Dynamic ranging and positioning method and device based on WSN (Wireless Sensor Network)

Abstract

The invention relates to a dynamic ranging and positioning method and device based on a WSN (Wireless Sensor Network). The device comprises a wireless sensor network module, an RSSI (Received Signal Strength Indicator) filtering processing module and a positioning module. The wireless sensor network module comprises a ZigBee CC2530, a target node and an anchor node, and is used for constructing a WSN network topology and collecting RSSI values of the nodes. And the RSSI filtering processing module preprocesses the data, and performs fitting operation on parameters through minimum error sum of squares by using distance information between nodes and a filtered RSSI value. And the positioning module comprises a dynamic distance measurement unit and a position coordinate calculation unit, comprehensively considers the influence of the distance between the anchor node and the unknown node on the unknown node, gives different weights to each anchor node, and calculates the geometric center of the polygon to obtain the positioning position of the unknown node. The positioning module provided by the invention mainly adopts an improved weighted centroid positioning method based on Kalman filtering, so that the positioning error is reduced, and the anti-interference capability of the device is improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of wireless sensor network positioning, in particular to a dynamic ranging positioning method and device based on WSN, and in particular to an indoor positioning system and implementation method based on Zigbee wireless sensor network. Background Art

[0002] With the development of mobile communication technology in my country, people's demand for spatial location information services continues to increase. With the rapid development of global satellite navigation systems in recent years, outdoor positioning navigation has become increasingly mature. Provide users with high-precision, all-time, convenient and fast positioning services in outdoor environments. However, satellite positioning is subject to the influence of signal strength and is easily affected by signal interference such as shielding and multipath effects, and the signal will be greatly attenuated and scattered. Therefore, in urban canyons with tall buildings or in closed indoor environments, it is impossible to provide accurate positioning services through satellite navigation signals. People spend 80% of their daily lives indoors, and their main activities are in high-rise office buildings, large shopping malls, smart parking lots, airports, railway stations and other densely populated public indoor environments. The demand for indoor positioning services continues to increase. Through the study of indoor positioning technology, people and objects in basements, mines and other environments can be more accurately located. For example, when carrying out rescue activities, accurate positioning can help rescuers quickly lock rescue targets and rescue routes. Therefore, high-precision indoor positioning technologies such as location navigation and environmental perception based on location information services have become the core issues for realizing indoor location-based services.

[0003] At present, the existing indoor positioning technology can use WiFi, Bluetooth, cellular network, ultra-wideband, geomagnetic fingerprint and pseudo-satellite positioning to achieve indoor navigation and indoor positioning and tracking of people and objects. However, these technologies have difficulties in application, such as high cost and power consumption, poor security and real-time performance, low positioning accuracy, and positioning signals are easily affected by environmental interference. The effect of existing methods has not yet reached the ideal state.

[0004] ZigBee is a wireless communication technology that has the characteristics of large network capacity, simplicity and reliability, low price, short latency, low power consumption and strong security. It is one of the mainstream technologies for indoor positioning.

[0005] Due to the complex and changeable indoor environment, the wireless signal will be affected by multipath fading, signal interference, link loss and other factors during transmission from the transmitter to the receiver, showing an irregular fluctuation, thereby affecting the positioning accuracy of the positioning method based on signal strength. The present invention proposes a dynamic ranging positioning method and device based on WSN, focusing on solving the problems of large indoor positioning error and unstable position coordinates. Summary of the invention

[0006] Based on the technical problems raised above, the present invention proposes a dynamic ranging positioning method and device based on WSN, which is used to solve the problems of large positioning error and unstable position coordinates in the existing indoor positioning methods. At the same time, it also overcomes the high hardware cost and installation and maintenance cost of using UWB positioning technology, and solves the problem that the existing equipment has high power consumption and cannot be operated and used for a long time.

[0007] The technical solution adopted by the present invention to achieve the above-mentioned purpose is:

[0008] A dynamic ranging and positioning device based on WSN, comprising:

[0009] Wireless sensor network module, used to build WSN network and collect the signal strength RSSI value of nodes in real time;

[0010] RSSI filtering processing module, used to pre-process the RSSI values ​​collected by the wireless network module and estimate the parameters of the ranging model through parameter fitting operation;

[0011] The positioning module is used to dynamically measure the distance to the target node and locate the target node in real time based on the ranging model.

[0012] The wireless sensor network module comprises:

[0013] The target node is used to monitor and collect the location information of the anchor nodes and send signal strength information by scanning the communication channel;

[0014] Anchor node is used to send its own reference location information in real time.

[0015] The target node and the anchor node are both ZigBee CC2530 nodes, and the ZigBee CC2530 node integrates an 8051 microcontroller and a wireless communication module.

[0016] The RSSI filtering processing module includes:

[0017] A logarithmic path loss module is used to convert the RSSI value into corresponding distance information through a logarithmic path loss model;

[0018] A data preprocessing module is used to filter out abnormal values ​​in the RSSI value and preprocess the RSSI value through Kalman filtering;

[0019] The parameter fitting operation module is used to search for the best function match for the data and dynamically correct the logarithmic path loss model parameters.

[0020] The positioning module comprises:

[0021] Dynamic ranging unit: used to obtain the RSSI value received between nodes in real time and convert it into distance information using the logarithmic path loss model;

[0022] Position coordinate calculation unit: used to calculate the position information of the target node through the geometric relationship between the distance information obtained in the dynamic ranging unit and the position information of the known anchor node.

[0023] A dynamic ranging and positioning method based on WSN includes the following steps:

[0024] 1) Construct the WSN network topology, fix the anchor node in the positioning area, and communicate with the target node;

[0025] 2) Obtain the RSSI values ​​received between nodes in real time, and convert the received RSSI values ​​into corresponding distance information through the logarithmic path loss model;

[0026] 3) The received RSSI signal is filtered using the Kalman filter algorithm, and a relationship mapping between the RSSI value and the anchor node position is established to obtain the RSSI set between the reference node and the unknown node: RSSI_set = {R1, R2, R3, ..., R k}, where R i is the RSSI value between the reference node and the unknown node i, i = 1, ..., k;

[0027] 4) Using the distance information between anchor nodes and the optimized RSSI value, select the adjacent anchor nodes in the environment, perform fitting calculation on the path loss index by minimizing the square error, and dynamically correct the path loss function model parameters. The adjacent anchor nodes are the anchor nodes that are adjacent in the sequence after all anchor nodes are sorted by distance;

[0028] 5) The weighted value is calculated using the sum of the radius distance and the square of the RSSI value. The target node sorts the anchor nodes from strong to weak according to the RSSI value, and establishes a mapping between the RSSI value and the distance from the node to the anchor node. The distances corresponding to the three adjacent anchor nodes are taken out through the Euclidean distance. Combined with the corresponding anchor node coordinate position information, the coordinates of the unknown node O(x, y) are obtained according to the improved weighted centroid positioning algorithm.

[0029] The logarithmic distance path loss model is:

[0030]

[0031] Among them, P d represents the path loss when the distance between the transceiver devices is d, d is the distance between the target node and the anchor node, μ is the path loss exponent, and x σ is a zero-mean normally distributed random, is the value of the signal strength received by the node at the distance between the parameters d0.

[0032] The Kalman filter algorithm includes a prediction phase and an update phase, wherein:

[0033] In the prediction stage, according to the analytical model, the state value of the future cycle is predicted through the state variables and external control inputs, that is, the predicted state estimate P of the kth step in the state before the kth step k / -1 and the known measured variable Z k The corrected state estimate E at step k is k / :

[0034] The analytical model is:

[0035] E k / -1 =AE k / A T +Q k

[0036] P k / -1 =AP k / -1 +Bu k-1

[0037] Among them, A is the state transfer equation, Bu k-1 is the estimated state control quantity, Q k is the system noise variance at the kth step.

[0038] In the update phase, according to the measurement results E k / and P k / -1 Correct the predicted state quantity to obtain a more accurate state value;

[0039] The corrected equation is:

[0040]

[0041]

[0042] P k / =P k / -1 +K k (Z k -H k P k / -1 )

[0043] Among them, E k / is the state error variance, K k is the filter gain matrix, R k is the observation noise variance, I is the unit matrix, H k is the state observation matrix;

[0044] The improved weighted centroid positioning algorithm is specifically:

[0045]

[0046]

[0047] Among them, ω1, ω2, ω3 are the weighted values ​​of the centroid, x1, x2, x3 are the horizontal coordinate values ​​of the three anchor nodes adjacent to the target node, y1, y2, y3 are the vertical coordinate values ​​of the three anchor nodes adjacent to the target node, x is the horizontal coordinate value of the target node obtained by calculation, and y is the vertical coordinate value of the target node obtained by calculation;

[0048]

[0049]

[0050]

[0051] Among them, d1, d2, and d3 are the test distances from reference nodes A, B, and C to node O, and R1, R2, and R3 are the RSSI values ​​of the three anchor nodes.

[0052] The present invention has the following beneficial effects and advantages:

[0053] 1. The present invention designs a dynamic ranging positioning method and device based on WSN, which mainly aims at the problem that accurate positioning services cannot be provided through satellite navigation signals in closed indoor environments. The dynamic ranging positioning method proposed in the present invention can be deployed in closed indoor environments such as underground parking lots and mines to provide users with high-precision and high-reliability positioning services.

[0054] 2. The present invention proposes a dynamic ranging positioning method based on WSN. Through the wireless sensor network module, RSSI filtering processing module and positioning module, a dynamic ranging positioning method based on WSN is constructed, which can dynamically transmit RSSI signal values ​​in real time and measure the location information of the target node. At the same time, the Kalman filter is combined and the weighted centroid positioning algorithm is improved to improve the positioning speed and positioning accuracy of the positioning device in complex indoor environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Figure 1 It is an overall schematic diagram of the dynamic ranging and positioning device of the present invention;

[0056] Figure 2 Schematic diagram of the RSSI filtering processing module of the present invention;

[0057] Figure 3 It is a working principle diagram of the present invention;

[0058] Figure 4 This is the positioning node distribution diagram of the present invention. DETAILED DESCRIPTION

[0059] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation method of the present invention is described in detail below in conjunction with the accompanying drawings. In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention can be implemented in many other ways different from those described herein, and those skilled in the art can make similar improvements without violating the connotation of the invention, so the present invention is not limited by the specific implementation disclosed below.

[0060] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art of the present invention. The terms used in the specification of the invention herein are only for the purpose of describing specific embodiments and are not intended to limit the present invention.

[0061] A dynamic ranging and positioning method and device based on WSN, comprising:

[0062] Wireless sensor network module, used to build WSN network and collect signal strength information of nodes in real time;

[0063] RSSI filtering processing module, used to perform data preprocessing on RSSI values ​​collected by the wireless sensor network module, and to estimate the parameters of the ranging model through parameter fitting operation;

[0064] The positioning module is used for dynamic ranging and real-time positioning of the target node.

[0065] The wireless sensor network module comprises:

[0066] ZigBee CC2530, used for wireless communication and data transmission, as well as control and management of sensor nodes;

[0067] The target node is used to monitor and collect information by scanning the communication channel to send signal strength information;

[0068] Anchor nodes are used to provide reference location information in wireless sensor networks so that other nodes can determine their own locations through anchor nodes;

[0069] The ZigBee CC2530 is a single-chip system that integrates an 8051 microcontroller and a wireless communication module, and can perform data processing and control. By receiving, processing and analyzing the data collected by the sensor nodes, wireless communication and data transmission are realized, as well as control and management of the sensor nodes.

[0070] The RSSI filtering processing module includes:

[0071] Logarithmic path loss module, used to convert the signal strength received at the receiving point into corresponding distance information;

[0072] The data preprocessing module is used to filter out abnormal values ​​of signal strength with large changes and reduce the impact of the environment;

[0073] Parameter fitting operation module, which searches for the best function match of the data and dynamically corrects the path loss function model parameters;

[0074] The data preprocessing module preprocesses the RSSI value through Kalman filtering, including the following steps:

[0075] 1) In the prediction stage, according to the Kalman filter model, the state variables and external control inputs at the moment can be used to predict the state values ​​of the future cycle. k / -1 is the predicted state estimate of the kth step in the state before the kth step, E k / is the known measured variable Z k The prediction process obtained under this setting is: E k / -1 =AE k / A T +Q k , P k / -1 =AP k / -1 +Bu k-1 . Where A is the state transfer equation, Bu k-1 is the estimated state control quantity, Q k is the system noise variance at step k, Z k is the filter input, that is, the measured RSSI signal value.

[0076] 2) In the update phase, according to the measurement results E k / and P k / -1 Correct the predicted state quantity to obtain a more accurate state value. The correction equation is: E k / =(IK k H k )E k / , P k / =P k / -1 +K k (Z k -H k P k / -1 ). Where E k / is the state error variance, Q is the system noise variance, K k is the filter gain matrix, R k is the observation noise variance, I is the unit matrix, H k is the state observation matrix.

[0077] The positioning module comprises:

[0078] Dynamic ranging unit: used to update the RSSI value received between nodes in real time and convert it into distance information using the signal transmission model;

[0079] Position coordinate calculation unit: used to calculate the position information of the target node through the geometric relationship between the distance information obtained in the dynamic ranging unit and the position information of the known anchor node.

[0080] The dynamic ranging unit includes a wireless module for receiving and sending wireless signals, and uses the Zstack protocol stack to achieve inter-node communication. In the dynamic ranging unit, anchor nodes are deployed near the target node to build the WSN network topology, measure the received signal strength, and calculate the distance between the node and the RSSI dynamic ranging unit by processing and analyzing the measurement results.

[0081] The position coordinate calculation unit connects all anchor nodes according to their coordinates to form a polygon, comprehensively considers the impact of the distance between the anchor node and the unknown node on the unknown node, assigns different weights to each anchor node, and calculates the geometric center of the polygon to obtain the positioning position of the unknown node.

[0082] A dynamic ranging and positioning method and device based on WSN, comprising: a wireless sensor network module, an RSSI filtering and processing module and a positioning module.

[0083] like Figure 1 As shown, a dynamic ranging and positioning method and device based on WSN include: a wireless sensor network module, an RSSI filtering and processing module and a positioning module.

[0084] The wireless sensor network module includes: ZigBee CC2530, target node and anchor node.

[0085] The ZigBee CC2530 unit integrates a single-chip system of an 8051 microcontroller and a wireless communication module, and can perform data processing and control. By receiving data collected by sensor nodes, processing and analyzing them, wireless communication and data transmission are achieved, as well as control and management of sensor nodes.

[0086] The target nodes are in a mobile state within the positioning area. They are terminal nodes in the wireless sensor network and are the last level in the network. They cannot forward data from other devices and need to join the network through the base station node first. After the system is started, the target node actively scans the communication channel, sends a network access request data frame to the base station node, obtains the network ID and joins the wireless sensor network. After joining the network, the positioning process begins, and the positioning request is broadcast to the surrounding base station nodes with a hop count of 1 from the network.

[0087] The anchor node collects data sent by each sensing node in the wireless sensor network, processes and analyzes it, completes data aggregation and transmission, and then realizes monitoring, control and management of the network status. At the same time, the anchor node coordinates and controls the behavior of the node, providing the network with stronger adaptability and flexibility. By reasonably deploying anchor nodes to build the network topology, the positions of these nodes are calculated based on the known position coordinates of the anchor nodes and the RSSI value and position relationship with the target node.

[0088] The data preprocessing method is to filter the RSSI value using the Kalman filter algorithm. Establish a relationship mapping between the RSSI value and the anchor node position to obtain the RSSI set between the reference node and the unknown node: RSSI_set = {R1, R2, R3, ..., R k The Kalman filter process can be divided into two stages: prediction and update. In the prediction stage, the state variables and external control inputs at the moment can be used to predict the state values ​​of future cycles according to the analytical model. In the update stage, the predicted state values ​​are corrected according to the measurement results to obtain more accurate state values. The basic principle of Kalman filtering is to weight the error between the measurement results and the prediction results to obtain more accurate results, such as Figure 2 As shown. The covariance matrix is ​​used to describe the error covariance between the measurement and state values, the Kalman gain is used to describe the error between the prediction and the measurement value, and the update of the state estimation is achieved by updating the Kalman gain in real time.

[0089] The dynamic ranging positioning method combines the logarithmic distance path loss model with the improved weighted centroid positioning algorithm. In the positioning process, due to the multipath effect and obstacles in complex environments, there is an error between the actual received signal strength and the actual value, resulting in the intersection of the three circles in the centroid positioning area and a certain error in the position of the test node. Figure 4 As shown in the figure, let d1, d2, d3 be the test distances from reference nodes A, B, C to node O, and O1, O2, O3 are the three intersection points of the intersection area. The RSSI value of each AP node is scanned at the target point O to form a set RSSI_set = {R1, R2, R3, ..., R k}, where R k is the RSSI value of the kth AP node. The RSSI values ​​of the three nearest AP nodes are extracted through the Euclidean distance as {R1, R2, R3}. In order to improve the weighted centroid positioning algorithm, reduce the positioning error, and make it suitable for complex and closed special environments, the weighted value is calculated by using the radius distance and the square sum of the RSSI value, which can better reflect the internal relationship between AP nodes. The weighted values ​​can be expressed as: Among them, ω1, ω2, ω3 are the weighted values ​​of the centroid. The improved weighted centroid positioning formula can be expressed as: Among them, x1, x2, and x3 are the horizontal coordinate values ​​of the three anchor nodes adjacent to the target node, y1, y2, and y3 are the vertical coordinate values ​​of the three anchor nodes adjacent to the target node, x is the horizontal coordinate value of the target node obtained by calculation, and y is the vertical coordinate value of the target node obtained by calculation.

[0090] The present invention designs a WSN-based indoor positioning method and device. In practical applications, the working principle is as follows: Figure 3 As shown in the figure. The wireless sensor network module consists of ZigBee CC2530, target node, anchor node and other parts. The anchor node is fixed in the positioning area and can communicate with the target node. After the networking is completed, all nodes join the network through the gateway node, and the target node is located through the surrounding anchor node communication. One of the anchor nodes is connected to the PC as a host node. The host software is developed in the PC to process real-time positioning information. The RS232 serial port line is used to connect to the gateway node in the ZigBee sensor network to obtain information from the network. All wireless sensor network nodes are evenly distributed in the positioning area so that the wireless signal covers the entire area. Deploy anchor nodes and configure network information and communicate with the host computer. After accessing the network, the target node actually becomes a mobile node in the wireless sensor network and broadcasts positioning information. The anchor node is responsible for receiving network access applications from other nodes, forwarding the signal strength value of the target node, and calculating the position coordinates through the anchor node connected to the PC to achieve the positioning purpose. In the deployment of the wireless sensor network module, a coordinate system is established according to the location scenario, and several points are selected as reference points. The RSSI values ​​of a group of reference points are collected using the Zigbee CC2530 device. The RSSI eigenvalues ​​are extracted using the Kalman filter algorithm, and the position coordinates of the reference points are recorded and stored in the database. Then, the indoor positioning model is constructed by improving the weighted centroid positioning algorithm. In the positioning module, when the target node enters the positioning area, it continuously sends positioning requests to the surrounding nodes. The anchor node connected to the host computer extracts the RSSI eigenvalue through the Kalman filter algorithm, and the weighted value is calculated using the radius distance and the square sum of the RSSI value, which can better reflect the internal relationship between AP nodes. The RSSI value of each AP node is scanned at the target point O to form a set RSSI_set={R1,R2,R3,…,R k}, where R kis the RSSI value of the kth AP node. The three nearest AP nodes are found through the Euclidean distance, and the RSSI values ​​of the three nearest AP nodes are extracted as {R1, R2, R3}. The test distances from the three reference nodes to the node to be tested are d1, d2, and d3. O1, O2, and O3 are the three intersection points of the intersection area. The weighted value is calculated using the sum of the radius distance and the square of the RSSI value to better reflect the internal relationship between AP nodes.

[0091] The actual measured value of RSSI has great volatility. The received signal strength value shows an overall attenuation trend as the distance increases, but it is not strictly attenuated, which shows that the RSSI value is not a one-to-one mapping relationship with the distance, and there is a certain degree of jitter. When the transmission distance is short, the RSSI value decays faster; the longer the transmission distance, the slower the attenuation, and the change of the receiving strength to the transmission distance is not obvious. In practice, the receiving node can receive n RSSI values ​​in a certain period of time. Due to the influence of non-line-of-sight and multipath, these RSSI values ​​have great volatility. Before substituting them into the formula for calculation, they are filtered to obtain a more accurate value, and then calculated. RSSI filtering can detect and process abnormal values ​​or erroneous values ​​in RSSI, and use filtering algorithms to predict the actual RSSI value, thereby reducing errors. By using the Kalman filter, the noise in RSSI can be eliminated to a certain extent, making it more accurate, thereby improving the reliability of communication and the accuracy of distance calculation.

[0092] The specific processing flow of the position coordinate calculation unit is as follows:

[0093] 1) The anchor node periodically broadcasts information to the surrounding area, including its own node ID and coordinates. After receiving the information, the target node takes the average RSSI of the same anchor node;

[0094] 2) When the target node has collected a certain amount of anchor node information, it will no longer receive new information. The target node sorts the anchor nodes from strong to weak according to RSSI, and establishes a mapping between RSSI value and the distance between the node and the anchor node. Two sets are established: RSSI set between anchor nodes and target nodes: RSSI_set = {R1, R2, R3, ..., R k}, anchor node position set: P_set = {(x1,y1),(x2,y2),…,(x n ,y n )}.

[0095] 3) Calculate the distance between the target node and each anchor node according to the logarithmic distance path loss model, corresponding to the set: d_set = {d1, d2, d3, ..., d k};

[0096] 4) The distances corresponding to the three anchor nodes are taken out through the Euclidean distance, and the coordinates of the unknown node O(x, y) are obtained according to the improved weighted centroid positioning algorithm based on the corresponding anchor node coordinate position information.

[0097] A dynamic ranging and positioning method and device based on WSN, comprising the following steps:

[0098] S1 builds a wireless sensor network through ZigBee CC2530 devices and constructs the WSN network topology. The anchor node is fixed in the positioning area and communicates with the target node. In the wireless sensor network, the anchor node provides reference location information so that the target node can determine its own location through the anchor node and collect the RSSI signal value between nodes in real time.

[0099] S2 updates the RSSI value received between nodes in real time, and converts the RSSI signal strength received at the receiving point into the corresponding distance information through the logarithmic path loss model. The formula of the logarithmic distance path loss model is: In the formula, d is the distance between the target node and the anchor node; μ is the path loss exponent, which is a parameter that depends on the surrounding environment and indicates how fast the path loss increases with distance. σ is a zero-mean normally distributed random number with a standard deviation of σ. is the signal strength value received by the node at the distance d0, P d It indicates the path loss when the distance between the transmitting and receiving devices is d, in dB. The parameter d0 is usually set to 1m.

[0100] S3 uses the Kalman filter algorithm to filter the received RSSI signal. Establish the relationship mapping between RSSI value and anchor node position, and obtain the RSSI set between the reference node and the unknown node: RSSI_set = {R1, R2, R3, ..., R k}, the Kalman filter process can be divided into two stages: prediction and update. In the prediction stage, according to the analytical model, the state variables and external control inputs at the moment can predict the state values ​​of the future cycle. In the update stage, the predicted state quantities are corrected according to the measurement results to obtain more accurate state values. Including the mean m and covariance P of the initial estimated state, by generating sigma points, predicting the state transfer matrix and model response in one step, calculating the covariance, calculating the filter gain, updating the state and covariance matrix, detecting and processing abnormal values ​​or error values ​​in RSSI, and predicting the actual RSSI value, thereby reducing errors.

[0101] S4 uses the distance information between anchor nodes and the optimized RSSI value to select nearby anchor nodes in the environment, fits the path loss index by minimizing the square error, and dynamically corrects the path loss function model parameters.

[0102] S5 calculates the weighted value using the sum of the radius distance and the square of the RSSI value. The target node sorts the anchor nodes from strong to weak according to RSSI, and establishes a mapping between the RSSI value and the distance from the node to the anchor node. The distances corresponding to the three adjacent anchor nodes are taken out through the Euclidean distance, and the coordinates of the unknown node O(x, y) are obtained according to the improved weighted centroid positioning algorithm in combination with the corresponding anchor node coordinate position information.

[0103] The above is a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should be regarded as within the scope of protection of the present invention.

Claims

1. A dynamic ranging and positioning device based on WSN, It is characterized in that include: Wireless sensor network module, used to build WSN network and collect the signal strength RSSI value of nodes in real time; RSSI filtering processing module, used to pre-process the RSSI values ​​collected by the wireless network module and estimate the parameters of the ranging model through parameter fitting operation; The positioning module is used to dynamically measure the distance to the target node and locate the target node in real time based on the ranging model.

2. A dynamic ranging and positioning device based on WSN according to claim 1, It is characterized in that The wireless sensor network module comprises: The target node is used to monitor and collect the location information of the anchor nodes and send signal strength information by scanning the communication channel; Anchor node is used to send its own reference location information in real time.

3. A dynamic ranging and positioning device based on WSN according to claim 2, It is characterized in that The target node and the anchor node are both ZigBee CC2530 nodes, and the ZigBee CC2530 node integrates an 8051 microcontroller and a wireless communication module.

4. A dynamic ranging and positioning device based on WSN according to claim 1, It is characterized in that The RSSI filtering processing module includes: A logarithmic path loss module is used to convert the RSSI value into corresponding distance information through a logarithmic path loss model; A data preprocessing module is used to filter out abnormal values ​​in the RSSI value and preprocess the RSSI value through Kalman filtering; The parameter fitting operation module is used to search for the best function match for the data and dynamically correct the logarithmic path loss model parameters.

5. A dynamic ranging and positioning device based on WSN according to claim 1, It is characterized in that The positioning module comprises: Dynamic ranging unit: used to obtain the RSSI value received between nodes in real time and convert it into distance information using the logarithmic path loss model; Position coordinate calculation unit: used to calculate the position information of the target node through the geometric relationship between the distance information obtained in the dynamic ranging unit and the position information of the known anchor node.

6. A dynamic ranging and positioning method based on WSN, It is characterized in that The following steps are involved: 1) Construct the WSN network topology, fix the anchor node in the positioning area, and communicate with the target node; 2) Obtain the RSSI values ​​received between nodes in real time, and convert the received RSSI values ​​into corresponding distance information through the logarithmic path loss model; 3) The received RSSI signal is filtered using the Kalman filter algorithm, and a relationship mapping between the RSSI value and the anchor node position is established to obtain the RSSI set between the reference node and the unknown node: RSSI_set = {R 1 ,R 2 ,R 3 ,…,R k }, where R i is the RSSI value between the reference node and the unknown node i, i = 1, ..., k; 4) Using the distance information between anchor nodes and the optimized RSSI value, select the adjacent anchor nodes in the environment, perform fitting calculation on the path loss index by minimizing the square error, and dynamically correct the path loss function model parameters. The adjacent anchor nodes are the anchor nodes that are adjacent in the sequence after all anchor nodes are sorted by distance; 5) The weighted value is calculated using the sum of the radius distance and the square of the RSSI value. The target node sorts the anchor nodes from strong to weak according to the RSSI value, and establishes a mapping between the RSSI value and the distance from the node to the anchor node. The distances corresponding to the three adjacent anchor nodes are taken out through the Euclidean distance. Combined with the corresponding anchor node coordinate position information, the coordinates of the unknown node O(x, y) are obtained according to the improved weighted centroid positioning algorithm.

7. A dynamic ranging and positioning method based on WSN according to claim 6, It is characterized in that The logarithmic distance path loss model is: Among them, P d represents the path loss when the distance between the transceiver devices is d, d is the distance between the target node and the anchor node, μ is the path loss exponent, and x σ is a zero-mean normally distributed random, For nodes in parameter d 0 The value of the received signal strength at the distance between.

8. The WSN-based dynamic ranging and positioning method according to claim 6, It is characterized in that The Kalman filter algorithm includes a prediction phase and an update phase, wherein: In the prediction stage, according to the analytical model, the state value of the future cycle is predicted through the state variables and external control inputs, that is, the predicted state estimate P of the kth step in the state before the kth step k / k-1 and the known measured variable Z k The corrected state estimate E at step k is k / k-1 : The analytical model is: AND k / k-1 =AE k / k-1 TO T +Q k P k / k-1 =AP k / k-1 +Bu k-1 Among them, A is the state transfer equation, Bu k-1 is the estimated state control quantity, Q k is the system noise variance of the kth step; In the update phase, according to the measurement results E k / k-1 and P k / k-1 Correct the predicted state quantity to obtain a more accurate state value; The corrected equation is: E k / k =(I-K k H k )E k / k-1 P k / k =P k / k-1 +K k (Z k -H k P k / k-1 ) Among them, E k / k is the state error variance, K k is the filter gain matrix, R k is the observation noise variance, I is the unit matrix, H k is the state observation matrix.

9. The WSN-based dynamic ranging and positioning method according to claim 6, It is characterized in that The improved weighted centroid positioning algorithm is specifically: Among them, ω 1 ,ω 2 ,ω 3 is the weighted value of the centroid, x 1 、x 2 、x 3 are the horizontal coordinate values ​​of the three anchor nodes adjacent to the target node, y 1 ,y 2 ,y 3 are the ordinate values ​​of the three anchor nodes adjacent to the target node, x is the abscissa value of the target node obtained by calculation, and y is the ordinate value of the target node obtained by calculation; Among them, d 1 d 2 d 3 is the test distance from reference nodes A, B, C to node O, R 1 ,R 2 ,R 3 are the RSSI values ​​of the three anchor nodes.

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