WO2020221049A1 - 基于时空域联合处理的分布式协作定位系统和方法 - Google Patents
基于时空域联合处理的分布式协作定位系统和方法 Download PDFInfo
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- 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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- 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
- G01S19/00—Satellite radio beacon positioning systems; Determining position, velocity or attitude using signals transmitted by such systems
- G01S19/38—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system
- G01S19/39—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system the satellite radio beacon positioning system transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
- G01S19/42—Determining position
- G01S19/45—Determining position by combining measurements of signals from the satellite radio beacon positioning system with a supplementary measurement
- G01S19/46—Determining position by combining measurements of signals from the satellite radio beacon positioning system with a supplementary measurement the supplementary measurement being of a radio-wave signal type
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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
- G01S5/0284—Relative positioning
- G01S5/0289—Relative positioning of multiple transceivers, e.g. in ad hoc networks
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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
- G01S5/0294—Trajectory determination or predictive filtering, e.g. target tracking or Kalman filtering
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- H04W—WIRELESS COMMUNICATION NETWORKS
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- H—ELECTRICITY
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- H04W—WIRELESS COMMUNICATION NETWORKS
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- H04W4/02—Services making use of location information
- H04W4/025—Services making use of location information using location based information parameters
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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
- G01S19/00—Satellite radio beacon positioning systems; Determining position, velocity or attitude using signals transmitted by such systems
- G01S19/38—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system
- G01S19/39—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system the satellite radio beacon positioning system transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
- G01S19/42—Determining position
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- 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
- H04W4/029—Location-based management or tracking services
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- 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
Definitions
- the invention belongs to the field of navigation, and is a distributed coordinated positioning system and method based on joint processing of time and space domains.
- GNSS Global Positioning System
- all nodes can be roughly divided into two categories: (1) reference nodes, which can locate themselves through traditional positioning methods; (2) non-reference nodes, which cannot perform self-positioning and need Obtain own location information through collaboration.
- the performance of cooperative positioning has a great relationship with the data fusion method.
- the extended Kalman filter algorithm and the confidence propagation algorithm are more commonly used cooperative positioning methods. Other methods include the unscented Kalman filter algorithm, the least square algorithm and the maximum similarity algorithm. However, estimation algorithms and so on.
- the cooperative positioning algorithm based on the extended Kalman filter algorithm has low complexity. It uses a linear system to approximate the nonlinear cooperative positioning system. This approximation greatly reduces the positioning performance of the system.
- the unscented Kalman filter algorithm uses a nonlinear transformation to approximate the nonlinear positioning system, which has better performance than the extended Kalman filter algorithm.
- Confidence propagation algorithms based on factor graphs such as the SPAWN algorithm, are also widely used.
- the algorithm is sensitive to the accuracy of the initial value of the solution. It can provide higher positioning accuracy in a static or quasi-static network environment, but when used in a high-speed environment, the error of the initial value of the algorithm is relatively large. , So its final positioning accuracy is also poor.
- the least squares algorithm or the maximum likelihood algorithm is asymptotically optimal under Gaussian white noise conditions, but because the target problem is non-convex, its solution is relatively difficult.
- the aforementioned collaborative positioning methods can be divided into two categories: centralized methods and distributed methods.
- a centralized cooperative positioning system all calculations are completed in the central processing node. Before this, the central processing node needs to collect the observation data of all nodes in the network. This type of method has high positioning accuracy, but when applied to a large-scale network, it will lead to greater communication burden and computational complexity. Distributed methods can effectively reduce communication burden and computational complexity at the expense of certain positioning accuracy.
- the calculation of the position of each node is completed within each node, and each node only needs to collect the observation information of neighboring nodes.
- all the aforementioned cooperative positioning algorithms only consider the observation information in the current time slice in each time slice. When the node movement speed is relatively large or the number of connections is insufficient, the positioning accuracy of these algorithms is relatively poor or even divergent.
- the present invention proposes a distributed cooperative positioning system and method based on joint processing in the spatio-temporal domain.
- the system is based on the trajectory information constraints of the nodes, and the position information of all nodes within several hops of the target node in multiple time slices and The relative distance information is fused to complete the positioning of the target node.
- the target node in each time slice, the target node first collects the position information and relative distance information of all nodes in the range of several hops in multiple time slices backtracking; then based on the historical measurement of the above-mentioned multi-hop node Information, predict all the measurement information of these nodes in multiple time slices up to the current moment; then based on the state information variables of these nodes at the current moment, invert the trajectory information of these nodes in these time slices; then based on history Measure the information, use the traditional collaborative positioning method to calculate the initial estimated value of the state information of these nodes at the current moment, as the initial solution value of the node trajectory information; finally, based on the node’s trajectory information constraint, the historical measurement information in multiple time slices And the prediction information is fused to obtain a higher-precision estimate of the state information of the target node at the current moment.
- the purpose of the present invention is to solve the problem that some nodes in a large-scale wireless network with high-speed movement cannot be positioned through traditional positioning methods, such as GNSS, and there will be insufficient connections under harsh environmental conditions.
- Distributed cooperative positioning system and method for joint processing are used to solve the problem that some nodes in a large-scale wireless network with high-speed movement cannot be positioned through traditional positioning methods, such as GNSS, and there will be insufficient connections under harsh environmental conditions.
- the present invention discloses a distributed cooperative positioning system based on joint processing of time and space.
- the input of the initial estimation module and the output of the multi-time slice observation information buffer module are used as the input of the observation information prediction module.
- the state variables of each node at the current moment are used as the input of the node trajectory information inversion module, the observation information prediction module and the node trajectory information inversion module
- the output of the node state information preliminary estimation module is also used as the input of the spatio-temporal joint processing module, and the output of the spatio-temporal joint processing module is the result.
- Another aspect of the present invention also discloses a distributed coordinated positioning method of the system, which includes the following steps:
- the multi-time slice observation information caching module caches all the observation information in multiple time slices that the node within a few hops of the target node backtracks at the current time, and sends these observation information to the observation Information prediction module;
- the target node needs to pass through the intermediate node for multi-hop communication to obtain the measurement information of the multi-hop node, that is to say, the target node cannot obtain the measurement information of the multi-hop node at the current moment.
- Observation information prediction module Based on the historical measurement information of multi-hop nodes, predict all measurement information of these nodes in multiple time slices up to the current moment, and send historical measurement information and prediction information to the spatio-temporal joint processing module;
- the node trajectory information inversion module based on the current state information variables of all nodes within the multi-hop range of the target node, calculates the position information of these nodes in multiple time slices back in the future, and obtains these nodes in these
- the trajectory information in the time slice is sent to the spatio-temporal domain joint processing module;
- the initial estimation module of node status information is based on historical measurement information, and uses a cooperative positioning method based on single time slice measurement information to make an initial estimate of the current state information of all nodes within a few hops of the target node; Estimated to be sent to the spatio-temporal joint processing module;
- the spatio-temporal domain joint processing module uses the node state information estimation provided by the node state information initial estimation module as the initial value of the solution, and uses the motion trajectory constraint to determine the historical measurement information of all nodes in multiple time slices within several hops of the target node Perform joint processing in the time and space domain with the prediction information to obtain the state estimation of these nodes at the current moment, which includes the estimated value of the target node position.
- the present invention Based on the movement trajectory constraints of each node in the system, the present invention performs joint processing in the spatio-temporal domain on the historical measurement information and prediction information of all nodes within several hops of the target node in multiple time slices. By increasing the cooperation in the time domain, Effectively improve the positioning accuracy of the node and the stability of the positioning result. In addition, the present invention distributes all calculations to each node in the system, which can effectively reduce the communication burden of the system and the calculation complexity of each node.
- Figure 1 is a block diagram of a distributed coordinated positioning method based on joint processing in the spatio-temporal domain
- Figure 2 is a graph of the cumulative density of positioning errors of non-reference nodes
- Figure 3 is a graph of the cumulative density of positioning errors of reference nodes.
- the invention designs a distributed cooperative positioning system and method.
- This system is aimed at the problem that some nodes in a large-scale mobile network cannot complete self-location. It uses the location information and relative distance information of all nodes in the multi-hop range of the target node to complete the target node's location in multiple time slices backtracking. .
- the proposed distributed coordinated positioning method is based on the current state information of all nodes within the multi-hop range of the target node to reverse the trajectory information of these nodes in multiple time slices backtracking. Then, based on the trajectory information constraints of the nodes, the observation information of these nodes in multiple time slices is processed in spatio-temporal domain to obtain the position estimation of the target node at the current moment. This method can effectively improve the positioning accuracy of nodes and the reliability of positioning results.
- Each node in the distributed cooperative positioning system based on the joint processing of the spatio-temporal domain contains the following five modules: multi-time slice observation information cache module, observation information prediction module, node trajectory information inversion module, node state information initial estimation module, and time-space Domain joint processing module.
- the distributed collaborative positioning method designed for this system includes the following steps:
- each node in the network is done by the node itself.
- m the node to be located, called the target node, and define ⁇ m as the set of all nodes within several hops of the target node m .
- g m,l represents the l-hop node of node m, Indicates the estimated position of node n at time t. If node n is not a reference node, Is empty. Send these observation information to the observation information prediction module;
- the observation information prediction module predicts all the measurement information of these nodes in multiple time slices up to the current moment, and defines the historical measurement information and prediction information of these node positions for The historical measurement information and prediction information of the relative distance between nodes are defined as among them Then send these historical measurement information and prediction information to the spatio-temporal domain joint processing module;
- ⁇ T t (tk) ⁇ T
- ⁇ T represents the length of the time slice.
- the above formula represents the trajectory information of node n from time ⁇ to time k at K time, and the state variable of the node is defined as
- trajectory information of node n can be expressed as
- the node state information initial estimation module adopts the traditional coordinated positioning method based on single time slice observation information, such as extended Kalman filter algorithm, unscented Kalman filter algorithm, etc., to calculate the state of all nodes in ⁇ m at the current moment k
- the initial estimated value of the information is used as the initial solution value of the node trajectory information in the joint processing of multi-time slice observation information.
- the spatio-temporal domain joint processing module is based on the initial estimated value of the node state information provided by the node state information initial estimation module, and uses the node's motion trajectory constraint to fuse the observation information of all nodes in ⁇ m in multiple time slices to obtain The estimated state of these nodes at the current moment includes the estimated position of the target node.
- the definition Observation information based on multiple time slices as well as The maximum likelihood estimate of ⁇ k is
- the system block diagram of the cooperative positioning system is shown in Figure 1, including a multi-time slice observation information buffer module 101, an observation information prediction module 102, a node trajectory information inversion module 103, a node state information initial estimation module 104, and a spatio-temporal joint processing module 105.
- the observation information is simultaneously input to the multi-time slice observation information buffer module 101 and the node state information preliminary estimation module 104.
- the output terminal of the multi-time slice observation information buffer module is connected to the input terminal 102 of the observation information prediction module, and each node state variable at the current moment is input To the node trajectory information inversion module 103, the output terminals of the observation information prediction module 102, the node trajectory information inversion module 103, and the node state information preliminary estimation module 104 are all connected to the input terminals of the spatio-temporal domain joint processing module 105.
- Set in a motion network there are 113 nodes, of which 13 nodes can obtain their own position information through GNSS. There is a certain error in the GNSS observation information. The rest of the nodes cannot complete self-positioning. All nodes are randomly distributed in one at the beginning. Within an area of 1000m ⁇ 1000m. Due to the limitation of distance and power consumption and the occlusion of obstacles, each node can only communicate with some nodes in the network.
- the communication radius of each node is set to be 200m.
- the relative distance information with neighboring nodes and the location information of neighboring nodes can be obtained through the communication link.
- the initial velocities of all nodes are set to 40m/s, and the acceleration meets a Gaussian distribution with a mean value of 0m/s 2 and a standard deviation of 0.2m/s 2 .
- the error standard deviation of the position of the reference node obtained through GNSS is 4m, and the error standard deviation of the relative distance information between nodes is 0.4m.
- Each node in the distributed cooperative positioning system based on the joint processing of the spatio-temporal domain contains the following five modules: multi-time slice observation information cache module, observation information prediction module, node trajectory information inversion module, node state information initial estimation module, Joint processing module in time and space.
- the distributed collaborative positioning method designed for this system includes the following steps:
- Is the position information of node n at time t Is the GNSS observation information of the reference node, Represents the position observation error, which satisfies the mean value of zero, and the covariance matrix is Gaussian distribution
- Is the relative distance information observation value between node n and node j Represents the true distance
- the target node needs to pass through the intermediate node for multi-hop communication to obtain the measurement information of the multi-hop node, and the multi-hop communication has a delay, that is to say, in the current time slice k, the target node m can only Obtain the position information of the node in g m,1 until k-1 and the relative distance information obtained by these nodes until k-1, and only the position information of the node in g m,2 until k-1 and these nodes The obtained relative distance information up to k-2, and so on.
- the transmission delay from the node in g m,l to the target node will be greater, the improvement of the positioning performance of the target node will gradually become smaller, and the computational complexity will also increase.
- ⁇ m ⁇ m ⁇ g m,1 ⁇ g m,2 .
- the multi-time slice measurement information caching module of the target node m buffers the position estimation information of the node in ⁇ m from time ⁇ to time k in K time slices and the relative distance information between these nodes:
- the multi-time slice measurement information caching module of the target node m cannot obtain all the observation information of all nodes in ⁇ m up to the current time k.
- the observation information prediction module is based on the historical measurement information of all nodes in ⁇ m , All measurement information in multiple time slices up to the current moment is predicted:
- the functions f m and f l represent the prediction function of the node position
- h l is the prediction function of the relative distance information.
- the present invention predicts the position information and relative distance information of the node based on the extended Kalman filter algorithm. Further, define:
- each node is in these K time slices.
- the interior is in a state of uniform acceleration linear motion.
- node n is The location information at the moment is:
- ⁇ T t (tk) ⁇ T
- ⁇ T represents the length of the time slice. It can be seen that for node n, all the position information from time ⁇ to time k represents the trajectory information of the node at these K times, and the state variable of the node is defined as
- trajectory information of node n can be expressed as
- I 2 represents a 2 ⁇ 2 unit array, and sends the trajectory information of all nodes to the space-time domain joint processing module;
- the node state information initial estimation module is based on the historical measurement information within a single time slice, and uses the extended Kalman filter algorithm to calculate the current time of all nodes in ⁇ m
- the initial estimated value of k-state information is used as the initial solution value of node trajectory information during the joint processing of multi-time slice information.
- the matrix F is
- 0 2 represents a 2 ⁇ 2 all-zero matrix. Represents an estimate The covariance matrix, Model the noise covariance matrix for the system. Then calculate the observation data on The Jacobian matrix:
- N n denotes the number of neighboring nodes of node n.
- matrix I is a diagonal array:
- the target node m can only get the historical measurement information of the node in g m,1 until time k-1, so the above method can only get the posterior estimation of the state from the node in g m,1 to time k-1 .
- One-step forecast As the state estimation value at time k, similarly, two-step prediction is needed for the nodes in g m,2 to get the state estimation value of these nodes at time k. Use these estimated information as the initial estimated value of node state information and send it to the spatio-temporal domain joint processing module;
- the spatio-temporal joint processing module is based on the initial estimated value of each node state information provided by the node state information initial estimation module, and uses the motion trajectory constraints of all nodes in ⁇ m to fuse the historical measurement information and prediction information of multiple time slices. Get the current state estimation of each node. Specifically, the definition Contains the state information of all nodes in ⁇ m at time k, based on the observation information of multiple time slices as well as The maximum likelihood estimate of ⁇ k is
- N m represents the number of neighboring nodes of node m, Indicates that node n and node j are connected, otherwise
- Figure 2 shows the cumulative density curve of positioning errors for non-reference nodes
- Figure 3 shows the cumulative density curve of positioning errors for reference nodes.
- JSTP-DMLE is a distributed cooperative positioning method based on spatio-temporal joint processing proposed in the present invention.
- EKF, UKF, and SPAWN are three comparison methods. Among them, EKF refers to the extended Kalman filtering algorithm, UKF refers to the unscented Kalman filtering algorithm, and SPAWN is a collaborative positioning method based on factor graphs.
- "GNSS" in Figure 3 represents the cumulative density curve of the node positioning error obtained by the reference node through the traditional GNSS positioning method.
- JSTP-DMLE when used in large-scale mobile networks, JSTP-DMLE can provide higher positioning accuracy than comparison algorithms. In addition, based on GNSS observation information, these cooperative positioning methods can further improve The positioning accuracy of the reference node.
- the definition Is the mean square error of node positioning.
- the RMSE of JSTP-DMLE is 0.4561m
- EKF is 0.6031m
- UKF is 0.5762m
- SPAWN is 0.6320m
- the RMSE of JSTP-DMLE is 0.4182m
- EKF is 0.5718m
- UKF is 0.4902m
- SPAWN is 0.5409m.
Abstract
本发明提供了一种基于时空域联合处理的分布式协作定位系统和方法。通过利用目标节点多跳范围内节点的位置信息及相对距离信息，完成目标节点的定位。针对该系统所设计的分布式协作定位方法在每个时间片，目标节点首先收集若干跳范围内节点在多个时间片内的位置信息及相对距离信息，然后预测这些节点到当前时刻为止的多个时间片内所有的测量信息，最后基于运动轨迹约束对多时间片的历史测量信息及预测信息进行时空域的联合处理，得到目标节点在当前时刻的位置估计。本发明通过对多跳范围内节点在多时间片内的信息进行联合处理，能够有效提高节点的定位精度和定位结果的稳定性。
Description
本申请基于申请号为201910349661.8、申请日为2019/04/28的中国专利申请提出，并要求该中国专利申请的优先权，该中国专利申请的全部内容在此引入本申请作为参考。
本发明属于导航领域，是一种基于时空域联合处理的分布式协作定位系统和方法。
实时、高精度的定位信息对于很多无线网络来说至关重要。通常情况下，传统的定位系统，如全球卫星定位系统(GNSS)，能够为这些网络提供较高精度的定位信息。但是，出于成本和功耗的考虑，给网络中每个节点都安装一个GNSS接收机是不现实的。另外，在恶劣环境条件下，例如城市峡谷、森林等，GNSS信号容易受到干扰，此时，GNSS提供的位置信息的精度大大降低。协作定位能够有效利用网络中节点间的协作来提高定位的性能。在协作定位系统中，所有的节点可以大致分为两类：(1)参考节点，这些节点能够通过传统的定位方法对自身进行定位；(2)非参考节点，这些节点无法进行自定位，需要通过协作获得自身位置信息。协作定位的性能与数据融合方法有很大的关系，扩展卡尔曼滤波算法和置信度传播算法是比较常用的协作定位方法，其他方法还包括无迹卡尔曼滤波算法、最小二乘算法和最大似然估计算法等。基于扩展卡尔曼滤波算法的协作定位算法具有较低的复杂度，它利用一个线性系统对非线性的协作定位系统进行近似，这个近似大大降低了系统的定位性能。无迹卡尔曼滤波算法利用一个非线性变换对非线性的定位系统进行近似，它具有比扩展卡尔曼滤波算法更好的性能。基于因子图的置信度传播算法，如SPAWN算法，也被广泛运用。该算法对于解算初值的精度比较敏感，在静态或者准静态的网络环境下可以提供较高的定位精度，但是当运用于高速运动的环境时，该算法的解算初值的误差比较大，因而其最终的定位精度也较差。最小二乘算法或者最大似然算法在高斯白噪声条件下渐近最优，但是由于目标问题是非凸的，其解算难度比较大。
上述的协作定位方法可以分为两类：集中式方法和分布式方法。在集中式协作定位系统中，所有的计算都在中心处理节点中完成，在这之前中心处理节点需要把网络中所有节点的观测数据收集起来。这类方法具有较高的定位精度，但是当运用于大规模网络时，会导致较大的通信负担和计算复杂度。分布式方法在牺牲一定定位精度的情况下能够有效减小通信负担和计算复杂度。具体来讲，在分布式协作定位系统中，各个节点位置的计算是在各自内部完成的，每个节点只需收集邻近节点的观测信息即可。另外，上述所有的协作定位算法在每个时间片只考虑了当前时间片内的观测信息，当节点运动速度比较大或者连接数不足时，这些算法的定位精度比较差甚至会发散。
本发明针对大规模网络中节点的定位问题，提出了一种基于时空域联合处理的分布式协作定位系统和方法。该系统针对大规模移动网络中部分节点无法完成自定位，并且会出现连接数不足等问题，基于节点的轨迹信息约束，对目标节点若干跳范围内所有节点在多个时间片内的位置信息以及相对距离信息进行融合，进而完成目标节点的定位。所提出的协作定位方法在每个时间片内，目标节点首先收集若干跳范围内所有节点在往前回溯的多个时间片内的位置信息以及相对距离信息；然后基于上述多跳节点的历史测量信息，对这些节点到当前时刻为止的多个时间片内所有的测量信息进行预测；接着基于这些节点在当前时刻的状态信息变量，反演出这些节点在这些时间片内的轨迹信息；随后基于历史测量信息，利用传统的协作定位方法计算出这些节点在当前时刻状态信息的初始估计值，作为节点轨迹信息的初始解算值；最后基于节点的轨迹信息约束，对多时间片内的历史测量信息以及预测信息进行融合，得到更高精度的关于当前时刻目标节点状态信息的估计值。
发明内容
本发明的目的是针对恶劣的环境条件下，高速运动的大规模无线网络中部分节点无法通过传统的定位方法，如GNSS，进行定位，并且会出现连接数不足的情况，提出一种基于时空域联合处理的分布式协作定位系统和方法。
本发明一方面公开了一种基于时空域联合处理的分布式协作定位系统，分布式协作定位系统中存在多个节点，各个节点通过与邻近节点的协作完成自身的定位；系统中的每个节点均包括多时间片观测信息缓存模块、观测信息预测模块、节点轨迹信息反演模块、节点状态信息初估计模块、时空域联合处理模块；观测信息同时作为多时间片观测信息缓存模块、节点状态信息初估计模块的输入，多时间片观测信息缓存模块的输出作为观测信息预测模块的输入，当前时刻各节点状态变量作为节点轨迹信息反演模块的输入，观测信息预测模块、节点轨迹信息反演模块、节点状态信息初估计模块的输出同时作为时空域联合处理模块的输入，时空域联合处理模块的输出作为结果。
本发明另一方面还公开了一种所述系统的分布式协作定位方法，包括如下步骤：
(1)在每个时间片，多时间片观测信息缓存模块缓存了目标节点若干跳范围内节点在当前时刻往前回溯的多个时间片内的所有观测信息，将这些观测信息送入到观测信息预测模块；
(2)在分布式网络中，目标节点需要经过中间节点进行多跳通信才能获得多跳节点的测量信息，也就是说目标节点并不能获得多跳节点在当前时刻的测量信息，观测信息预测模块基于多跳节点的历史测量信息，对这些节点到当前时刻为止的多个时间片内所有的测量信息进行预测，将历史测量信息和预测信息送入到时空域联合处理模块；
(3)节点轨迹信息反演模块基于目标节点多跳范围内的所有节点在当前时刻的状态信息变量，推算出这些节点在往前回溯的多个时间片内的位置信息，得到这些节点在这些时间片内的轨迹信息，将轨迹信息送入到时空域联合处理模块；
(4)节点状态信息初估计模块基于历史测量信息，利用基于单时间片测量信息的协作定位方法对目标节点若干跳范围内的所有节点在当前时刻的状态信息进行初估计；将这些状态信息初估计送入到时空域联合处理模块；
(5)时空域联合处理模块将节点状态信息初估计模块提供的节点状态信息估计作为解算的初值，利用运动轨迹约束对目标节点若干跳范围内所有节点在多时间片内的历史测量信息和预测信息进行时空域的联合处理，得到这些节点在当前时刻的状态估计，这其中就包括了目标节点位置的估计值。
本发明基于系统中各节点的运动轨迹约束，对目标节点若干跳范围内的所有节点在多个时间片内的历史测量信息及预测信息进行时空域的联合处理，通过增加时间域的协作，可以有效提高节点的定位精度和定位结果的稳定性。另外，本发明将所有的计算分散到系统中的各个节点，可以有效减小系统的通信负担和各节点的计算复杂度。
图1是基于时空域联合处理的分布式协作定位方法的框图；
图2是非参考节点的定位误差累积密度图；
图3是参考节点的定位误差累积密度图。
本发明设计了一种分布式的协作定位系统和方法。该系统是针对大规模的移动网络中部分节点无法完成自定位的问题，利用目标节点多跳范围内所有节点在往前回溯的多个时间片内的位置信息及相对距离信息完成目标节点的定位。所提出的分布式协作定位方法基于目标节点多跳范围内所有节点在当前时刻的状态信息反演出这些节点在往前回溯的多个时间片内的轨迹信息。然后基于节点的轨迹信息约束对这些节点在多个时间片内的观测信息进行时空域的联合处理，得到目标节点在当前时刻的位置估计。该方法能够有效提高节点的定位精度以及定位结果的可靠性。
基于时空域联合处理的分布式协作定位系统中每个节点包含了以下五个模块：多时间片观测信息缓存模块、观测信息预测模块、节点轨迹信息反演模块、节点状态信息初估计模块、时空域联合处理模块。针对该系统所设计的分布式协作定位方法包括如下步骤：
(1)在分布式协作定位中，网络中各个节点的定位由节点自身完成，定义m为待定位节点，称之为目标节点，定义ξ
m为在目标节点m若干跳范围内所有节点的集合。首先建立系统的状态模型，确定系统的观测方程：
其中，
为节点n在t时刻的位置信息，
为参考节点的位置观测信息，
表示位置观测误差；
为节点n与节点j之间的相对距离信息观测值，
表示的是真实距离，
表示的是相对距离信息观测误差；
(2)在当前时间片k，缓存目标节点m若干跳范围所有内节点在τ＝k-K+1时刻到k时刻的K个时间片内的位置估计信息以及这些节点间的相对距离信息：
其中，g
m,l表示节点m的l跳节点，
表示节点n在t时刻的位置估计值。如果节点n不是参考节点，
为空。将这些观测信息送入到观测信息预测模块；
(3)观测信息预测模块基于ξ
m中所有节点的历史测量信息，对这些节点到当前时刻为止的多个时间片内所有的测量信息进行预测，将这些节点位置的历史测量信息和预测信息定义为
将节点间相对距离的历史测量信息和预测信息定义为
其中
然后将这些历史测量信息和预测信息送入到时空域联合处理模块；
(4)基于节点n∈ξ
m在当前时刻位置变量
结合节点的运动状态变量，如速度
加速度
等，推算出该节点在前K-1个时刻的位置信息。假定各个节点在这K个时间片内处于匀加速直线运动状态，基于k时刻节点的运动状态，定义节点n在
时刻的位置信息为：
其中，ΔT
t＝(t-k)ΔT，ΔT表示时间片的长度。上式表示了节点n从τ时刻到k时刻的K个时刻的轨迹信息，定义节点的状态变量为
则节点n的轨迹信息可以表示为
将这些节点的轨迹信息送入到时空域联合处理模块；
(5)节点状态信息初估计模块采用传统的基于单时间片观测信息的协作定位方法，如扩展卡尔曼滤波算法、无迹卡尔曼滤波算法等，计算出ξ
m中所有节点在当前时刻k状态信息的初始估计值，作为多时间片观测信息联合处理时节点轨迹信息的初始解算值。将节点状态信息的初始估计值送入到时空域联合处理模块；
(6)时空域联合处理模块基于节点状态信息初估计模块提供的节点状态信息的初始估计值，利用节点的运动轨迹约束对ξ
m中所有节点在多个时间片内的观测信息进行融合，得到这些节点在当前时刻的状态估计，这其中就包括了目标节点的位置估计值。具体来讲，定义
基于多时间片的观测信息
以及
α
k的最大似然估计为
(7)将时空域联合处理模块的结果输出，即可得到在当前时刻目标节点状态的最终估计值，其中就包括了目标节点位置的估计值。
协作定位系统的系统框图如图1所示，包括了多时间片观测信息缓存模块101、观测信息预测模块102、节点轨迹信息反演模块103、节点状态信息初估计模块104、时空域联合处理模块105。观测信息同时输入到多时间片观测信息缓存模块101、节点状态信息初估计模块104，多时间片观测信息缓存模块的输出端连接到观测信息预测模块的输入端102，当前时刻各节点状态变量输入到节点轨迹信息反演模块103，观测信息预测模块102、节点轨迹信息反演模块103、节点状态信息初估计模块104的输出端均连接到时空域联合处理模块105的输入端。
实施例
以扩展卡尔曼滤波算法作为节点状态信息初估计算法，然后基于最大似然准则对目标节点两跳范围内所有节点在K＝3个时间片内的观测信息进行融合，利用SQP算法来求解目标问题为例。设定在一个运动网络中，有113个节点，其中13个节点可以通过GNSS获得自身的位置信息，GNSS观测信息存在一定的误差，其余的节点不能完成自定位，所有节点一开始随机分布在一个1000m×1000m的区域内。由于距离与功耗的限制以及障碍物的遮挡，每一个节点只能与网络中的部分节点进行通信，这里设定各个节点的通信半径均为200m。通过通信链路可以得到与邻近节点间的相对距离信息以及邻近节点的位置信息。另外，所有节点的初始速度均设定为40m/s，加速度满足均值为0m/s
2，标准差为0.2m/s
2的高斯分布。 参考节点通过GNSS所得位置的误差标准差为4m，节点间相对距离信息的误差标准差为0.4m。
基于时空域联合处理的分布式协作定位系统中的每个节点包含了以下五个模块：多时间片观测信息缓存模块、观测信息预测模块、节点轨迹信息反演模块、节点状态信息初估计模块、时空域联合处理模块。针对该系统所设计的分布式协作定位方法包括如下步骤：
(1)针对目标节点m的定位,首先建立系统的状态模型，确定系统的观测方程：
其中，
为节点n在t时刻的位置信息，
为参考节点的GNSS观测信息，
表示位置观测误差，其满足均值为零，协方差矩阵为
的高斯分布；
为节点n与节点j之间的相对距离信息观测值，
表示的是真实距离，
表示的是相对距离信息观测误差，其满足均值为零，方差为
的高斯分布；
(2)在分布式网络中，目标节点需要经过中间节点进行多跳通信才能获得多跳节点的测量信息，并且多跳通信存在延时，也就是说在当前时间片k，目标节点m只能获得g
m,1中节点直到k时刻的位置信息以及这些节点所获得的直到k-1时刻的相对距离信息，也只能获得g
m,2中节点直到k-1时刻的位置信息以及这些节点所获得的直到k-2时刻的相对距离信息，以此类推。随着跳数l的增加，g
m,l中的节点到目标节点的传输时延就越大，对于目标节点定位性能的提升也会逐渐变小，还会增加计算复杂度。这里只考虑目标节点两跳范围内的节点，此时，ξ
m＝{m}∪g
m,1∪g
m,2。在当前时间片k，目标节点m的多时间片测量信息缓存模块缓存ξ
m中节点在τ时刻到k时刻的K个时间片内的位置估计信息以及这些节点间的相对距离信息：
其中，
表示节点n在t时刻的位置估计值。如果节点n不是参考节点，
为空。将这些观测信息送入到观测信息预测模块；
(3)目标节点m的多时间片测量信息缓存模块并不能获得ξ
m中所有节点直到当前时刻k的所有观测信息，观测信息预测模块基于ξ
m中所有节点的历史测量信息，对这些节点到当前时刻为止的多个时间片内所有的测量信息进行预测：
其中，函数f
m和f
l表示节点位置的预测函数，h
l是相对距离信息的预测函数，本发明均基于扩展卡尔曼滤波算法对节点的位置信息和相对距离信息进行预测。进一步，定义：
(4)从τ时刻到k时刻的K个时间片的测量信息不仅与当前时刻k的节点位置有关，还与前面K-1个时刻内节点的位置有关。因此，基于当前时刻节点的位置变量，结合节点的运动状态变量，如速度、加速度等，推算出前K-1个时刻所有节点的位置信息。定义k时刻节点n∈ξ
m的速度变量为
加速度为
考虑到较多时间片之前的测量信息对当前时刻节点的定位性能影响比较小，因此K值不宜太大，这里选取K＝3，在这种情况下，可以假定各个节点在这K个时间片内处于匀加速直线运动状态。此时，基于k时刻节点的运动状态，我们可以定义节点n在
时刻的位置信息为：
其中，ΔT
t＝(t-k)ΔT，ΔT表示时间片的长度。可以看出，对于节点n，从τ时刻到k时 刻的所有位置信息表示的是该节点在这K个时刻的轨迹信息，定义节点的状态变量为
则节点n的轨迹信息可以表示为
其中，矩阵F
t为
I
2表示2×2的单位阵，将所有节点的轨迹信息送入到时空域联合处理模块；
(5)为了提高多时间片历史测量信息以及预测信息的融合效率，节点状态信息初估计模块基于单时间片内的历史测量信息，利用扩展卡尔曼滤波算法计算出ξ
m中所有节点在当前时刻k状态信息的初始估计值，作为多时间片信息联合处理时节点轨迹信息的初始解算值。定义
为t时刻节点n的待估计状态量，其中
首先，基于t-1时刻的后验估计
对当前时刻的
进行预测
其中，矩阵F为
0
2表示2×2的全零矩阵。
表示估计量
的协方差矩阵，
为系统建模噪声的协方差矩阵。然后计算出观测数据
关于
的雅克比矩阵：
接着，计算出测量余量及测量余量协方差矩阵：
其中
然后计算出卡尔曼增益：
最后，计算出
的后验估计及其协方差矩阵：
考虑到在k时刻，目标节点m只能得到g
m,1中节点直到k-1时刻的历史测量信息，所以上述方法只能得到g
m,1中节点到k-1时刻的状态后验估计，为了得到k时刻的状态信息，我们基于k-1时刻状态后验估计做一个一步预测：
将一步预测值
作为k时刻的状态估计值，同理，需要对g
m,2中节点做两步预测才能得到这些节点在k时刻的状态估计值。将这些估计信息作为节点状态信息的初始估计值，送入到时空域联合处理模块；
(6)时空域联合处理模块基于节点状态信息初估计模块提供的各个节点状态信息的初始估计值，利用ξ
m中所有节点的运动轨迹约束对多时间片的历史测量信息以及预测信息进行融合，得到当前时刻各个节点的状态估计。具体来讲，定义
包含了ξ
m中所有节点在k时刻的状态信息，基于多时间片的观测信息
以及
α
k的最大似然估计为
在高斯白噪声的假设下，上式等效于一个非线性最小二乘问题：
N
m表示节点m的邻近节点数目，
表示节点n与节点j之间是连通的，否则
(7)利用MATLAB的SQP算法求解上述问题，即可得到当前时刻目标节点状态的最终估计值，其中就包括了目标节点位置的估计值。
定义
为节点m在k时刻的定位误差，其中
为估计值，
为真实位置。图2表示非参考节点的定位误差累计密度曲线，图3表示参考节点的定位误差累计密度曲线。JSTP-DMLE是本发明所提出的一种基于时空域联合处理的分布式协作定位方法。EKF、UKF以及SPAWN是三个对比方法，其中，EKF指的是扩展卡尔曼滤波算法，UKF指的是无迹卡尔曼滤波算法，SPAWN是一种基于因子图的协作定位方法。图3中“GNSS”表示参考节点通过传统的GNSS定位方法所得到的节点定位误差累积密度曲线。
从图2和图3中可以看出来，当运用于大规模移动网络中时，JSTP-DMLE能够提供比对比算法更高的定位精度，另外，基于GNSS观测信息，这些协作定位方法还能够进一步提高参考节点的定位精度。具体来讲，定义
为节点定位的均方误差，则对于非参考节点，JSTP-DMLE的RMSE为0.4561m，EKF为0.6031m，UKF为0.5762m，SPAWN为0.6320m；对于参考节点来讲，JSTP-DMLE的RMSE为0.4182m，EKF为0.5718m，UKF为0.4902m，SPAWN为0.5409m。
Claims (2)
- 一种基于时空域联合处理的分布式协作定位系统的分布式协作定位方法，所述基于时空域联合处理的分布式协作定位系统中存在多个节点，每个节点通过与邻近节点的协作完成自身的定位；系统中的每个节点均包括多时间片观测信息缓存模块(101)、观测信息预测模块(102)、节点轨迹信息反演模块(103)、节点状态信息初估计模块(104)、时空域联合处理模块(105)；观测信息同时作为多时间片观测信息缓存模块(101)、节点状态信息初估计模块(104)的输入，多时间片观测信息缓存模块(101)的输出作为观测信息预测模块(102)的输入，当前时刻各节点状态变量作为节点轨迹信息反演模块(102)的输入，观测信息预测模块(102)、节点轨迹信息反演模块(103)、节点状态信息初估计模块(104)的输出同时作为时空域联合处理模块(105)的输入，时空域联合处理模块(105)的输出作为结果；其特征在于，所述分布式协作定位方法包括如下步骤：(1)在每个时间片，多时间片观测信息缓存模块缓存了目标节点若干跳范围内节点在当前时刻往前回溯的多个时间片内的所有观测信息，将这些观测信息送入到观测信息预测模块；(2)在分布式网络中，目标节点需要经过中间节点进行多跳通信才能获得多跳节点的测量信息，也就是说目标节点并不能获得多跳节点在当前时刻的测量信息，观测信息预测模块基于多跳节点的历史测量信息，对这些节点到当前时刻为止的多个时间片内所有的测量信息进行预测，将历史测量信息和预测信息送入到时空域联合处理模块；所述的步骤(2)具体为：目标节点需要经过中间节点进行多跳通信才能获得多跳节点的测量信息，也就是说在当前时间片k，目标节点m只能获得一跳节点直到k时刻的位置信息以及这些节点所获得的直到k-1时刻的相对距离信息，也只能获得两跳节点直到k-1时刻的位置信息以及这些节点所获得的直到k-2时刻的相对距离信息，观测信息预测模块基于上述的历史测量信息，对ξ m中所有节点到当前时刻k为止的K个时间片内所有的测量信息进行预测，将这些节点位置的历史测量信息和预测信息定义为节点间相对距离的历史测量信息和预测信息定义为
其中，
将这些信息送入到时空域联合处理模块；
(3)节点轨迹信息反演模块基于目标节点多跳范围内的所有节点在当前时刻的状态信息变量，推算出这些节点在往前回溯的多个时间片内的位置信息，得到这些节点在这些时间片内的轨迹信息，将轨迹信息送入到时空域联合处理模块；所述的步骤(3)具体为：基于节点n∈ξ m在当前时刻位置变量结合节点的运动状态变量，推算出该节点在前 K-1个时刻的位置信息；假定各个节点在这K个时间片内处于匀加速直线运动状态，基于k时刻节点的运动状态，定义节点n在
时刻的位置信息为：
其中，表示节点n在k时刻的速度信息，
表示加速度，ΔT t＝(t-k)ΔT，ΔT表示时间片的长度；上式表示了节点n从τ时刻到k时刻的K个时刻的轨迹信息，定义节点的状态变量为
则节点n的轨迹信息可以表示为
其中，I 2表示2×2的单位矩阵，将这些节点的轨迹信息送入到时空域联合处理模块；(4)节点状态信息初估计模块基于历史测量信息，利用基于单时间片测量信息的协作定位方法对目标节点若干跳范围内的所有节点在当前时刻的状态信息进行初估计；将这些状态信息初估计送入到时空域联合处理模块；定义为t时刻节点n的待估计状态量，其中
首先，基于t-1时刻的后验估计
对当前时刻的
进行预测
其中，矩阵F为
0 2表示2×2的全零矩阵，表示估计量
的协方差矩阵，
为系统建模噪声的协方差矩阵，然后计算出观测数据
关于
的雅克比矩阵：
接着，计算出测量余量及测量余量协方差矩阵：
其中
表示节点n的所有邻近节点，N n表示节点n的邻近节点数目，矩阵
是一个对角阵：
然后计算出卡尔曼增益：
最后，计算出的后验估计及其协方差矩阵：
考虑到在k时刻，目标节点m只能得到g m,1中节点直到k-1时刻的历史测量信息，所以上述方法只能得到g m,1中节点到k-1时刻的状态后验估计，为了得到k时刻的状态信息，基于k-1时刻状态后验估计做一个一步预测：
将一步预测值作为k时刻的状态估计值，同理，需要对g m,2中节点做两步预测才能得到这些节点在k时刻的状态估计值，将这些估计信息作为节点状态信息的初始估计值，送入到时空域联合处理模块；
(5)时空域联合处理模块将节点状态信息初估计模块提供的节点状态信息估计作为解算的初值，利用运动轨迹约束对目标节点若干跳范围内所有节点在多时间片内的历史测量信息和预测信息进行时空域的联合处理，得到这些节点在当前时刻的状态估计，这其中就包括了目标节点位置的估计值；所述的步骤(5)具体为：时空域联合处理模块基于节点状态信息初估计模块提供的节点状态信息的初始估计值，利用节点的运动轨迹约束对多时间片的历史测量信息及预测信息进行融合，得到当前时刻ξ m中所有节点的状态估计，这其中就包括了目标节点位置的估计值；具体来讲，定义基于多时间片的观测信息
以及
α k的最大似然估计为
- 如权利要求1所述的分布式协作定位方法，其特征在于所述的步骤(1)具体为：分布式协作定位中各个节点的定位由节点自身完成，定义m为待定位节点，称之为目标节点，ξ m为在目标节点若干跳范围内所有节点的集合，在当前时间片k，多时间片观测信息 缓存模块将ξ m中所有节点在当前时刻往前回溯K个时间片的所有时间片内的观测数据缓存下来，这些观测信息包括了节点的位置估计信息以及所有这些节点间的相对距离信息；将这些观测信息送入观测信息预测模块。
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