CN107631727A - A kind of indoor CSS/INS integrated navigation systems - Google Patents

A kind of indoor CSS/INS integrated navigation systems Download PDF

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CN107631727A
CN107631727A CN201710817165.1A CN201710817165A CN107631727A CN 107631727 A CN107631727 A CN 107631727A CN 201710817165 A CN201710817165 A CN 201710817165A CN 107631727 A CN107631727 A CN 107631727A
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mrow
msub
mtd
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css
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CN107631727B (en
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岳克强
黄金鹏
尚俊娜
李蕊江
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Hangzhou Dianzi University
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Abstract

The invention discloses a kind of indoor CSS/INS integrated navigation systems, the blind nodes of CSS, the INS inertial navigation devices of the present invention is separately mounted on pedestrian's shoulder and foot, and communicated respectively with data processing module, CSS anchor nodes are arranged in the located space of robot operation.The blind nodes of CSS be used for obtain mobile robot to the range information of anchor node and resolving be positioning result, upload to data processing module, INS devices are used to obtain the movement state information of individual and upload data processing module, final data processing module is merged using EKF to INS navigation information with the CSS results positioned, output stabilization, accurate positioning result.The present invention significantly reduces influence of the multipath to CSS under indoor complex environment, meets the demand of indoor decimeter grade high accuracy positioning, and technical support is provided for high accuracy positioning navigation application in wide area room.

Description

A kind of indoor CSS/INS integrated navigation systems
Technical field
The present invention relates to integrated navigation and location technology under indoor environment, more particularly to a kind of indoor CSS/INS integrated navigations System.
Background technology
With the continuous development of modern society, Development of China's Urbanization is accelerated, smart city, wisdom logistics, the neck such as intelligent plant The construction in domain is all brought into schedule, and Internet of Things ecological chain gradually moves to maturity, and the demand of positioning will greatly be increased, either Indoor positioning or outdoor positioning field will all welcome the fulminant market opportunity.In outdoor, by GPS and GLONASS, the Big Dipper, It is fixed that GNSS (Global Navigation Satellite System) system of the compositions such as GALILEO can provide preferably navigation Position service, and the indoor last one kilometer for being described as positioning, with mobile terminal device be significantly increased and people room in it is living The dynamic time is elongated, and indoor positioning is increasingly becoming firm need.But indoors, because satellite-signal is seriously blocked, signal intensity It is all very poor with signal quality, the people often interior of work and activity can hardly be covered, navigator fix service can not be provided.
Indoor positioning is considered as one of main application technology of development in science and technology of future, and it, which possesses, is widely applied space and business Industry is worth, and the research of indoor positioning technologies was more and more popular in the last few years.Currently conventional indoor positioning technologies mainly have pseudo- letter Demarcate position, ultrasonic wave positioning, Zig Bee wireless locations, ultra wide band (Ultra Wide Band, UWB) positioning, WiFi positioning, indigo plant Tooth positions, and is positioned based on radio frequency identification (Radio Frequency Identification, RFID), inertia measurement positioning Deng, but these location technologies are had nothing in common with each other in positioning precision, system cost, robustness and applicable environmental field, and it is current single Positioning method be difficult to meet the requirement of high accuracy, high real-time, high reliability, low cost, low complex degree simultaneously.Therefore select Multiple indoor orientation methods blend, that is, select the mode of a variety of location technology integrated navigations, turn into current indoor navigator fix Main direction of studying.
Current indoor integrated navigation and location effectively raises the overall performance of alignment system, therefore has selection WiFi and row People's reckoning (pedestrian dead reckoning.PDR) carries out fusion positioning, and positioning precision mean error is reachable MEMS (MEMS, Micro-Electro-Mechanical System) on 1.24m, or WiFi and handheld device The method of the indoor pedestrian navigation positioning of sensor, maximum positioning error 2.5m, but with indoor integrated navigation technology Development, the positioning precision based on WiFi can not meet the high-precision requirement of indoor positioning further；Selection INS is studied simultaneously with having System is combined navigation to the ultra wide band (ultra wideband, UWB) of unique detent edge, although the group based on UWB technology Closing localization method positioning precision, to can reach sub-meter grade other, meets the needs of high accuracy positioning, but UWB base station signal bandwidth is larger, So that the complexity of hardware device is higher, it is expensive, while UWB communication distances are limited, it is difficult to meet the room of wide area large area Interior navigator fix demand.Therefore in the case where meeting the needs of wide area indoor position accuracy, select hardware cost suitable, it is wireless to other Communication system interference is smaller, and the wider array of communication mode of communication range is significant for integrated navigation.
The content of the invention
The present invention is for problem present in present combination navigator fix, it is proposed that a kind of indoor CSS/INS integrated navigations System, CSS technologies are used in the system to be obtained as master positioning technology in integrated navigation using EKF to INS The result of pedestrian navigation information and CSS positioning carry out data fusion, effectively increase positioning precision.
A kind of indoor CSS/INS integrated navigation systems, including：The blind nodes of CSS, CSS anchor nodes, INS inertial navigation devices And data processing module；The blind nodes of CSS, INS inertial navigation devices communicate with data processing module respectively；The inertial navigation device Part (INS) is arranged on the foot of pedestrian, for measuring the position of pedestrian, speed, attitude information；The blind node installations of CSS exist The shoulder of pedestrian, for measure pedestrian to the range information of CSS anchor nodes and resolve be positioning result；The CSS anchor nodes peace In bit space undetermined, for measuring the distance between blind node；The Data Data processing module is used for upload Location information and INS information carry out data fusion.
Described data processing module is positioned using the pedestrian navigation information that EKF obtains to INS and CSS Result carry out data fusion.The state equation of described extended Kalman filter is：
Wherein δgyroAnd δacceThe deviation of gyroscope and accelerometer is represented, is generally remembered as a stochastic variable and one Individual random error sum, it is as follows to be modeled as single order Gauss markoff process：
Whereintgyro, tacceRespectively gyroscope and accelerometer error with The correlation time of machine model, i.e. sampling error be within correlation time do not have it is related；wgyroAnd wacceFor white Gaussian noise.
δ q are the error of attitude quaternion, and the error equation of attitude quaternion is obtained by following formula：
Wherein q is quaternary number, and Ω is antisymmetric matrix, and its form is as follows：
Wherein, ωx、ωyAnd ωzIt is three axis component values of gyroscope angular speed.
CSS system error equation：δ r are the error between section point distance measurement：Had according to kinetics equation：R=c × t, (c=3 × 108M/s), wherein c is that the speed of service, t are the time to electromagnetic wave in atmosphere, therefore can obtain range error equation For：
The clock jitter b of CSS node receiversR：
Wherein, wgFor white Gaussian noise.
The observational equation of extended Kalman filter described in it is：
In formula：δr、δθ、The attitude angle difference of attitude angle and previous moment for current time IMU outputs, δ x, δ y are The difference of the position location coordinate that current time inertial navigation system resolves and the position coordinates of CSS system positioning.
Beneficial effects of the present invention：The present invention uses CSS technologies to use expansion card as master positioning technology in integrated navigation The pedestrian navigation information that Kalman Filtering obtains to INS carries out data fusion with CSS positioning results, effectively increases positioning precision, Solve the problems, such as traditional INS accumulations of error, simultaneously effective reduce influence of the multipath to CSS under indoor complex environment, it is full The foot demand of indoor decimeter grade high accuracy positioning, for high accuracy positioning navigation application in wide area room provides technical support.
Brief description of the drawings
A kind of indoor CSS/INS integrated navigation systems operating diagrams of Fig. 1.
Embodiment
There is further cognition for convenience of the feature to the present invention and function and understand, structure below in conjunction with the accompanying drawings is entered Row detailed description, if Fig. 1 is a kind of indoor CSS/INS integrated navigation systems, including the blind nodes of CSS, CSS anchor nodes, INS are used to Property navigational material, data processing module, the blind nodes of CSS, INS inertial navigation devices communicate with data processing module respectively；It is described Inertial navigation device (INS) is arranged on the foot of pedestrian, for measuring the position of pedestrian, speed, attitude information；
The blind node installations of CSS pedestrian shoulder, for measuring range information and resolving of the pedestrian to CSS anchor nodes For positioning result；
The CSS anchor nodes are arranged in bit space undetermined, for measuring the distance between blind node；
The Data Data processing module is used to carry out data fusion to the range information and INS information of upload；
Described data processing module is positioned using the pedestrian navigation information that EKF obtains to INS and CSS Result carry out data fusion, the state equation of the extended Kalman filter described in it is：
Wherein δgyroAnd δacceThe deviation of gyroscope and accelerometer is represented, is generally remembered as a stochastic variable and one Individual random error sum, it is as follows to be modeled as single order Gauss markoff process：
Whereintgyro, tacceRespectively gyroscope and accelerometer error with The correlation time of machine model, i.e. sampling error be within correlation time do not have it is related；wgyroAnd wacceFor white Gaussian noise.
δ q are the error of attitude quaternion, and the error equation of attitude quaternion is obtained by following formula：
Wherein q is quaternary number, and Ω is antisymmetric matrix, and its form is as follows：
Wherein, ωx、ωyAnd ωzIt is three axis component values of gyroscope angular speed.
CSS system error equation：Range errors of the δ r between node：Had according to kinetics equation：R=c × t, (c=3 × 108M/s), wherein c is that the speed of service, t are the time to electromagnetic wave in atmosphere, therefore can obtain range error equation For：
The clock jitter b of CSS node receiversR：
Wherein, wgFor white Gaussian noise.
The observational equation of extended Kalman filter described in it is：
In formula：δr、δθ、The attitude angle difference of attitude angle and previous moment for current time IMU outputs, δ x, δ y are The difference of the position location coordinate that current time inertial navigation system resolves and the position coordinates of CSS system positioning.

Claims (4)

  1. A kind of 1. indoor CSS/INS integrated navigation systems, it is characterised in that：Including：The blind nodes of CSS, CSS anchor nodes, INS inertia Navigational material and data processing module；The blind nodes of CSS, INS inertial navigation devices communicate with data processing module respectively；It is described INS inertial navigations device is arranged on the foot of pedestrian, for measuring the position of pedestrian, speed, attitude information；The blind sections of CSS Point installed in pedestrian shoulder, for measure pedestrian to the range information of CSS anchor nodes and resolving be positioning result；The CSS Anchor node is arranged in bit space undetermined, for measuring the distance between blind node；The data processing module be used for The location information and INS information of biography carry out data fusion.
  2. A kind of 2. indoor CSS/INS integrated navigation systems as claimed in claim 1, it is characterised in that：Described data processing Module is carried out using the pedestrian navigation information that EKF obtains to INS inertial navigations device and the result of CSS positioning Data fusion.
  3. A kind of 3. indoor CSS/INS integrated navigation systems as claimed in claim 2, it is characterised in that：Expansion card described in it The state equation of Thalmann filter is：
    <mrow> <mi>X</mi> <mo>=</mo> <mfenced open = "[" close = "]"> <mtable> <mtr> <mtd> <msub> <mi>&amp;delta;</mi> <mrow> <mi>g</mi> <mi>y</mi> <mi>r</mi> <mi>o</mi> </mrow> </msub> </mtd> </mtr> <mtr> <mtd> <msub> <mi>&amp;delta;</mi> <mrow> <mi>a</mi> <mi>c</mi> <mi>c</mi> <mi>e</mi> </mrow> </msub> </mtd> </mtr> <mtr> <mtd> <mrow> <mi>&amp;delta;</mi> <mi>q</mi> </mrow> </mtd> </mtr> <mtr> <mtd> <mi>&amp;delta;</mi> <mi>r</mi> </mtd> </mtr> <mtr> <mtd> <msub> <mi>b</mi> <mi>R</mi> </msub> </mtd> </mtr> </mtable> </mfenced> <mo>=</mo> <mfenced open = "[" close = "]"> <mtable> <mtr> <mtd> <mrow> <mo>-</mo> <msub> <mi>T</mi> <mrow> <mi>g</mi> <mi>y</mi> <mi>r</mi> <mi>o</mi> </mrow> </msub> <msub> <mi>&amp;delta;</mi> <mrow> <mi>g</mi> <mi>y</mi> <mi>r</mi> <mi>o</mi> </mrow> </msub> <mo>+</mo> <msub> <mi>w</mi> <mrow> <mi>g</mi> <mi>y</mi> <mi>r</mi> <mi>o</mi> </mrow> </msub> </mrow> </mtd> </mtr> <mtr> <mtd> <mrow> <mo>-</mo> <msub> <mi>T</mi> <mrow> <mi>a</mi> <mi>c</mi> <mi>c</mi> <mi>e</mi> </mrow> </msub> <msub> <mi>&amp;delta;</mi> <mrow> <mi>a</mi> <mi>c</mi> <mi>c</mi> <mi>e</mi> </mrow> </msub> <mo>+</mo> <msub> <mi>w</mi> <mrow> <mi>a</mi> <mi>c</mi> <mi>c</mi> <mi>e</mi> </mrow> </msub> </mrow> </mtd> </mtr> <mtr> <mtd> <mrow> <mi>&amp;Omega;</mi> <mrow> <mo>(</mo> <mi>&amp;omega;</mi> <mo>)</mo> </mrow> <mo>.</mo> <mi>q</mi> <mo>-</mo> <mi>&amp;Omega;</mi> <mrow> <mo>(</mo> <mover> <mi>&amp;omega;</mi> <mo>^</mo> </mover> <mo>)</mo> </mrow> <mo>.</mo> <mover> <mi>q</mi> <mo>^</mo> </mover> </mrow> </mtd> </mtr> <mtr> <mtd> <mrow> <mi>c</mi> <mo>&amp;times;</mo> <mi>&amp;delta;</mi> <mi>t</mi> </mrow> </mtd> </mtr> <mtr> <mtd> <mrow> <mi>&amp;delta;</mi> <mi>t</mi> <mo>+</mo> <msub> <mi>w</mi> <mi>g</mi> </msub> </mrow> </mtd> </mtr> </mtable> </mfenced> </mrow>
    Wherein δgyroAnd δacceRepresent the deviation of gyroscope and accelerometer, be generally remembered as a stochastic variable with one with Chance error difference sum, it is as follows to be modeled as single order Gauss markoff process：
    <mfenced open = "{" close = ""> <mtable> <mtr> <mtd> <msub> <mi>&amp;delta;</mi> <mrow> <mi>g</mi> <mi>y</mi> <mi>r</mi> <mi>o</mi> </mrow> </msub> <mo>=</mo> <mo>-</mo> <msub> <mi>T</mi> <mrow> <mi>g</mi> <mi>y</mi> <mi>m</mi> </mrow> </msub> <msub> <mi>&amp;delta;</mi> <mrow> <mi>g</mi> <mi>y</mi> <mi>r</mi> <mi>o</mi> </mrow> </msub> <mo>+</mo> <msub> <mi>w</mi> <mrow> <mi>g</mi> <mi>y</mi> <mi>r</mi> <mi>o</mi> </mrow> </msub> </mtd> </mtr> <mtr> <mtd> <mrow> <msub> <mi>&amp;delta;</mi> <mrow> <mi>a</mi> <mi>c</mi> <mi>c</mi> <mi>e</mi> </mrow> </msub> <mo>=</mo> <mo>-</mo> <msub> <mi>T</mi> <mrow> <mi>a</mi> <mi>c</mi> <mi>c</mi> <mi>e</mi> </mrow> </msub> <msub> <mi>&amp;delta;</mi> <mrow> <mi>a</mi> <mi>c</mi> <mi>c</mi> <mi>e</mi> </mrow> </msub> <mo>+</mo> <msub> <mi>w</mi> <mrow> <mi>a</mi> <mi>c</mi> <mi>c</mi> <mi>e</mi> </mrow> </msub> </mrow> </mtd> </mtr> </mtable> </mfenced>
    Whereintgyro, tacceThe respectively random mould of gyroscope and accelerometer error The correlation time of type, i.e. sampling error be within correlation time do not have it is related；wgyroAnd wacceFor white Gaussian noise.
    δqFor the error of attitude quaternion, the error equation of attitude quaternion is obtained by following formula：
    <mrow> <mi>&amp;delta;</mi> <mi>q</mi> <mo>=</mo> <mi>q</mi> <mo>-</mo> <mover> <mi>q</mi> <mo>^</mo> </mover> <mo>=</mo> <mi>&amp;Omega;</mi> <mrow> <mo>(</mo> <mi>&amp;omega;</mi> <mo>)</mo> </mrow> <mo>.</mo> <mi>q</mi> <mo>-</mo> <mi>&amp;Omega;</mi> <mrow> <mo>(</mo> <mover> <mi>&amp;omega;</mi> <mo>^</mo> </mover> <mo>)</mo> </mrow> <mo>.</mo> <mover> <mi>q</mi> <mo>^</mo> </mover> </mrow>
    Wherein q is quaternary number, and Ω is antisymmetric matrix, and its form is as follows：
    <mrow> <mi>&amp;Omega;</mi> <mrow> <mo>(</mo> <mi>&amp;omega;</mi> <mo>)</mo> </mrow> <mo>=</mo> <mfrac> <mn>1</mn> <mn>2</mn> </mfrac> <mfenced open = "[" close = "]"> <mtable> <mtr> <mtd> <mn>0</mn> </mtd> <mtd> <mrow> <mo>-</mo> <msub> <mi>&amp;omega;</mi> <mi>x</mi> </msub> </mrow> </mtd> <mtd> <mrow> <mo>-</mo> <msub> <mi>&amp;omega;</mi> <mi>y</mi> </msub> </mrow> </mtd> <mtd> <mrow> <mo>-</mo> <msub> <mi>&amp;omega;</mi> <mi>z</mi> </msub> </mrow> </mtd> </mtr> <mtr> <mtd> <msub> <mi>&amp;omega;</mi> <mi>x</mi> </msub> </mtd> <mtd> <mn>0</mn> </mtd> <mtd> <msub> <mi>&amp;omega;</mi> <mi>z</mi> </msub> </mtd> <mtd> <mrow> <mo>-</mo> <msub> <mi>&amp;omega;</mi> <mi>y</mi> </msub> </mrow> </mtd> </mtr> <mtr> <mtd> <msub> <mi>&amp;omega;</mi> <mi>y</mi> </msub> </mtd> <mtd> <mrow> <mo>-</mo> <msub> <mi>&amp;omega;</mi> <mi>z</mi> </msub> </mrow> </mtd> <mtd> <mn>0</mn> </mtd> <mtd> <msub> <mi>&amp;omega;</mi> <mi>x</mi> </msub> </mtd> </mtr> <mtr> <mtd> <msub> <mi>&amp;omega;</mi> <mi>z</mi> </msub> </mtd> <mtd> <msub> <mi>&amp;omega;</mi> <mi>y</mi> </msub> </mtd> <mtd> <mrow> <mo>-</mo> <msub> <mi>&amp;omega;</mi> <mi>x</mi> </msub> </mrow> </mtd> <mtd> <mn>0</mn> </mtd> </mtr> </mtable> </mfenced> </mrow>
    Wherein, ωx、ωyAnd ωzIt is three axis component values of gyroscope angular speed.
    CSS system error equation：δrRange error between node：Had according to kinetics equation：R=c × t, (c=3 × 108M/s), wherein c is that the speed of service, t are the time to electromagnetic wave in atmosphere, therefore can obtain range error equation and be：
    <mrow> <mi>&amp;delta;</mi> <mi>r</mi> <mo>=</mo> <mi>c</mi> <mo>&amp;times;</mo> <mrow> <mo>(</mo> <mi>t</mi> <mo>-</mo> <mover> <mi>t</mi> <mo>^</mo> </mover> <mo>)</mo> </mrow> <mo>=</mo> <mi>c</mi> <mo>&amp;times;</mo> <mi>&amp;delta;</mi> <mi>t</mi> </mrow>
    The clock jitter b of CSS node receiversR：
    <mrow> <msub> <mi>b</mi> <mi>R</mi> </msub> <mo>=</mo> <mrow> <mo>(</mo> <mi>t</mi> <mo>-</mo> <mover> <mi>t</mi> <mo>^</mo> </mover> <mo>)</mo> </mrow> <mo>+</mo> <msub> <mi>w</mi> <mi>g</mi> </msub> <mo>=</mo> <mi>&amp;delta;</mi> <mi>t</mi> <mo>+</mo> <msub> <mi>w</mi> <mi>g</mi> </msub> </mrow>
    Wherein, wgFor white Gaussian noise.
  4. A kind of 4. indoor CSS/INS integrated navigation systems as claimed in claim 2, it is characterised in that：Expansion card described in it The observational equation of Thalmann filter is：
    In formula：δr、δθ、The attitude angle difference of attitude angle and previous moment for current time IMU outputs, δ x, δ y are current The difference of the position location coordinate that moment inertial navigation system resolves and the position coordinates of CSS system positioning.
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