WO2020210923A1 - 一种基于uwb和蓝牙技术的室内定位融合方法 - Google Patents

一种基于uwb和蓝牙技术的室内定位融合方法 Download PDF

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WO2020210923A1
WO2020210923A1 PCT/CN2019/000151 CN2019000151W WO2020210923A1 WO 2020210923 A1 WO2020210923 A1 WO 2020210923A1 CN 2019000151 W CN2019000151 W CN 2019000151W WO 2020210923 A1 WO2020210923 A1 WO 2020210923A1
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uwb
base station
bluetooth
receiver
positioning
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PCT/CN2019/000151
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王博
赵冲
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北京理工大学
王博
赵冲
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    • 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
    • G01S5/0009—Transmission of position information to remote stations
    • 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
    • G01S5/0257—Hybrid positioning
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B17/00—Monitoring; Testing
    • H04B17/30—Monitoring; Testing of propagation channels
    • H04B17/309—Measuring or estimating channel quality parameters
    • H04B17/318—Received signal strength
    • 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
    • H04W64/006—Locating users or terminals or network equipment for network management purposes, e.g. mobility management with additional information processing, e.g. for direction or speed determination

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  • the invention belongs to the technical field of navigation, guidance and control, and specifically relates to an indoor positioning fusion method based on UWB and Bluetooth technology.
  • LBS Location-aware computing and location-based services
  • GPS Global Positioning System
  • WSNs Wireless Sensor Networks
  • LBS location-aware computing and location-based services
  • the emergence of positioning technology has greatly facilitated people’s lives, and its influence has extended to include military, technology, and all aspects of people’s ordinary lives, and the help of indoor positioning for people’s daily life also makes it more and more popular in various fields.
  • the location-based service information push in the supermarket helps people quickly locate a store or even a commodity in a large supermarket; another example is when emergency events such as fire, earthquake, hostage hijacking, bomb threats, etc. occur, only accurate indoor positioning can be performed Optimal rescue route planning and fast and safe rescue work.
  • GPS is currently the most widely used and most successful positioning technology.
  • the current mainstream GPS applications are suitable for outdoor places, and the propagation environment in indoor environments is complicated, and GPS signals are easily absorbed by buildings, metal coverings, etc., and the existence of obstructions makes wireless signals refract, reflect and energy Attenuation and other phenomena, coupled with its high cost, have seriously affected the development of GPS applications in indoor positioning. Therefore, in order to meet personal and commercial needs, various indoor positioning technologies have emerged.
  • Existing typical indoor positioning technologies include Wi-Fi technology, Bluetooth technology, infrared technology, ultra-wideband (UWB) technology and so on.
  • the existing indoor positioning algorithms are divided into range-based and rang-free algorithms. Because the distance-based positioning method is convenient It has been widely used in engineering practice. Typical ranging technologies include signal arrival time TOA (time of arrival), signal arrival time difference TDOA (time difference of arrival), signal arrival angle AOA (angle of arrival), signal strength RSSI (received signal strength indication), etc.
  • TOA time of arrival
  • TDOA time difference of arrival
  • AOA angle of arrival
  • signal strength RSSI received signal strength indication
  • UWB technology based on TOF (time of flight) measurement can achieve high-precision positioning in an indoor environment, while RSSI-based Bluetooth technology has a high coverage rate due to its low price.
  • the existing single indoor positioning solutions are mainly based on a single positioning technology. In order to achieve higher precision positioning, higher economic costs are required. As a result, the existing technologies are not well applied to practical applications. .
  • the purpose of the present invention is to provide an indoor positioning fusion method based on UWB and Bluetooth technology, which can improve positioning accuracy and reduce costs at the same time.
  • An indoor positioning fusion method that uses one UWB base station and at least two Bluetooth base stations to achieve indoor positioning, including the following steps:
  • Step 1 Express the UWB ranging model by the following formula:
  • p r and p b are the positions of the receiver and the UWB base station
  • r TOF is the distance between the UWB base station and the receiver
  • r D ⁇ N(0, ⁇ 2 ) are the measurement errors caused by clock errors. calibration obtained; respectively receive and transmit time signal t r and t b is the receiver, c is the speed of light;
  • RSSI(d) and P 0 are the signal receiving strength between the receiver and the base station in the Bluetooth ranging mode at the distance d and the reference distance d 0 respectively, n is the signal path loss factor, ⁇ (0, ⁇ 2 ) It is the white noise interference caused by the shadow fading effect, and ⁇ represents the mean square error of white noise;
  • d i is the distance between the receiver position (x, y) and the i-th Bluetooth base station position (x i , y i ), Is the difference between the signal strength measurement value RSSI i of the i-th base station signal received by the receiver and the theoretical value, that is, the measurement error;
  • Step 2 According to the UWB model in Step 1, the limited range after UWB ranging is obtained:
  • (x a , y a ) represents the UWB base station coordinates, and r Dmax represents the maximum value of clock error;
  • Step 3 Calculate the probability distribution of the measurement signal of each Bluetooth base station, and define the evaluation function as f e , then:
  • equation (7) After substituting equation (4) into the following equation (7), solve equation (7) within the limited range of equation (5), so that f c in equation (7) takes the minimum value, then the estimated position of the receiver ( x, y), to achieve positioning;
  • the method for solving the formula (7) includes the Kuentuck condition solving method and the Lagrangian multiplier method.
  • the PSO algorithm is used to solve the equation (7).
  • the indoor positioning fusion method based on UWB and Bluetooth technology proposed by the present invention combines the advantages of two indoor positioning schemes, utilizes the feature of small UWB ranging error, limits the possible area of estimated position, and reduces the solution range;
  • the traditional method of estimating the position based on the distance measurement directly uses the RSSI value of the Bluetooth signal to obtain the probability distribution in the solution area through Bayesian inference to solve the position, which avoids the introduction of errors when the RSSI signal is calculated into the distance and improves the positioning Accuracy
  • the present invention uses a particle swarm optimization algorithm to solve the extreme value problem under inequality constraints. Compared with the direct extreme value solution method, the local optimal solution can be obtained when the RSSI measurement value error is large and the minimum value deviates from the true solution. Have better actual use effect.
  • Fig. 1 is a flowchart of the indoor positioning fusion algorithm based on UWB and Bluetooth technology of the present invention.
  • Figure 2 is a comparison diagram of the positioning results of the positioning method of the present invention and several known positioning methods.
  • the indoor positioning fusion method proposed by the present invention is composed of hardware devices of at least one UWB base station and two Bluetooth base stations.
  • the fusion positioning is carried out according to the characteristics of the positioning accuracy and economic cost of the two technologies.
  • UWB the indoor positioning fusion algorithm of Bluetooth technology is used to solve the estimated position and improve the positioning accuracy.
  • the basic principle of the fusion algorithm is to obtain a UWB ranging model based on the error analysis of UWB ranging, and establish a mathematical model of the receiving signal strength (RSSI) of the receiving terminal and the distance between the base station and the receiving terminal according to the attenuation characteristics of the wireless sensor signal in the room.
  • RSSI receiving signal strength
  • the estimated position limit range from the UWB measurement distance and UWB base station position, and obtain the probability distribution function of the estimated position point according to the measured RSSI value and error distribution according to Bayesian inference, and use the particle swarm optimization algorithm to obtain the global minimum within the limit.
  • the optimal solution is output as the estimated position.
  • Step 1 Since the error of UWB ranging is mainly caused by the clock error between UWB base station and receiver, the UWB ranging model can be expressed by the following formula:
  • p r and p b are the positions of the UWB base station and the receiver respectively
  • r TOF is the distance between the UWB base station and the receiver measured by TOF in the UWB ranging method
  • r D ⁇ N(0, ⁇ 2 ) is a clock error caused by measurement error, obtained through prior calibration
  • t r and the time t b respectively receive and transmit signals to a receiver
  • c is the speed of light
  • RSSI(d) (in dBm) and P 0 are the signal receiving strength between the receiver and the Bluetooth base station at a distance d and a reference distance d 0 , respectively, n is the path loss factor of the signal, ⁇ (0, ⁇ 2 ) Is the white noise interference caused by the shadow fading effect, in which the size of the white noise mean square error ⁇ depends on the interference of the propagation path. Therefore, after measuring the relevant parameters of the model in the environment, the received signal strength RSSI(d) and the estimated position, the probability that the position is the true position can be obtained according to Bayesian inference:
  • d i is the distance between the receiver position (x, y) and the i-th base station position (x i , y i ), It is the difference between the signal strength measurement value RSSI i of the i-th base station signal received by the receiver and the theoretical value, that is, the measurement error.
  • I 1, 2,...N, N represents the number of Bluetooth base stations; when we are collecting the signal strength RSSI, the random variable ⁇ has been included in it, so there must be an error between the estimated distance calculated by the model and the true distance.
  • Step 2 According to the UWB model in Step 1, the limited range after UWB ranging can be obtained as:
  • (x a , y a ) represents the coordinates of the UWB base station;
  • r Dmax represents the maximum value of the clock error;
  • Step 3 Since there are at least two Bluetooth base stations in the system, it is necessary to calculate the probability distribution of the measurement signal of each Bluetooth base station. Set the evaluation function to f e , then:
  • the real position is the maximum value solution of the function f e . Since the number of base stations is fixed, in order to simplify the calculation, we only need to solve the minimum value solution of the equation f c :
  • the position solving process becomes the process of solving the most value solution of the equation under the inequality constraint.
  • Common solving methods include Kuentuck condition solving method, Lagrangian multiplier method, etc.
  • the extreme value solution of the equation solution may not be the true solution.
  • the present invention uses the PSO algorithm to solve the estimated position, specifically:
  • the following particle swarm optimization algorithm is used to update the speed and position:
  • V j (t+1) ⁇ V j (t)+c 1 r 1 [pbest j (t)-X j (t)]+c 2 r 2 [gbest-X j (t)]
  • V j (t) and X j (t) respectively represent the velocity and position of the j-th particle when the number of iterations is t.
  • FIG. 2 is a comparison diagram of the positioning results of the positioning method of the present invention and several known positioning methods. Among them are the positioning effect proofs of 5 examples. Each example uses separate UWB positioning, Bluetooth positioning, and traditional least squares methods. The positioning and fusion positioning algorithms are verified. It can be seen from the example that the Bluetooth positioning error is large, and the UWB positioning effect is the best. Compared with Bluetooth positioning, the result of unconverged positioning is improved, but there is a big gap between the effect and UWB positioning. After fusion positioning, the positioning result error level reaches the UWB positioning level, but because only one UWB base station is used, the positioning cost is greatly reduced.

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  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Radar, Positioning & Navigation (AREA)
  • Remote Sensing (AREA)
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Abstract

一种基于UWB和蓝牙技术的室内定位融合方法，该方法结合了两种室内定位方案的优势，利用UWB测距误差小的特点，限定了估计位置的可能区域，减小了求解范围；相对于传统根据测距估算出位置的方法，直接利用蓝牙信号的RSSI值，通过贝叶斯推理得到求解区域内的概率分布来求解位置，避免了RSSI信号在计算成距离时的误差引入，提高了定位精度；利用一个UWB基站和2个蓝牙基站即可在大多数的情况下获得UWB技术的定位精度，相比于使用4个UWB基站的UWB定位方案，成本可降低至原成本的四分之一；利用粒子群优化算法来求解不等式约束下的极值问题，在RSSI测量值误差较大导致最小值偏离真实解的情况下能够获得局部最优解，有更好的实际使用效果。

Description

一种基于UWB和蓝牙技术的室内定位融合方法 技术领域
本发明属于导航、制导与控制技术领域，具体涉及一种基于UWB和蓝牙技术的室内定位融合方法。
背景技术
在现代社会中，随着如通信、网络。全球定位系统(Global Positioning System，GPS)、无线传感器网络(Wireless Sensor Networks，WSNs)等技术的迅速发展，位置感知计算和基于位置的服务(Location Based Services，LBS)在现实生活中显得越来越重要。定位技术的出现极大的方便了人们的生活，其影响已经延伸到了包括军事、科技、以及人们普通生活中的各个方面，而室内定位对于人们日常生活的帮助也使其越来越得到各领域的青睐。如超市里基于位置服务的信息推送，帮助人们在大型超市里面快速定位到某个商铺甚至是商品；又如当发生火灾、地震、人质劫持、炸弹威胁等紧急事件时只有精确的室内定位才能进行最优的救援路线规划和快速安全的救援工作。
GPS是当前应用最广泛最成功的定位技术。然而目前主流的GPS应用都适用于室外的场所，而在室内环境下传播环境复杂，而且GPS信号易被建筑物、金属遮盖物等吸收，而且遮挡物的存在使得无线信号发生折射、反射以及能量衰减等现象，加之其成本较高，严重影响了GPS在室内定位方面应用的发展。因此，为了满足个人和商业需求，各种室内定位技术应运而生。现有的典型的室内定位技术有Wi-Fi技术、蓝牙技术、红外技术、超宽带(UWB)技术等。根据在定位过程中是否需要测量接收端与发射端 的实际距离，现有的室内定位算法分为基于测距(range-based)和无需测距(rang-free)算法，由于基于测距定位方法便于应用于工程实践中而得到了更加广泛的应用。典型的测距技术有信号到达时间TOA(time of arrive)、信号到达时间差TDOA(time difference of arrive)、信号到达角AOA(angle of arrive)、信号强度RSSI(received signal strength indication)等。但是由于信号在室内传播时存在路径短、传播环境复杂等问题，TOA、TDOA、AOA等方法难以实现精确测量，所以很难用这些方法得到较为精确的定位结果。
现有的技术中，基于TOF(time of flight)测量的UWB技术能在室内的环境下能实现高精度的定位，而基于RSSI的蓝牙技术由于价格低廉，拥有很高的覆盖率。但是现有的单一的室内定位方案都是以单一的定位技术为主，为了实现较高精度的定位就需要付出较高的经济成本，导致现有的技术都没有很好的运用到实际应用中。
发明内容
有鉴于此，本发明的目的是提供一种基于UWB和蓝牙技术的室内定位融合方法，可以提高定位精度，同时降低成本。
一种室内定位融合方法，采用一个UWB基站和至少两个蓝牙基站实现室内定位，包括如下步骤：
步骤1，将UWB测距模型由下式表达：
Figure PCTCN2019000151-appb-000001
其中，p r和p b分别为接收器和UWB基站的位置，r TOF是UWB基站与接收器之间距离，r D～N(0，δ 2)为时钟误差带来的测量误差，通过事先标定得到；t r和t b分别为接收器接收和发送信号的时间，c为光速；
将蓝牙测距方式中蓝牙信号强度RSSI的数学模型表示如下：
Figure PCTCN2019000151-appb-000002
其中，RSSI(d)和P 0分别为蓝牙测距方式中接收器与基站在距离d和参考距离d 0下的信号接收强度，n是信号的路径损耗因子，ζ∈(0，σ 2)是由于阴影衰落效应而造成的白噪声干扰，σ表示白噪声均方差；
根据贝叶斯推理得到估计的位置为真实位置的概率：
Figure PCTCN2019000151-appb-000003
其中：
Figure PCTCN2019000151-appb-000004
d i是接收器位置(x，y)到第i个蓝牙基站位置(x i，y i)之间的距离，
Figure PCTCN2019000151-appb-000005
是接收器接收第i个基站信号的信号强度测量值RSSI i和理论值之间的差值，即测量误差；
步骤2，根据步骤1中的UWB模型得到UWB测距之后限定范围为：
Figure PCTCN2019000151-appb-000006
其中，(x a，y a)表示UWB基站坐标，r Dmax表示时钟误差的最大值；
步骤3，计算每个蓝牙基站的量测信号的概率分布，并定义评价函数为f e，则有：
Figure PCTCN2019000151-appb-000007
将式(4)代入下式(7)后，在公式(5)的限定范围内求解式(7)，使得式(7)中f c取极小值，则求出接收器的估算位置(x，y)，实现定位；
Figure PCTCN2019000151-appb-000008
较佳的，所述步骤3中，对式(7)的求解方法包括库恩塔克条件求解法和拉格朗日乘子法。
较佳的，所述步骤3中，采用PSO算法对式(7)进行求解。
本发明具有如下有益效果：
本发明提出的基于UWB和蓝牙技术的室内定位融合方法，结合了两种室内定位方案的优势，利用UWB测距误差小的特点，限定了估计位置的可能区域，减小了求解范围；相对于传统根据测距估算出位置的方法，直接利用蓝牙信号的RSSI值，通过贝叶斯推理得到求解区域内的概率分布来求解位置，避免了RSSI信号在计算成距离时的误差引入，提高了定位精度；
利用一个UWB基站和2个蓝牙基站即可在大多数的情况下获得UWB技术的定位精度，相比于使用4个UWB基站的UWB定位方案，成本可降低至原成本的四分之一。
本发明利用粒子群优化算法来求解不等式约束下的极值问题，相比于直接极值求解的方法，在RSSI测量值误差较大导致最小值偏离真实解的情况下能够获得局部最优解，有更好的实际使用效果。
附图说明
图1为本发明基于UWB和蓝牙技术的室内定位融合算法的流程图。
图2为本发明定位方法与已知几种定位方法的定位结果比较图。
具体实施方式
下面结合附图并举实施例，对本发明进行详细描述。
本发明提出的室内定位融合方法定位硬件由至少一个UWB基站和两个蓝牙基站的硬件设备组成，根据两种技术定位精度和经济成本等特点进行融合定位，并在该框架下提出了基于UWB和蓝牙技术的室内定位融合算法用来求解估计位置，提高定位精度。融合算法的基本原理是：根据UWB测距的误差分析得到UWB测距模型，根据无线传感器信号在室内传播衰减特点，建立接收终端接的接受信号强度(RSSI)与基站和接收终端距离数学模型。由UWB测量距离和UWB基站位置得到估计位置限定范围，并根据测量RSSI值和误差分布根据贝叶斯推理获得估计位置点的概率分布函数，并利用粒子群优化算法求得限定范围内的全局最优解作为估计位置输出。
本发明提出的基于UWB和蓝牙技术的室内定位融合方法，如图1所示，具体步骤如下：
步骤1，由于UWB测距的误差主要由UWB基站和接收器之间的时钟误差造成，故UWB测距模型可由下式表达：
Figure PCTCN2019000151-appb-000009
其中，p r和p b分别为UWB基站和接收器的位置，r TOF是UWB测距方式中采用TOF测量得到的UWB基站与接收器之间距离，r D∈N(0，δ 2)为时钟误差带来的测量误差，通过事先标定得到；t r和t b分别为接收器接收和发送信号的时间，c为光速，故通过数据测量可以得到UWB的误差分布大小。
根据无线传感器信号在室内传播衰减特点，蓝牙信号强度(RSSI)的 数学模型如下：
Figure PCTCN2019000151-appb-000010
其中，RSSI(d)(单位为dBm)和P 0分别为接收器与蓝牙基站在距离d和参考距离d 0下的信号接收强度，n是信号的路径损耗因子，ζ∈(0，σ 2)是由于阴影衰落效应而造成的白噪声干扰，其中白噪声均方差σ的大小取决于传播路径的干扰。因此，当测得环境中的模型的相关参数，接收信号强度RSSI(d)和估计位置之后即可根据贝叶斯推理得到该位置为真实位置的概率：
Figure PCTCN2019000151-appb-000011
其中：
Figure PCTCN2019000151-appb-000012
d i是接收器位置(x，y)到第i个基站位置(x i，y i)之间的距离，
Figure PCTCN2019000151-appb-000013
是接收器接收第i个基站信号的信号强度测量值RSSI i和理论值之间的差值，即测量误差。I＝1，2，…N，N表示蓝牙基站数量；当我们在采集信号强度RSSI时，随机变量ζ已经包含在其中，因此利用模型推算的估计距离与真实距离一定存在误差。
步骤2，根据步骤1中的UWB模型可以得到UWB测距之后限定范围为：
Figure PCTCN2019000151-appb-000014
其中，(x a，y a)表示UWB基站坐标；r Dmax表示时钟误差的最大值；
步骤3，由于在系统中存在至少两个蓝牙基站，所以需要对每个蓝牙基站的量测信号进行概率分布计算，设定评价函数为f e，则有：
Figure PCTCN2019000151-appb-000015
显然，真实位置为函数f e极大值解，由于基站个数固定，为了简化计算，我们只需求解方程f c的极小值解：
Figure PCTCN2019000151-appb-000016
将式(4)代入式(7)后，在公式(5)的限定范围内求解式(7)，使得f c取极小值，则可求出接收器的估算位置(x，y)。
根据如上变换，位置求解过程变成不等式约束条件下的方程最值解求解过程。常见的求解方法有库恩塔克条件求解法、拉格朗日乘子法等，但是由于蓝牙信号带有误差，因此方程解的极值解不一定为真实解。为了取得更好的求解效果，本发明利用PSO算法求解估算位置，具体为：
将几何坐标作为粒子状态，f c作为粒子的适应度函数
Figure PCTCN2019000151-appb-000017
在限定范围内完成粒子群的初始化，并根据适度函数
Figure PCTCN2019000151-appb-000018
计算每个粒子的适度值，更新个体最优值pbest j(t)和种群最优值gbest(t)：用pbest j(t)保存到第t次迭代时j粒子的适度值最小的位置，并比较所有pbest j(t)，将适应度最小的粒子的位置作为全局最优量保存在gbest(t)中。
其中，用如下粒子群优化算法进行速度和位置更新：
Figure PCTCN2019000151-appb-000019
Figure PCTCN2019000151-appb-000020
V j(t+1)＝ωV j(t)+c 1r 1[pbest j(t)-X j(t)]+c 2r 2[gbest-X j(t)]
X j(t+1)＝X j(t)+V j(t+1)
Figure PCTCN2019000151-appb-000021
其中V j(t)和X j(t)分别表示第j个粒子在迭代次数为t时的速度和位置。
判断设定定位精度或者最大迭代是否满足，若满足条件，则输出此时的全局最优解gbest(t)作为估计位置输出，算法结束，否则返回步骤4，继续迭代过程。
图2所示，为本发明定位方法与已知几种定位方法的定位结果比较图，其中为5个实例的定位效果证明，每个实例都利用单独UWB定位、蓝牙定位、传统最小二乘方法定位和融合定位算法进行验证。从实例中可以看到蓝牙定位误差较大，UWB定位效果最好。在未融合的定位结果相比于蓝牙定位有所提升，但效果和UWB定位有较大差距。经过融合定位后定位结果误差量级达到UWB的定位量级，但是由于只使用一个UWB基站，定位成本大大降低。
综上所述，以上仅为本发明的较佳实施例而已，并非用于限定本发明的保护范围。凡在本发明的精神和原则之内，所作的任何修改、等同替换、改进等，均应包含在本发明的保护范围之内。

Claims (3)

  1. 一种室内定位融合方法，其特征在于，采用一个UWB基站和至少两个蓝牙基站实现室内定位，包括如下步骤：
    步骤1，将UWB测距模型由下式表达：
    Figure PCTCN2019000151-appb-100001
    其中，p r和p b分别为接收器和UWB基站的位置，r TOF是UWB基站与接收器之间距离，r D为时钟误差带来的测量误差，通过事先标定得到；t r和t b分别为接收器接收和发送信号的时间，c为光速；
    将蓝牙测距方式中蓝牙信号强度RSSI的数学模型表示如下：
    Figure PCTCN2019000151-appb-100002
    其中，RSSI(d)和P 0分别为蓝牙测距方式中接收器与基站在距离d和参考距离d 0下的信号接收强度，n是信号的路径损耗因子，ζ∈(0，σ 2)是由于阴影衰落效应而造成的白噪声干扰，σ表示白噪声均方差；
    根据贝叶斯推理得到估计的位置为真实位置的概率：
    Figure PCTCN2019000151-appb-100003
    其中：
    Figure PCTCN2019000151-appb-100004
    d i是接收器位置(x，y)到第i个蓝牙基站位置(x i，y i)之间的距离，
    Figure PCTCN2019000151-appb-100005
    是接收器接收第i个基站信号的信号强度测量值RSSI i和理论值之间的差值，即测量误差；
    步骤2，根据步骤1中的UWB模型得到UWB测距之后限定范围为：
    Figure PCTCN2019000151-appb-100006
    其中，(x a，y a)表示UWB基站坐标，r Dmax表示时钟误差的最大值；
    步骤3，计算每个蓝牙基站的量测信号的概率分布，并定义评价函数为f e，则有：
    Figure PCTCN2019000151-appb-100007
    将式(4)代入下式(7)后，在公式(5)的限定范围内求解式(7)，使得式(7)中f c取极小值，则求出接收器的估算位置(x，y)，实现定位；
    Figure PCTCN2019000151-appb-100008
  2. 如权利要求1所述的一种室内定位融合方法，其特征在于，所述步骤3中，对式(7)的求解方法包括库恩塔克条件求解法和拉格朗日乘子法。
  3. 如权利要求1所述的一种室内定位融合方法，其特征在于，所述步骤3中，采用PSO算法对式(7)进行求解。
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