WO2020177225A1 - 一种车路协同下基于超宽带的车辆高精度定位方法 - Google Patents
一种车路协同下基于超宽带的车辆高精度定位方法 Download PDFInfo
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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
- G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
- G01S13/87—Combinations of radar systems, e.g. primary radar and secondary radar
- G01S13/878—Combination of several spaced transmitters or receivers of known location for determining the position of a transponder or a reflector
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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
- G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
- G01S13/74—Systems using reradiation of radio waves, e.g. secondary radar systems; Analogous systems
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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
- G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
- G01S13/87—Combinations of radar systems, e.g. primary radar and secondary radar
- G01S13/876—Combination of several spaced transponders or reflectors of known location for determining the position of a receiver
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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
- G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
- G01S13/88—Radar or analogous systems specially adapted for specific applications
- G01S13/91—Radar or analogous systems specially adapted for specific applications for traffic control
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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
- G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
- G01S13/88—Radar or analogous systems specially adapted for specific applications
- G01S13/93—Radar or analogous systems specially adapted for specific applications for anti-collision purposes
- G01S13/931—Radar or analogous systems specially adapted for specific applications for anti-collision purposes of land vehicles
- G01S2013/9327—Sensor installation details
- G01S2013/93273—Sensor installation details on the top of the vehicles
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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
- G01S2205/00—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations
- G01S2205/01—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations specially adapted for specific applications
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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
- G01S5/0205—Details
- G01S5/0218—Multipath in signal reception
Definitions
- the invention relates to the field of vehicle navigation and positioning, in particular to a vehicle high-precision positioning method in a vehicle-road collaborative environment.
- IVICS Intelligent Vehicle Infrastructure Cooperative Systems
- vehicle positioning technology is one of the foundation and core content of research on vehicle-road collaboration and even intelligent transportation.
- Dead reckoning and inertial navigation mainly use low-cost vehicle-mounted sensors, such as electronic compasses, wheel speed sensors, micro-mechanical gyroscopes, etc., to achieve vehicle navigation and positioning. Because sensor measurement errors will continue to accumulate over time, positioning accuracy is usually not High; Among various positioning methods, Global Navigation Satellite System (GNSS) is the most widely used in the field of vehicle positioning. It generally refers to all satellite navigation systems. The more mature technology is the United States' Global Positioning System (Global Positioning System).
- GNSS can provide vehicles with three-dimensional position, speed, time and other information in real time, realizing all-weather, all-round navigation and positioning functions.
- the positioning accuracy of GNSS is relatively high.
- urban environment such as urban canyons, intersections surrounded by tall buildings, etc.
- satellite signals are easily blocked due to the high-rise buildings on both sides of the road. If the path effect is serious, the GNSS positioning accuracy will drop sharply, and even when the occlusion is severe, the positioning will be impossible due to insufficient number of visible satellites.
- UWB positioning technology is mainly used for indoor positioning. Fields such as personnel, smart cars, robot positioning, etc. Due to UWB's extremely wide bandwidth, strong pulse signal penetration, good multipath resolution and other technical advantages, it is feasible to achieve vehicle positioning in outdoor environments based on UWB, but compared to indoor environments, outdoor scenes are usually It is more complicated. In a dynamic traffic environment, UWB signals are more susceptible to interference from surrounding vehicles, trees and buildings on both sides of the road, resulting in non-line-of-sight propagation.
- the present invention proposes a vehicle-road coordination method for high-precision positioning of vehicles.
- UWB nodes By rationally arranging UWB nodes and combining non-line-of-sight error processing algorithms, positioning errors are reduced, thereby realizing urban environment High-precision positioning of the under vehicle.
- the present invention adopts the following technical solutions: firstly, UWB fixed nodes are arranged on both sides of the road, and the position coordinates of the UWB fixed nodes are obtained through high-precision differential GNSS; then, a UWB mobile node is arranged on the top of the vehicle, The time difference of arrival method is used to obtain the distance between the UWB mobile node and each UWB fixed node; then, the ARIMA model is established based on the historical distance observation value before the current moment, and the ARIMA model is used to perform NLOS identification for each UWB fixed node one by one, and according to the identification results Compensate and correct the distance observation; further, calculate the coordinates of the UWB mobile node according to the corrected distance from the UWB mobile node to each UWB fixed node and the coordinates of the UWB fixed node to obtain the precise position of the vehicle.
- a UWB-based vehicle high-precision positioning method under vehicle-road coordination is characterized in that, in a typical urban vehicle-road coordination scene, through a reasonable layout of UWB nodes, combined with a non-line-of-sight error compensation algorithm, the UWB signals propagate the positioning errors caused by non-line-of-sight, thereby improving the accuracy of UWB positioning and realizing accurate, reliable, and real-time positioning of urban vehicles.
- the method includes the following steps:
- Step 1) Determine the UWB layout plan, arrange UWB fixed nodes on both sides of the road, and measure the position coordinates of the UWB fixed nodes;
- the number and installation location of UWB fixed nodes should be based on actual traffic scenarios, comprehensively considering the cost and positioning accuracy requirements, and determine a reasonable layout plan.
- the present invention designs a layout plan for a typical urban intersection area. : A total of nine UWB fixed nodes are arranged on both sides of the road in four different directions at the intersection and the center of the intersection area. This layout method can cover the entire intersection to the greatest extent, and realize the vehicle in the intersection area. Complete positioning, and the UWB node arranged in the center of the roundabout is not susceptible to interference from occlusion, which helps to improve positioning accuracy;
- Step 2 Arrange a UWB mobile node on the roof directly above the center of mass of the vehicle.
- the mobile node will move with the vehicle. Therefore, the coordinates of the mobile node are regarded as the coordinates of the vehicle.
- the UWB mobile node will periodically send and receive each fixed
- the signal returned by the node uses the Time Difference of Arrival (TDOA) method to calculate the distance between the vehicle-mounted UWB mobile node and each UWB fixed node according to the time information of the signal transmission and return.
- TDOA Time Difference of Arrival
- the calculated distance observation value is close to the real value, but in the dynamic traffic scenario shown in Figure 3, the straight line between UWB nodes Propagation is easily blocked by surrounding vehicles, trees and buildings on both sides of the road, reflection, diffraction and other phenomena in the signal propagation path, that is, non-line of sight propagation (NLOS), the distance observation value contains larger Error, if the positioning solution is introduced, it will seriously affect the positioning accuracy;
- LOS line of sight
- Step 3 Establish a differential integrated moving average autoregressive model (ARIMA) based on the historical distance observations before the current moment, use the ARIMA model to perform NLOS identification for each UWB fixed node, and observe the distance according to the identification results
- ARIMA differential integrated moving average autoregressive model
- Step 4 According to the corrected distance from the UWB mobile node to each UWB fixed node and the coordinates of the UWB fixed node, a coordinate equation is established based on the principle of ranging and positioning, and the least square method is used to calculate the coordinates of the UWB mobile node to obtain the precise position of the vehicle .
- a UWB non-line-of-sight node identification and error compensation algorithm based on the ARIMA model is proposed, which can effectively reduce the UWB ranging error in a dynamic environment, thereby improving the positioning accuracy of UWB;
- the method in the present invention solves the problem of applying UWB positioning technology to the field of vehicle navigation under the actual traffic environment to realize high-precision positioning of urban vehicles.
- FIG. 1 is a flowchart of the present invention.
- Figure 2 is a schematic diagram of the layout of UWB nodes in a typical urban crossroad environment.
- Figure 3 is a schematic diagram of line-of-sight and non-line-of-sight UWB nodes in a dynamic traffic scene.
- Figure 4 is a comparison diagram of vehicle positioning trajectories before and after using the non-line-of-sight error processing algorithm based on the ARIMA model.
- Figure 5 is a comparison diagram of vehicle positioning errors before and after using the non-line-of-sight error processing algorithm based on the ARIMA model.
- IVICS Intelligent Vehicle Infrastructure Cooperative Systems
- vehicle positioning technology is one of the foundation and core content of research on vehicle-road collaboration and even intelligent transportation.
- Dead reckoning and inertial navigation mainly use low-cost vehicle-mounted sensors, such as electronic compasses, wheel speed sensors, micro-mechanical gyroscopes, etc., to achieve vehicle navigation and positioning. Because sensor measurement errors will continue to accumulate over time, positioning accuracy is usually not High; Among various positioning methods, Global Navigation Satellite System (GNSS) is the most widely used in the field of vehicle positioning. It generally refers to all satellite navigation systems. The more mature technology is the United States' Global Positioning System (Global Positioning System).
- GNSS can provide vehicles with three-dimensional position, speed, time and other information in real time, realizing all-weather, all-round navigation and positioning functions.
- the positioning accuracy of GNSS is relatively high.
- urban environment such as urban canyons, intersections surrounded by high-rise buildings, etc.
- satellite signals are easily blocked due to the high-rise buildings on both sides of the road. If the path effect is serious, the GNSS positioning accuracy will drop sharply, and even when the occlusion is severe, the positioning will be impossible due to insufficient number of visible satellites.
- UWB positioning technology is mainly used for indoor positioning. Fields such as personnel, smart cars, robot positioning, etc. Due to UWB's extremely wide bandwidth, strong pulse signal penetration, good multipath resolution and other technical advantages, it is feasible to achieve vehicle positioning in outdoor environments based on UWB, but compared to indoor environments, outdoor scenes are usually It is more complicated. In a dynamic traffic environment, UWB signals are more susceptible to interference from surrounding vehicles, trees and buildings on both sides of the road, resulting in non-line-of-sight propagation.
- the present invention proposes a high-precision positioning method for vehicles under vehicle-road coordination.
- UWB nodes By rationally arranging UWB nodes and combining non-line-of-sight error processing algorithms, positioning errors are reduced, thereby realizing vehicles in urban environments. High-precision positioning.
- FIG. 1 shows the flow of an embodiment of a method for implementing a high-precision vehicle positioning method according to the method of the present invention:
- the UWB node in this embodiment specifically uses the RK-101 UWB module.
- a high-precision vehicle positioning method based on UWB which is characterized in that, in a typical urban vehicle-road collaboration scenario, through a reasonable layout of UWB nodes, combined with a non-line-of-sight error compensation algorithm, it can effectively reduce the non-line-of-sight error due to signal propagation in dynamic traffic scenarios.
- the method includes the following steps:
- Step 1) Arrange UWB fixed nodes on both sides of the road, and obtain the position coordinates of UWB fixed nodes through high-precision differential GPS;
- the number and installation location of UWB fixed nodes should be based on actual traffic scenarios, comprehensively considering the cost and positioning accuracy requirements, and determine a reasonable layout plan.
- the present invention is a layout plan proposed for a typical urban intersection area. : A total of nine UWB fixed nodes are arranged on both sides of the road in four different directions at the intersection and the center of the intersection area. This layout method can cover the entire intersection to the greatest extent, and realize the vehicle in the intersection area. Complete positioning, and the UWB node arranged in the center of the roundabout is not susceptible to interference from occlusion, which helps to improve positioning accuracy;
- Step 2 Arrange a UWB mobile node on the roof directly above the center of mass of the vehicle.
- the mobile node will move with the vehicle. Therefore, the coordinates of the mobile node are regarded as the coordinates of the vehicle.
- the UWB mobile node will periodically send and receive each fixed
- the signal returned by the node uses the Time Difference of Arrival (TDOA) method to calculate the distance between the vehicle-mounted UWB mobile node and each UWB fixed node according to the time information of the signal transmission and return.
- TDOA Time Difference of Arrival
- the calculated distance observation value is close to the real value, but in the dynamic traffic scenario shown in Figure 3, the straight line between UWB nodes Propagation is easily blocked by surrounding vehicles, trees and buildings on both sides of the road, reflection, diffraction and other phenomena in the signal propagation path, that is, non-line of sight propagation (NLOS), the distance observation value contains larger Error, if the positioning solution is introduced, it will seriously affect the positioning accuracy;
- LOS line of sight
- Step 3 Establish a differential integrated moving average autoregressive model (ARIMA) based on the historical distance observations before the current time.
- the ARIMA model is a famous time series analysis method proposed by Box and Jenkins, also known as Box -Jenkins model, the specific model can refer to the literature: "PankratzA. Forecasting with univariate Box-Jenkins models: Concepts and cases[M]. JohnWiley&Sons, 2009.”
- the present invention proposes a NLOS identification and compensation method based on the ARIMA model, which uses the ARIMA model to perform NLOS identification on each UWB fixed node one by one, and compares them according to the identification results.
- the distance observation value is compensated and corrected, the specific process is as follows:
- Step 4 According to the corrected distance from the UWB mobile node to each UWB fixed node and the coordinates of the UWB fixed node, a coordinate equation is established based on the principle of ranging and positioning, and the least square method is used to calculate the coordinates of the UWB mobile node to obtain the precise position of the vehicle .
- Figure 4 is a comparison chart of vehicle positioning trajectory of a set of test results.
- Figure 5 is a comparison diagram of the front and back positioning errors using the non-line-of-sight error processing algorithm based on the ARIMA model. It can be clearly seen from the trajectory in Figure 4 and the positioning error in Figure 5 that the non-line-of-sight error is used After processing the algorithm, the vehicle positioning accuracy is significantly improved.
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Abstract
一种车路协同场景下的车辆高精度定位方法，利用超宽带(Ultra-Wideband，UWB)定位技术多径分辨能力强，动态环境下抗干扰性能好等优点，通过合理布局UWB节点，结合非视距(Not Line ofSight，NLOS)误差处理算法，减小定位误差，从而实现车辆在典型城市环境下的准确、可靠、实时定位。
Description
本发明涉及车辆导航定位领域，特别涉及一种车路协同环境下的车辆高精度定位方法。
随着经济社会的发展与进步，我国的机动车保有量快速增加，道路交通面临着巨大的挑战，为了解决日益严峻的城市交通问题，智能车路协同系统(Intelligent Vehicle Infrastructure Cooperative Systems，IVICS)应运而生，并逐步成为智能交通系统(Intelligent Transport Systems，ITS)研究的最新发展方向。无论是车路协同的应用，还是智能交通的实现，都离不开高精度车辆定位技术：只有在实现准确、可靠车辆定位的前提下，全方位实施车车、车路动态实时信息交互，并在全时空动态交通信息采集与融合的基础上开展车辆主动安全控制和道路协同管理，充分实现人车路的有效协同，才能够有效地指挥调度车辆，改善城市交通，保证车辆安全行驶。因此，车辆定位技术是车路协同乃至智能交通等研究的基础与核心内容之一。
目前常见的车辆导航定位技术包括：航位推算(Dead Reckoning,DR)，惯性导航系统(Inertial Navigation System,INS)，卫星导航(Global Navigation Satellite System,GNSS)等。航位推算和惯性导航主要采用低成本的车载传感器，如电子罗盘，轮速传感器、微机械陀螺仪等来实现车辆的导航定位，由于传感器的测量误差会随时间不断的累积，定位精度通常不高；在各种定位方法中全球卫星导航系统(Global Navigation Satellite System，GNSS)在车辆定位领域的应用最为广泛，它泛指所有的卫星导航系统，技术较为成熟的有美国的全球定位系统(Global Positioning System，GPS)，俄罗斯的格洛纳斯导航系统以及中国的北斗卫星导航系统，GNSS能够实时的为车辆提供三维位置、速度、时间等信息，实现全天候、全方位的导航定位功能。在相对开阔的环境下，GNSS的定位精度较高，但是当车辆行驶在城市环境(如城市峡谷、高楼环立的交叉路口等)时，由于道路两旁的高层建筑，卫星信号易受到遮 挡，多路径效应严重，GNSS定位精度将急剧下降，甚至在遮挡严重时，会由于可见卫星数目不足而无法定位。
除了上述传统定位技术外，近年来，基于超宽带(Ultra-Wideband，UWB)的无线定位技术的兴起和迅速发展为实现车辆精确定位提供了新思路，目前，UWB定位技术主要被用于室内定位领域如人员、智能小车、机器人定位等。由于UWB的带宽极宽，脉冲信号穿透力强，多径分辨能力良好等技术优点，基于UWB实现室外环境下的车辆定位亦具备了一定的可行性，但相比于室内环境，室外场景通常较为复杂，在动态交通环境下，UWB信号更易受周围行驶的车辆、道路两旁的树木以及建筑物的干扰而出现非视距传播，非视距的UWB节点的距离观测值含有较大的误差，若引入定位解算，将导致较大的定位误差，因此，如何针对相对复杂的交通场景(如十字交叉路口、环岛路口等)，结合道路交通设施，合理布局路侧UWB节点，在车路协同下实现人车路信息动态交互，进而选取合适的方法，辨识与补偿UWB非视距误差，提高定位精度，是基于UWB实现车辆的高精度定位亟需解决的关键问题。
发明内容
技术问题：针对现有技术的不足，本发明提出了一种车路协同下的车辆高精度定位方法，通过合理布局UWB节点，结合非视距误差处理算法，减小定位误差，从而实现城市环境下的车辆高精度定位。
技术方案：为实现上述目的，本发明采用如下技术方案：首先，在道路两旁布置UWB固定节点，并通过高精度差分GNSS获取UWB固定节点的位置坐标；然后，在车辆顶部布置一个UWB移动节点，采用到达时间差法，获得该UWB移动节点到各个UWB固定节点的距离；接着，根据当前时刻之前的历史距离观测值建立ARIMA模型，利用ARIMA模型逐一对各个UWB固定节点进行NLOS辨识，并根据辨识结果对距离观测值进行补偿校正；进而，根据修正后的UWB移动节点到各个UWB固定节点的距离以及UWB固定节点的坐标，计算UWB移动节点的坐标，获取车辆的精确位置。
下面结合附图1，对本发明的思路作进一步的说明：
本发明的流程如附图1所示。
一种车路协同下基于UWB的车辆高精度定位方法，其特征在于，在典型城 市车路协同场景下，通过合理布局UWB节点，结合非视距误差补偿算法，有效减小动态交通场景下由于UWB信号传播非视距导致的定位误差，从而提高UWB定位的精度，实现城市车辆的准确、可靠、实时定位，所述方法包括如下步骤：
步骤1)确定UWB布局方案，在道路两旁布置UWB固定节点，并测量UWB固定节点的位置坐标；
其中UWB固定节点的数目与安装位置要针对实际的交通场景，综合考虑成本与定位精度需求，确定合理的布局方案，如图2中所示为本发明为典型城市交叉路口区域设计一种布局方案：在十字交叉路口的四个不同方向的道路两旁以及交叉路口环岛区域的中心，总共布设了九个UWB固定节点，这种布局方式，能够最大限度的覆盖整个交叉路口，实现车辆在路口区域的完整定位，并且在环岛中心布置的UWB节点，不易受遮挡干扰，有助于定位精度的提高；
步骤2)在车辆质心正上方的车顶上布置一个UWB移动节点，移动节点会跟随车辆移动，因此，移动节点的坐标即认为是车辆的坐标，UWB移动节点将周期性的发送并接收各个固定节点返回的信号，根据信号发送与返回的时间信息，采用到达时间差法(Time Difference of Arrival，TDOA)计算出车载UWB移动节点到各个UWB固定节点间的距离；
若UWB节点间无遮挡，即视距(Line of sight，LOS)传播，则计算出的距离观测值近似于真实值，但在如图3中所示的动态交通场景下，UWB节点间的直线传播易受周围行驶的车辆，道路两旁的树木以及建筑物等遮挡，信号传播路径出现反射、衍射等现象，即非视距传播(Not Line of Sight，NLOS)，则距离观测值含有较大的误差，若引入定位解算，会严重影响定位精度；
步骤3)根据当前时刻之前的历史距离观测值建立差分整合移动平均自回归模型(Autoregressive Integrated Moving Average model，ARIMA)，利用ARIMA模型逐一对各个UWB固定节点进行NLOS辨识，并根据辨识结果对距离观测值进行补偿校正，具体过程如下：
1.检验由初始时刻到当前时刻的距离观测量组成的时间序列的平稳性，若为非平稳的时间序列，则对序列进行差分处理，差分处理的阶数为d， 使之转化为平稳时间序列；
2.平稳化处理后，根据平稳时间序列的自相关函数和偏相关函数，确定ARIMA模型的阶数，ARIMA模型的阶数分别为p和q；
3.采用条件最小二乘法，估计ARIMA模型中的自回归部分以及滑动平均部分的参数；
4.对已建立ARIMA(p，d，q)模型的残差序列进行单位根检验，判断其是否为白噪声序列，若通过检验，则可进行预测，否则，需重新进行模型参数估计直至通过检验为止；
5.利用通过检验的ARIMA模型对当前时刻UWB移动节点与UWB固定节点间的距离进行预测，得到当前时刻的距离预测值
当前时刻的距离观测值为d(t)，预测值与观测值差的绝对值为
6.根据交通场景的复杂程度以及定位精度的需求设置NLOS辨识的阈值θ
thr，若
则判定UWB移动节点与UWB固定节点之间为NLOS传播，其距离观测值d(t)存在较大的非视距误差，并根据ARIMA模型的预测值，修正移动节点与UWB固定节点间的距离；
步骤4)根据修正后的UWB移动节点到各个UWB固定节点的距离以及UWB固定节点的坐标，基于测距定位原理建立坐标方程，采用最小二乘法，计算UWB移动节点的坐标，获取车辆的精确位置。
本发明的有益效果为：
1.本发明中提出了基于ARIMA模型的UWB非视距节点辨识及误差补偿算法，可以有效减小动态环境下的UWB测距误差，从而提高UWB的定位精度；
2.本发明中的方法，解决了实际交通环境下，将UWB定位技术应用于车辆导航领域，实现城市车辆的高精度定位问题。
图1是本发明的流程图。
图2是典型城市十字交叉路口环境下的UWB节点布局示意图。
图3是动态交通场景下视距与非视距UWB节点示意图。
图4是采用了基于ARIMA模型的非视距误差处理算法的前后的车辆定位轨迹对比图。
图5是采用了基于ARIMA模型的非视距误差处理算法的前后的车辆定位误差对比图。
下面结合附图和实施例对本申请作进一步的详细说明。可以理解的是，此处所描述的具体实施例仅用于解释相关发明，而非对该发明的限定。
随着经济社会的发展与进步，我国的机动车保有量快速增加，道路交通面临着巨大的挑战，为了解决日益严峻的城市交通问题，智能车路协同系统(Intelligent Vehicle Infrastructure Cooperative Systems，IVICS)应运而生，并逐步成为智能交通系统(Intelligent Transport Systems，ITS)研究的最新发展方向。无论是车路协同的应用，还是智能交通的实现，都离不开高精度车辆定位技术：只有在实现准确、可靠车辆定位的前提下，全方位实施车车、车路动态实时信息交互，并在全时空动态交通信息采集与融合的基础上开展车辆主动安全控制和道路协同管理，充分实现人车路的有效协同，才能够有效地指挥调度车辆，改善城市交通，保证车辆安全行驶。因此，车辆定位技术是车路协同乃至智能交通等研究的基础与核心内容之一。
目前常见的车辆导航定位技术包括：航位推算(Dead Reckoning,DR)，惯性导航系统(Inertial Navigation System,INS)，卫星导航(Global Navigation Satellite System,GNSS)等。航位推算和惯性导航主要采用低成本的车载传感器，如电子罗盘，轮速传感器、微机械陀螺仪等来实现车辆的导航定位，由于传感器的测量误差会随时间不断的累积，定位精度通常不高；在各种定位方法中全球卫星导航系统(Global Navigation Satellite System，GNSS)在车辆定位领域的应用最为广泛，它泛指所有的卫星导航系统，技术较为成熟的有美国的全球定位系统(Global Positioning System，GPS)，俄罗斯的格洛纳斯导航系统以及中国的北斗卫星导航系统，GNSS能够实时的为车辆提供三维位置、速度、时间等信息，实现全天候、全方位的导航定位功能。在相对开阔的环境下，GNSS的定位精度较高，但是当车辆行驶在城市环境(如城市峡谷、 高楼环立的交叉路口等)时，由于道路两旁的高层建筑，卫星信号易受到遮挡，多路径效应严重，GNSS定位精度将急剧下降，甚至在遮挡严重时，会由于可见卫星数目不足而无法定位。
除了上述传统定位技术外，近年来，基于超宽带(Ultra-Wideband，UWB)的无线定位技术的兴起和迅速发展为实现车辆精确定位提供了新思路，目前，UWB定位技术主要被用于室内定位领域如人员、智能小车、机器人定位等。由于UWB的带宽极宽，脉冲信号穿透力强，多径分辨能力良好等技术优点，基于UWB实现室外环境下的车辆定位亦具备了一定的可行性，但相比于室内环境，室外场景通常较为复杂，在动态交通环境下，UWB信号更易受周围行驶的车辆、道路两旁的树木以及建筑物的干扰而出现非视距传播，非视距的UWB节点的距离观测值含有较大的误差，若引入定位解算，将导致较大的定位误差，因此，如何针对相对复杂的交通场景(如十字交叉路口、环岛路口等)，结合道路交通设施，合理布局路侧UWB节点，在车路协同下实现人车路信息动态交互，进而选取合适的方法，辨识与补偿UWB非视距误差，提高定位精度，是基于UWB实现车辆的高精度定位亟需解决的关键问题。
针对现有技术的不足，本发明提出了一种车路协同下的车辆高精度定位方法，通过合理布局UWB节点，结合非视距误差处理算法，减小定位误差，从而实现城市环境下的车辆高精度定位。
请参考图1，其示出了根据本发明中的方法实现车辆高精度定位方法的一个实施例的流程：
首先，在道路两旁布置UWB固定节点，并通过高精度差分GNSS获取UWB固定节点的位置坐标；然后，在车辆顶部布置一个UWB移动节点，采用到达时间差法，获得该UWB移动节点到各个UWB固定节点的距离；接着，根据当前时刻之前的历史距离观测值建立ARIMA模型，利用ARIMA模型逐一对各个UWB固定节点进行NLOS辨识，并根据辨识结果对距离观测值进行补偿校正；进而，根据修正后的UWB移动节点到各个UWB固定节点的距离以及UWB固定节点的坐标，计算UWB移动节点的坐标，获取车辆的精确位置。
本实施例中UWB节点具体采用的是RK-101型UWB模块。
下面结合附图，对本发明的思路作进一步的说明：
一种基于UWB的车辆高精度定位方法，其特征在于，在典型城市车路协同场景下，通过合理布局UWB节点，结合非视距误差补偿算法，有效减小动态交通场景下由于信号传播非视距导致的定位误差，从而提高UWB定位的精度，实现城市车辆的准确、可靠、实时定位，所述方法包括如下步骤：
步骤1)在道路两旁布置UWB固定节点，并通过高精度差分GPS获取UWB固定节点的位置坐标；
其中UWB固定节点的数目与安装位置要针对实际的交通场景，综合考虑成本与定位精度需求，确定合理的布局方案，请参考图2中，本发明为典型城市交叉路口区域提出的一种布局方案：在十字交叉路口的四个不同方向的道路两旁以及交叉路口环岛区域的中心，总共布设了九个UWB固定节点，这种布局方式，能够最大限度的覆盖整个交叉路口，实现车辆在路口区域的完整定位，并且在环岛中心布置的UWB节点，不易受遮挡干扰，有助于定位精度的提高；
步骤2)在车辆质心正上方的车顶上布置一个UWB移动节点，移动节点会跟随车辆移动，因此，移动节点的坐标即认为是车辆的坐标，UWB移动节点将周期性的发送并接收各个固定节点返回的信号，根据信号发送与返回的时间信息，采用到达时间差法(Time Difference of Arrival，TDOA)计算出车载UWB移动节点到各个UWB固定节点间的距离；
若UWB节点间无遮挡，即视距(Line of sight，LOS)传播，则计算出的距离观测值近似于真实值，但在如图3中所示的动态交通场景下，UWB节点间的直线传播易受周围行驶的车辆，道路两旁的树木以及建筑物等遮挡，信号传播路径出现反射、衍射等现象，即非视距传播(Not Line of Sight，NLOS)，则距离观测值含有较大的误差，若引入定位解算，会严重影响定位精度；
步骤3)根据当前时刻之前的历史距离观测值建立差分整合移动平均自回归模型(Autoregressive Integrated Moving Average model，ARIMA)，ARIMA模型是由Box和Jenkins提出的著名的时间序列分析方法，又称为Box-Jenkins模型，具体模型可参考文献：“PankratzA.Forecasting with univariate Box-Jenkins models: Concepts and cases[M].JohnWiley&Sons,2009.”
本发明中针对NLOS的UWB节点距离观测值存在较大误差的问题，提出了一种基于ARIMA模型的NLOS辨识与补偿方法，利用ARIMA模型逐一对各个UWB固定节点进行NLOS辨识，并根据辨识结果对距离观测值进行补偿校正，具体过程如下：
1.检验由初始时刻到当前时刻的距离观测量组成的时间序列的平稳性，若为非平稳的时间序列，则对序列进行差分处理，差分处理的阶数为d，使之转化为平稳时间序列；
2.平稳化处理后，根据平稳时间序列的自相关函数和偏相关函数，确定ARIMA模型的阶数，ARIMA模型的阶数分别为p和q；
3.采用条件最小二乘法，估计ARIMA模型中的自回归部分以及滑动平均部分的参数；
4.对已建立ARIMA(p,d,q)模型的残差序列进行单位根检验，判断其是否为白噪声序列，若通过检验，则可进行预测，否则，需重新进行模型参数估计直至通过检验为止；
5.利用通过检验的ARIMA模型对当前时刻UWB移动节点与UWB固定节点间的距离进行预测，得到当前时刻的距离预测值
当前时刻的距离观测值为d(t)，预测值与观测值差的绝对值为
6.根据交通场景的复杂程度以及定位精度的需求设置NLOS辨识的阈值θ
thr，若
则判定UWB移动节点与UWB固定节点之间为NLOS传播，其距离观测值d(t)存在较大的非视距误差，并根据ARIMA模型的预测值，修正移动节点与UWB固定节点间的距离；
步骤4)根据修正后的UWB移动节点到各个UWB固定节点的距离以及UWB固定节点的坐标，基于测距定位原理建立坐标方程，采用最小二乘法，计算UWB移动节点的坐标，获取车辆的精确位置。
在本实施实例中，为了检验提出的基于ARIMA模型的NLOS误差补偿算法的有益效果，进行了实车试验，并对采用了基于ARIMA模型的非视距误差补偿 算法处理前、后的一组实验结果进行了对比。本实施例中，具体采用的是ARIMA(2,1,1)模型，NLOS辨识阈值θ
thr为0.5米。
图4为一组试验结果车辆定位轨迹对比图。图5为采用了基于ARIMA模型的非视距误差处理算法的前、后的定位误差对比图，由图4中的轨迹以及图5中的定位误差可以明显看出，采用了非视距的误差处理算法后，车辆定位精度显著提高。
以上所述仅为本发明的优选实施例而已，并不用于限制本发明，对于本领域的技术人员来说，本发明可以有各种更改和变化。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 (1)
- 一种车路协同下基于超宽带的车辆高精度定位方法，其特征在于，所述方法包括如下步骤：步骤1)确定UWB布局方案，在道路两旁布置UWB固定节点，并测量UWB固定节点的位置坐标；步骤2)在车辆顶部布置一个UWB移动节点，采用到达时间差法获得该UWB移动节点到各个UWB固定节点的距离观测值；步骤3)根据当前时刻之前的历史距离观测值建立差分整合移动平均自回归模型，利用ARIMA模型逐一对各个UWB固定节点进行NLOS辨识，并根据辨识结果对距离观测值进行补偿校正，具体过程如下：1.检验由初始时刻到当前时刻的距离观测值组成的时间序列的平稳性，若为非平稳的时间序列，则对序列进行差分处理，使之转化为平稳时间序列；2.平稳化处理后，根据平稳时间序列的自相关函数和偏相关函数，确定ARIMA模型的阶数；3.采用条件最小二乘法，估计ARIMA模型中的自回归部分以及滑动平均部分的参数；4.对已建立的ARIMA模型的残差序列进行单位根检验，判断其是否为白噪声序列，若通过检验，则可进行预测，否则，需重新进行模型参数估计直至通过检验为止；5.利用通过检验的ARIMA模型对当前时刻UWB移动节点与UWB固定节点间的距离进行预测，得到当前时刻的距离预测值当前时刻的距离观测值为d(t)，预测值与观测值差的绝对值为
6.根据交通场景的复杂程度以及定位精度的需求设置NLOS辨识的阈值θ thr，若则判定UWB移动节点与UWB固定节点之间为NLOS传播，其距离观测值d(t)存在较大的非视距误差，并根据ARIMA模型的预测值，修正移动节点与UWB固定节点间的距离；
步骤4)根据修正后的UWB移动节点到各个UWB固定节点的距离以及 UWB固定节点的坐标，基于测距定位原理建立坐标方程，采用最小二乘法，计算UWB移动节点的坐标，获取车辆的精确位置。
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| US20220179061A1 (en) | 2022-06-09 |
| CN109946648A (zh) | 2019-06-28 |
| CN109946648B (zh) | 2022-04-29 |
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