WO2019091164A1 - 一种智能球场定位***及方法 - Google Patents

一种智能球场定位***及方法 Download PDF

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WO2019091164A1
WO2019091164A1 PCT/CN2018/099235 CN2018099235W WO2019091164A1 WO 2019091164 A1 WO2019091164 A1 WO 2019091164A1 CN 2018099235 W CN2018099235 W CN 2018099235W WO 2019091164 A1 WO2019091164 A1 WO 2019091164A1
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time
positioning
base station
point
state
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PCT/CN2018/099235
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English (en)
French (fr)
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吴建成
张也雷
韩步勇
罗向望
郭岱硕
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简极科技有限公司
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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
    • G01S5/02—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
    • G01S5/06—Position of source determined by co-ordinating a plurality of position lines defined by path-difference measurements
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01P—MEASURING LINEAR OR ANGULAR SPEED, ACCELERATION, DECELERATION, OR SHOCK; INDICATING PRESENCE, ABSENCE, OR DIRECTION, OF MOVEMENT
    • G01P3/00—Measuring linear or angular speed; Measuring differences of linear or angular speeds
    • G01P3/64—Devices characterised by the determination of the time taken to traverse a fixed distance
    • G01P3/66—Devices characterised by the determination of the time taken to traverse a fixed distance using electric or magnetic means
    • 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
    • G01S5/0081—Transmission between base 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
    • G01S5/0205—Details
    • G01S5/021—Calibration, monitoring or correction

Definitions

  • the invention relates to the field of big data technology, and particularly relates to an intelligent stadium positioning system and method.
  • the intelligent stadium positioning system is based on intelligent equipment and big data processing to realize real-time collection and analysis of sports data in the stadium. Due to the wide variety of sports points on the court, accurate collection and accurate analysis of various types of sports point data is the unremitting pursuit of big data processing. In order to accurately collect the sports points of the stadium, such as players, balls, referees, etc., and analyze the precise motion information of the above various types of sports points, this is an extremely complicated big data processing project itself, in the existing big data processing technology. Due to the diversity of data collection points, the complexity of data processing sources, and the complexity of data processing, the errors of real-time dynamic positioning of moving points are caused, and it is impossible to accurately and efficiently locate in real time.
  • the present invention provides an intelligent positioning system for accurately positioning, including a position and speed collection terminal of a motion point, at least three base station devices, a management server, and a data server, wherein the position and speed collection terminal are disposed at The movement point is displaced along with the movement point, and the position and speed collection terminal is connected to each base station device through a wireless network, and each base station device is respectively connected to the data server through a wireless or wired network, the management server And the data server is deployed through the same network, and the location and speed collection terminal broadcasts its own identifier and signal to each base station device.
  • the sports points include a ball, a player, and/or a course staff.
  • the present invention also provides an intelligent positioning method for accurately positioning, which is applicable to the above system, wherein the position and speed collecting terminal acquires a broadcast signal at a time t of a motion point and transmits the signal to the base station device, and the base station device according to the received broadcast signal Calculating the propagation time signal from the time of the motion point t to each base station and transmitting it to the data server, and the data server calculates the distance difference r i,j,t from any two base station devices according to the propagation time signal, and according to the distance The difference r i,j,t is preprocessed, the abnormal base station propagation time signal is removed, the normal base station propagation time signal received at the motion point t is obtained, and the normal base station propagation time signal number received according to the motion point t is obtained after arrival.
  • the time difference multi-point positioning step calculates the positioning position at the movement point t, and then determines the positioning state of the positioning position as the positioning abnormal state or the positioning effective state according to the positioning state detection step according to the positioning position at the movement point t, and finally according to the positioning state
  • the positioning position and the positioning state at the time of the movement point t are corrected and the vector step is calculated.
  • the vectors, p x, t , p y, t represent the coordinates of the motion point on the x, y axis at time t, respectively.
  • the distance difference r i,j,t from the nearest two base station devices at the time of the motion point t is calculated as follows:
  • d i,t is the distance from the base station i at the time t of the motion point
  • ⁇ i,j,t is the time difference calculated from the signal propagation time value received by the base station i and the base station j at the time t
  • this c can be the speed of light.
  • the pre-processing step is specifically as follows: a distribution of distance differences calculated from the past five seconds of time t [r i,j,t ,r i,j,t-1 ,r i,j,t-2 ...r i,j,tK ], Calculating the variance when Greater than the preset variability threshold Then, the i-th base station and the j-th base station are given a point at time t. If a base station scores more than three points at time t, it is determined that the base station is an abnormal base station, and the propagation time signal transmitted by the base station is excluded.
  • the pre-processing determines the quality of the propagation time signal, detects and eliminates the high-noise message, and thereby reduces the positioning error caused by the frequency unsynchronized and non-direct-viewing errors caused by the network factor. Further, the multi-point positioning step of the arrival time difference is specifically as follows:
  • the multi-point arrival time difference algorithm based on the constrained weighted least squares method is used to calculate the position of the motion point at time t.
  • the set parameter of the arrival time difference multi-point positioning algorithm is the maximum number of iterations 2,
  • the positional position of the motion point at time t is calculated by a Spherical Interpolation-based arrival time difference multi-point localization algorithm.
  • the positioning status detecting step is specifically as follows:
  • d valid d 0 * ⁇ t*f s ,
  • d 0 is the initial value of the distance threshold and is set to 1 meter
  • ⁇ t is the difference between time t and time (t - k)
  • f s is the system sampling frequency and is set to 20 Hz.
  • the linear Kalman filter is used to calculate the velocity vector of the ball/player and correct the fixed position calculated by the multi-point positioning step of the arrival time difference.
  • the settings and parameters are as follows:
  • the state vector s t at time t is set to:
  • the sampling rate is set to 0.05 seconds.
  • the covariance matrix Q of the process noise at time t is set to:
  • the observation model matrix H at time t is set to:
  • the covariance matrix R of the observed noise at time t is set to:
  • the exponential decay function is used to calculate the speed at which the current time t is predicted:
  • ⁇ is the attenuation parameter and is set to 0.90.
  • the present invention were screened by the position P t t t time and velocity V of the moving point of the first collection, excluding the abnormal signal propagation time of the base station, and then sequentially passes through the arrival time difference of the multi-point positioning step, the step of positioning status detection, location and correction
  • the calculation speed vector is progressively layer by layer, and finally the velocity vector at the precise movement point t is obtained, which greatly improves the positioning efficiency and improves the processing efficiency of the intelligent stadium big data.
  • the location tracking algorithm of the present invention can perform pre-processing to determine the quality of the propagation time message, detect and eliminate the high-noise message, thereby reducing the frequency unsynchronization caused by the network factor, and the positioning caused by the error caused by the non-direct-view propagation. error.
  • the location tracking algorithm of the present invention can continuously and accurately predict the position and velocity information of the volume label in the case of insufficient propagation time information.
  • FIG. 1 is a schematic structural view of an embodiment of the present invention.
  • a precise positioning intelligent stadium positioning system of the embodiment includes a motion point location and speed collection terminal, at least three base station devices, a management server, and a data server, where the position and speed collection terminal is set in The movement point is displaced along with the movement point.
  • the position and speed collection terminal is connected to each base station device via a wireless network, and each base station device is connected to the data server via a wireless or wired network, and the wireless network includes But not limited to wifi, 4G, 5G, WCDMA, cdma200 network.
  • the data server is connected to the management server, and the management server undertakes data access and update management, data integrity management, data security management, database retrieval and modification, data import/export management, database structure maintenance, data recovery function and performance of the entire system. Monitoring service, the management server undertakes the system configuration and management of the entire system, the parallel operation mechanism, and the simultaneous processing of multiple events.
  • the management server and the data server are deployed through the same network, and the network may be a local area network or a wide area network.
  • the management server and the data server can be configured by using the same hardware device for network configuration, or can be configured by hardware devices respectively.
  • the location and speed collection terminal broadcasts its own identifier and broadcast signal to each base station device.
  • the location and speed collection terminal includes a UWB tag, and the UWB tag is used to broadcast a UWB signal, that is, the broadcast signal can be, but is not limited to, including a UWB signal. In other applications, other types of broadcast signals can also be used.
  • the acquisition terminal broadcast frequency is set to 20 Hz.
  • the base station device calculates the propagation time signal according to the information signal and transmits it to the data server via Wi-Fi.
  • the data server calculates the location and speed information of the terminal according to the propagation time signal.
  • the movement point is defined as a moving object whose position changes in the course of the course and changes in the course.
  • the movement point includes: a ball, a player, a referee, and Course staff who must be displaced during the course.
  • the position and speed collection terminal of the motion point differs according to the object of the motion point collected by the motion point. For example, when the motion point is a ball object, the position and speed collection terminal includes a first UWB tag disposed in the ball, and the first UWB tag is used to broadcast the own identity code and the UWB signal to the base station device.
  • the position and speed collection terminal includes a wearable device disposed on the player, the wearable device includes a second UWB tag, and the second UWB tag is used to broadcast the own identification code,
  • the UWB signal is sent to the base station device.
  • the embodiment further provides an intelligent positioning method for accurately positioning, which is applicable to the above system.
  • the position and speed collecting terminal acquires a UWB signal at time t of the moving point and sends the signal to the base station device, and the base station device according to the received UWB
  • the signal calculates the distance from the base station device at the time of the motion point t, and calculates the propagation time signal from the time of the motion point t to each base station and transmits it to the data server, and the data server calculates the motion point t distance according to the propagation time signal.
  • the distance difference r i,j,t , and the base station equipment are preprocessed according to the distance difference r i,j,t , the abnormal base station propagation time signal is removed, and the normal base station propagation time signal received at the motion point t is obtained, and according to The number of normal base station propagation time signals received at the time of the motion point t, after the arrival time difference multi-point positioning step, the positioning position at the motion point t is calculated, and then according to the positioning position at the motion point t, the positioning state detection step is determined.
  • the positioning state of the positioning position is a positioning abnormal state or a positioning effective state, and finally according to the time of the motion point t
  • the bit position and the positioning state are corrected by positioning and calculating the velocity vector step, and the velocity vector at the time of the motion point t is obtained.
  • the dimensional position vector, p x,t ,p y,t represents the coordinate of the motion point on the x, y axis at time t, respectively.
  • the distance difference r i,j,t from the nearest two base station devices at the time of the motion point t is calculated as follows:
  • d i,t is the distance from the base station i at the time t of the motion point
  • ⁇ i,j,t is the propagation time difference calculated from the signal propagation time value received by the base station i and the base station j at the time t of the motion point
  • c is the signal propagation speed
  • c is assumed to be the speed of light.
  • the multi-point positioning step of the arrival time difference is specifically as follows:
  • the position of the anchor point at time t is calculated by the Constrained Weighted Least Square (Strained Weighted Least Square) time difference multipoint positioning algorithm [1].
  • the setting and parameters of the arrival time difference multipoint positioning algorithm are the largest. Number of iterations 2,
  • the positional position of the motion point at time t at time t is calculated using a time-of-arrival multi-point localization algorithm [2] based on Spherical Interpolation.
  • the position of the positioning point cannot be calculated by using the arrival time difference multi-point positioning algorithm.
  • the positioning status detecting step is specifically as follows:
  • d valid d 0 * ⁇ t*f s ,
  • d 0 is the initial value of the distance threshold and is set to 1 meter
  • ⁇ t is the difference between time t and time (t - k)
  • f s is the system sampling frequency and is set to 20 Hz.
  • the linear Kalman filter [3] is used to calculate the velocity vector of the ball/player and correct the fixed position calculated in the multi-point positioning step of the arrival time difference.
  • the settings and parameters are as follows:
  • the state vector s t at time t is set to:
  • the state transition model matrix A at time t is set to:
  • ⁇ t is the sampling rate and is set to 0.05 seconds
  • the covariance matrix Q of the process noise at time t is set to:
  • the observation model matrix H at time t is set to:
  • the covariance matrix R of the observed noise at time t is set to:
  • the exponential decay function is used to calculate the speed at which the current time t is predicted:
  • v t ⁇ * v t-1 , 0 ⁇ ⁇ ⁇ 1, ⁇ is the attenuation parameter and is set to 0.90.
  • the localization tracking algorithm of the present invention can perform pre-processing to determine the quality of the propagation time message, detect and eliminate the high-noise message, and thereby reduce the frequency unsynchronization caused by the network factor. Positioning error caused by errors such as direct vision transmission.
  • the location tracking algorithm of the present invention is capable of continuously and accurately predicting the position and velocity information of the volume label in the event that the propagation time message is insufficient (eg, less than three propagation time signals from different base stations are received).

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  • General Physics & Mathematics (AREA)
  • Engineering & Computer Science (AREA)
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Abstract

一种智能球场定位***及方法，该***包括运动点的位置及速度采集终端、基站设备、管理服务器及数据服务器，位置及速度采集终端、基站设备、管理服务器及数据服务器通过网络相互通信连接。智能球场定位方法，包括位置及速度采集终端获取运动点的t时刻的位置pt及速度vt并发送至基站设备，并依次通过预处理步骤、到达时间差多点定位步骤、定位状态侦测步骤及修正定位与计算速度向量步骤最终得到精准的运动点t时刻的速度向量，极大提高了定位效率，提高了智能球场大数据处理效率。

Description

一种智能球场定位***及方法 技术领域
本发明大数据技术领域，具体涉及一种智能球场定位***及方法。
背景技术
智能球场定位***基于智能设备及大数据处理实现对球场中运动数据的实时采集及分析。由于在球场上的运动点类型繁多，各种类型的运动点数据的精准采集和精准分析是目前大数据处理中不懈追求的目标。为了精准采集球场的运动点，例如球员、球、裁判等，并分析上述各类型运动点的精准动作信息，这本身是一项极为复杂的大数据处理工程，在现有的大数据处理技术中，由于数据采集点的多样性，数据处理源的复杂性，以及数据处理复杂程度，都造成了个运动点实时动态定位的误差，无法精准高效实时地定位。
发明内容
为此，需要提供一种精准定位的智能球场定位***及方法，该***结合了设置在各种类运动点的智能设备或智能可穿戴设备，并通过设计一个全新的通信网络架构，使处于该通信网络架构下的各运动点之间数据的交互和数据传输变得更为高效，再结合基于立体时空维度的定位追踪演算法，来得到各运动点的精准实时定位信息。
为实现上述目的，本发明提供了一种精准定位的智能球场定位***，包括运动点的位置及速度采集终端、至少三个基站设备、管理服务器及数据服务器，所述位置及速度采集终端设置在运动点上并随着运动点的移动而发生位移，所述位置及速度采集终端与每一基站设备通过无线网络连接，每一基站设备分别与数据服务器通过无线或有线网络连接，所述管理服务器和数据服务器通过同一网络部署，所述位置及速度采集终端广播自身标识符与讯号至各个基站设备。
进一步的，所述运动点包括球、球员和/或球场工作人员。
本发明还提供了一种精准定位的智能球场定位方法，适用于上述***， 所述位置及速度采集终端获取运动点的t时刻广播讯号并发送至基站设备，所述基站设备根据接收的广播讯号计算出运动点t时刻到每一基站的传播时间讯号并传输到数据服务器，数据服务器根据所述传播时间讯号计算出距离任意两个基站设备的距离差r i,j,t，，并根据距离差r i,j,t进行预处理，剔除异常基站传播时间讯号，得到运动点t时刻接收的正常基站传播时间讯号，并根据运动点t时刻接收到的正常基站传播时间讯号个数，经过到达时间差多点定位步骤，计算出运动点t时刻的定位位置，再根据运动点t时刻的定位位置，经过定位状态侦测步骤判定该定位位置的定位状态为定位异常状态或定位有效状态，最后根据运动点t时刻的定位位置及定位状态经过修正定位与计算速度向量步骤，得到运动点t时刻的速度向量。
进一步的，所述广播讯号包括位置p t及速度v t，t时刻的位置为p t＝[p x,t p y,t]，其中，p t为运动点在时间t时的2维位置向量，p x,t,p y,t分别代表运动点在时间t时x,y轴上的座标。
进一步的，t时刻的速度为v t＝[v x,t v y,t]，其中，v t为运动点在时间t时的2维速度向量，v x,t,v y,t分别代表运动点在时间t时x,y轴上的速度分量。
进一步的，运动点t时刻距离最近两基站设备的距离差r i,j,t计算公式如下：
r i,j,t＝d i,t-d j,t＝δ i,j,t＊c，r i,j,t为运动点在时间t时与基站i跟基站j之间距离差，d i,t为运动点在时间t时离基站i的距离，δ i,j,t为运动点在时间t时根据基站i跟基站j所接收到的讯号传播时间值所计算的时间差，c为信号传播速度，该c可为光速。
进一步的，所述预处理步骤具体如下，从时间t过去五秒所计算出的距离差的分布[r i,j,t,r i,j,t-1,r i,j,t-2...r i,j,t-K],
Figure PCTCN2018099235-appb-000001
计算变异数
Figure PCTCN2018099235-appb-000002
当
Figure PCTCN2018099235-appb-000003
大于预设的变异数阀值
Figure PCTCN2018099235-appb-000004
则在时间t时给予第i基站与第j基站一分，若当一个基站在时间t的时候分数超过三分，则判定该基站为异常基站，并排除由该基站传送的传播时间讯号。该预处理判断传播时间讯号质量，侦测并排除有高噪声的讯息，进 而减低因网络因素而产生的频率不同步、非直视性等误差所造成的定位误差。进一步的，所述到达时间差多点定位步骤具体如下：
当***在时间t时接收到大于3个来自不同的基站传播时间讯号:
利用基于约束加权最小二乘法的到达时间差多点定位演算法来计算运动点在时间t时的定位位置.，该到达时间差多点定位演算法的设定参数为最大叠代次数2，
当***在时间t时接收到3个来自不同的基站传播时间讯号:
利用基于球面内插(Spherical Interpolation)的到达时间差多点定位演算法来计算运动点在时间t时的定位位置。
进一步的，所述定位状态侦测步骤具体如下：
(1)若在时间t里接收到小于三个来自不同基站传播时间讯号因而无法利用到达时间差多点定位演算法来计算定位点的位置,则判断在时间t时的定位状态为定位异常状态。
(2)若在时间t里的第二阶段利用到达时间差多点定位演算法来计算定位点的位置p t与在上一个时间(t-k)判断定位有效状态的位置p t-k之间的距离超过预设的阀值d valid,判断在时间t时的定位状态为定位异常状态，
(3)若在时间t里的第二阶段利用到达时间差多点定位演算法来计算定位点的位置p t与在上一个时间(t-k)判断定位有效状态的位置p t-k之间的距离小于或等于预设的阀值d valid,判断在时间t时的定位状态为定位有效状态，
d valid的计算方式如下:d valid＝d 0＊δt＊f s，
d 0为距离阀值初始值并设定为1米,δ t为时间t与时间(t–k)之间的差异而f s为***采样频率并且设定为20Hz。
进一步的，所述修正定位与计算速度向量步骤具体如下：
当定位状态为定位有效状态:
利用线性卡尔曼滤波器(Kalman Filter)来计算球/球员的速度向量并且修正在到达时间差多点定位步骤所计算出的定对位置，其设定与参数如下:
在时间t时的状态向量s t设定为:
s t＝[p t v t]＝[p x,t p y,t v x,t v y,t]
在时间t时的状态变换模型矩阵A设定为:
Figure PCTCN2018099235-appb-000005
为采样率并设定为0.05秒.
在时间t时的过程噪声的共变异数矩阵Q设定为:
Figure PCTCN2018099235-appb-000006
q p＝1,q v＝5
在时间t时的观测模型矩阵H设定为:
Figure PCTCN2018099235-appb-000007
在时间t时的观测噪声的共变异数矩阵R设定为:
Figure PCTCN2018099235-appb-000008
r x＝r y＝10
当定位状态为定位异常状态:
根据在时间(t-1)时预测的位置与速度计算现在时间t的位置:
Figure PCTCN2018099235-appb-000009
另外根据在时间(t-1)时所预测的速度,利用指数衰减函数来计算预测现在时间t时的速度:
v t＝α*v t-1,0<α<1,
α为衰减参数并设定为0.90。
区别于现有技术，上述技术方案具有以下有益效果：
本发明通过全新的定位点及定位基站网络架构设置，所有定位点上部署位置及速度采集终端，并且该位置及速度采集终端通过最高速的数据交互通道来实现信号的实时传递，极大提高了数据的传输效率。
本发明通过对首先采集的运动点的t时刻的位置p t及速度v t进行初筛，剔除异常基站传播时间讯号，再依次经过到达时间差多点定位步骤、定位状态侦测步骤、修正定位与计算速度向量逐层递进，最终得到精准的运动点t时刻的速度向量，极大提高了定位效率，提高了智能球场大数据处理效率。
本发明的定位追踪算法能先做预处理判断传播时间讯息质量，侦测并排除有高噪声的讯息，进而减低因网络因素而产生的频率不同步，非直视性传播等误差所造成的定位误差。
本发明的定位追踪算法能够在传播时间讯息不足的情况下持续准确的预测卷标的位置与速度信息。
附图说明
图1为本发明实施例的结构示意图。
具体实施方式
为详细说明技术方案的技术内容、构造特征、所实现目的及效果，以下结合具体实施例并配合附图详予说明。
请参阅图1，本实施例的一种精准定位的智能球场定位***，包括运动点的位置及速度采集终端、至少三个基站设备、管理服务器及数据服务器，所 述位置及速度采集终端设置在运动点上并随着运动点的移动而发生位移，所述位置及速度采集终端与每一基站设备通过无线网络连接，每一基站设备与数据服务器通过无线或有线网络连接，所述无线网络包括但不局限于wifi,4G,5G，WCDMA,cdma200网络。数据服务器与管理服务器连接，管理服务器承接整个***的数据存取与更新管理、数据完整性管理、数据安全性管理、数据库检索和修改、数据导入/导出管理，数据库结构维护、数据恢复功能和性能监测服务，管理服务器承接整个***的***配置与管理、并行运行机制，多个事件的同时发生处理功能。所述管理服务器和数据服务器通过同一网络部署，该网络可以为局域网也可以为广域网。该管理服务器和数据服务器既可以采用同一硬件装置进行网络配置，也可以分别采用硬件装置进行网络配置。所述位置及速度采集终端广播自身标识符与广播讯号至各个基站设备。所述位置及速度采集终端包括UWB标签，所述UWB标签用于广播UWB信号，也就是说所述广播讯号可以但不局限与包括UWB信号，在其他运用中，也可以采用其他类型的广播讯号作为计算参数。采集终端广播频率设定为20Hz。基站设备根据信息讯号计算传播时间讯号并透过Wi-Fi传输至数据服务器。数据服务器根据传播时间讯号计算终端的位置及速度信息。
在本实施例中，所述运动点定义为在球场比赛中随着时间轴演变，其位置在球场中发生改变的运动对象，在本实施例中，该运动点包括：球、球员、裁判以及在球场比赛中必须产生位移的球场工作人员。进一步的，该运动点的位置及速度采集终端，应其采集的运动点的对象不同而有所差别。例如，对于运动点为球对象时候，所述位置及速度采集终端包括设置在球内的第一UWB标签，该第一UWB标签用于广播自身识别码和UWB信号至基站设备。例如，对于运动点为球员对象时候，所述位置及速度采集终端包括设置在球员身上的可穿戴设备，所述可穿戴设备包括第二UWB标签，该第二UWB标签用于广播自身识别码、UWB信号至基站设备。
本实施例还提供一种精准定位的智能球场定位方法，适用于上述***， 所述位置及速度采集终端获取运动点的t时刻的UWB信号并发送至基站设备，所述基站设备根据接收的UWB信号计算出运动点t时刻距离基站设备的距离，并且计算出运动点t时刻到每一基站的传播时间讯号并传输到数据服务器，数据服务器根据所述传播时间讯号计算出运动点t距离任意两个基站设备的距离差r i,j,t，，并根据距离差r i,j,t进行预处理，剔除异常基站传播时间讯号，得到运动点t时刻接收的正常基站传播时间讯号，并根据运动点t时刻接收到的正常基站传播时间讯号个数，经过到达时间差多点定位步骤，计算出运动点t时刻的定位位置，再根据运动点t时刻的定位位置，经过定位状态侦测步骤判定该定位位置的定位状态为定位异常状态或定位有效状态，最后根据运动点t时刻的定位位置及定位状态经过修正定位与计算速度向量步骤，得到运动点t时刻的速度向量。
本实施例中，所述UWB信号包括位置p t及速度v t，t时刻的位置为p t＝[p x,t p y,t]，其中，p t为运动点在时间t时的2维位置向量，p x,t,p y,t分别代表运动点在时间t时x,y轴上的座标。
本实施例中，t时刻的速度为v t＝[v x,t v y,t]，其中，v t为运动点在时间t时的2维速度向量，v x,t,v y,t分别代表运动点在时间t时x,y轴上的速度分量。
本实施例中，运动点t时刻距离最近两基站设备的距离差r i,j,t计算公式如下：
r i,j,t＝d i,t-d j,t＝δ i,j,t＊c，r i,j,t为运动点在时间t时与基站i跟基站j之间距离差，d i,t为运动点在时间t时离基站i的距离，δ i,j,t为运动点在时间t时根据基站i跟基站j所接收到的讯号传播时间值所计算的传播时间差，c为信号传播速度，c假设为光速。
所述到达时间差多点定位步骤具体如下：
当***在时间t时接收到大于3个来自不同的基站传播时间讯号:
利用基于约束加权最小二乘法(Constrained Weighted Least Square)的到达时间差多点定位演算法[1]来计算在时间t时定位点的位置，该到达时间差多点定位 演算法的设定与参数为最大叠代次数2，
当***在时间t时接收到3个来自不同的基站传播时间讯号:
利用基于球面内插(Spherical Interpolation)的到达时间差多点定位演算法[2]来计算在时间t时运动点在时间t时的定位位置。
当***在时间t时接收到小于3个来自不同的基站传播时间讯号:
根据到达时间差多点定位演算法的基础概念下,在这样的情况下无法利用到达时间差多点定位演算法计算出定位点的位置。
进一步的，所述定位状态侦测步骤具体如下：
(1)若在时间t里接收到小于三个来自不同基站传播时间讯号因而无法利用到达时间差多点定位演算法来计算定位点的位置,则判断在时间t时的定位状态为定位异常状态。
(2)若在时间t里的第二阶段利用到达时间差多点定位演算法来计算定位点的位置p t与在上一个时间(t-k)判断定位有效状态的位置p t-k之间的距离超过预设的阀值d valid,判断在时间t时的定位状态为定位异常状态，
(3)若在时间t里的第二阶段利用到达时间差多点定位演算法来计算定位点的位置p t与在上一个时间(t-k)判断定位有效状态的位置p t-k之间的距离小于或等于预设的阀值d valid,判断在时间t时的定位状态为定位有效状态，
d valid的计算方式如下:d valid＝d 0＊δt＊f s，
d 0为距离阀值初始值并设定为1米,δ t为时间t与时间(t–k)之间的差异而f s为***采样频率并且设定为20Hz。
进一步的，所述修正定位与计算速度向量步骤具体如下：
当定位状态为定位有效状态:
利用线性卡尔曼滤波器(Kalman Filter)[3]来计算球/球员的速度向量并且修正 在到达时间差多点定位步骤所计算出的定对位置，其设定与参数如下:
在时间t时的状态向量s t设定为:
s t＝[p t v t]＝[p x,t p y,t v x,t v y,t]
在时间t时的状态变换模型矩阵A设定为:
Figure PCTCN2018099235-appb-000010
Δt为采样率并设定为0.05秒；
在时间t时的过程噪声的共变异数矩阵Q设定为:
Figure PCTCN2018099235-appb-000011
q p＝1,q v＝5。
在时间t时的观测模型矩阵H设定为:
Figure PCTCN2018099235-appb-000012
在时间t时的观测噪声的共变异数矩阵R设定为:
Figure PCTCN2018099235-appb-000013
r x＝r y＝10。
当定位状态为定位异常状态:
根据在时间(t-1)时预测的位置与速度计算现在时间t的位置:
Figure PCTCN2018099235-appb-000014
另外根据在时间(t-1)时所预测的速度,利用指数衰减函数来计算预测现在时间t时的速度:
v t＝α*v t-1,0<α<1，α为衰减参数并设定为0.90。
相比与传统的多点定位算法，本发明的定位追踪算法能先做预处理判断传播时间讯息质量，侦测并排除有高噪声的讯息，进而减低因网络因素而产生的频率不同步，非直视性传播等误差所造成的定位误差。此外，本发明的定位追踪算法能够在传播时间讯息不足的情况下(例如接收到小于3个来自不同基站传播时间讯号)持续准确的预测卷标的位置与速度信息。
需要说明的是，在本文中，诸如第一和第二等之类的关系术语仅仅用来将一个实体或者操作与另一个实体或操作区分开来，而不一定要求或者暗示这些实体或操作之间存在任何这种实际的关系或者顺序。而且，术语“包括”、“包含”或者其任何其他变体意在涵盖非排他性的包含，从而使得包括一系列要素的过程、方法、物品或者终端设备不仅包括那些要素，而且还包括没有明确列出的其他要素，或者是还包括为这种过程、方法、物品或者终端设备所固有的要素。在没有更多限制的情况下，由语句“包括……”或“包含……”限定的要素，并不排除在包括所述要素的过程、方法、物品或者终端设备中还存在另外的要素。此外，在本文中，“大于”、“小于”、“超过”等理解为不包括本数；“以上”、“以下”、“以内”等理解为包括本数。
尽管已经对上述各实施例进行了描述，但本领域内的技术人员一旦得知了基本创造性概念，则可对这些实施例做出另外的变更和修改，所以以上所述仅为本发明的实施例，并非因此限制本发明的专利保护范围，凡是利用本发明说明书及附图内容所作的等效结构或等效流程变换，或直接或间接运用在其他相关的技术领域，均同理包括在本发明的专利保护范围之内。

Claims (10)

  1. 一种精准定位的智能球场定位***，其特征在于：包括运动点的位置及速度采集终端、至少三个基站设备、管理服务器及数据服务器，所述位置及速度采集终端设置在运动点上并随着运动点的移动而发生位移，所述位置及速度采集终端与每一基站设备通过无线网络连接，每一基站设备分别与数据服务器通过无线或有线网络连接，所述管理服务器和数据服务器通过同一网络部署，所述位置及速度采集终端广播自身标识符与讯号至各个基站设备。
  2. 根据权利要求1所述的一种精准定位的智能球场定位***，其特征在于：所述运动点包括球、球员和/或球场工作人员。
  3. 一种精准定位的智能球场定位方法，适用于权利要求1或2的***，其特征在于：所述位置及速度采集终端获取运动点的t时刻的广播讯号并发送至基站设备，所述基站设备根据接收的广播讯号计算出运动点t时刻到每一基站的传播时间讯号并传输到数据服务器，数据服务器根据所述传播时间讯号计算出运动点t距离任意两个基站设备的距离差r i,j,t，，并根据距离差r i,j,t进行预处理，剔除异常基站传播时间讯号，得到运动点t时刻接收的正常基站传播时间讯号，并根据运动点t时刻接收到的正常基站传播时间讯号个数，经过到达时间差多点定位步骤，计算出运动点t时刻的定位位置，再根据运动点t时刻的定位位置，经过定位状态侦测步骤判定该定位位置的定位状态为定位异常状态或定位有效状态，最后根据运动点t时刻的定位位置及定位状态经过修正定位与计算速度向量步骤，得到运动点t时刻的速度向量。
  4. 根据权利要求3所述的一种精准定位的智能球场定位方法，其特征在于：所述广播讯号包括位置p t及速度v t，t时刻的位置为p t＝[p x,t p y,t]，其中，p t为运动点在时间t时的2维位置向量，p x,t,p y,t分别代表运动点在时间t时x,y轴上的座标。
  5. 根据权利要求3所述的一种精准定位的智能球场定位方法，其特征在于：t时刻的速度为v t＝[v x,t v y,t]，其中，v t为运动点在时间t时的2维速度向量，v x, t,v y,t分别代表运动点在时间t时x,y轴上的速度分量。
  6. 根据权利要求3所述的一种精准定位的智能球场定位方法，其特征在于：运动点t时刻距离最近两基站设备的距离差r i,j,t计算公式如下：
    r i,j,t＝d i,t-d j,t＝δ i,j,t＊c，r i,j,t为运动点在时间t时与基站i跟基站j之间距离差，d i,t为运动点在时间t时离基站i的距离，δ i,j,t为运动点在时间t时根据基站i跟基站j所接收到的讯号传播时间值所计算的传播时间差，c为信号传播速度。
  7. 根据权利要求4所述的一种精准定位的智能球场定位方法，其特征在于：所述预处理步骤具体如下，从时间t过去五秒所计算出的距离差的分布[r i,j,t,r i,j,t -1,r i,j,t-2...r i,j,t-K],
    Figure PCTCN2018099235-appb-100001
    计算变异数σ 2 r,i,j，当σ 2 r,i,j.大于预设的变异数阀值σ 2 max＝10，则在时间t时给予第i基站与第j基站一分，若当一个基站在时间t的时候分数超过三分，则判定该基站为异常基站，并排除由该基站传送的传播时间讯号。
  8. 根据权利要求4所述的一种精准定位的智能球场定位方法，其特征在于：所述到达时间差多点定位步骤具体如下：
    当***在时间t时接收到大于3个来自不同的基站传播时间讯号:
    利用基于约束加权最小二乘法的到达时间差多点定位演算法来计算运动点在时间t时的定位位置，该到达时间差多点定位演算法的设定参数为最大叠代次数2，
    当***在时间t时接收到3个来自不同的基站传播时间讯号:
    利用基于球面内插的到达时间差多点定位演算法来计算运动点在时间t时的定位位置。
  9. 根据权利要求4所述的一种精准定位的智能球场定位方法，其特征在于：所述定位状态侦测步骤具体如下：
    (1)若在时间t里接收到小于三个来自不同基站传播时间讯号因而无法利用到 达时间差多点定位演算法来计算定位点的位置,则判断在时间t时的定位状态为定位异常状态，
    (2)若在时间t里的第二阶段利用到达时间差多点定位演算法来计算定位点的位置p t与在上一个时间(t-k)判断定位有效状态的位置p t-k之间的距离超过预设的阀值d valid,判断在时间t时的定位状态为定位异常状态，
    (3)若在时间t里的第二阶段利用到达时间差多点定位演算法来计算定位点的位置p t与在上一个时间(t-k)判断定位有效状态的位置p t-k之间的距离小于或等于预设的阀值d valid,判断在时间t时的定位状态为定位有效状态，
    d valid的计算方式如下:d valid＝d 0＊δ t＊f s，
    d 0为距离阀值初始值并设定为1米,δ t为时间t与时间(t–k)之间的差异而f s为***采样频率并且设定为20Hz。
  10. 根据权利要求5所述的一种精准定位的智能球场定位方法，其特征在于：
    所述修正定位与计算速度向量步骤具体如下：
    当定位状态为定位有效状态:
    利用线性卡尔曼滤波器来计算球/球员的速度向量并且修正在到达时间差多点定位步骤所计算出的定对位置，其设定与参数如下:
    在时间t时的状态向量s t设定为:
    s t＝[p t v t]＝[p x,t p y,t v x,t v y,t]
    在时间t时的状态变换模型矩阵A设定为:
    Figure PCTCN2018099235-appb-100002
    Δt为采样率并设定为0.05秒；
    在时间t时的过程噪声的共变异数矩阵Q设定为:
    Figure PCTCN2018099235-appb-100003
    q p＝1,q v＝5
    在时间t时的观测模型矩阵H设定为:
    Figure PCTCN2018099235-appb-100004
    在时间t时的观测噪声的共变异数矩阵R设定为:
    Figure PCTCN2018099235-appb-100005
    r x＝r y＝10
    当定位状态为定位异常状态:
    根据在时间(t-1)时预测的位置与速度计算现在时间t的位置:
    Figure PCTCN2018099235-appb-100006
    另外根据在时间(t-1)时所预测的速度,利用指数衰减函数来计算预测现在时间t时的速度:
    v t＝α*v t-1,0<α<1,
    α为衰减参数并设定为0.90。
PCT/CN2018/099235 2017-11-10 2018-08-07 一种智能球场定位***及方法 WO2019091164A1 (zh)

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