state quantities, taking the distance between the mobile robot and a UWB reference node obtained by UWB measurement as a system observed quantity, and constructing a filtering model;
means for constructing m different sub-filters considering colored measurement noise based on the extended finite impulse response filter according to different local filtering windows selected at the off-line stage;
and the device is used for fusing the outputs of the constructed sub-filters in an IMM mode to obtain the optimal position prediction of the mobile robot at the current moment and realize the positioning of the mobile robot.
In other embodiments, the following technical solutions are adopted:
a terminal device comprising a processor and a memory, the processor being arranged to implement instructions; the memory is for storing a plurality of instructions adapted to be loaded by the processor and to perform the above described mobile robot positioning method taking into account variable colored measurement noise.
In other embodiments, the following technical solutions are adopted:
a computer readable storage medium having stored therein a plurality of instructions adapted to be loaded by a processor of a terminal device and to perform the above described mobile robot positioning method taking into account variable colored measurement noise.
Compared with the prior art, the invention has the beneficial effects that:
in the invention, the proportion of colored measurement noise of the sub-EFIR filters is obtained through independent data in an off-line stage, the sub-EFIR filters under different colored measurement noise are constructed, and on the basis, the output of each sub-filter is fused through an interactive multi-model (IMM) algorithm to finally obtain the optimal position estimation of the mobile robot measured by the optimal UWB; the method can be used for realizing high-precision positioning in indoor environment.
Additional features and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention.
Drawings
FIG. 1 is a schematic structural diagram of a mobile robot positioning system in accordance with an embodiment of the present invention, in which variable colored measurement noise is taken into account;
fig. 2 is a flowchart of a mobile robot positioning method in consideration of variable colored measurement noise in an embodiment of the present invention.
Detailed Description
It should be noted that the following detailed description is exemplary and is intended to provide further explanation of the disclosure. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments according to the present application. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, and it should be understood that when the terms "comprises" and/or "comprising" are used in this specification, they specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof, unless the context clearly indicates otherwise.
Example one
In one or more embodiments, disclosed is a mobile robot positioning method considering variable colored measurement noise, which is implemented based on the system shown in fig. 1, and referring to fig. 1, UWB includes a UWB reference node and a UWB positioning tag, the UWB reference node is fixed on a known coordinate in advance, and the UWB positioning tag is fixed on any mobile machine; an INS is primarily composed of an IMU secured to the foot of a target pedestrian. UWB obtains the distance information between the target pedestrian and the reference node at the current moment; the INS acquires distance information between the target pedestrian and the reference node at the current moment.
Referring to fig. 2, the mobile robot positioning method considering variable colored measurement noise specifically includes:
(1) taking the east and north positions and speeds of the mobile robot at the moment k as state quantities, and taking the distance between the mobile robot and a UWB reference node obtained by UWB measurement as a system observed quantity to construct a filtering model;
(2) on the basis of the extended finite impulse response filter, m different sub-filters considering Colored Measurement Noise (CMN) are constructed according to different local filtering windows selected in an off-line stage, wherein m is a set value.
The number of different local filtering windows selected in the off-line stage is selected according to the requirement on the performance of the algorithm.
In this embodiment, the sub-filter is a sub-EFIR filter (sub-chefir filter) under colored measurement noise.
The output of the sub-cEFIR filter is the estimation of the sub-filter to the current time position of the mobile robot, and the input of the sub-cEFIR filter is the observation vector of each time.
The equation of state for the sub-chefir filter is:
Figure BDA0002958006910000071
wherein (x)k,yk)、(xk-1,yk-1) The positions of the east and north of the mobile robot at the moment k and the moment k-1 respectively;
Figure BDA0002958006910000072
the speed of the mobile robot in the east direction and the north direction at the moment k and k-1; tau is a sampling period; w is akIs the system noise at time k.
The observed equation for the sub-chefir filter is:
Figure BDA0002958006910000073
wherein (x)i,yi),i∈[1,4]The position of the ith UWB reference node; v. ofkThe observed noise at the system k moment;
Figure BDA0002958006910000074
and the observed distance between the mobile robot and the UWB reference node is measured by UWB at the time k.
The iterative algorithm for the sub-chefir filter is:
setting intermediate variables m and s:
mcE＝k-NcE+1,s＝mcE+McE-1
wherein N iscEIs the size of the local filter window of the sub-chefir filter; mcEIs the dimension of the state vector;
when the sampling time is more than NcEThen, setting the intermediate variable j to range from time s +1 to time k, the iteration performed by the sub-chefir filter is as follows:
zl＝yl-γyl-1
Cl＝Hl-γHlF-1
Figure BDA0002958006910000081
Figure BDA0002958006910000082
Figure BDA0002958006910000083
Figure BDA0002958006910000084
Figure BDA0002958006910000085
Figure BDA0002958006910000086
Figure BDA0002958006910000087
Figure BDA0002958006910000088
Figure BDA0002958006910000089
on the basis of the above-mentioned technical scheme,
Figure BDA00029580069100000810
wherein F is a system matrix;
Figure BDA00029580069100000811
and
Figure BDA00029580069100000812
state directions at time k and time k-1, respectivelyQuantity iteration intermediate quantity;
Figure BDA00029580069100000813
iterating the intermediate quantity for the state vector at the time k obtained from the time k-1; gkGeneralized noise power gain at time k; y iskIs an observation vector at the k moment;
Figure BDA00029580069100000814
estimating a state vector for k time;
Figure BDA00029580069100000815
to relate to
Figure BDA00029580069100000816
A function of (a);
Figure BDA0002958006910000091
is an error matrix from k-1 to k times,
Figure BDA0002958006910000092
Is the error matrix from the time k-1, Q is the system noise wkCovariance of (H)kThe observation equation at time j, RkTo observe the covariance of the noise.
Figure BDA0002958006910000093
Is the weight value C of the current timek＝Hk-γiHk-1F-1、γiIs the ratio of CMN in the ith sub-filter,
Figure BDA0002958006910000094
error matrix for k-time derived from k-1 time, R observed noise covariance,
Figure BDA0002958006910000095
φk＝QkγiHk-1F-1Wherein Q iskIs the covariance of the system noise at time k.
(3) Fusing the constructed output of the sub-filters in an IMM mode to obtain the optimal position estimation of the mobile robot of the UWB positioning system at the current moment;
the process of fusing the outputs of the constructed sub-filters in an IMM manner specifically comprises the following steps:
1) constructing a Markov transition probability matrix, and introducing a weight matrix on the basis;
2) calculating the weight of each sub-cEFIR filter; the specific calculation method is as follows:
Figure BDA0002958006910000096
Figure BDA0002958006910000097
Figure BDA0002958006910000098
wherein the content of the first and second substances,
Figure BDA0002958006910000099
the weight value of the previous moment; i is the number of sub-chefir filters.
3) And obtaining the total output of the data fusion filter according to the weight of each sub-cEFIR filter and the state vector prediction of the sub-cEFIR filters.
The total output of the data fusion filter is specifically:
Figure BDA00029580069100000910
wherein the content of the first and second substances,
Figure BDA00029580069100000911
is the output of the main filter at the time k,
Figure BDA00029580069100000912
The state of the sub-filter at time k is estimated.
Example two
In one or more embodiments, a mobile robot positioning system is disclosed that considers variable colored measurement noise, specifically comprising:
the device is used for taking the east and north positions and speeds of the mobile robot k as state quantities, taking the distance between the mobile robot and a UWB reference node obtained by UWB measurement as a system observed quantity, and constructing a filtering model;
means for constructing m different sub-filters considering colored measurement noise based on the extended finite impulse response filter according to different local filtering windows selected at the off-line stage;
and the device is used for fusing the outputs of the constructed sub-filters in an IMM mode to obtain the optimal position prediction of the mobile robot at the current moment and realize the positioning of the mobile robot.
It should be noted that specific implementation manners of the modules are already described in the first embodiment, and are not described again.
EXAMPLE III
In one or more implementations, a terminal device is disclosed that includes a server including a memory, a processor, and a computer program stored on the memory and executable on the processor, the processor implementing the mobile robot positioning method of example one that takes into account variable colored measurement noise when executing the program. For brevity, no further description is provided herein.
It should be understood that in this embodiment, the processor may be a central processing unit CPU, and the processor may also be other general purpose processors, digital signal processors DSP, application specific integrated circuits ASIC, off-the-shelf programmable gate arrays FPGA or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, and so on. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The memory may include both read-only memory and random access memory, and may provide instructions and data to the processor, and a portion of the memory may also include non-volatile random access memory. For example, the memory may also store device type information.
In implementation, the steps of the above method may be performed by integrated logic circuits of hardware in a processor or instructions in the form of software.
The mobile robot positioning method considering the variable colored measurement noise in the first embodiment may be directly implemented by a hardware processor, or implemented by a combination of hardware and software modules in the processor. The software modules may be located in ram, flash, rom, prom, or eprom, registers, among other storage media as is well known in the art. The storage medium is located in a memory, and a processor reads information in the memory and completes the steps of the method in combination with hardware of the processor. To avoid repetition, it is not described in detail here.
Those of ordinary skill in the art will appreciate that the various illustrative elements, i.e., algorithm steps, described in connection with the embodiments disclosed herein may be implemented as electronic hardware or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
Although the embodiments of the present invention have been described with reference to the accompanying drawings, it is not intended to limit the scope of the present invention, and it should be understood by those skilled in the art that various modifications and variations can be made without inventive efforts by those skilled in the art based on the technical solution of the present invention.

Claims (10)

1. A mobile robot positioning method taking into account variable colored measurement noise, comprising:
taking the east and north positions and speeds of the mobile robot at the moment k as state quantities, and taking the distance between the mobile robot and a UWB reference node obtained by UWB measurement as a system observed quantity to construct a filtering model;
on the basis of expanding the finite impulse response filter, according to different local filtering windows selected in an off-line stage, m different sub-filters considering colored measurement noise are constructed, wherein m is a set value;
and fusing the constructed output of the sub-filters in an IMM mode to obtain the optimal position prediction of the mobile robot at the current moment, so as to realize the positioning of the mobile robot.
2. A method for mobile robot localization taking into account variable colored measurement noise according to claim 1, characterized in that the state equation of each sub-filter is embodied as:
Figure FDA0002958006900000011
wherein (x)k,yk)、(xk-1,yk-1) The positions of the east and north of the mobile robot at the moment k and the moment k-1 respectively;
Figure FDA0002958006900000012
the speed of the mobile robot in the east direction and the north direction at the moment k and k-1 respectively; tau is a sampling period; w is akIs the system noise at time k.
3. A method for mobile robot localization taking into account variable colored measurement noise according to claim 1, characterized in that the observation equation of each sub-filter is embodied as:
Figure FDA0002958006900000013
wherein (x)i,yi),i∈[1,4]The position of the ith UWB reference node; v. ofkThe observed noise at the system k moment;
Figure FDA0002958006900000021
and the observed distance between the mobile robot and the UWB reference node is measured by UWB at the time k.
4. A method for mobile robot localization taking into account variable colored measurement noise according to claim 1, characterized in that the number of different local filtering windows selected in the off-line phase is selected according to the need for algorithm performance.
5. The method for positioning the mobile robot by considering the variable colored measurement noise as claimed in claim 1, wherein the constructed sub-filters are fused by means of IMM, and the specific process comprises:
constructing a Markov transition probability matrix, and introducing a weight matrix on the basis;
calculating the weight of each sub-filter;
and obtaining the total output of the data fusion filter according to the weight of each sub-filter and the state vector prediction of the sub-filters.
6. The method of claim 5, wherein the calculating the weights of the sub-filters comprises:
Figure FDA0002958006900000022
Figure FDA0002958006900000023
Figure FDA0002958006900000024
wherein the content of the first and second substances,
Figure FDA0002958006900000025
the weighted value of the k moment obtained from the k-1 moment; i is the number of the sub-filters;
Figure FDA0002958006900000026
is the weight value C of the current timek＝Hk-γiHk-1F-1，HkIs the observation equation at time j, gammaiIs the ratio of CMN in the ith sub-filter,
Figure FDA0002958006900000027
for the error matrix at time k derived from time k-1, R observes the noise covariance, φk＝QkγiHk-1F-1，QkAnd F is a system matrix.
7. A mobile robot localization method considering variable colored measurement noise according to claim 5, characterized by obtaining the total output of the data fusion filter, in particular:
Figure FDA0002958006900000031
wherein the content of the first and second substances,
Figure FDA0002958006900000032
is the output of the main filter at the time k,
Figure FDA0002958006900000033
The state of the sub-filter at time k is estimated.
8. A mobile robot positioning system that accounts for variable colored measurement noise, comprising:
the device is used for taking the east and north positions and speeds of the mobile robot k as state quantities, taking the distance between the mobile robot and a UWB reference node obtained by UWB measurement as a system observed quantity, and constructing a filtering model;
means for constructing m different sub-filters considering colored measurement noise based on the extended finite impulse response filter according to different local filtering windows selected at the off-line stage;
and the device is used for fusing the outputs of the constructed sub-filters in an IMM mode to obtain the optimal position prediction of the mobile robot at the current moment and realize the positioning of the mobile robot.
9. A terminal device comprising a processor and a memory, the processor being arranged to implement instructions; the memory for storing a plurality of instructions, characterized in that the instructions are adapted to be loaded by the processor and to perform the method of mobile robot positioning taking into account variable colored measurement noise according to any of claims 1-7.
10. A computer-readable storage medium, in which a plurality of instructions are stored, characterized in that said instructions are adapted to be loaded by a processor of a terminal device and to perform a mobile robot localization method taking into account a variable colored measurement noise according to any of claims 1-7.
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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113970331A (en) * 2021-09-06 2022-01-25 济南大学 Four-rotor positioning method and system based on reconstruction observed quantity

Citations (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102025345A (en) * 2010-11-12 2011-04-20 河南理工大学 Particle filtering ultra-wideband pulse timing tracker with feedback control
CN107289941A (en) * 2017-06-14 2017-10-24 湖南格纳微信息科技有限公司 A kind of indoor orientation method and device based on inertial navigation
CN107402375A (en) * 2017-08-08 2017-11-28 济南大学 A kind of indoor pedestrian of band observation time lag positions EFIR data fusion systems and method
CN107966143A (en) * 2017-11-14 2018-04-27 济南大学 A kind of adaptive EFIR data fusion methods based on multiwindow
CN107966142A (en) * 2017-11-14 2018-04-27 济南大学 A kind of adaptive UFIR data fusion methods of indoor pedestrian based on multiwindow
CN109141413A (en) * 2018-08-06 2019-01-04 济南大学 EFIR filtering algorithm and system with shortage of data UWB pedestrian positioning
CN110895146A (en) * 2019-10-19 2020-03-20 山东理工大学 Synchronous positioning and map construction method for mobile robot
CN112325880A (en) * 2021-01-04 2021-02-05 中国人民解放军国防科技大学 Distributed platform relative positioning method and device, computer equipment and storage medium

Patent Citations (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102025345A (en) * 2010-11-12 2011-04-20 河南理工大学 Particle filtering ultra-wideband pulse timing tracker with feedback control
CN107289941A (en) * 2017-06-14 2017-10-24 湖南格纳微信息科技有限公司 A kind of indoor orientation method and device based on inertial navigation
CN107402375A (en) * 2017-08-08 2017-11-28 济南大学 A kind of indoor pedestrian of band observation time lag positions EFIR data fusion systems and method
CN107966143A (en) * 2017-11-14 2018-04-27 济南大学 A kind of adaptive EFIR data fusion methods based on multiwindow
CN107966142A (en) * 2017-11-14 2018-04-27 济南大学 A kind of adaptive UFIR data fusion methods of indoor pedestrian based on multiwindow
CN109141413A (en) * 2018-08-06 2019-01-04 济南大学 EFIR filtering algorithm and system with shortage of data UWB pedestrian positioning
CN110895146A (en) * 2019-10-19 2020-03-20 山东理工大学 Synchronous positioning and map construction method for mobile robot
CN112325880A (en) * 2021-01-04 2021-02-05 中国人民解放军国防科技大学 Distributed platform relative positioning method and device, computer equipment and storage medium

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
XU YUAN,ET AL: "INS/UWB-Based Quadrotor Localization Under Colored Measurement Noise", 《IEEE SENSORS JOURNAL》 *
XU YUAN,ETAL: "Tightly Coupled Integration of INS and UWB Using Fixed-Lag Extended UFIR Smoothing for Quadrotor Localization", 《IEEE INTERNET OF THINGS JOURNAL,》 *

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113970331A (en) * 2021-09-06 2022-01-25 济南大学 Four-rotor positioning method and system based on reconstruction observed quantity

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