A Monte Carlo localization method based on variable probability sampling of anchor box regions

Abstract

The present invention provides a Monte Carlo positioning method based on variable probability sampling of anchor box areas, including initializing sensor network nodes, the number of sample nodes collected at the initial moment is N; predicting the initialized nodes; filtering the predicted nodes, filtering out samples that do not meet the filtering conditions in the anchor node coordinates within the one-hop and two-hop range of the located node, when there are multiple anchor nodes, the sampling areas of the multiple anchor nodes will generate a common sampling area, and the common sampling area is adjusted to obtain better sampling nodes, thereby improving the node positioning accuracy. In the filtering stage, if the number of sample nodes obtained in the end does not reach N, an iteration is performed until N nodes are collected; and then the filtered sample nodes are weighted averaged to obtain the final positioning position. The present invention solves the problem of too many iterations in the MCL algorithm and the problem of reduced positioning accuracy due to unreasonable sampling nodes in the MCB algorithm.

Description

Technical Field

[0001] The present invention relates to the technical field of wireless sensor network (WSN) node positioning, and in particular to a Monte Carlo positioning method based on variable probability sampling of anchor box regions. Background Art

[0002] WSN is a product of the combination of multiple technologies such as computers, sensor networks and communications. It is also a hot research topic in the information field technology in the world today, and is also regarded as one of the ten most influential emerging technologies in the future human life. WSN mainly processes information through a distributed architecture, which can increase the coverage area while reducing the coverage blind area, and enhance the fault tolerance of the system. It has very broad application prospects in the fields of medical care, industrial monitoring and target tracking. In practical applications, WSN often involves coverage information or routing mechanisms that depend on the location information of other nodes. In many scenarios of WSN practical applications, only when the location of the data source of the sensor node when receiving data is obtained, the information obtained will be of practical significance.

[0003] The MCL algorithm is a typical mobile node positioning algorithm. This algorithm mainly uses probability statistics to solve numerical problems. The MCL algorithm is widely used in machine intelligence, physics, finance, economics and other fields, and later it was widely used in WSN mobile node positioning algorithms. However, in the traditional MCL algorithm, there are defects such as poor sampling efficiency and low positioning accuracy. Therefore, some improved MCL algorithms have appeared later, such as: Monte Carlo positioning algorithm based on sampling anchor box (MCB), Monte Carlo positioning algorithm with optimized constraints (COMCL) and enhanced Monte Carlo positioning algorithm (EMCL). The basic principles of some typical mobile node positioning algorithms will be introduced in detail below.

[0004] In the traditional MCL algorithm, due to the imperfect sampling area, a large number of samples are required to obtain enough samples, which increases the time complexity of the algorithm and makes the cost of calculation and algorithm execution time high. At the same time, in the cyclic sampling, the samples selected at a time may be insufficient and may not meet the predetermined sampling requirements, resulting in increased positioning errors.

[0005] In the MCB algorithm, although the expanded sampling area improves the positioning accuracy and reduces the number of iterations, the sampled nodes that are not in the original MCL sampling area may exceed the sampling range of the original prediction stage. In other words, the node sampled in the sampling area in the MCB algorithm may be an invalid node, which will also cause certain errors. Summary of the invention

[0006] According to the technical problems that the number of iterations in the MCL algorithm is too many and the positioning accuracy is reduced due to the existence of unreasonable sampling nodes in the MCB algorithm, a Monte Carlo positioning method based on variable probability sampling of anchor box areas is provided.

[0007] The technical means adopted by the present invention are as follows:

[0008] A Monte Carlo positioning method based on variable probability sampling of anchor box regions, comprising:

[0009] Initialize sensor network nodes;

[0010] Make predictions on initialized sensor network nodes;

[0011] Filter the predicted sensor network nodes;

[0012] Assuming that the number of sample nodes collected at the initial moment is N, in the filtering stage, if the number of sample nodes finally obtained does not reach N, an iteration is performed until N nodes are collected;

[0013] The filtered sample nodes are weighted averaged to obtain the final positioning position.

[0014] Further, the initializing the sensor network node includes:

[0015] Initialize the positions of anchor nodes and sampled sample nodes.

[0016] Furthermore, the initializing the sensor network node further includes:

[0017] Set the communication radius of the sensor and the maximum movement speed of the anchor node when moving; also need to pre-set a parameter ns_range.

[0018] Furthermore, the predicting of the initialized sensor network nodes includes:

[0019] The anchor node starts to move, and sampling needs to be performed at the anchor node at every moment. The sampling area is: centered at the anchor node, and the maximum movement speed of the node is V max A circular area with a radius of ; each anchor node performs such sampling once at this moment;

[0020] If the nodes are uniformly distributed, then the anchor node L at the current moment t The position is at the previous moment L t-1 The maximum movement speed of the node is within the radius of the center, as shown in the following formula:

[0021]

[0022] In the formula, d(l t |l t-1 ) represents the Euclidean distance between two adjacent anchor nodes.

[0023] Furthermore, filtering the predicted sensor network nodes includes:

[0024] The samples whose coordinates of anchor nodes within one hop and two hops of the located node do not meet the filtering conditions will be filtered out. That is, when there are multiple anchor nodes, the sampling areas of multiple anchor nodes will generate a common sampling area, and the generated common sampling area will be appropriately adjusted to obtain better sampling nodes to improve the positioning accuracy of the node.

[0025] Furthermore, filtering the predicted sensor network nodes specifically includes:

[0026] The MCB algorithm is used to obtain an anchor box area for sampling. The MCB sampling area is divided into two parts, one part is the part of the original MCL sampling area in the MCB sampling area, and the other part is the other part of the MCB area; the sampling probability of the original MCL part is 0.8, and the other parts of the MCB sampling area are uniformly sampled;

[0027] Based on MATLAB, the algorithm is simulated and variable probability sampling is realized through the normal distribution function normrnd. The mean of the X and Y coordinates is the center of the anchor box, that is:

[0028] μ x =(X sambox_min +X sambox_max ) / 2

[0029] μ y =(Y sambox_min +Y sambox_max ) / 2

[0030] The standard deviation is:

[0031]

[0032]

[0033] Compared with the prior art, the present invention has the following advantages:

[0034] 1. The Monte Carlo positioning method based on variable probability sampling of anchor box regions provided by the present invention solves the technical problems of excessive number of iterations in the MCL algorithm and decreased positioning accuracy due to the existence of unreasonable sampling nodes in the MCB algorithm.

[0035] 2. The Monte Carlo positioning method based on variable probability sampling of anchor box areas provided by the present invention has a significantly improved positioning accuracy of the VPS-MCB algorithm compared to the traditional MCL algorithm and MCB algorithm.

[0036] Based on the above reasons, the present invention can be widely promoted in the fields of wireless sensor network applications. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0038] Figure 1 The figure is a flow chart of the method of the present invention.

[0039] Figure 2 A schematic diagram of a sampling range defined by a traditional MCL algorithm in a prediction phase according to an embodiment of the present invention.

[0040] Figure 3 A schematic diagram of the sampling area of ​​the traditional MCL algorithm in the filtering stage provided in an embodiment of the present invention.

[0041] Figure 4 A schematic diagram of the sampling area of ​​the MCB algorithm provided in an embodiment of the present invention during the filtering stage.

[0042] Figure 5 A comparison diagram of the number of iterations at each moment of the traditional MCL algorithm and the MCB algorithm provided in an embodiment of the present invention.

[0043] Figure 6 A comparison diagram of the positioning errors of the traditional MCL algorithm and the MCB algorithm provided in an embodiment of the present invention.

[0044] Figure 7 A schematic diagram of the sampling area of ​​the VPS-MCB algorithm of the present invention during the filtering stage provided in an embodiment of the present invention.

[0045] Figure 8 A comparison diagram of the positioning errors of the traditional MCL algorithm, the MCB algorithm and the VPS-MCB algorithm of the present invention provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0046] It should be noted that, in the absence of conflict, the embodiments of the present invention and the features in the embodiments can be combined with each other. The present invention will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0047] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. The following description of at least one exemplary embodiment is actually only illustrative and is by no means intended to limit the present invention and its application or use. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0048] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit exemplary embodiments according to the present invention. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should be understood that when the terms "comprising" and / or "including" are used in this specification, it indicates the presence of features, steps, operations, devices, components and / or combinations thereof.

[0049] Unless otherwise specifically stated, the relative arrangement of the parts and steps described in these embodiments, the numerical expressions and numerical values ​​do not limit the scope of the present invention. At the same time, it should be clear that, for ease of description, the sizes of the various parts shown in the drawings are not drawn according to the actual proportional relationship. The technology, methods and equipment known to ordinary technicians in the relevant field may not be discussed in detail, but in appropriate cases, the technology, methods and equipment should be regarded as part of the authorization specification. In all examples shown and discussed here, any specific value should be interpreted as merely exemplary, rather than as a limitation. Therefore, other examples of exemplary embodiments may have different values. It should be noted that similar numbers and letters represent similar items in the following drawings, so once an item is defined in one drawing, it does not need to be further discussed in subsequent drawings.

[0050] In the description of the present invention, it is necessary to understand that the directions or positional relationships indicated by directional words such as "front, back, up, down, left, right", "lateral, vertical, perpendicular, horizontal" and "top, bottom" are usually based on the directions or positional relationships shown in the drawings. They are only for the convenience of describing the present invention and simplifying the description. Unless otherwise specified, these directional words do not indicate or imply that the device or element referred to must have a specific direction or be constructed and operated in a specific direction. Therefore, they cannot be understood as limiting the scope of protection of the present invention: the directional words "inside and outside" refer to the inside and outside relative to the contours of each component itself.

[0051] For ease of description, spatially relative terms such as "above", "above", "on the upper surface of", "above", etc. may be used here to describe the spatial positional relationship between a device or feature and other devices or features as shown in the figure. It should be understood that spatially relative terms are intended to include different orientations of the device in use or operation in addition to the orientation described in the figure. For example, if the device in the accompanying drawings is inverted, the device described as "above other devices or structures" or "above other devices or structures" will be positioned as "below other devices or structures" or "below their position devices or structures". Thus, the exemplary term "above" can include both "above" and "below". The device can also be positioned in other different ways (rotated 90 degrees or in other orientations), and the spatially relative descriptions used here are interpreted accordingly.

[0052] In addition, it should be noted that the use of terms such as "first" and "second" to limit components is only for the convenience of distinguishing the corresponding components. If not otherwise stated, the above terms have no special meaning and therefore cannot be understood as limiting the scope of protection of the present invention.

[0053] like Figure 1 As shown, the present invention provides a Monte Carlo positioning method based on variable probability sampling of anchor box regions, comprising:

[0054] Initialize sensor network nodes;

[0055] Make predictions on initialized sensor network nodes;

[0056] Filter the predicted sensor network nodes;

[0057] Assuming that the number of sample nodes collected at the initial moment is N, in the filtering stage, if the number of sample nodes finally obtained does not reach N, an iteration is performed until N nodes are collected;

[0058] The filtered sample nodes are weighted averaged to obtain the final positioning position.

[0059] In specific implementation, as a preferred embodiment of the present invention, the initialization of the sensor network node includes:

[0060] Initialize the positions of the anchor nodes and sampled nodes. At the same time, set the communication radius of the sensor and the maximum movement speed V of the anchor node when moving. max; At the same time, a parameter ns_range needs to be set in advance. In this embodiment, the purpose of setting the parameter ns_range is: in the filtering stage, it is necessary to sample N sample nodes before the iterative sampling ends. However, if at a certain moment of iterative sampling, the sample nodes sampled in each iteration are very few, or even not collected, then the number of sampling iterations in this case will be very large, that is, the time complexity of the algorithm will be very large. Therefore, this parameter needs to be set to deal with such extreme situations to avoid excessive complexity of the algorithm.

[0061] In specific implementation, as a preferred embodiment of the present invention, the predicting of the initialized sensor network nodes includes:

[0062] The anchor node starts to move, and sampling needs to be performed at the anchor node at every moment. The sampling area is: centered at the anchor node, and the maximum movement speed of the node is V max A circular area with a radius of ; each anchor node performs such sampling once at this moment; Figure 2 shown.

[0063] If the nodes are uniformly distributed, then the anchor node L at the current moment t The position is at the previous moment L t-1 The maximum movement speed of the node is within the radius of the center, as shown in the following formula:

[0064]

[0065] In the formula, d(l t |l t-1 ) represents the Euclidean distance between two adjacent anchor nodes. In this embodiment, in the prediction stage, only the sampling range of the node is given. In the following filtering stage, specific sampling work is performed.

[0066] In specific implementation, as a preferred embodiment of the present invention, filtering the predicted sensor network nodes includes:

[0067] The coordinates of the anchor nodes within one hop and two hops of the located node will filter out samples that do not meet the filtering conditions. That is, when there are multiple anchor nodes, the sampling areas of the multiple anchor nodes will generate a common sampling area. The generated common sampling area will be appropriately adjusted to obtain better sampling nodes to improve the positioning accuracy of the node. The sampling area of ​​the traditional Monte Carlo positioning algorithm (Monte Carlo, MCL) is as follows Figure 3 shown. Figure 3The shaded parts in the figure are the public areas surrounded by the anchor node areas, which are also the sampling areas of the traditional MCL algorithm. In the traditional MCL algorithm, due to the imperfect sampling area, a large number of samples are required to obtain enough samples, which increases the time complexity of the algorithm and makes the calculation and execution time of the algorithm high. Secondly, the MCL algorithm processes the samples through the filtering link, and the filtering conditions are completely dependent on the one-hop and two-hop anchor node information around the target node. Finally, the same weight is given to each sample obtained in the end, and the sample diversity is seriously insufficient. At the same time, in the cyclic sampling, the sample selected at a certain time may not meet the predetermined sampling requirements, resulting in an increase in positioning error.

[0068] In order to solve the problem of high time and computational cost caused by a large number of samples in the traditional MCL algorithm, the sampling area is expanded in the Monte Carlo localization algorithm (MCB) based on anchor box area sampling. Each sampling can obtain enough samples, thus reducing the number of sampling times. Figure 4 As shown in Figure 2. The sampling area of ​​the MCB algorithm is enlarged compared to the MCL algorithm, and the number of iterations of the MCB algorithm is reduced compared to the MCL algorithm. The comparison of the number of iterations of the MCL algorithm and the MCB algorithm at each moment is shown in Figure 2. Figure 5 As shown in the figure, due to the expansion of the sampling area in the MCB algorithm, the sampled nodes will be more than that of the MCL algorithm. Therefore, when the weighted average is performed at the end, the MCB algorithm will make its positioning accuracy more stable due to the more sample nodes it collects, and the positioning error will also be reduced. The comparison of the positioning errors of the MCL algorithm and the MCB algorithm is shown in the figure. Figure 6 shown.

[0069] In this embodiment, specifically, an anchor box area for sampling is obtained by using the MCB algorithm, and the MCB sampling area is divided into two parts, one part is the part of the original MCL sampling area in the MCB sampling area, and the other part is the other part of the MCB area; in this embodiment, the sampling probability of the original MCL part is 0.8, and the other part of the MCB sampling area adopts a uniform sampling method; the distribution of the sampling area is as follows Figure 7 shown.

[0070] Based on MATLAB, the algorithm is simulated and variable probability sampling is realized through the normal distribution function normrnd. The mean of the X and Y coordinates is the center of the anchor box, that is:

[0071] μ x =(X sambox_min +X sambox_max ) / 2

[0072] μ y=(Y sambox_min +Y sambox_max ) / 2

[0073] The standard deviation is:

[0074]

[0075]

[0076] In the MCB algorithm, although the expanded sampling area improves the positioning accuracy and reduces the number of iterations, the sampled nodes that are not in the original MCL sampling area may exceed the sampling range of the original prediction stage. In other words, the node sampled in the sampling area of ​​the MCB algorithm may be an invalid node, which will also produce a certain error. Therefore, we try to make more sampled nodes fall in the original MCL nodes, and then reduce the probability of regional sampling in the MCB. This makes the two parts use different sampling probabilities, which is the VPS-MCB algorithm. Comparison of positioning errors among the MCL algorithm, the MCB algorithm, and the VPS-MCB algorithm Figure 8 As shown, compared with the traditional MCL algorithm and MCB algorithm, the positioning accuracy of the VPS-MCB algorithm of the present invention is greatly improved.

[0077] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A Monte Carlo localization method based on variable probability sampling of anchor box regions. It is characterized in that include: Initialize sensor network nodes; Make predictions on initialized sensor network nodes; Filter the predicted sensor network nodes, including: The MCB algorithm is used to obtain an anchor box area for sampling. The MCB sampling area is divided into two parts, one part is the part of the original MCL sampling area in the MCB sampling area, and the other part is the other part of the MCB area; the sampling probability of the original MCL part is 0.8, and the other parts of the MCB sampling area are uniformly sampled; The number of sample nodes collected at the initial moment is N. In the filtering stage, if the number of sample nodes obtained at the end does not reach N, an iteration is performed until N nodes are collected; The filtered sample nodes are weighted averaged to obtain the final positioning position.

2. The Monte Carlo positioning method based on variable probability sampling of anchor box regions according to claim 1, It is characterized in that The initialization of the sensor network node comprises: Initialize the positions of anchor nodes and sampled sample nodes.

3. The Monte Carlo positioning method based on variable probability sampling of anchor box regions according to claim 2, It is characterized in that The initialization of the sensor network node also includes: Set the communication radius of the sensor and the maximum movement speed of the anchor node when in motion.

4. The Monte Carlo positioning method based on variable probability sampling of anchor box regions according to claim 1, It is characterized in that The predicting of the initialized sensor network nodes includes: The anchor node starts to move, and sampling needs to be performed at the anchor node at every moment. The sampling area is: centered at the anchor node, and the maximum movement speed of the node is V max A circular area with a radius of ; each anchor node performs such sampling once at this moment; If the nodes are uniformly distributed, then the anchor node l at the current moment t The position is at the previous moment l t-1 The maximum movement speed of the node is within the radius of the center, as shown in the following formula: In the formula, d(l t |l t-1 ) represents the Euclidean distance between two adjacent anchor nodes.

5. The Monte Carlo positioning method based on variable probability sampling of anchor box regions according to claim 1, It is characterized in that The filtering of the predicted sensor network nodes includes: The samples whose anchor node coordinates within one hop and two hops of the located node do not meet the filtering conditions are filtered out.

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