WO2021196656A1 - 一种适用于稀疏锚节点wsn的测距定位方法 - Google Patents

一种适用于稀疏锚节点wsn的测距定位方法 Download PDF

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WO2021196656A1
WO2021196656A1 PCT/CN2020/130076 CN2020130076W WO2021196656A1 WO 2021196656 A1 WO2021196656 A1 WO 2021196656A1 CN 2020130076 W CN2020130076 W CN 2020130076W WO 2021196656 A1 WO2021196656 A1 WO 2021196656A1
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positioning
ranging
nodes
wsn
blind
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PCT/CN2020/130076
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French (fr)
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黄美根
王维平
井田
周鑫
王彦锋
何华
朱一凡
王涛
李小波
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中国人民解放军国防科技大学
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W12/00—Security arrangements; Authentication; Protecting privacy or anonymity
    • H04W12/009—Security arrangements; Authentication; Protecting privacy or anonymity specially adapted for networks, e.g. wireless sensor networks, ad-hoc networks, RFID networks or cloud networks
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S5/00—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations
    • G01S5/02—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations using radio waves
    • G01S5/0278—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations using radio waves involving statistical or probabilistic considerations
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W12/00—Security arrangements; Authentication; Protecting privacy or anonymity
    • H04W12/60—Context-dependent security
    • H04W12/63—Location-dependent; Proximity-dependent
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
    • H04W4/02—Services making use of location information
    • H04W4/023—Services making use of location information using mutual or relative location information between multiple location based services [LBS] targets or of distance thresholds
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
    • H04W4/30—Services specially adapted for particular environments, situations or purposes
    • H04W4/38—Services specially adapted for particular environments, situations or purposes for collecting sensor information
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W64/00—Locating users or terminals or network equipment for network management purposes, e.g. mobility management
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S5/00—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations
    • G01S5/02—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations using radio waves
    • G01S5/14—Determining absolute distances from a plurality of spaced points of known location
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W84/00—Network topologies
    • H04W84/18—Self-organising networks, e.g. ad-hoc networks or sensor networks

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  • the invention belongs to the technical field of WSN positioning, and in particular relates to a ranging and positioning method suitable for a sparse anchor node WSN.
  • WSN has a wide range of applications. Because the cost of sensor nodes is relatively low, and GPS and other positioning modules are usually not equipped, there is a high demand for coordinated and precise positioning between sensor nodes. Generally, WSN positioning methods can be divided into ranging positioning methods and non-ranging positioning methods. The accuracy of non-ranging positioning methods is usually low, and sometimes it is difficult to meet actual application requirements.
  • the general process of the WSN cooperative positioning method is as follows: the anchor node with a known location broadcasts its own location information in the network; then the blind node to be located with an unknown location obtains the distance characteristic information from the anchor node, such as the received information strength indicator, the number of propagation hops, etc. Synchronously obtain anchor node location information; finally, after obtaining multiple anchor node information, the distance characterization information can be converted into ranging information to complete its own positioning.
  • the existing non-range positioning technology has the problem of low positioning accuracy, while the existing ranging positioning technology has the defects of accumulation of positioning errors and high node cost.
  • the present invention provides a ranging and positioning method suitable for sparse anchor node WSN.
  • the present invention solves the positioning problem of the sparse anchor node WSN from the perspective of utilizing the ranging information between all sensor nodes as much as possible.
  • the ranging information between the blind node is the root cause of the high positioning error. Therefore, by incorporating the distance measurement information between blind nodes and constructing a positioning model, the positioning accuracy can be effectively improved.
  • the multi-node positioning is transformed into a single-objective optimization problem, and the positioning is performed synchronously, thus avoiding the problem of error accumulation while only requiring a small number of anchor nodes.
  • WSN As the key supporting technology of the Internet of Everything, WSN has broad application prospects in smart cities, smart homes, smart military camps, smart agriculture, smart logistics and other fields. Sensor node positioning is the basis for smart and smart applications. The sensory information of missing locations in some scenarios is worthless. Solving the WSN positioning problem suitable for the sparse anchor node environment will effectively expand the application scenarios of WSN.
  • the technical solution of the present invention is: a ranging and positioning method suitable for sparse anchor node WSN, and the ranging and positioning method suitable for sparse anchor node WSN includes:
  • the positioning center obtains the anchor node coordinate parameters in the network and the ranging information between all sensor nodes, constructs a positioning model, and generates a blind node positioning matrix;
  • the second step is to generate a fitness function based on the positioning model and construct an adaptive operator
  • the third step is to run the adaptive firework algorithm for iterative optimization, output the best elite individuals, and resolve them into the coordinates of all blind nodes.
  • the positioning model of the distance measurement positioning method suitable for the sparse anchor node WSN incorporates the distance measurement information between blind nodes, and the positioning optimization factor ⁇ is designed to adjust the weight of different types of distance measurement information to be suitable for different types of distance measurement information. WSN environment.
  • the blind node positioning matrix characterization method suitable for the WSN ranging and positioning method of sparse anchor nodes converts the multi-objective optimization problem of multi-node synchronous positioning into a single-objective optimization problem, and finally resolves it back to all blind nodes coordinate.
  • Another object of the present invention is to provide a WSN ranging and positioning simulation platform applying the WSN ranging and positioning method suitable for sparse anchor nodes.
  • the present invention proposes a ranging and positioning method suitable for sparse anchor nodes WSN.
  • the beneficial effect is that: the positioning method proposed in this solution can significantly improve the positioning accuracy of sparse anchor nodes WSN without increasing network cost, while supporting two Two-dimensional plane and three-dimensional space positioning scenes have better applicability; compared with the existing non-ranging positioning method under the sparse anchor node environment, the present invention uses the ranging information between sensing nodes and has higher positioning accuracy; Compared with the existing distance measurement and positioning method under the sparse anchor node environment, the positioning model of the present invention incorporates the distance measurement information between the blind nodes, and at the same time, the blind node positioning matrix representation method is adopted to convert the multi-node positioning problem into a single-object optimization problem. Synchronous positioning effectively avoids the problem of error accumulation.
  • Figure 1 is a schematic flow chart of a ranging and positioning method suitable for sparse anchor nodes WSN proposed by the present invention
  • Figure 2 is a composition diagram of the WSN ranging and positioning simulation platform proposed by the present invention.
  • a ranging and positioning method suitable for sparse anchor node WSN includes:
  • the positioning center obtains the anchor node coordinate parameters in the network and the ranging information between all sensor nodes, constructs a positioning model, and generates a blind node positioning matrix.
  • the positioning center is responsible for gathering sensor node parameters in the network and running positioning algorithms. After obtaining the anchor node coordinate parameters and the ranging information between all sensor nodes in the network, the position coordinates of the sensor nodes are preprocessed.
  • Is a collection of sensor nodes Is the k-th sensor node
  • m c is the number of sensor nodes
  • the anchor node set I-th anchor node m a is the number of anchor nodes
  • blind node set J blind node m b is the number of blind nodes. Mark the positioning area as D.
  • the difference between the distance d r and the geographic distance d g between a given neighboring node is called the positioning error d:
  • the positioning model is established with the goal of minimizing the positioning error:
  • the objective function of the model is designed to minimize the sum of the positioning errors of all nodes.
  • R is the node communication radius, so Restricted node With node Is a pair of neighbor nodes;
  • ⁇ is a positioning optimization factor, which characterizes the sensitivity of the model positioning error to the distance measurement information between nodes, and its value has a greater impact on the positioning accuracy.
  • ⁇ the larger the value of ⁇ , the greater the contribution of the distance measurement information between blind nodes to the model positioning result, while the effect of the distance measurement information between blind nodes and anchor nodes is more obvious.
  • the weight of the ranging information between the anchor node and the blind node must be greater than 0, so ⁇ 1. Therefore, by reasonably adjusting the value of ⁇ , the adaptability of the model to different networks can be enhanced. For example, when the anchor node in the network has a higher signal transmission power than the blind node, the weight of the ranging information between the anchor node and the blind node is increased by appropriately lowering the value of ⁇ .
  • the positioning of multiple blind nodes is a multi-objective optimization problem. Therefore, by integrating the position coordinates of all blind nodes to be obtained into the blind node positioning matrix, the multi-objective optimization problem is transformed into a single-objective optimization problem, and synchronous positioning is realized to avoid error accumulation.
  • evolutionary entities denoted as E
  • E evolutionary entities
  • the evolutionary individual E is a positioning matrix with m b rows and v columns, the row vectors are blind nodes (m b ), and the elements are a dimension (v dimension) of the corresponding node coordinates.
  • a fitness function is generated according to the positioning model, and an adaptive operator is constructed. Combined with the above model, the fitness function of the single-objective optimization problem is given:
  • the positioning error Among the constraints, Indicates blind node And anchor node Should be Neighbor nodes, Express with Must be located in the positioning area D.
  • the given adaptive firework algorithm operator includes adaptive explosion operator, adaptive mutation operator and selection strategy, which together affect the algorithm’s adaptive search performance .
  • the adaptive explosion operator is implemented in a polar coordinate system, where with They are the polar diameter matrix and polar angle matrix.
  • the polar coordinate system can make the explosion sparks better randomly distributed in the circular area.
  • n is the size of the firework population
  • ⁇ is the minimum amount of the machine to avoid possible division by zero operations
  • ⁇ is an adaptive search factor designed to improve the search performance of the algorithm:
  • ⁇ represents the evolution efficiency of the adaptive firework algorithm
  • ⁇ 1; ⁇ max and ⁇ min are the maximum and minimum values of ⁇ respectively.
  • the firework can produce explosion sparks within the explosion radius, and the number of explosion sparks produced by the rth firework E r
  • the number of sparks should be an integer, and at the same time, prevent fireworks with too large fitness value from producing too few sparks or fireworks with too small fitness value to produce too many sparks, which will explode the number of sparks Amended to That is, round the number of explosion sparks and set the lower threshold:
  • h is the correction parameter
  • ceil( ⁇ ) and round( ⁇ ) are the rounding up and rounding functions respectively.
  • the operation of adaptively generating mutation sparks for a given firework is called an adaptive mutation operator, denoted as ⁇ :
  • the adaptive mutation operator uses a rectangular coordinate system for displacement, and performs random mutation based on Gaussian distribution.
  • the Gaussian mutation matrix is e ⁇ N(1,1), A′ E is the radius of variation, calculated according to the fitness function:
  • the firework can generate mutated sparks within the radius of mutation, and the number of mutated sparks generated by the rth firework E r
  • ⁇ [0,1] is the coefficient of variation sparks, which together with the adaptive search factor ⁇ optimizes the number of variation sparks.
  • ⁇ only acts on the number of explosion sparks, and does not affect the explosion spark radius. Similar to explosion sparks, the number of variant sparks is corrected to
  • the third step is to run the adaptive firework algorithm for iterative optimization, and output the best elite individuals, which are resolved into the coordinates of all blind nodes.
  • the fireworks After the fireworks produce explosive sparks and mutation sparks, it is necessary to select excellent evolutionary individuals from them to pass on to the next generation, so as to continuously search for the best.
  • the element corresponding to f min is selected from the set of evolutionary individuals K to become the next generation of fireworks. Then, use the roulette strategy to select n-1 from the remaining elements of K, and form the next generation of fireworks population together with the elite evolutionary individuals
  • the probability of the roulette strategy is determined by the degree of crowding of the elements, that is, the denser the elements, the lower the probability of being selected.
  • the degree of crowding is derived from the location of the element, giving the probability p(E r ) of the evolutionary individual E r being selected:
  • the termination condition of the adaptive firework algorithm is set as the algorithm iteration reaches the specified number or the individual fitness value of the elite evolution meets the same for g consecutive times.
  • the elite evolution individual positioning matrix output by the firework algorithm is parsed into the position coordinates of each blind node by row, which is the optimized positioning result.
  • Another object of the present invention is to provide a WSN ranging and positioning simulation platform applying the WSN ranging and positioning method suitable for sparse anchor nodes.
  • the implementation process of the WSN ranging and positioning simulation platform in the sparse anchor node environment provided by the embodiment of the present invention is as follows:
  • the data import module is responsible for importing the collected WSN positioning data from the outside, and supports the import of RSSI and TOA ranging data.
  • the import of distance data is optional and is only used to evaluate the data positioning error.
  • the data parameter configuration is used to configure the attributes of the imported positioning data, such as the ranging information type, anchor node information, node position coordinate dimension, etc.;
  • the model parameter configuration is responsible for setting the parameters of the positioning model, such as Positioning optimization factor, node communication radius, etc.;
  • algorithm parameter configuration is responsible for setting the parameters of the firework algorithm operation process, such as the initial firework population number, maximum iteration number, variation spark coefficient, adaptive search factor boundary value, etc.; result parameter configuration Mainly configure the positioning result output information, including positioning time, number of iterations, positioning accuracy, etc.
  • the algorithm operation module is responsible for controlling the operation of the algorithm.
  • the initial control of the population is mainly responsible for completing the population initialization;
  • the operation control of the operator is responsible for generating explosive sparks and mutation sparks, and processing sparks that cross the boundary;
  • population evolution control is responsible for iterative optimization of fireworks Control, and terminate the algorithm to output the best elite individuals when the conditions are met.
  • the positioning coordinate analysis is responsible for analyzing the optimal individual positioning matrix into the position coordinates of each blind node by row; positioning result evaluation statistics algorithm iteration times, algorithm running time, and positioning error calculation based on imported distance data, etc. ; Positioning error output The positioning result is output by means of average positioning error curve, node positioning error graph, etc.
  • the positioning method proposed by the present invention can significantly improve the positioning accuracy of the sparse anchor node WSN without increasing the network cost, while supporting two-dimensional and three-dimensional positioning, and has better applicability;
  • the present invention utilizes the ranging information between sensor nodes and has higher positioning accuracy;
  • the present invention locates The model incorporates the distance measurement information between blind nodes, and at the same time, the blind node positioning matrix representation method is adopted to convert the multi-node positioning problem into a single-objective optimization problem, which realizes synchronous positioning and effectively avoids the problem of error accumulation.

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Abstract

本发明公开一种适用于稀疏锚节点WSN的测距定位方法，所述适用于稀疏锚节点WSN的测距定位方法包括：定位中心获取网络中锚节点坐标参数及所有传感节点间的测距信息，构造定位模型，并生成盲节点定位矩阵；依据定位模型生成适应度函数，构建自适应算子；运行自适应烟花算法进行迭代优化，输出最优精英个体，解析为所有盲节点坐标。本发明中定位模型通过利用所有传感节点间的测距信息，特别是纳入盲节点间测距信息，显著提升了传感节点定位精度，并减少了对锚节点密度的依赖，此外可同时支持二维平面和三维空间定位，使本定位方法可以很好地适用于稀疏锚节点的WSN。

Description

一种适用于稀疏锚节点WSN的测距定位方法
本申请要求于2020年03月31日提交中国专利局、申请号为202010240155.8、发明名称为“一种适用于稀疏锚节点WSN的测距定位方法”的中国专利申请的优先权，其全部内容通过引用结合在本申请中。
技术领域
本发明属于WSN定位技术领域，尤其涉及一种适用于稀疏锚节点WSN的测距定位方法。
背景技术
WSN作为物联网中感知层的关键支撑技术，应用十分广泛，由于传感节点成本较为低廉，通常不配备GPS等定位模块，因此对传感节点间协作精确定位的需求较高。通常，WSN定位方法可分为测距定位方法与非测距定位方法，缘于非测距定位方法精度通常较低，有时难以满足实际应用需求。
WSN协作定位方法的一般流程如下：位置已知的锚节点在网络中广播自身位置信息；然后位置未知的待定位盲节点获得与锚节点间距离表征信息如接收信息强度指示、传播跳数等，同步获得锚节点位置信息；最后在获取多个锚节点信息后，通过将距离表征信息转换为测距信息即可完成自身定位。
但是，由于传感节点通信半径较短，每个盲节点通信范围内不一定会存在三个(二维平面定位)或四个(三维空间定位)及以上的锚节点，即实际应用时WSN经常面临稀疏锚节点环境。通常，非测距定位方法是稀疏锚节点环境下定位的首选，如DV-Hop、APIT等定位算法。但是，缘于非测距定位方法中距离表征信息误差较差，导致该类定位方法存在定位精度不高的缺陷。针对此，适用于稀疏锚节点环境的测距定位方法逐渐成为学者研究的重点。
因此，有学者研究提出将已完成定位的盲节点升级为锚节点以应对锚节点稀疏问题，并参与定位后续盲节点，但这种方式存在误差累积问题，即盲节点定位误差会在升级为锚节点后进一步传导至后续盲节点，从而导致定位误差逐渐增大。也有学者提出通过调整锚节点发射功率来覆盖更多 盲节点，尽可能保证盲节点能同时收到三个锚节点位置信息，但这种可调发射功率的无线通信模块会显著增加锚节点成本，制约了方案的适应性。
综上所述，针对稀疏锚节点的WSN定位需求，现有非测距定位技术存在定位精度不高的问题，现有测距定位技术则存在定位误差累积和节点成本过高的缺陷。
发明内容
针对现有技术存在的问题，本发明提供了一种适用于稀疏锚节点WSN的测距定位方法。
解决上述技术问题的思路：
本发明从尽可能利用所有传感节点间测距信息的角度解决稀疏锚节点WSN的定位问题。通过深入分析定位误差来源，发现忽略盲节点与盲节点间测距信息(以下简称为盲节点间测距信息)是导致定位误差居高不下的根本原因。因此，通过纳入盲节点间测距信息，构建定位模型，可以有效提升定位精度。同时，通过将所有待求盲节点坐标表征为盲节点定位矩阵，将多节点定位转换为单目标优化问题，同步进行定位，因而在仅需要少量锚节点的同时避免了误差累积问题。
解决上述技术问题的意义：
WSN作为万物互联世界的关键支撑技术，在智慧城市、智能家居、智慧军营、智慧农业、智能物流等领域都有着广泛的应用前景，而传感节点定位是实现智能、智慧应用的基础，甚至在部分场景下缺失位置的传感信息毫无价值。解决适用于稀疏锚节点环境的WSN定位问题将有效扩大WSN的应用场景。
为达到上述目的，本发明的技术方案为：一种适用于稀疏锚节点WSN的测距定位方法，所述适用于稀疏锚节点WSN的测距定位方法包括：
第一步，定位中心获取网络中锚节点坐标参数及所有传感节点间的测距信息，构造定位模型，并生成盲节点定位矩阵；
第二步，依据定位模型生成适应度函数，构建自适应算子；
第三步，运行自适应烟花算法进行迭代优化，输出最优精英个体，解 析为所有盲节点坐标。
优选的，所述适用于稀疏锚节点WSN的测距定位方法的定位模型纳入了盲节点间测距信息，并通过设计定位优化因子β用以调整不同类型测距信息的权重，以适用于不同WSN环境。
优选的，所述适用于稀疏锚节点WSN的测距定位方法的盲节点定位矩阵表征方法，将多节点同步定位的多目标优化问题转换为单目标优化问题，并最后反向解析为所有盲节点坐标。
本发明的另一目的在于提供一种应用所述适用于稀疏锚节点WSN的测距定位方法的WSN测距定位仿真平台。
本发明提出的一种适用于稀疏锚节点WSN的测距定位方法，有益效果在于：本方案提出的定位方法可以在不增加网络成本的基础上显著提高稀疏锚节点WSN的定位精度，同时支持二维平面和三维空间定位场景，具有较好的适用性；与现有稀疏锚节点环境下非测距定位方法相比，本发明利用了传感节点间测距信息，具有更高的定位精度；与现有稀疏锚节点环境下测距定位方法相比，本发明定位模型纳入了盲节点间测距信息，同时采取盲节点定位矩阵表征方法将多节点定位问题转换为单目标优化问题，实现了同步定位，有效避免了误差累积问题。
说明书附图
下面结合附图对本发明作进一步说明：
图1是本发明提出的一种适用于稀疏锚节点WSN的测距定位方法的流程示意图；
图2是本发明提出的WSN测距定位仿真平台的组成图。
具体实施方式
下面将结合本发明实施例中的附图，对本发明实施例中的技术方案进行清楚、完整地描述，显然，所描述的实施例仅仅是本发明一部分实施例，而不是全部的实施例。
参照图1：一种适用于稀疏锚节点WSN的测距定位方法，所述适用于稀疏锚节点WSN的测距定位方法包括：
第一步，定位中心获取网络中锚节点坐标参数及所有传感节点间的测距信息，构造定位模型，并生成盲节点定位矩阵。
定位中心负责汇聚网络中传感节点参数，并运行定位算法。在获取网络中锚节点坐标参数及所有传感节点间的测距信息后，对传感节点位置坐标进行预处理。记
Figure PCTCN2020130076-appb-000001
为传感节点集合，
Figure PCTCN2020130076-appb-000002
为第k个传感节点，m c为传感节点数量，v∈{2，3}为节点坐标维度(v＝2代表二维平面定位，v＝3代表三维空间定位)。类似地，锚节点集合
Figure PCTCN2020130076-appb-000003
第i个锚节点
Figure PCTCN2020130076-appb-000004
m a为锚节点数量；盲节点集合
Figure PCTCN2020130076-appb-000005
第j个盲节点
Figure PCTCN2020130076-appb-000006
m b为盲节点数量。记定位区域为D。
给定邻居节点间测距距离d r与地理距离d g之差称为定位误差d：
Figure PCTCN2020130076-appb-000007
通过充分利用盲节点间测距信息，以最小化定位误差为目标建立定位模型：
Figure PCTCN2020130076-appb-000008
模型目标函数设计为所有节点定位误差总和最小。约束条件中，R为节点通信半径，因此
Figure PCTCN2020130076-appb-000009
限定节点
Figure PCTCN2020130076-appb-000010
与节点
Figure PCTCN2020130076-appb-000011
为邻居节点对；β为定位优化因子，表征模型定位误差对节点间测距信息的敏感程度，其取值对定位精度影响较大。通常，β取值越大，盲节点间测距信息对模型定位结果贡献越大，反之盲节点与锚节点间测距信息作用更为明显。特别地，β＝0表示忽略盲节点间测距信息。同时，考虑到定位过程必须要有锚节点参与，即锚节点与盲节点间测距信息的权重必须大于0，故β＜1。因此， 通过合理调整β取值可以增强模型对不同网络的适应能力。例如，当网络中锚节点相比盲节点具有更高的信号发送功率时，通过适当调低β取值以提升锚节点与盲节点间测距信息的权重。
依据定位数学模型，多盲节点定位属于多目标优化问题。因此，通过将所有盲节点待求位置坐标整合为盲节点定位矩阵，从而将多目标优化问题转变为单目标优化问题，并实现同步定位避免误差累积。
给定烟花、爆炸火花和变异火花统称为进化个体，记为E，采用盲节点定位矩阵表征，即
Figure PCTCN2020130076-appb-000012
显然，进化个体E为m b行v列的定位矩阵，行向量为盲节点(m b个)，而元素则为相应节点坐标的一个维度(v维)。
第二步，依据定位模型生成适应度函数，构建自适应算子。结合上述模型，给出单目标优化问题的适应度函数：
Figure PCTCN2020130076-appb-000013
其中，定位误差
Figure PCTCN2020130076-appb-000014
Figure PCTCN2020130076-appb-000015
约束条件中，
Figure PCTCN2020130076-appb-000016
表示盲节点
Figure PCTCN2020130076-appb-000017
和锚节点
Figure PCTCN2020130076-appb-000018
均应为
Figure PCTCN2020130076-appb-000019
的邻居节点，
Figure PCTCN2020130076-appb-000020
表示
Figure PCTCN2020130076-appb-000021
和
Figure PCTCN2020130076-appb-000022
需位于定位区域D内。
选用烟花算法求解上述单目标优化问题，并改进其适应性，给定自适应烟花算法算子包括自适应爆炸算子、自适应变异算子和选择策略，三者共同影响算法的自适应搜索性能。
烟花自适应产生爆炸火花的操作称为自适应爆炸算子，记为Φ:
Figure PCTCN2020130076-appb-000023
自适应爆炸算子采用极坐标系进行实现，其中
Figure PCTCN2020130076-appb-000024
和
Figure PCTCN2020130076-appb-000025
分别为极径矩阵和极角矩阵。极坐标系可以使爆炸火花更好地随机分布 于圆形区域。在极角矩阵
Figure PCTCN2020130076-appb-000026
中，第j个极角向量
Figure PCTCN2020130076-appb-000027
其中
Figure PCTCN2020130076-appb-000028
在极径矩阵中，随机矩阵
Figure PCTCN2020130076-appb-000029
λ＝rand(0,1)；
Figure PCTCN2020130076-appb-000030
为爆炸半径，由适应度函数计算得出：
Figure PCTCN2020130076-appb-000031
其中，
Figure PCTCN2020130076-appb-000032
为烟花种群，n为烟花种群大小；ε为机器最小量，用以避免可能出现的除零操作；ω为改善算法搜索性能而设计的自适应搜索因子：
Figure PCTCN2020130076-appb-000033
其中，γ代表自适应烟花算法进化效率，且有γ≥1；ω max和ω min分别为ω的最大值和最小值，显然有ω∈[ω min,ω max]，ω∝γ -1。因此，当算法进化较快时，ω可以有效加速全局搜索能力；而当算法进化较慢时，通常表示算法进入精细寻优阶段，因而ω取值变大，使爆炸半径
Figure PCTCN2020130076-appb-000034
变小，从而进一步增强局部搜索性能。
依据自适应爆炸算子，烟花即可在爆炸半径范围内产生爆炸火花，第r个烟花E r产生爆炸火花数目
Figure PCTCN2020130076-appb-000035
Figure PCTCN2020130076-appb-000036
其中，
Figure PCTCN2020130076-appb-000037
考虑到火花数目应为整数，同时防止适应度值过大的烟花产生火花数过少或适应度值过小的烟花产生火花数过多，将爆炸火花数目
Figure PCTCN2020130076-appb-000038
修正为
Figure PCTCN2020130076-appb-000039
即对爆炸火花数目取整并设定下限阈值：
Figure PCTCN2020130076-appb-000040
其中，h为修正参数，ceil(·)和round(·)分别为向上取整和四舍五入取整函数。
给定烟花自适应产生变异火花的操作称为自适应变异算子，记为Γ:
Figure PCTCN2020130076-appb-000041
自适应变异算子采用直角坐标系进行位移，并基于高斯分布进行随机变异。其中，高斯变异矩阵为
Figure PCTCN2020130076-appb-000042
e～N(1,1)，A′ E为变异半径，依据适应度函数计算得出：
Figure PCTCN2020130076-appb-000043
依据自适应变异算子，烟花即可在变异半径范围内产生变异火花，第r个烟花E r产生变异火花数目
Figure PCTCN2020130076-appb-000044
Figure PCTCN2020130076-appb-000045
其中，δ∈[0,1]为变异火花系数，其与自适应搜索因子ω共同优化变异火花数量。此外，为进一步防止算法在优化搜索性能时陷入局部最优，ω仅作用于爆炸火花数目，而不影响爆炸火花半径。类似于爆炸火花，将变异火花数目修正为
Figure PCTCN2020130076-appb-000046
需要说明的是，自适应爆炸算子和自适应变异算子产生的进化个体可能超出可行域D，违背模型约束条件。因此，当进化个体某一坐标维度
Figure PCTCN2020130076-appb-000047
越界时，通过如下坐标映射规则将该越界坐标重新映射至D内：
Figure PCTCN2020130076-appb-000048
其中，
Figure PCTCN2020130076-appb-000049
和
Figure PCTCN2020130076-appb-000050
分别为D在该维度上的最大值和最小值，“％”为取模运算符。
第三步，运行自适应烟花算法进行迭代优化，输出最优精英个体，解析为所有盲节点坐标。
烟花产生爆炸火花和变异火花后，需要从中选择优秀的进化个体传递至下一代，从而不断寻优。首先，依据精英保留策略从进化个体集合K中 选择f min对应的元素成为下一代烟花。然后，采用轮盘赌策略从K的剩余元素中选择n-1个，与精英进化个体共同构成下一代烟花种群
Figure PCTCN2020130076-appb-000051
为增强进化效果，轮盘赌策略概率由元素拥挤程度决定，即越密集的元素被选择概率越低。拥挤程度由元素所在位置得出，给出进化个体E r被选择概率p(E r)：
Figure PCTCN2020130076-appb-000052
考虑到能量有效性及定位时效性，自适应烟花算法终止条件设定为算法迭代达到指定次数或精英进化个体适应度值满足连续g次相同。
定位结果输出时，将烟花算法输出的精英进化个体定位矩阵按行解析为各个盲节点的位置坐标，即优化完成的定位结果。
实施例
本发明的另一目的在于提供一种应用所述适用于稀疏锚节点WSN的测距定位方法的WSN测距定位仿真平台。
如图2所示，本发明实施例提供的稀疏锚节点环境下WSN测距定位仿真平台实施过程为：
S1：数据导入模块负责从外部导入已经采集的WSN定位相关数据，支持导入RSSI和TOA两种测距数据，距离数据导入为可选项，仅用于评估数据定位误差。
S2：参数配置模块中，数据参数配置用于对导入定位数据的属性进行配置，如测距信息类型、锚节点信息、节点位置坐标维度等；模型参数配置负责对定位模型的参数进行设置，如定位优化因子、节点通信半径等；算法参数配置负责对烟花算法运行过程上的参数进行设定，如初始烟花种群数、最大迭代次数、变异火花系数、自适应搜索因子边界值等；结果参数配置主要对定位结果输出信息进行配置，包括定位时间、迭代次数、定位精度等。
S3：算法运行模块负责对算法运行进行控制，种群初始控制主要负 责完成种群初始化；算子运行控制负责产生爆炸火花和变异火花，并对火花越界进行处理；种群进化控制负责对烟花迭代寻优进行控制，并在满足条件时终止算法输出最优精英个体。
S4：结果输出模块中，定位坐标解析负责将最优精英个体定位矩阵按行解析为各个盲节点的位置坐标；定位结果评估统计算法迭代次数、算法运行时间以及依据导入的距离数据计算定位误差等；定位误差输出通过平均定位误差曲线、节点定位误差图等方式输出定位结果。
综上所述：本发明提出的定位方法可以在不增加网络成本的基础上显著提高稀疏锚节点WSN的定位精度，同时支持二维平面与三维空间定位，具有较好的适用性；与现有稀疏锚节点环境下非测距定位方法相比，本发明利用了传感节点间测距信息，具有更高的定位精度；与现有稀疏锚节点环境下测距定位方法相比，本发明定位模型纳入了盲节点间测距信息，同时采取盲节点定位矩阵表征方法将多节点定位问题转换为单目标优化问题，实现了同步定位，有效避免了误差累积问题。
提供以上实施例仅仅是为了描述本发明的目的，而并非要限制本发明的范围。本发明的范围由所附权利要求限定。不脱离本发明的精神和原理而做出的各种等同替换和修改，均应涵盖在本发明的范围之内。

Claims (4)

  1. 一种适用于稀疏锚节点WSN的测距定位方法，其特征在于，所述适用于稀疏锚节点WSN的测距定位方法包括：
    第一步：定位中心获取网络中锚节点坐标参数及所有传感节点间的测距信息，构造定位模型，并生成盲节点定位矩阵；
    第二步：依据定位模型生成适应度函数，构建自适应算子；
    第三步：运行自适应烟花算法进行迭代优化，输出最优精英个体，解析为所有盲节点坐标。
  2. 如权利要求1所述的适用于稀疏锚节点WSN的测距定位方法，其特征在于，所述适用于稀疏锚节点WSN的测距定位方法的定位模型纳入了盲节点间测距信息，并通过设计定位优化因子β用以调整不同类型测距信息的权重，以适用于不同WSN环境。
  3. 如权利要求1所述的适用于稀疏锚节点WSN的测距定位方法，其特征在于，所述适用于稀疏锚节点WSN的测距定位方法的盲节点定位矩阵表征方法，将多节点同步定位的多目标优化问题转换为单目标优化问题，并最后反向解析为所有盲节点坐标。
  4. 一种应用权利要求1～3任意一项所述适用于稀疏锚节点WSN的测距定位方法的WSN测距定位仿真平台。
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Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114173281A (zh) * 2021-12-24 2022-03-11 长安大学 室内nlos环境下基于toa的定位系统信标节点的优化布局方法
CN115766779A (zh) * 2022-11-03 2023-03-07 北京邮电大学 物联网中目标节点高精度定位方法、系统、设备及介质
CN116930864A (zh) * 2023-06-27 2023-10-24 中铁第四勘察设计院集团有限公司 一种室内外无缝统一基准构建方法及装置

Families Citing this family (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111432368B (zh) * 2020-03-31 2022-10-21 中国人民解放军国防科技大学 一种适用于稀疏锚节点wsn的测距定位方法
CN113038475B (zh) * 2021-03-15 2023-01-20 中山大学 基于稀疏项恢复的恶意锚节点检测和目标节点定位方法
CN113923590B (zh) * 2021-09-24 2023-07-21 西北工业大学 一种锚节点位置不确定情况下的toa定位方法
CN113959448B (zh) * 2021-10-26 2023-08-29 江苏海洋大学 一种基于改进樽海鞘群算法的水下地形辅助导航方法

Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN108668358A (zh) * 2018-05-09 2018-10-16 宁波大学 一种应用于无线传感网络的基于到达时间的协作定位方法
CN108924734A (zh) * 2018-05-24 2018-11-30 北京理工大学 一种三维传感器节点定位方法及系统
US20190069263A1 (en) * 2017-08-23 2019-02-28 Locix Inc. Systems and methods for adaptively selecting distance estimates for localization of nodes based on error metric information
CN111432368A (zh) * 2020-03-31 2020-07-17 中国人民解放军国防科技大学 一种适用于稀疏锚节点wsn的测距定位方法

Family Cites Families (10)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN104066179B (zh) * 2014-07-10 2017-09-22 浙江银江研究院有限公司 一种改进的自适应迭代ukf的wsn节点定位方法
CN105277917B (zh) * 2015-10-30 2017-11-03 湖南大学 一种基于反馈机制的动态指纹库室内定位方法
CN105554873B (zh) * 2015-11-10 2019-03-15 胡燕祝 一种基于pso-ga-rbf-hop的无线传感器网络定位算法
CN105682026A (zh) * 2016-01-08 2016-06-15 南昌大学 基于跳数阈值优化平均跳距的改进DV-Hop定位方法
CN106211079A (zh) * 2016-09-23 2016-12-07 武汉创驰蓝天信息科技有限公司 基于 iBeacons 节点的RSSI 测距的室内定位方法及系统
CN107426817B (zh) * 2017-08-28 2020-09-22 西安电子科技大学 距离修正混沌粒子群多维标度无线传感器网络定位方法
CN107734479A (zh) * 2017-09-11 2018-02-23 广东广业开元科技有限公司 一种基于无线传感技术的消防人员定位方法、系统和装置
CN108600959A (zh) * 2018-01-03 2018-09-28 中山大学 一种基于改进布谷鸟搜索算法的wsn节点定位方法
TWI671740B (zh) * 2018-06-07 2019-09-11 光禾感知科技股份有限公司 基於地磁訊號結合電腦視覺的室內定位系統及方法
CN110856252B (zh) * 2019-11-11 2020-10-16 山东大学 一种考虑测量误差的二维平面传感器定位方法及系统

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20190069263A1 (en) * 2017-08-23 2019-02-28 Locix Inc. Systems and methods for adaptively selecting distance estimates for localization of nodes based on error metric information
CN108668358A (zh) * 2018-05-09 2018-10-16 宁波大学 一种应用于无线传感网络的基于到达时间的协作定位方法
CN108924734A (zh) * 2018-05-24 2018-11-30 北京理工大学 一种三维传感器节点定位方法及系统
CN111432368A (zh) * 2020-03-31 2020-07-17 中国人民解放军国防科技大学 一种适用于稀疏锚节点wsn的测距定位方法

Non-Patent Citations (3)

* Cited by examiner, † Cited by third party
Title
MA YAN;ZHAO HAN-DONG;ZHANG WEI;CHEN BAI-YU;SHAO XIAN-FENG;ZHANG XIAO-DONG;ZHANG YUE-YUE: "Task Allocation for Multiple UAVs Based on Adaptive Fireworks Algorithm", ELECTRONICS OPTICS & CONTROL, vol. 25, no. 1, 31 January 2018 (2018-01-31), pages 37 - 43, XP009531057, ISSN: 1671-637X *
TAN, YING ET AL.: "Recent Advances in Fireworks Algorithm", CAAI TRANSACTIONS ON INTELLIGENT SYSTEMS, vol. 9, no. 5, 31 October 2014 (2014-10-31) *
YE WENWEN; WEN JIECHANG: "Adaptive Fireworks Algorithm Based on Simulated Annealing", 2017 13TH INTERNATIONAL CONFERENCE ON COMPUTATIONAL INTELLIGENCE AND SECURITY (CIS), IEEE, 15 December 2017 (2017-12-15), pages 371 - 375, XP033318451, DOI: 10.1109/CIS.2017.00087 *

Cited By (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114173281A (zh) * 2021-12-24 2022-03-11 长安大学 室内nlos环境下基于toa的定位系统信标节点的优化布局方法
CN114173281B (zh) * 2021-12-24 2023-10-27 长安大学 室内nlos环境下基于toa的定位系统信标节点的优化布局方法
CN115766779A (zh) * 2022-11-03 2023-03-07 北京邮电大学 物联网中目标节点高精度定位方法、系统、设备及介质
CN115766779B (zh) * 2022-11-03 2023-07-07 北京邮电大学 物联网中目标节点高精度定位方法、系统、设备及介质
CN116930864A (zh) * 2023-06-27 2023-10-24 中铁第四勘察设计院集团有限公司 一种室内外无缝统一基准构建方法及装置
CN116930864B (zh) * 2023-06-27 2024-02-23 中铁第四勘察设计院集团有限公司 一种室内外无缝统一基准构建方法及装置

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