CN202562486U - Course angle based wireless sensor network (WSN)/ inertial navigation system (INS) integrated navigation system - Google Patents

Course angle based wireless sensor network (WSN)/ inertial navigation system (INS) integrated navigation system Download PDF

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CN202562486U
CN202562486U CN2012201903929U CN201220190392U CN202562486U CN 202562486 U CN202562486 U CN 202562486U CN 2012201903929 U CN2012201903929 U CN 2012201903929U CN 201220190392 U CN201220190392 U CN 201220190392U CN 202562486 U CN202562486 U CN 202562486U
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ins
module
unknown node
navigation
reference mode
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陈熙源
徐元
李庆华
黄浩乾
申冲
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Southeast University
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Abstract

A course angle based WSN/INS integrated navigation system belongs to the multi-sensor data fusion filed. The integrated navigation system comprises a reference node (RN) portion and a blind node (BN) portion, wherein the RN portion comprises a RN wireless network receiving module, an ultrasonic distance measuring module and a time synchronization module; and the BN portion comprises a BN wireless network receiving module, an INS navigation module and a central data processing module. The extended Kalman filter (EKF) is used for obtaining the optimal discreet values of the position error and the velocity error measured by the INS in the system, and a difference is made between the navigation information measured by the INS and the optimal discreet values so as to obtain the optical navigation information. According to the WSN/INS integrated navigation system, the problem that errors of the INS drift along with time in environments such as underground passages and long and narrow tunnels where GPS signal outages for a long time is solved, and middle and low accuracy requirements for urban transportation, long and narrow tunnels, small-sized intelligent robot and the like can be satisfied.

Description

WSN/INS integrated navigation system based on course angle
Technical field
The utility model relates to a kind of WSN/INS integrated navigation system based on course angle, belongs to multi-sensor data and merges the field.
Background technology
GPS (Global positioning systems, GPS) and inertial navigation system (Inertial navigation system INS) is one of present most widely used navigational system.Wherein GPS can provide accurately, has the navigation information of continual and steady navigation accuracy, but under environment such as the urban district indoor, that skyscraper is intensive, mine, tunnel, the gps signal losing lock can not position.That though INS has is complete autonomous, movable information and comprehensively, in short-term, high-precision advantage, though can realize independent navigation, error accumulates in time, will cause navigation accuracy seriously to descend under the service condition during long boat.Therefore; INS can only be the short-term compensation to the compensation of GPS navigation information; And the navigation accuracy of the GPS/INS integrated navigation system of using always the most at present depends on the navigation accuracy of GPS, and under the situation of the long-time losing lock of GPS, integrated navigation system can't provide precise navigation information.
In recent years, (Wireless Sensors Network, WSN) characteristics with its low cost, low-power consumption and low system complexity show very big potentiality in the short distance positioning field to wireless sensor network.WSN is in no gps signal area, and when promptly so-called " blind area ", providing like unknown node location under the environment such as the urban district indoor, that skyscraper is intensive, mine, tunnel maybe.But because the communication technology that WSN adopts is generally short-distance wireless communication technology (like ZigBee, WIFI etc.), therefore if want to accomplish the target following location of long distance, need a large amount of network nodes to accomplish jointly, this has increased the network burden of WSN.In addition, WSN can only provide position and velocity information, and comprehensive movable information can not be provided.
In order under the long losing lock environment of GPS, to obtain stable navigation information for a long time; Many scholars propose the WSN location technology is incorporated into cheaply in the INS system; Make up the WSN/INS integrated navigation system; Like the Y. Xu of Southeast China University; Though the long distance objective that this array mode has well solved under the underground closed environment is followed the tracks of and the high problem of navigator cost, this method has just used the range information between speed, unknown node and the reference mode of carrier to come the navigation information of carrier is estimated, and the attitude information that does not have the carrier that INS is obtained is (like course angle; Accelerometer etc.) take into account the environmental information around not having effectively to utilize.
The utility model content
In order to address the above problem, the utility model provides a kind of WSN/INS integrated navigation system based on course angle.
The utility model adopts following technical scheme for solving its technical matters:
A kind of WSN/INS integrated navigation system based on course angle; Comprise reference mode part and unknown node part; Reference mode partly comprises reference mode wireless network receiver module, supersonic sounding module and time synchronized module, and wherein, the reference mode wireless network receiver module in the reference mode part is responsible for accomplishing the transmitting-receiving of remote command; At first the control time synchronization module is accomplished the time synchronized of finding range after receiving remote command; Then open the supersonic sounding module and measure corresponding range information, last, the range information that the supersonic sounding module is measured returns to unknown node through reference mode wireless network receiver module; Unknown node partly comprises unknown node wireless network receiver module, INS navigation module and central data processing module; Wherein, Unknown node wireless network receiver module in the unknown node part is responsible for accomplishing the collection of the range information between unknown node and the reference mode, and sends the data that collect to the central data processing module; What the INS navigation module was accomplished is the collection that gyro adds table information, and sends the data that collect to the central data processing unit; The central data processing unit is responsible for the data that unknown node wireless network receiver module and INS navigation module collect are carried out data fusion, thereby obtains navigation information.
The control method of described WSN/INS integrated navigation system based on course angle comprises the following steps:
(1) navigation procedure is divided into training process and adaptive equalization process two parts, will has the navigation procedure of WSN signal to be called training process, and have only the navigation procedure of INS signal to be referred to as the adaptive equalization process;
(2) in training process, in local relative coordinate system, INS and WSN are carried out integratedly, through EKF the synchronous navigation data that obtains is carried out data fusion;
(3) system equation of structure extended Kalman filter; This system equation with INS each constantly site error
Figure 87170DEST_PATH_IMAGE001
, velocity error , accelerometer error
Figure 410146DEST_PATH_IMAGE003
and the course angle
Figure 579525DEST_PATH_IMAGE004
of both direction as state variable; The system equation of wave filter is suc as formula shown in (1); Wherein
Figure 269264DEST_PATH_IMAGE005
is the sampling period; is system noise;
Figure 984858DEST_PATH_IMAGE007
is the course angle of carrier,
Figure 59124DEST_PATH_IMAGE008
be the sampling period counting of system;
(1)
(4) through INS measure each constantly unknown node in the position of x and y direction the positional information of positional information that obtains and reference mode is calculated unknown node that INS measures and the distance
Figure 977161DEST_PATH_IMAGE011
between
Figure 347466DEST_PATH_IMAGE010
individual reference mode suc as formula shown in (2) through range formula:
Figure 589539DEST_PATH_IMAGE012
(2)
Wherein, The coordinate of the unknown node that measures for INS;
Figure 168867DEST_PATH_IMAGE014
is the coordinate of i reference mode, and
Figure 662690DEST_PATH_IMAGE015
is the number of reference mode; Measure the distance
Figure 456651DEST_PATH_IMAGE016
between unknown node and
Figure 813180DEST_PATH_IMAGE010
individual reference mode through the supersonic sounding module; Square differing from square with
Figure 271471DEST_PATH_IMAGE016
with ; Difference is defined as
Figure 225652DEST_PATH_IMAGE017
; On this basis; With
Figure 598996DEST_PATH_IMAGE018
observed quantity as wave filter; Meanwhile; The speed that the tachogenerator that comprises among the speed of each both direction constantly that INS is measured and the WSN measures is poor; With difference also as the observed quantity of wave filter; Simultaneously; The course angle that INS is measured is also as observed quantity; Make up observation equation through above-mentioned observed quantity, the wave filter observation equation is suc as formula shown in (3):
Figure 350135DEST_PATH_IMAGE019
(3)
Wherein,
Figure 686393DEST_PATH_IMAGE020
is the position of reference mode in relative coordinate system;
Figure 568899DEST_PATH_IMAGE021
;
Figure 796749DEST_PATH_IMAGE022
;
Figure 638101DEST_PATH_IMAGE023
;
Figure 951402DEST_PATH_IMAGE024
is the observation equation noise matrix, be the sampling period counting of system;
Figure 719954DEST_PATH_IMAGE007
is the course angle of carrier;
(5) wave filter carries out data filtering, and in the process of filtering, optimum constantly estimation of error and time of this that KF is obtained is input in the intelligent algorithm, builds relative navigation information that INS the estimates model of deviation in time through the BP neural network;
(6) if leaving the zone of building WSN, unknown node gets into the adaptive equalization process; In this process; Integrated navigation system obtains the relative navigation information of measuring less than WSN; Can only rely on the MINS system to accomplish this a part of independent navigation, the error model that INS is utilized in the training process training carries out error compensation to the absolute navigation information of measuring, and obtains optimum navigation information.
The beneficial effect of the utility model is following:
1,, overcome the problem that the error of INS navigation under the environment of the long-term losing locks of gps signal such as underground passage, long and narrow tunnel is drifted about in time through the method for utilization WSN/INS integrated navigation.
2, in used filtering method; With adding in the system equation that Watch Error is incorporated into wave filter of the course angle of INS, both direction, thereby used more fully in following just very limited navigation carrier ambient condition information of the environment of the long-term losing lock of gps signal.
3, in the observation equation of wave filter; Do not have on the inclined to one side model based in tradition; Through adopting
Figure 708770DEST_PATH_IMAGE018
, eliminated the weak coupling problem that exists in traditional no inclined to one side model fully as the method for observed quantity.
Description of drawings
Fig. 1 is the WSN/INS integrated navigation system structural representation based on course angle.
Fig. 2 is the WSN/INS Combinated navigation method synoptic diagram based on course angle.
The process flow diagram that Fig. 3 realizes for Combinated navigation method.
Fig. 4 is that reference mode, the unknown node of embodiment distributes.
Embodiment
Below in conjunction with accompanying drawing the invention is explained further details.
A kind of WSN/INS integrated navigation system based on course angle is as shown in Figure 1; Comprise with reference to (RN) node section and the unknown (BN) node section; The RN node section is made up of reference mode wireless network receiver module, supersonic sounding module and time synchronized module, and wherein, reference mode wireless network receiver module mainly is responsible for accomplishing the transmitting-receiving of remote command; At first the control time synchronization module is accomplished the time synchronized of finding range after receiving remote command; Then open the supersonic sounding module and measure corresponding range information, last, the range information that the supersonic sounding module is measured returns to the unknown (BN) node through reference mode wireless network receiver module; The BN node section is made up of unknown node wireless network receiver module, INS navigation module, central data processing module; Wherein, Unknown node wireless network receiver module on the unknown node mainly is responsible for accomplishing the collection of the range information between unknown node and the reference mode, and sends the data that collect to the central data processing module; What the INS navigation module was mainly accomplished is the collection that gyro adds table information, and sends the data that collect to the central data processing unit; The central data processing unit mainly is responsible for the data that unknown node wireless network receiver module and INS navigation module collect are carried out data fusion, thereby obtains navigation information.
Combinated navigation method is as shown in Figure 2, and (Extened Kalman Filter, system equation EKF) is with the site error of each moment both direction of INS for the extended Kalman filter of proposition
Figure 509367DEST_PATH_IMAGE001
, velocity error
Figure 733675DEST_PATH_IMAGE002
, accelerometer error
Figure 670538DEST_PATH_IMAGE003
And course angle
Figure 830255DEST_PATH_IMAGE004
As state variable, the system equation of wave filter is suc as formula shown in (1):
Figure 242782DEST_PATH_IMAGE025
(1)
Wherein
Figure 146147DEST_PATH_IMAGE005
is the sampling period;
Figure 203096DEST_PATH_IMAGE006
is system noise;
Figure 268135DEST_PATH_IMAGE007
is the course angle of carrier,
Figure 43324DEST_PATH_IMAGE008
be the sampling period counting of system.Observation equation through INS measure each constantly unknown node (Blind Node, BN) in the position of x and y direction, with the positional information that obtains and reference mode (Reference Node, positional information RN) is passed through range formula suc as formula shown in (2):
Figure 484801DEST_PATH_IMAGE012
（2）
Calculate BN node that INS measures and the distance
Figure 900705DEST_PATH_IMAGE011
between
Figure 789399DEST_PATH_IMAGE010
individual RN node; The coordinate of
Figure 38557DEST_PATH_IMAGE013
unknown node of measuring for INS wherein;
Figure 283725DEST_PATH_IMAGE014
is the coordinate of
Figure 426517DEST_PATH_IMAGE010
individual reference mode, and
Figure 950867DEST_PATH_IMAGE015
is the number of reference mode.Measure the distance
Figure 234660DEST_PATH_IMAGE016
between BN node and
Figure 310435DEST_PATH_IMAGE010
individual RN node through the supersonic sounding module; Square differing from square with
Figure 91593DEST_PATH_IMAGE016
with
Figure 248271DEST_PATH_IMAGE011
; Difference is defined as
Figure 63092DEST_PATH_IMAGE017
; On the basis of
Figure 650062DEST_PATH_IMAGE017
, with observed quantity as wave filter.Meanwhile; The observed quantity of wave filter also comprises each velocity error of both direction constantly; I.e.
Figure 906304DEST_PATH_IMAGE026
; Specific practice is that the speed that measures of the tachogenerator that comprises among speed and the WSN of each both direction constantly that INS is measured is poor; With the velocity error of difference as this direction; I.e.
Figure 240465DEST_PATH_IMAGE022
;
Figure 490180DEST_PATH_IMAGE023
; Simultaneously; The course angle that INS is measured makes up observation equation suc as formula shown in (3) also as observed quantity through above-mentioned observed quantity:
Figure 350820DEST_PATH_IMAGE019
（3）
Wherein,
Figure 535945DEST_PATH_IMAGE020
is the position of reference mode in relative coordinate system;
Figure 482035DEST_PATH_IMAGE021
;
Figure 410808DEST_PATH_IMAGE022
;
Figure 125955DEST_PATH_IMAGE023
;
Figure 481981DEST_PATH_IMAGE024
is the observation equation noise matrix,
Figure 649788DEST_PATH_IMAGE008
be the sampling period counting of system.Make up new observation equation through above-mentioned observed quantity, completely abolish the coupling situation that observation equation exists in the conventional model, make observation equation become linear system, and system equation still is a NLS.
Combined method is divided into training process and adaptive equalization process two parts with navigation procedure.To there be the navigation procedure of WSN signal to be called training process.And the navigation procedure that has only the INS signal is called the adaptive equalization process.In training process; Combined method is characterized in that in local relative coordinate system, carrying out INS and WSN integrated; The unknown node that two kinds of methods are measured is input among the EKF to the range information of known node, thereby obtains a kind of than the higher navigation information of above-mentioned any single air navigation aid precision.Carry out in the process of data filtering at wave filter, optimum constantly estimation of error and time of this that EKF is obtained is input in the intelligent algorithm, and the relative navigation information of estimating through BP neural network structure INS is the model of deviation in time.If unknown node is left the zone of building WSN and is got into the adaptive equalization process; In this process; Integrated navigation system obtains the relative navigation information of measuring less than WSN; Can only rely on the MINS system to accomplish this a part of independent navigation, the error model that INS is utilized in the training process training carries out error compensation to the absolute navigation information of measuring, and obtains optimum navigation information.
The idiographic flow that Combinated navigation method is realized is as shown in Figure 3, Fig. 4 provided the reference mode of embodiment, environment that unknown node distributes and the BN node that obtains by Fig. 3 flow process along illustrated orbiting motion.The concrete steps of method are following:
(1) gathers through INS and add table information, resolve after adding table information collecting, obtain the position and the velocity information of the BN node of INS measurement; Through measuring; The positional information of the BN that at a time measures through INS for (129.5388,57.2544) (m), be (22.7675 through resolving the velocity information of BN node in local relative coordinate system that obtains; 10.9697) (m/s), course angle is 70 degree.RN node coordinate around this moment BN node is respectively (120,48.6764), and (125,50.4963), (130,52.3161), (125,40.4963) are (m).
(2) the BN node measures the distance between the corresponding RN node in BN node and the step (1) through the supersonic sounding module, is respectively 8.0076m, 3.9013m, 6.5727m, 6.1673m.The velocity information of the unknown node that the sillometer that carries through unknown node obtains in local relative coordinate system for (23.0237,8.3785) (m/s), we regard this information as WSN measured speed information.
(3) after getting access to above-mentioned information; Make up the system equation of extended Kalman filter; This system equation with INS each constantly site error
Figure 382252DEST_PATH_IMAGE001
, velocity error
Figure 975342DEST_PATH_IMAGE002
, accelerometer error and the course angle
Figure 924417DEST_PATH_IMAGE004
of both direction as state variable; The system equation of wave filter is suc as formula shown in (1); Wherein is the sampling period;
Figure 619152DEST_PATH_IMAGE006
is system noise;
Figure 582559DEST_PATH_IMAGE007
is the carrier course angle,
Figure 724959DEST_PATH_IMAGE008
be the sampling period counting of system;
Figure 799225DEST_PATH_IMAGE009
(1)
(4) measure each moment unknown node in the position of x and y direction through INS, the positional information of positional information that obtains and reference mode is passed through range formula suc as formula shown in (2): (
Figure 202525DEST_PATH_IMAGE012
) (2)
Calculate BN node that INS measures and the distance
Figure 966530DEST_PATH_IMAGE011
between
Figure 336834DEST_PATH_IMAGE010
individual RN node; The coordinate of
Figure 703542DEST_PATH_IMAGE013
unknown node of measuring for INS wherein;
Figure 102293DEST_PATH_IMAGE014
is the coordinate of
Figure 407503DEST_PATH_IMAGE010
individual reference mode, and
Figure 383550DEST_PATH_IMAGE015
is the number of reference mode.Measure the distance
Figure 318456DEST_PATH_IMAGE016
between unknown node and
Figure 799619DEST_PATH_IMAGE010
individual reference mode through the supersonic sounding module; Square differing from square with
Figure 257910DEST_PATH_IMAGE016
with ; Difference is defined as
Figure 212091DEST_PATH_IMAGE017
; On this basis; With observed quantity as wave filter; Meanwhile; The speed that the tachogenerator that comprises among the speed of each both direction constantly that INS is measured and the WSN measures is poor; With difference also as the observed quantity of wave filter; Simultaneously; The course angle that INS is measured is also as observed quantity; Make up observation equation through above-mentioned observed quantity, the wave filter observation equation is suc as formula shown in (3):
Figure 91502DEST_PATH_IMAGE019
(3)
Wherein,
Figure 183086DEST_PATH_IMAGE020
is the position of reference mode in relative coordinate system;
Figure 940958DEST_PATH_IMAGE021
;
Figure 168808DEST_PATH_IMAGE022
; ;
Figure 300023DEST_PATH_IMAGE024
is the observation equation noise matrix;
Figure 596006DEST_PATH_IMAGE008
is the sampling period counting of system, and
Figure 943942DEST_PATH_IMAGE007
is the carrier course angle.
(5) through extended Kalman filter the navigation information predictor error of INS is estimated, this optimal location estimation of error that obtains constantly for (1.7394,11.0215) (m).With this constantly in the navigation information (129.5388,57.2544) measured of INS Optimal error that (m) deducts senior filter itself estimate, obtain revised result (127.7994,46.2329) at last (m).

Claims (1)

1. WSN/INS integrated navigation system based on course angle; It is characterized in that comprising reference mode part and unknown node part; Reference mode partly comprises reference mode wireless network receiver module, supersonic sounding module and time synchronized module, and wherein, the reference mode wireless network receiver module in the reference mode part is responsible for accomplishing the transmitting-receiving of remote command; At first the control time synchronization module is accomplished the time synchronized of finding range after receiving remote command; Then open the supersonic sounding module and measure corresponding range information, last, the range information that the supersonic sounding module is measured returns to unknown node through reference mode wireless network receiver module; Unknown node partly comprises unknown node wireless network receiver module, INS navigation module and central data processing module; Wherein, Unknown node wireless network receiver module in the unknown node part is responsible for accomplishing the collection of the range information between unknown node and the reference mode, and sends the data that collect to the central data processing module; What the INS navigation module was accomplished is the collection that gyro adds table information, and sends the data that collect to the central data processing unit; The central data processing unit is responsible for the data that unknown node wireless network receiver module and INS navigation module collect are carried out data fusion, thereby obtains navigation information.
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Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102636166A (en) * 2012-05-02 2012-08-15 东南大学 Course angle-based WSN/INS integrated navigation system and method
CN103699126A (en) * 2013-12-23 2014-04-02 中国矿业大学 Intelligent tour guide robot

Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102636166A (en) * 2012-05-02 2012-08-15 东南大学 Course angle-based WSN/INS integrated navigation system and method
CN102636166B (en) * 2012-05-02 2014-10-08 东南大学 Course angle-based WSN/INS integrated navigation system and method
CN103699126A (en) * 2013-12-23 2014-04-02 中国矿业大学 Intelligent tour guide robot

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