CN113242519B - Mobile wireless sensor node positioning method based on multi-information fusion - Google Patents
Mobile wireless sensor node positioning method based on multi-information fusion Download PDFInfo
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
- H04W4/02—Services making use of location information
- H04W4/029—Location-based management or tracking services
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- H04W64/00—Locating users or terminals or network equipment for network management purposes, e.g. mobility management
- H04W64/006—Locating users or terminals or network equipment for network management purposes, e.g. mobility management with additional information processing, e.g. for direction or speed determination
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Abstract
The invention discloses a mobile wireless sensor node positioning method based on multi-information fusion, which is used for positioning based on a mobile wireless sensor node positioning system based on multi-information fusion, wherein the mobile wireless sensor node positioning system based on multi-information fusion comprises a main base station, a plurality of slave base stations, a plurality of relay wireless sensor nodes and a plurality of common wireless sensor nodes; the relay wireless sensor node and the common wireless sensor node can acquire real-time inertia, speed, direction and multiple RSSI data, effectively fuse multiple positioning data through a multi-information fusion and alternative error minimization processing module according to a built-in algorithm, and finally determine the current position information of the node.
Description
The technical field is as follows:
the invention relates to a positioning method of a wireless sensor node, in particular to a mobile wireless sensor node positioning method based on multi-information fusion.
Background art:
a Wireless Sensor Network (WSN) is a distributed Network composed of many Wireless Sensor nodes, and the Wireless Sensor nodes are used for collecting, processing and transmitting useful information in the environment, such as temperature, humidity, vibration information, and the like. Since the WSN has the characteristics of wireless communication, high flexibility, low price, self-organization and the like, the WSN gradually plays an increasingly important role in a plurality of application fields.
The self-positioning of the wireless sensor node is to determine the self-position of the node according to a certain positioning algorithm according to a few nodes with known positions, so as to carry out corresponding event response. The specific position of the event monitored by the wireless sensor node can be effectively determined only after the position of the wireless sensor node is correctly estimated. Meanwhile, a plurality of wireless sensor nodes cooperate with one another, and monitored data are fused to make event decisions. And meanwhile, according to the event decision result, determining the moving track of each node in the wireless sensor network, and adjusting the distribution state of the wireless sensor nodes. It can be said that there is no sense of monitoring data of the location information, and thus the location information of the wireless sensor node is crucial to the whole wireless sensor network.
The current positioning technology of the wireless sensor network comprises the following steps: global Positioning System (GPS), electromagnetic navigation, inertial navigation, and radio frequency signal strength positioning technologies.
The Global Positioning System (GPS) is the most widely used positioning System at present, and has the advantages of high positioning accuracy, good real-time performance, and strong anti-interference capability. However, the GPS positioning system is suitable for an open outdoor scene without occlusion, and is almost ineffective in an occluded scene. Meanwhile, the GPS has large power consumption and high cost, and is not suitable for a low-cost self-organized wireless sensor network.
Electromagnetic navigation is the embedding of a specially made metal navigation signal wire approximately 20-30 millimeters below the ground in a work area. The principle is that the sensors are respectively arranged at the left end and the right end of the wireless sensor node and used for detecting signals, and then the wireless sensor node is controlled to move along a preset track. The disadvantage is that the navigation line is fixed once laid and is difficult to change or expand, and the limitation of the method is relatively large in navigation of paths with complex environments.
The inertial navigation is to measure the angular rate and acceleration information of the carrier relative to an inertial reference system, can automatically calculate all navigation and guidance parameters such as the current position information, speed information, attitude information and the like of the carrier, and has the advantages of no dependence on external environment, difficult interference and high positioning precision. The disadvantage is that accumulated positioning errors are easily generated.
The radio frequency signal strength (RSSI) positioning technology is used for relatively accurately triangulating a person and a vehicle through the wireless signal strength of a mobile device and three wireless network access points and a difference algorithm. Another is to record the signal strength of the huge amount at a certain location point in advance and determine the location by comparing the signal strength of the newly added device with a database holding huge amount data. Although the RSSI positioning method is simple to implement, the positioning error is large and the accuracy is low.
In summary, the conventional wireless sensor node positioning technology generally utilizes a positioning technology or a combination of two positioning technologies, so as to realize the positioning of nodes, and once a certain positioning mode in the nodes has a large error, the positioning result is greatly affected, and the problems of poor positioning reliability and low accuracy exist.
The invention content is as follows:
the invention aims to provide a mobile wireless sensor node positioning method based on multi-information fusion.
The purpose of the invention is implemented by the following technical scheme:
a mobile wireless sensor node positioning method based on multi-information fusion is used for positioning based on a multi-information fusion mobile wireless sensor node positioning system, wherein the multi-information fusion mobile wireless sensor node positioning system comprises a main base station, a plurality of slave base stations, a plurality of relay wireless sensor nodes and a plurality of common wireless sensor nodes;
the positions of the master base station and the slave base station are fixed; the relay wireless sensor node and the common wireless sensor node are mobile nodes;
the relay wireless sensor node and the common wireless sensor node have the same physical structure and respectively comprise a mobile device, a radio frequency sensor, an inertial sensor, a speed sensor, a direction sensor and a multi-information fusion and alternating error minimization processing module;
the multi-information fusion and alternation error minimization processing module of the common wireless sensor node sends the position information of the node to the multi-information fusion and alternation error minimization processing module of the relay wireless sensor node, the multi-information fusion and alternation error minimization processing module of the relay wireless sensor node sends the received position information of the common wireless sensor node and the position information of the relay wireless sensor node to the slave base station, and the slave base station sends the received position information of the common wireless sensor node and the position information of the relay wireless sensor node to the master base station;
the master base station plans motion tracks of the ordinary wireless sensor nodes and the relay wireless sensor nodes according to the received position information of the ordinary wireless sensor nodes and the position information of the relay wireless sensor nodes, sends the planned motion tracks to the slave base station in the communication range of the master base station as a control command, and sends the received control command to the mobile device of the relay wireless sensor nodes and the mobile device of the ordinary wireless sensor nodes in the communication range of the slave base station.
The method comprises the following steps:
(a) collecting multi-information positioning data: n types of positioning data which are acquired by each wireless sensor node and correspond to each wireless sensor node at a certain moment are respectively represented as s i ,(i＝1,2,……,n)；
(b) Calculating the position measurement change rate: calculating n positioning data at a certain moment and n positioning data at the previous moment in the multi-information positioning data acquired in the step (a) by a multi-information fusion and alternative minimization error processing module to obtain n position measurement change rates delta s i ,(i＝1,2,……,n)；
(c) Solving the true change rate of the position: assuming a true rate of change of position of
Establishing a rate of change of position measurement Δ s i And true rate of change of position of
The evaluation mechanism between the weight factor omega is constructed i And (i-1, 2, … …, n) alternately solving the true change rate of the position at the current moment by a gradient descent method
And a weight factor omega i ；
(d) Calculating current position information: solving the true rate of change of the n positions obtained from the true rate of change of the positions by the step (c)
Determining the current position information of the node by combining the position information of the node at the previous moment;
(e) data transmission: and sending the position information corresponding to each wireless sensor node to the main base station.
Further, in the step (a) of collecting the multi-information positioning data, the wireless sensor nodes include a plurality of relay wireless sensor nodes and a plurality of common wireless sensor nodes.
Further, in said step (b) of calculating the rate of change of position measurement,
wherein the content of the first and second substances,
representing n types of positioning data at time t,
representing n types of positioning data at time t-1.
Further, in the step (c) of solving the real change rate of the position, the position is measuredRate of change of quantity Δ s i And true rate of change of position of
In between evaluation mechanisms
Further, said (c) solving a location alternative error minimization model in the true rate of change of the location
Wherein, ω is i For n multiple types of positioning data s associated with a certain time i N weighting factors corresponding to (i ═ 1,2, … …, n), and
further, the step (g) of predicting the position information at the next time between the step (d) of calculating the current position information and the step (e) of transmitting data further comprises: and predicting future position information by using a plurality of position information in a past time period through a Kalman filtering algorithm.
Further, in the data transmission in the step (e), the position information corresponding to the common wireless sensor node is sent to the relay wireless sensor node, the relay wireless sensor node sends the received position information corresponding to the common wireless sensor node and the position information corresponding to the relay wireless sensor node to the slave base station, and the slave base station sends the received position information to the master base station.
The invention has the advantages that:
according to the system provided by the invention, the relay wireless sensor node and the common wireless sensor node can acquire real-time inertia, speed and direction data, effectively fuse a plurality of positioning data through a multi-information fusion and alternative minimized error processing module according to a built-in algorithm, finally determine the current position information of the node, upload the current position information to a main base station, plan the motion track of each wireless sensor node according to task requirements by the main base station, issue a moving direction instruction to each wireless sensor node, and execute a moving device of the wireless sensor node according to the received instruction.
The method provided by the invention is characterized in that a plurality of positioning data collected by a multi-information fusion and alternation minimization error processing module are uniformly converted into positioning data without a measurement unit, the positioning data are converted into data related to time, and the dynamic characteristic of the node position is represented by the positioning data change rate on a time sequence. An alternating error minimization model is constructed through multi-information positioning data, so that effective fusion of various positioning data is realized; by dynamically adjusting the weight factor of each positioning data, the dynamic high-precision positioning of each node is realized, and the positioning precision of the mobile wireless sensor network node is improved.
Description of the drawings:
in order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a communication schematic diagram in embodiment 1;
fig. 2 is a schematic structural diagram of a relay wireless sensor node and a normal wireless sensor node in embodiment 1;
FIG. 3 shows that the true change rate of the current position is alternatively solved by the gradient descent method in example 2
And a weight factor omega i And (i-1, 2, … …, n).
The specific implementation mode is as follows:
the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Example 1:
fig. 1 and fig. 2 show a positioning system of a multi-information-fusion mobile wireless sensor node, which includes a master base station, a plurality of slave base stations, a plurality of relay wireless sensor nodes, and a plurality of common wireless sensor nodes;
the positions of the master base station and the slave base station are fixed and uniformly distributed, the position information is known, and the master base station and the slave base station have larger transmitting power; the relay wireless sensor node and the common wireless sensor node are mobile nodes;
the relay wireless sensor node and the common wireless sensor node have the same physical structure and respectively comprise a mobile device, a radio frequency sensor, an inertial sensor, a speed sensor, a direction sensor and a multi-information fusion and alternation minimization error processing module;
the multi-information fusion and alternation minimization error processing module of the common wireless sensor node sends the position information of the node to the multi-information fusion and alternation minimization error processing module of the relay wireless sensor node, the multi-information fusion and alternation minimization error processing module of the relay wireless sensor node sends the received position information of the common wireless sensor node and the position information of the relay wireless sensor node to the slave base station, the slave base station sends the received position information of the common wireless sensor node and the position information of the relay wireless sensor node to the master base station, and a star network is adopted between the master base station and the slave base station;
the master base station plans motion tracks of the ordinary wireless sensor nodes and the relay wireless sensor nodes according to the received position information of the ordinary wireless sensor nodes and the position information of the relay wireless sensor nodes, sends the planned motion tracks to the slave base station in the communication range of the master base station as a control command, and sends the received control command to the mobile device of the relay wireless sensor nodes and the mobile device of the ordinary wireless sensor nodes in the communication range of the slave base station.
In the system, a relay wireless sensor node and a common wireless sensor node can acquire real-time inertia, speed, direction and RSSI data, effectively fuse a plurality of positioning data through a multi-information fusion and alternation minimized error processing module according to a built-in algorithm, finally determine the current position information of the node, upload the current position information to a main base station, plan the motion track of each wireless sensor node according to task requirements by the main base station, issue a moving direction instruction to each wireless sensor node, and execute a mobile device of the wireless sensor node according to the received instruction.
In this embodiment, each wireless sensor node transmits the positioning data to the master base station in a multi-hop manner. Specifically, since the transmission power of the common wireless sensor node is low, the common wireless sensor node can only transmit data to the slave base station through the relay wireless sensor node, and then the data is forwarded to the master base station by the slave base station; the common wireless sensor node can not directly receive the data sent by the main base station, and the data can be received only after being forwarded by the slave base station, so that the energy consumption of the common wireless sensor node can be reduced. The relay wireless sensor node also needs to transmit data to the main base station after being forwarded by the slave base station.
Meanwhile, the radio frequency identification device of each wireless sensor node can only obtain the RSSI signal value of one wireless sensor node around the wireless sensor node at a certain moment, so that each wireless sensor node can obtain the RSSI signal values of a plurality of nodes in a small time period, and a plurality of RSSI signal values measurable in a measurement interval are used as a plurality of effective RSSI data of the positioning. Since the square of the distance between two nodes is inversely proportional to the RSSI, the distance between two nodes can be known according to the magnitude of the RSSI.
In conclusion, the system fully combines various types of positioning data of the nodes, and can realize dynamic high-precision positioning of the nodes.
Example 2:
the method for positioning by using the multi-information fusion mobile wireless sensor node positioning system provided by the embodiment 1 comprises the following steps:
(a) collecting multi-information positioning data: n types of positioning data which are acquired by each wireless sensor node and correspond to each wireless sensor node at a certain moment are respectively represented as s i (i ═ 1,2, … …, n); the embodiment includes the inertial data s of the node at a certain time 1 Speed data s 2 Direction data s 3 And the time and the previous time period deltat 2 K RSSI data s within 4 ,s 5 ,……,s 3+k (k +3 ═ n); due to Δ t 2 The corresponding time period is very small, so that the k pieces of RSSI can be approximately regarded as a plurality of pieces of RSSI data at the moment;
the wireless sensor nodes comprise a plurality of relay wireless sensor nodes and a plurality of common wireless sensor nodes.
(b) Calculating the position measurement change rate: calculating n positioning data at a certain moment and n positioning data at the previous moment in the multi-information positioning data acquired in the step (a) by a multi-information fusion and alternative minimization error processing module to obtain n position measurement change rates delta s i ,(i＝1,2,……,n)；
Wherein the content of the first and second substances,
representing n types of positioning data at time t,
representing n types of positioning data at time t-1.
Thus, the inertia, speed, direction and RSSI data of the node at the moment can be obtainedThe corresponding measured rates of change, respectively denoted as Δ s 1 ,Δs 2 ,……,Δs n 。
(c) Solving the true change rate of the position: assuming a true rate of change of position of
Establishing a rate of change of position measurement Δ s i And true rate of change of position of
In between evaluation mechanisms
Reconstruction of the vector containing the weight factor omega i A positioning alternation error minimization model of (i ═ 1,2, … …, n):
wherein, ω is i (i-1, 2, … …, n) is a plurality of types of n positioning data s associated with a certain time i N weight factors corresponding to (i ═ 1,2, … …, n), and
then, the true change rate of the position at the current moment is solved by a gradient descent method
And a weight factor omega i ,(i＝1,2,……,n)；
In particular, corresponding to inertial data s 1 The weighting factor is marked as omega 1 Corresponding speed data s 2 The weighting factor is marked as omega 2 Corresponding direction data s 3 The weighting factor is marked as omega 3 And weighting factors corresponding to the RSSI data are respectively marked as omega 4 ,ω 5 ,……,ω n (ii) a And always maintain
Before the positioning system provided in embodiment 1 is operated, an initial value is set for each weight factor, each weight factor may be evenly distributed as an initial value, that is, the initial value of each weight factor is determined, and is substituted into the positioning alternation error minimization model, and a convergence threshold is set, and the initial value is solved by a gradient descent method
The specific process is as follows:
wherein the content of the first and second substances,
taking the initial value as the last iteration result value at the current moment as the initial value of the current iteration at the current moment;
is a step size factor;
is a gradient algorithm;
the value of the iteration result at the current moment is used as the initial value of the next iteration at the current moment.
The formula iterates for many times in one positioning process until a convergence threshold is met.
Then, fix
Will be provided with
Substituting the position alternating error into the positioning alternating error minimization model again to solve omega 1 ,ω 2 ,ω 3 ,ω 4 ,ω 5 ,……,ω n As shown in fig. 3; here, the convergence threshold is a criterion for determining whether or not the iteration is stopped, as a constraint condition.
In the operation process of the positioning system provided in embodiment 1, each weight factor solved at the previous time is used as an initial value of each weight factor at the next time, and the larger the corresponding weight factor is, the larger the contribution of the type of positioning data to determining the real position is, and the weight factor is dynamically adjusted according to this method.
(d) Calculating current position information: solving the true rate of change of the n positions obtained from the true rate of change of the positions by the step (c)
Determining the current position information d of the node at the moment t by combining the position information of the node at the last moment t ；
(g) Predicting location information at the next time: a time period deltat to be elapsed 3 M pieces of position information { d } t-1 ，d t-2 ，……，d t-m Predicting future position information through a Kalman filtering algorithm;
(e) data transmission: the position information corresponding to each wireless sensor node is sent to a master base station, specifically, the position information corresponding to the common wireless sensor node is sent to a relay wireless sensor node, the relay wireless sensor node sends the received position information corresponding to the common wireless sensor node and the position information corresponding to the relay wireless sensor node to a slave base station, and then the slave base station sends the received position information to the master base station.
(f) Instruction feedback: the main base station plans the motion trail of each wireless sensor node according to the operation requirement and sends a moving direction instruction to each wireless sensor node according to the received current position information of the plurality of relay wireless sensor nodes and the plurality of common wireless sensor nodes.
In this embodiment, the mobile device of the wireless sensor node may be an unmanned aerial vehicle for monitoring data such as temperature and humidity in the environment, or other mobile devices for performing other tasks.
A plurality of positioning data collected by the wireless sensor nodes are uniformly converted into positioning data without a measurement unit by a multi-information fusion and alternate minimization error processing module, the positioning data are converted into data related to time, and the dynamic characteristics of the positions of the nodes are represented by the variation rate of the positioning data in a time sequence. And an alternating error minimization model is constructed through multi-information positioning data, so that effective fusion of various positioning data is realized.
When a certain type of positioning data has a large contribution to the real position data at the instant, it indicates that the reliability of the type of positioning data is higher, and the weight factor of the type of positioning data is correspondingly increased. When the contribution of a certain type of positioning data to the real position data at the instant is small, it indicates that the reliability of the type of positioning data is relatively low, and the size of the weighting factor is correspondingly reduced. The weight factor of each positioning data is dynamically adjusted, so that high-precision positioning is realized.
The specific process for adjusting the weighting factor is as follows:
wherein the content of the first and second substances,
the initial value is the last iteration result value at the current time and is used as the initial value of the current iteration at the current time, wherein the initial value of the first iteration at the current time is the last iteration result value at the last time; alpha is a step size factor;
a gradient algorithm;
is the current timeAnd the iteration result value is used as the initial value of the next iteration at the current moment.
The formula iterates for many times in one positioning process until a convergence threshold is met.
The system fully utilizes various positioning data of the nodes, realizes dynamic high-precision positioning of each node, and improves the positioning precision of the mobile wireless sensor nodes.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that fall within the spirit and principle of the present invention are intended to be included therein.
Claims (7)
1. A mobile wireless sensor node positioning method based on multi-information fusion is characterized in that the method is based on a multi-information fusion mobile wireless sensor node positioning system for positioning, and the multi-information fusion mobile wireless sensor node positioning system comprises a main base station, a plurality of slave base stations, a plurality of relay wireless sensor nodes and a plurality of common wireless sensor nodes;
the positions of the master base station and the slave base station are fixed; the relay wireless sensor node and the common wireless sensor node are mobile nodes;
the relay wireless sensor node and the common wireless sensor node have the same physical structure and respectively comprise a mobile device, a radio frequency sensor, an inertial sensor, a speed sensor, a direction sensor and a multi-information fusion and alternation error minimization processing module;
the multi-information fusion and alternation error minimization processing module of the common wireless sensor node sends the position information of the node to the multi-information fusion and alternation error minimization processing module of the relay wireless sensor node, the multi-information fusion and alternation error minimization processing module of the relay wireless sensor node sends the received position information of the common wireless sensor node and the position information of the relay wireless sensor node to the slave base station, and the slave base station sends the received position information of the common wireless sensor node and the position information of the relay wireless sensor node to the master base station;
the master base station plans the motion tracks of the ordinary wireless sensor nodes and the relay wireless sensor nodes according to the received position information of the ordinary wireless sensor nodes and the position information of the relay wireless sensor nodes, sends the planned motion tracks to the slave base station in the communication range of the master base station as a control instruction, and sends the received control instruction to the mobile device of the relay wireless sensor nodes and the mobile device of the ordinary wireless sensor nodes in the communication range of the slave base station;
the method comprises the following steps:
(a) collecting multi-information positioning data: n types of positioning data which are acquired by each wireless sensor node and correspond to each wireless sensor node at a certain moment are respectively represented as s i ,(i＝1,2,……,n)；
(b) Calculating the position measurement change rate: calculating n positioning data of a certain moment and n positioning data of a previous moment acquired in the step (a) of acquiring multi-information positioning data by a multi-information fusion and alternative minimized error processing module to obtain n position measurement change rates deltas i ,(i＝1,2,……,n)；
(c) Solving the true change rate of the position: assuming a true rate of change of position of
Establishing a rate of change of position measurement Δ s i And true rate of change of position of
The evaluation mechanism between the weight factor omega is constructed i And (i-1, 2, … …, n) alternately solving the true change rate of the position at the current moment by a gradient descent method
And a weight factor omega i ；
(d) Calculating the current positionInformation: solving the true rate of change of the n positions obtained from the true rate of change of the positions by the step (c)
Determining the current position information of the node by combining the position information of the node at the previous moment;
(e) data transmission: and sending the position information corresponding to each wireless sensor node to the main base station.
2. The method of claim 1, wherein the step (a) collects multi-information positioning data, and the wireless sensor nodes comprise a plurality of relay wireless sensor nodes and a plurality of common wireless sensor nodes.
3. The method of claim 1, wherein in the step (b) of calculating the change rate of the position measurement,
wherein the content of the first and second substances,
representing n types of positioning data at time t,
representing n types of positioning data at time t-1.
4. The method according to claim 1, wherein the step (c) of solving the true change rate of the position comprises solving the change rate of the position measurement Δ s i And true rate of change of position of
In between evaluation mechanisms
5. The method according to claim 1, wherein the step (c) of solving the positioning alternation error minimization model in the true change rate of the position
Wherein, ω is i For n multiple types of positioning data s associated with a certain time i N weight factors corresponding to (i ═ 1,2, … …, n), and
6. the method of claim 1, wherein between the step (d) of calculating the current location information and the step (e) of data transmission, the method further comprises the step (g) of predicting the location information at the next time: and predicting future position information by using a plurality of position information in a past time period through a Kalman filtering algorithm.
7. The method as claimed in claim 2, wherein in the step (e), during data transmission, the position information corresponding to the normal wireless sensor node is sent to the relay wireless sensor node, the relay wireless sensor node sends the received position information corresponding to the normal wireless sensor node and the position information corresponding to the relay wireless sensor node to the slave base station, and the slave base station sends the received position information to the master base station.
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