A method for positioning in irregular spaces

Abstract

This invention discloses a method for positioning in irregular spaces, relating to the field of spatial positioning technology. The method comprises the following steps: S1: Monitoring and tracking are performed by deploying a monitoring sensor network; S2: Node self-localization is achieved based on a positioning algorithm; S3: The scalability and fault tolerance of the positioning algorithm are evaluated from a practical perspective. In this invention, the scalability and fault tolerance of the positioning algorithm are evaluated from a practical perspective. Based on this positioning algorithm, accurate positioning of each node within a grid matrix can be achieved indoors. Wireless nodes determine their own location information based on the RSSI ranging principle, and signal information at various points inside the room is monitored in real time, ultimately achieving positioning in irregular spaces. Furthermore, while meeting network system performance requirements, signals are transmitted with relatively low overall power using a low-power wireless transceiver chip, improving positioning accuracy while reducing energy consumption during the positioning process.

Description

Technical Field

[0001] This invention relates to the field of spatial positioning technology, specifically a method for achieving positioning in irregular spaces. Background Technology

[0002] Wireless sensor network (WSN) technology is a natural extension of existing network technologies, significantly shortening the distance between humans and the physical world. It allows for the collection and appropriate control of information about the physical world anytime, anywhere. Furthermore, the emergence and development of WSNs are of paramount importance in promoting ubiquitous computing. In recent years, WSN technology and its applications have gradually increased, playing a crucial role in specific fields. The "sensing" characteristic of WSNs is a vital foundation for the future ubiquity of general services. Simultaneously, WSN technology can be well integrated with internet technology to generate new converged services, greatly expanding the scope of existing businesses and making future urban management and service operations more accurate and intelligent.

[0003] A search revealed patent document CN102364983B, which describes a WLS node self-localization method based on RSSI ranging in a wireless sensor network. The method first calculates the channel fading factor n using weighted averages; then, it uses RSSI ranging to measure the distance between the unknown node and the beacon node; finally, it estimates the coordinates of the unknown node using a weighted least squares (WLS) estimation algorithm. This localization algorithm achieves significantly improved positioning accuracy with the same hardware complexity as the least squares (LS) estimation algorithm. Therefore, this invention is simple, easy to implement, and has strong practical value. Patent document CN105228103B describes an RSSI ranging and localization method based on Bluetooth signals. By performing Gaussian filtering and Savitzky-Golay filtering on the received RSSI values, it eliminates severe jitter in the RSSI values, obtaining smooth and continuous RSSI values. These smooth and continuous RSSI values ​​are then substituted into the distance estimation formula provided by this invention to calculate the distance between the signal receiver and the signal transmitter. Patent document CN102981164A discloses a method for positioning in finite irregular spaces. This method uses two units—a sensing receiver and a target object—to locate specific objects in regular or slightly irregular spaces. Patent document CN114025311A discloses an RSSI ranging and positioning method based on spatial projection. This method calculates the distance between the target node and the anchor node using a positioning model, and then introduces spatial projection technology to calculate the angle information between the target node and the anchor node. Finally, it employs several distance-based positioning algorithms to obtain the coordinates of the target node, further achieving high-precision positioning.

[0004] Indoor positioning technology is one of the key and hot topics in wireless sensor network technology research. In indoor spatial positioning technology, how to achieve a positioning method with low power consumption and high positioning accuracy in a simple way is an urgent problem to be solved. Based on this, we propose an irregular spatial positioning method to solve the shortcomings of existing technologies. Summary of the Invention

[0005] The purpose of this invention is to provide a method for achieving positioning in irregular spaces, so as to solve the problems mentioned in the background art.

[0006] To achieve the above objectives, the present invention provides the following technical solution:

[0007] A method for localization in irregular spaces, comprising a monitoring sensor network and a localization algorithm, includes the following steps:

[0008] S1: Monitoring and tracking are achieved by deploying a network of monitoring sensors;

[0009] S2: Based on the positioning algorithm, the node can achieve self-localization;

[0010] S3: Evaluate the scalability and fault tolerance of the positioning algorithm from a practical perspective.

[0011] As a further aspect of the present invention: In S1, pillars are evenly placed in an independent room, and nodes are evenly placed on each pillar from the bottom to the top of the room. Wireless nodes are placed on the top of the pillars, and wired nodes are placed inside the room. The nodes on a single pillar are connected through a bus structure. The wireless nodes placed on the top of the pillars and the main control node of the room constitute a wireless sensor network for room management. The positions of the pillars are planned in advance.

[0012] As a further aspect of the present invention: the positioning algorithm is based on the RSSI ranging principle. By setting the node distribution of the monitoring area as a uniform n×n grid, the implementation principle of the positioning algorithm is analyzed by induction.

[0013] As a further aspect of this invention: In step S3, when evaluating the scalability of the positioning algorithm, since the monitoring area in actual application environments is mostly not a standard square area, but an area of ​​arbitrary shape, if it is necessary to expand the monitoring area by adding network nodes and increasing the monitoring area, then the scalability of the positioning algorithm needs to be evaluated and analyzed. Positioning is based on a 4×4 square frame node matrix, where the 4×4 square frame node matrix represents the already located nodes. By using known nodes to locate surrounding unknown nodes, the following situation may occur during the positioning calculation process: Known node A sends 01 positioning information. At this time, unknown nodes labeled G, H, and I will receive the 01 positioning information and reply with the RSSI value corresponding to the received positioning information, such as... Figure 5 As shown, the unknown nodes G and I are equidistant from the known nodes, making it impossible for the known node A to distinguish between them. To differentiate G and I, the known node B is used as a reference. After sending 0 / 1 positioning information, the known node A receives two closely spaced RSSI values ​​and still sends out a comparison of the RSSI values. The unknown node H receives the strongest RSSI value and uses its stored location database to locate itself, thus becoming a known node. Node A then sends auxiliary positioning information to its neighboring node B. Node B first locates the unknown nodes I and J. After detecting the completion of node B's positioning process, node A locates the unknown node G. Thus, in any region, the positioning of a node will encounter situations where two unknown nodes are equidistant from a known node. By using the neighboring node B as a reference... With the assistance of known nodes, the final location of unknown nodes can be achieved. In the area expansion implementation, the area surrounded by irregular lines is the original monitoring area, the squares inside the area are known nodes, and the nodes outside the area are unknown nodes. The area where they are located is the expansion area. Each unknown node stores a database of unknown nodes in the expansion area. Consider using known nodes adjacent to the expansion area to locate unknown nodes. The specific implementation steps of area expansion are as follows: Unknown nodes in the expansion area are initialized to a listening state. Only unknown nodes adjacent to the original monitoring area can hear the data information of known nodes. Unknown nodes that hear the data information use the CSMA / CA mechanism to obtain the channel and send a location request information to the known nodes. After receiving the location request information, the known nodes in the original area assist the unknown nodes in the expansion area in locating.

[0014] As a further aspect of this invention: When performing fault tolerance evaluation and analysis of the positioning algorithm, nodes cannot be distinguished by comparing the RSSI values ​​of unknown nodes. In extreme cases, a known node is designated as A, and nodes with unknown locations are designated as B and C. A is a known node, while nodes B and C are unknown nodes. The known node A is used to locate the unknown nodes B and C. The maximum fault tolerance radius for node placement is set to A. Nodes can be placed anywhere within a circular area, with a node spacing of r. Taking the most extreme case of node placement as an example, when nodes A, B, and C are all placed on the edge of the maximum fault tolerance radius, the coordinates of A are (A, r), the coordinates of B are (-A, 0), and the coordinates of C are (rA, 0). The calculation method for the central axis coordinates of nodes B and C is shown in Formula 1.

[0015] x mid =(x B +x C ) / 2=(-a+ra) / 2 Formula 1

[0016] Since the x-coordinate of node A is 'a', to avoid the situation where unknown nodes B and C are equidistant from node A, the x-coordinate of the axis of the unknown nodes B and C should be greater than the x-coordinate of node A, as shown in Formula 2:

[0017] xmid >x a Formula 2

[0018] Substituting Formula 1 into Formula 2, we get Formula 3:

[0019] (-a+ra) / 2>a Formula 3

[0020] Solving Equation 3, we can obtain the range of values ​​for the maximum tolerance radius A of the node placement:

[0021] a < r / 4 Formula 4

[0022] Therefore, to avoid situations where two unknown nodes are equidistant from a known node, the maximum tolerance deviation for node placement is less than 1 / 4 of the node spacing. The above conclusion regarding the maximum tolerance for node placement is derived under ideal RSSI ranging accuracy conditions. It verifies that under maximum transmission power, the ranging accuracy within 15m is 2m. Based on an RSSI ranging accuracy of 2m, if the node spacing is within 15m, then Formula 2 can be rewritten as Formula 5:

[0023] x mid -x a >2 Formula 5

[0024] The range of values ​​for the tolerance radius A is calculated as shown in Formula 6:

[0025] a<(r-4) / 4 Formula 6

[0026] Formula 6 derives the maximum fault tolerance radius when deploying nodes, which is smaller than the fault tolerance radius under ideal ranging accuracy. Furthermore, in order to avoid positioning deviations, the fault tolerance radius must not be exceeded during node installation.

[0027] As a further aspect of the present invention: by using CC1020 and CC2420 wireless transceiver chips, the relationship curve between transmission power and current consumption was tested. When the transmission power of the node is set to a higher level, the current consumption increases significantly, further verifying the relationship between the transmission power and current consumption of CC1020 and CC2420.

[0028] As a further aspect of the present invention: the positioning algorithm is based on the RSSI ranging principle to determine the location information of wireless nodes and monitor the signal information of various points inside the room in real time.

[0029] Compared with the prior art, the beneficial effects of the present invention are:

[0030] In this invention, pillars are evenly distributed in an independent room, with nodes evenly distributed from the bottom to the top of each pillar. Wireless nodes are located at the top of the pillars, while wired nodes are located inside the room. Nodes on a single pillar are connected via a bus structure. The wireless nodes are responsible for transmitting grain condition parameters collected by other nodes on a single pillar to the main control node of the entire room, which then transmits the data to a monitoring computer. The wireless nodes at the top of the pillars and the main control node of the room constitute a wireless sensor network for room management. Based on the RSSI ranging principle, the wireless nodes determine their own location information and monitor the signal information of various points inside the room in real time. By using a sensor network as a foundation, large-scale monitoring and tracking functions can be achieved, and further, targeting the grid topology... Given the known conditions of the node location database, a node self-localization algorithm is set. The scalability and fault tolerance of the algorithm are evaluated from a practical perspective. Based on this algorithm, accurate positioning of each node within the grid matrix can be achieved indoors. Wireless nodes determine their own location information based on RSSI ranging principles, and signal information at various points inside the room is monitored in real time. Ultimately, positioning in irregular spaces is achieved. Furthermore, while meeting network system performance requirements, signals are transmitted with relatively low overall power using low-power wireless transceiver chips. This improves positioning accuracy while reducing energy consumption during positioning. The overall implementation is low-cost, simple, and energy-efficient, providing a new solution for node positioning in irregular spaces. Attached Figure Description

[0031] Figure 1 A flowchart illustrating the method for locating objects in irregular spaces.

[0032] Figure 2A schematic diagram of the sensor network layout in the method for localization in irregular spaces.

[0033] Figure 3 A schematic diagram illustrating the node positioning principle of a 2×2 square grid in an irregular spatial positioning method.

[0034] Figure 4 A schematic diagram illustrating the (n+1)×(n+1) uniform grid positioning implementation in the irregular space positioning method.

[0035] Figure 5 This is a schematic diagram illustrating the node positioning process in an arbitrary monitoring area within an irregular spatial positioning implementation method.

[0036] Figure 6 A schematic diagram of the extended positioning process of the monitoring area in the method for positioning in irregular spaces.

[0037] Figure 7 A schematic diagram illustrating the fault tolerance of node placement in the method for positioning in irregular spaces.

[0038] Figure 8 A schematic diagram showing the relationship between the transmit power and current consumption of CC1020 and CC2420 in the method for positioning in irregular spaces.

[0039] Figure 9 A schematic diagram illustrating the optimal transmit power setting for nodes in the positioning algorithm of an irregular space positioning implementation method. Detailed Implementation

[0040] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0041] Please see Figure 1-9 In this embodiment of the invention, a method for positioning in irregular spaces includes a monitoring sensor network and a positioning algorithm. The positioning algorithm is based on the RSSI ranging principle, where wireless nodes determine their own location information and monitor signal information at various points inside a room in real time. The method steps are as follows:

[0042] S1: Monitoring and tracking are achieved by deploying a network of monitoring sensors;

[0043] S2: Based on the positioning algorithm, the node can achieve self-localization;

[0044] S3: Evaluate the scalability and fault tolerance of the positioning algorithm from a practical perspective.

[0045] In step S1, pillars are evenly placed in an independent room, with nodes evenly placed on each pillar from the bottom to the top. Wireless nodes are placed at the top of the pillars, and wired nodes are placed inside the room. The nodes on a single pillar are connected via a bus structure. The wireless node is the master control node of a single pillar, responsible for transmitting the parameters collected by other nodes on the single pillar to the master control node of the entire room, and then the master control node transmits them to the monitoring computer. The wireless nodes placed at the top of the pillars and the master control node of the room constitute a wireless sensor network for room management. The positions of the pillars are pre-planned, but the wireless nodes are randomly placed at the top of the pillars. The wireless nodes need to determine their own position information so that the master control node of the room can monitor the signal information of each point inside the room in real time.

[0046] In S2, the positioning algorithm is based on the RSSI ranging principle. It sets the node distribution in the monitoring area as a uniform n×n grid, and then analyzes the implementation principle of the positioning algorithm through inductive reasoning. First, node positioning is performed based on the 2×2 grid positioning concept, including A... 1，1 A 1，2 A 2，1 A 2，2 ,like Figure 3 As shown, there are 4 squares A. 1，1 A 1，2 A 2，1 A 2，2 These are wireless network nodes, randomly deployed on a square grid. Each node stores a database of its deployment locations, but none of the nodes know their exact location. A master node A is added to the entire wireless network system. m To achieve node self-location, the master node and node A 1，1 The distance is the pixel spacing of the square grid. The master node knows its own location information. The positioning method for a 2×2 square grid includes the following steps: Step 1: All nodes are initialized. The master node is in the transmitting state, and the unknown location nodes are in the receiving state; Step 2: The master node sets the wireless signal transmission power through programming. The initial transmission power is relatively low, and it sends 01 (01 is a representative value) positioning information. After the master node finishes sending, it switches to the receiving state. After receiving the 01 positioning information, the unknown location nodes switch back to the transmitting state and send out the RSSI value of the received signal; Step 3: If the master node does not receive the location information from two unknown location nodes A within the set time, the master node will be considered for further action. 1，1 and A 1，2 RSSI response information for 01 location information; Step 4: Repeat step 3 until the master control node receives two unknown location nodes A 1，1 and A 1，2RSSI response information for 01 location information; Step 5: The master control node compares the received RSSI values, switches to sending mode, and sends the comparison result to the unknown location node A. 1，1 and A 1，2 Node A 1，1 and A 1，2 After receiving the RSSI comparison result, the distance between each coordinate point in the stored location database and the master node is calculated. The node with the larger RSSI comparison result is the coordinate point with the smallest distance from the master node, and the node with the smaller RSSI comparison result is the coordinate point with the next smallest distance from the master node; Step Six: Determine the location of node A. 1，1 and A 1，2 The process transitions to transmit mode, acquires the channel via CSMA / CA mechanism, and sends 01 positioning information. After transmission, it transitions to receive mode; nodes that fail to compete for the channel transition to receive mode. Step 7: Repeat steps 3, 4, and 5, except that the node receiving the 01 positioning information is labeled A. 2，1 and A 2，2 For unknown nodes, nodes with known locations receive 0 / 1 positioning information during the execution process and do not perform any processing; Step 8: Node A 2，1 and A 2，2 After receiving the RSSI comparison result, the system uses the stored location database to calculate the distance between each coordinate point in the location database and the node that sent the 01 location information, thereby determining its own coordinates. During the execution process, only the coordinates of the nodes whose ordinates in the location database are greater than those of the nodes that sent the 01 location information are considered, and finally the positioning process is completed.

[0047] In step S2, an inductive approach is used to locate nodes based on a 2×2 grid. This allows for self-localization of an n×n uniform node grid. The self-localization of an n×n uniform node grid is then verified. The principle behind the self-localization of an (n+1)×(n+1) grid node is as follows: Figure 4 As shown, it includes a master control node, whose placement and performance parameters are similar to those of the master control node. Figure 3 As shown in the diagram, gray squares form an n×n uniform node grid, where the positions of all nodes within the grid are known. Light-colored squares represent nodes with unknown positions. Unknown nodes are located by gradually locating their neighboring known nodes. The method for locating unknown nodes is similar to the principle of 2×2 matrix positioning. Unknown nodes are initially in a receiving state. The self-localization steps for a (n+1)×(n+1) grid node include: Step 1: Label A n，1 It is known that the node enters the transmitting state and sends 01 location information. Repeating the above positioning process will locate the unknown node A. n+1，1 An+1，2 Location: During the location process, only coordinates in the database that are close to the coordinates of the node that sent the 01 location information are considered, and the ordinate of these coordinates must be greater than the ordinate of the node that sent the 01 information; Step 2: Through node A n，1 When returning the RSSI value comparison result returned by the unknown node to the unknown node, A n，2 The node will also receive this information to determine the unknown node A. n+1，1 A n+1，2 Node A has been located. n，2 Send confirmation that a neighboring node has been located; the neighboring known node A n，3 After receiving the information, repeat step one to add the unknown node A. n+1，3 A n+1，1 Location; Step 3: During the location of unknown nodes in the horizontal row of the network, the unknown nodes in the vertical row also start from A. 1，n+1 The process begins with gradually moving upwards, following the same steps and principles as locating unknown nodes in a horizontal row; Step 4: If n is even, the horizontal row positioning ends with known node A. n，n-1 Position A n+1，n-1 A n+1，n Unknown node, the vertical positioning ends at known node A. n-1，n Position A n-1，n+1 A n，n+1 Unknown node, when node A is known n，n Received A n，n-1 and A n-1，n Send confirmation that neighboring nodes have been located, send 01 location information, and then send the unknown node A. n+1，n+1 For location, if n is odd, locate the unknown nodes in the horizontal row to the unknown point A. n+1，n-2 A n+1，n-1 The unknown nodes in the vertical row are located to unknown node A. n-2，n+1 A n-1，n+1 Given node A n，n With unknown node A n+1，n and AA n，n+1 If the distances are the same, and we use the known node A... n，n Node A cannot be... n+1，n and A n，n+1 Distinguish them, in the known node A n，n-2 Unknown node A n+1，n-2 A n+1，n-1 After location, the nearest known node A n，n-1 Instead of sending location confirmation information, it directly sends 01 location information. As long as one unknown node replies with an RSSI value, the known node A... n，n-1 Upon receiving the RSSI reply value, the system transitions to transmit mode and sends out information containing only one RSSI value. (Unknown Node A) n+1，n Received known node An，n-1 After receiving the reply, A obtains its own location coordinates by comparing them with the stored location database. n，n-1 Neighboring known node A n，n Upon receiving A n，n-1 After receiving the RSSI reply information, send 01 location information to the unknown node A. n+1，n A n+1，n+1 The localization process is complete, with all unknown nodes located. Based on the principle of mathematical induction, the localization algorithm is feasible. The localization process utilizes the built-in RSSI function of most commercial wireless transceiver chips, eliminating the need for any external auxiliary localization devices. Ultimately, this localization method offers advantages such as low cost, simple process, and low overall energy consumption.

[0048] In step S3, when evaluating the scalability of the positioning algorithm, since the monitoring area in real-world applications is mostly not a standard square area but an area of ​​arbitrary shape, if it is necessary to expand the monitoring area by adding network nodes and increasing the monitoring area, then the scalability of the positioning algorithm needs to be evaluated and analyzed. Figure 5 As shown, positioning is performed based on a 4×4 square bounding box node matrix. The 4×4 square bounding box node matrix represents the already located nodes. By utilizing known nodes to locate surrounding unknown nodes, the following scenario may occur during the positioning calculation: Known node A sends 0 / 1 positioning information. In this case, unknown nodes labeled G, H, and I will receive the 0 / 1 positioning information and reply with the corresponding RSSI value, such as... Figure 5 As shown, it can be seen that the distances between unknown nodes G and I and known nodes are equal. Therefore, known node A cannot distinguish between unknown nodes G and I. To differentiate nodes G and I, known node B is used as a reference. After sending 0 / 1 positioning information, known node A receives two closely spaced RSSI values ​​and still sends out the RSSI value comparison. Unknown node H receives the strongest RSSI value and uses its stored location database information to locate itself, thus becoming a known node. Node A sends auxiliary positioning information to its neighboring node B. Node B first locates unknown nodes I and J. After detecting that node B's positioning process is complete, node A locates unknown node G. Thus, in any region, the positioning of a node will encounter situations where two unknown nodes are exactly equidistant from known nodes. By using nearby known nodes for assistance, the final positioning of unknown nodes can be achieved. In the implementation of region expansion, as shown... Figure 6As shown, the area surrounded by irregular lines is the original monitoring area, the squares inside the area are nodes with known locations, and the nodes outside the area are nodes with unknown locations. The area where these nodes are located is the extended area. Each unknown node stores a database of unknown nodes within the extended area. Considering locating unknown nodes by using known nodes adjacent to the extended area, the specific implementation steps of the area expansion are as follows: The unknown nodes within the extended area are initialized to a listening state. Only unknown nodes adjacent to the original monitoring area can hear the data information of the known nodes. The unknown nodes that hear the data information use the CSMA / CA mechanism to obtain the channel and send a location request information to the known nodes. After receiving the location request information, the known nodes in the original area assist the unknown nodes in the extended area in locating.

[0049] In S3, during the fault tolerance evaluation and analysis of the localization algorithm, the placement of nodes may deviate during the implementation of the node self-localization algorithm. The basic steps of the node self-localization algorithm are that a single known node locates two unknown nodes. If a positional deviation occurs during the placement process, it may lead to the extreme case where the distances between the two unknown nodes and the known node are equal. Therefore, it is necessary to analyze the impact of node placement deviation on the performance of the localization algorithm. Comparing the RSSI values ​​of unknown nodes cannot distinguish them. In extreme cases, such as... Figure 7 As shown: Let node A be a known node, and nodes B and C be unknown nodes. A has a known location, while nodes B and C have unknown locations. Use the known node A to locate the unknown nodes B and C. Set the maximum tolerance radius for node placement to A. Figure 7 The circle shown can be used as an example. Nodes can be placed anywhere within the circular area, with a node spacing of r. Taking the most extreme case of node placement, when nodes A, B, and C are all placed on the edge of the maximum tolerance radius, the coordinates of A are (A, r), the coordinates of B are (-A, 0), and the coordinates of C are (rA, 0). The coordinates of the midline of nodes B and C are calculated as shown in Formula 1:

[0050] x mid =(x B +x C ) / 2=(-a+ra) / 2 Formula 1

[0051] Since the x-coordinate of node A is 'a', to avoid the situation where unknown nodes B and C are equidistant from node A, the x-coordinate of the axis of the unknown nodes B and C should be greater than the x-coordinate of node A, as shown in Formula 2:

[0052] x mid >x a Formula 2

[0053] Substituting Formula 1 into Formula 2, we get Formula 3:

[0054] (-a+ra) / 2>a Formula 3

[0055] Solving Equation 3, we can obtain the range of values ​​for the maximum tolerance radius A of the node placement:

[0056] a < r / 4 Formula 4

[0057] Therefore, to avoid the situation where two unknown nodes are equidistant from a known node, the maximum tolerance deviation for node placement is less than 1 / 4 of the node spacing.

[0058] The above conclusion regarding the maximum tolerance for node placement is derived under ideal RSSI ranging accuracy. Based on our physical node experiments, the ranging accuracy within 15m is 2m under maximum transmit power conditions. Using an RSSI ranging accuracy of 2m, and assuming node spacing is within 15m, Formula 2 can be rewritten as Formula 5:

[0059] x mid -x a >2 Formula 5

[0060] The range of values ​​for the tolerance radius A is calculated as shown in Formula 6:

[0061] a<(r-4) / 4 Formula 6

[0062] Formula 6 takes into account the actual ranging accuracy of the node and derives the maximum fault tolerance radius when the node is deployed. It is smaller than the fault tolerance radius under the ideal ranging accuracy. In order to avoid positioning deviation, the node installation cannot exceed the fault tolerance radius.

[0063] Since the power supply for wireless nodes cannot be replenished, energy saving is a key consideration for the operation of sensor networks. Therefore, in order to further reduce the power consumption of the system, signals are transmitted with a lower overall power while meeting the performance requirements of the network system.

[0064] Example 1

[0065] like Figure 8 As shown, the relationship between transmit power and current consumption was tested using CC1020 and CC2420 wireless transceiver chips. When the node's transmit power is set higher, the current consumption increases significantly. The experiment verifies the relationship between the transmit power and current consumption of the CC1020 and CC2420. Figure 8 As shown: When the CC2420 node transmits at maximum power, the ranging accuracy of RSSI is high within 15m, and the ranging accuracy of RSSI is poor after 15m. Furthermore, due to the influence of dynamic environmental factors, the RSSI value and distance are not a stable exponential relationship. When the distance is close, there is less interference and the ranging accuracy is high, while when the distance is far, there is more interference and the ranging accuracy is poor.

[0066] Analysis of optimal transmit power settings for nodes in the positioning algorithm, as follows: Figure 9 As shown in the figure, dark squares represent nodes with known locations, and light squares represent nodes with unknown locations. The distance between nodes is a constant r, and the distance between diagonal nodes is 1 and 4r. The positioning process is initiated by node A1. Node A1 gradually increases the transmission power of the wireless signal through programming and sends 01 positioning information until it receives the RSSI values ​​corresponding to the 01 positioning information from the two unknown nodes. However, the following energy consumption and transmission power problems will occur in the application.

[0067] With the node spacing r set to 5m, and the CC2420 used as the wireless transceiver unit, the CC2420's minimum transmit power is -25dBm. At this power, the signal can be transmitted over 10m. Figure 9 As shown, it is known that location node A1 transmits 01 location information with the lowest transmission power, and its wireless signal coverage area is... Figure 9 Within the area enclosed by the large circle, unknown nodes R1, B2, A3, and C1 can all receive 0 / 1 positioning information. However, the distances between unknown nodes A3 and C1 and the known node A1 are the same. Therefore, RSSI values ​​cannot be used to locate the four unknown nodes R1, B2, A3, and C1. Experiments show that at a transmit power of -25dBm, the ranging accuracy is high within 3m, but beyond 3m, RSSI ranging accuracy is poor. When the node spacing exceeds 5m, RSSI values ​​cannot be used to accurately distinguish the nodes.

[0068] When the above situation occurs during the positioning process, it is resolved by increasing the transmission power. The known node A1 increases its transmission signal power, and all unknown nodes will receive A1's 01 positioning information. A1 uses its location information and its own coordinates to calculate and deduce the distances r and 1,4r between the two nearest unknown nodes. Based on the relationship between RSSI and distance calibrated in the actual application environment, the RSSI value corresponding to 1,4r is derived. When the known node A1 sends the 01 positioning information, it also sends the RSSI corresponding to 1,4r. The unknown nodes parse the received 01 positioning information to obtain the corresponding RSSI value. Only when the RSSI value is greater than or equal to the RSSI value attached to the 01 information will the unknown nodes reply with the 01 information. Figure 9 As shown, the positioning process can only be realized when unknown nodes B1 and B2 reply with RSSI values. In this case, when A1 sends the 01 positioning information, it may not include the RSSI value corresponding to 1,4r, but may include the transmission power. The unknown nodes first parse the RSSI value corresponding to the 01 positioning information, and then, according to the relationship between RSSI and distance under the corresponding transmission power calibrated in the actual application environment, only unknown nodes with a distance less than or equal to 1,4r reply with RSSI values.

[0069] When the node spacing is large, the unknown nodes B1 and B2 are located using the known node A1. The distances between A1 and B1, and B2 are r and 1, 4r, respectively. The distance between A1 and the unknown nodes A3, C1 is 2r. Considering the error of RSSI ranging, if accurate positioning is to be implemented, the maximum wireless signal coverage of the known node A1 should be in the area between the large-radius circle and the small-radius circle. To avoid deviations during positioning, the wireless signal coverage of A1 should be as close as possible to the small-radius circle. Further analysis shows that this not only improves the positioning accuracy but also reduces the energy consumed in the positioning process.

[0070] Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for positioning in irregular spaces, comprising a monitoring sensor network and a positioning algorithm, characterized in that: The method and steps are as follows: S1: Monitoring and tracking are achieved by deploying a network of monitoring sensors; S2: Based on the positioning algorithm, achieve self-localization of nodes; S3: Evaluate the scalability and fault tolerance of the positioning algorithm from a practical perspective. When evaluating the scalability of the positioning algorithm, if it is necessary to expand the monitoring area, add network nodes, and expand the monitoring area, then the scalability of the positioning algorithm needs to be evaluated and analyzed. Positioning is based on a 4×4 square frame node matrix, where the 4×4 square frame node matrix represents the already located nodes. By using known nodes to locate surrounding unknown nodes, the following situation may occur during the positioning calculation: Known node A sends 01 positioning information. At this time, unknown nodes labeled G, H, and I will all receive the 01 positioning information and reply with the corresponding RSSI value. The distances between unknown nodes G and I and known nodes are equal, so known node A cannot distinguish between unknown nodes G and I. To distinguish nodes G and I, known node B is used to differentiate them. Known node A sends 01... After receiving the location information, if two similar RSSI values ​​are received, the RSSI value comparison is still sent out. When the unknown node H receives the information with the strongest RSSI value, it uses the stored location database information to locate itself, and node H becomes a known node. Node A sends auxiliary location information to its neighboring node B. Node B first locates unknown nodes I and J. After node A hears that node B's location process is complete, it locates unknown node G again. In this way, the location of any node in any area will encounter a situation where two unknown nodes are exactly the same distance from known nodes. With the help of nearby known nodes, the final location of unknown nodes can be achieved. In the area expansion implementation, the area surrounded by irregular lines is the original monitoring area. The squares inside the area are known nodes, and the nodes outside the area are unknown nodes. The area where they are located is the expansion area. Each unknown node stores the database of unknown nodes in the expansion area. The unknown node is located by the known nodes adjacent to the expansion area. The specific implementation steps of the area expansion are as follows: Unknown nodes within the expanded area are initialized to a listening state. Only unknown nodes adjacent to the original monitoring area can detect data information from known nodes. Unknown nodes in the data information use the CSMA / CA mechanism to obtain the channel and send location request information to nodes with known locations. After receiving the location request information, the known nodes in the original area assist the unknown nodes in the extended area in locating.

2. The method for achieving irregular spatial positioning according to claim 1, characterized in that: When performing fault tolerance evaluation analysis of the positioning algorithm, comparing the RSSI values ​​of unknown nodes cannot distinguish them. In extreme cases, let's define a known node as A, and nodes with unknown locations as B and C. A is in a known location, while nodes B and C are in unknown locations. We use the known node A to locate the unknown nodes B and C. Let A be the maximum fault tolerance radius for node placement. Nodes can be placed anywhere within a circular area, with a node spacing of r. Taking the most extreme case of node placement as an example, when nodes A, B, and C are all placed on the edge of the maximum fault tolerance radius, the coordinates of A are (A, r), the coordinates of B are (-A, 0), and the coordinates of C are (r-A, 0). The calculation method for the centerline coordinates of nodes B and C is shown in Formula 1. Formula 1 Since the x-coordinate of node A is 'a', to avoid the situation where unknown nodes B and C are equidistant from node A, the x-coordinate of the axis of the unknown nodes B and C should be greater than the x-coordinate of node A, as shown in Formula 2: Formula 2 Substituting Formula 1 into Formula 2, we get Formula 3: Formula 3 Solving Equation 3, we can obtain the range of values ​​for the maximum tolerance radius A of the node placement: Formula 4 Therefore, to avoid situations where two unknown nodes are equidistant from a known node, the maximum tolerance deviation for node placement is less than 1 / 4 of the node spacing. The above conclusion regarding the maximum tolerance for node placement is derived under ideal RSSI ranging accuracy conditions. It verifies that under maximum transmission power, the ranging accuracy within 15m is 2m. Based on an RSSI ranging accuracy of 2m, if the node spacing is within 15m, then Formula 2 can be rewritten as Formula 5: Formula 5 The range of values ​​for the tolerance radius A is calculated as shown in Formula 6: Formula 6 Formula 6 derives the maximum fault tolerance radius when deploying nodes, which is smaller than the fault tolerance radius under ideal ranging accuracy. Furthermore, in order to avoid positioning deviations, the fault tolerance radius must not be exceeded during node installation.

3. The method for achieving irregular spatial positioning according to claim 1, characterized in that: By using CC1020 and CC2420 wireless transceiver chips, the relationship between transmission power and current consumption was tested. When the node's transmission power is set higher, the current consumption increases significantly, further verifying the relationship between the transmission power and current consumption of CC1020 and CC2420.

4. The method for achieving irregular spatial positioning according to claim 1, characterized in that: The positioning algorithm is based on the RSSI ranging principle, which allows wireless nodes to determine their own location information and monitor signal information at various points inside the room in real time.

Images