CN114537477B - Train positioning tracking method based on TDOA - Google Patents
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Abstract
The invention provides a train positioning tracking method based on TDOA, which is characterized by comprising the following steps: the head terminal acquires downlink signals of three base station positioning signals governed by the k time tracking base station group in real time, and simultaneously the tail terminal acquires downlink signals of three base station positioning signals governed by the k time tracking base station group in real time,respectively calculating TDOA values corresponding to the head terminal and the tail terminal at the moment k according to the downlink signals acquired by the head terminal and the downlink signals acquired by the tail terminal; then adopting UKF algorithm to obtain the positioning value of the position of the head terminal of the train at k moment according to the obtained TDOA value
，
) The positioning value of the position of the head terminal of the train at the moment k is calculated
，
) As a positioning value for the train. The method of the invention is used for positioning and tracking the train, and the positioning accuracy is greatly improved.
Description
Technical Field
The invention relates to the technical field of transportation, in particular to a train positioning and tracking method based on TDOA.
Background
The railway is an important infrastructure for economic development of China, the train operation control system is a core system for controlling the safe operation of a train, the train positioning is one of key technologies in the train control system, is a foundation for guaranteeing the safe operation of the train, and simultaneously provides train position information for an Automatic Train Protection (ATP) system in the train control system. The speed position and other information of the train are fed back in real time through train positioning, so that the train control system is guaranteed to timely make corresponding decisions, the train is reliably and safely indicated to make proper and correct deceleration operation, safety accidents are avoided, safe running of the train is guaranteed, train operation is effectively controlled, and transportation efficiency is improved.
The most commonly used train positioning method at present is a positioning mode based on GPS/INS. However, in the process of realizing train positioning, the navigation satellite signals are easily blocked by shielding objects such as buildings, mountains, trees and the like, and the speed measurement errors, multipath reflection errors, clock errors, atmospheric delays and instrument delays can all cause the deterioration of satellite signals, for example, when a train enters a place with more shielding surrounding areas such as a station, a mountain area and the like, the positioning accuracy can be reduced. In particular, when a train enters a mountainous multi-tunnel environment, no GNSS signal is available for positioning. Inertial navigation systems are capable of providing short-term, high-precision positioning results, but suffer from the problem that result errors accumulate over time. The combined positioning system based on the GPS/INS has overlarge dependence on GPS signals, when satellite signals are interfered, INS errors are increased continuously along with time, and the accuracy of the train positioning system is reduced to a great extent.
In the prior art, a scheme of realizing train positioning by adopting a wireless positioning technology such as TDOA (Time Difference of Arrival arrival time difference) is also available, and is a method for positioning by utilizing time difference, specifically, by comparing absolute time differences of signals of all base stations reaching a mobile terminal and converting the time differences into distance differences, a hyperbola taking the base station as a focus and the distance differences as long axes can be made, and the intersection point of the hyperbolas is the position of the signals. However, the measurement accuracy of the method is affected by various factors, such as time synchronization errors between base stations, transmitting power errors between base stations, etc., and the positioning accuracy of the train measured by the method needs to be further improved.
Disclosure of Invention
Aiming at the problems of the background technology, the invention provides a train positioning tracking method based on TDOA, which aims to solve the problem of low positioning tracking precision of a train in the prior art.
In order to achieve the purpose of the invention, the invention provides a train positioning and tracking method based on TDOA, which has the innovation points that:
the train running line is composed of a plurality of tracking road sections, each tracking road section is provided with three base stations for tracking the train, and the three base stations corresponding to the single tracking road section are marked as a tracking base station group corresponding to the tracking road section; the method comprises the steps that a positioning signal receiving terminal device is arranged at the head part and the tail part of a train respectively, the positioning signal receiving terminal device arranged at the head part of the train is marked as a head terminal, and the positioning signal receiving terminal device arranged at the tail part of the train is marked as a tail terminal; marking a tracking base station group corresponding to a tracking road section where the train is positioned at the moment k as an x tracking base station group;
the train positioning tracking method comprises the following steps: the head terminal acquires downlink signals of three base station positioning signals managed by the x tracking base station group at k moment in real time, and simultaneously the tail terminal acquires downlink signals of three base station positioning signals managed by the x tracking base station group at k moment in real time, and respectively calculates TDOA values corresponding to the head terminal and the tail terminal at k moment according to the downlink signals acquired by the head terminal and the downlink signals acquired by the tail terminal; then, according to the obtained TDOA value, a UKF algorithm is adopted to obtain a positioning value (x k ，y k ) The position value (x of the position of the head end of the train at time k k ，y k ) As a positioning value of the train;
the state equation related to the UKF algorithm is as follows: x is X k ＝f(X k-1 )+W k-1 The method comprises the steps of carrying out a first treatment on the surface of the The observation equation related to the UKF algorithm is as follows: z is Z k ＝h(X k )+V k The method comprises the steps of carrying out a first treatment on the surface of the Wherein,,
X k a system state vector at time k, defined as:
wherein x is k And y k The abscissa and the ordinate, v, of the position of the train head terminal at the moment k are respectively k And alpha k The speed and the angle of the train at the moment k are respectively;
f(X k-1 ) Is a nonlinear state equation function defined as:
wherein X is k-1 Is the system state vector of the train at the time k-1,
wherein x is k-1 And y k-1 The abscissa and the ordinate, v, of the position of the train head terminal at the moment k-1 respectively k-1 And alpha k-1 The speed and the angle of the train at the moment k-1 are respectively; Δt is the time interval between the time of k-1 and the time of k;
W k-1 the system noise vector is the k-1 moment;
Z k a system measurement vector for time k, defined as:
wherein d 2,1 ＝d 2 -d 1 ，d 3,1 ＝d 3 -d 1 ，d′ 2,1 ＝d′ 2 -d′ 1 ，d′ 3,1 ＝d′ 3 -d′ 1 The d is 1 、d 2 And d 3 The distances from the train head terminal to the first base station, the second base station and the third base station governed by the x tracking base station group are respectively d' 1 、d′ 2 And d' 3 The distance from the tail end terminal of the train to the first base station, the second base station and the third base station governed by the x tracking base station group is respectively;
h(X k ) Is a nonlinear observation equation function defined as:
wherein x' k And y' k The abscissa and the ordinate of the position of the tail end terminal of the train at the moment k are respectively; x is x 1 、x 2 And x 3 Respectively tracking the abscissa of the positions of the first base station, the second base station and the third base station managed by the base station for x; y is 1 、y 2 And y 3 Respectively tracking the ordinate of the positions of the first base station, the second base station and the third base station managed by the base station for the x; wherein x' k ＝x k -Lcosα，y′ k ＝y k -Lsin a, said L being the distance from the head terminal to the tail terminal;
V k is the measured noise vector at time k.
As an optimization, the communication among the base station, the head terminal and the tail terminal adopts a 5G-R network system.
As an optimization, the tracking road section comprises an open road section, and three base stations governed by a single tracking base station group of the open road section are arranged in the following manner: the first base station is arranged on the right side of the track, the second base station and the third base station are both arranged on the left side of the track, the vertical distances from the second base station and the third base station to the central axis of the track are equal, and the linear distances from the second base station and the third base station to the first base station are equal.
As an optimization, it is characterized in that: the tracking road section comprises a platform road section, and the platform road section adopts a pico-cell mode to realize 5G-R network coverage.
As optimization, the tracking road section comprises a tunnel road section, and the 5G-R network coverage is realized by adopting a mode of laying leaky coaxial cable.
The principle of the invention is as follows:
in the prior art, although the TDOA technology can be adopted to acquire the positioning information of the train, the observation data adopting the technology comprises the influence of measurement noise and interference, and the measurement data has larger deviation from the actual position of the train. Data filtering is a data processing technique for solving the above problems, which can restore real data by removing noise, for example, to estimate the coordinate position and velocity of an object from a limited set of observation sequences containing noise. The data filtering technology comprises KF (Kalman filter), EKF (extended Kalman filter), UKF (unscented Kalman filter), PF (particle filter) and the like, and the train positioning and tracking problem to be solved by the invention is a nonlinear system problem, while the KF only can solve the linear system problem, and the EKF and the UK can only solve the linear system problemF and PF can solve the nonlinear system problem, but EKF and PF calculate complicated, calculate speed slow, input cost high, the inventor finds, for train positioning trace this nonlinear system, UKF because its calculation accuracy is high, calculate the speed block, more suitable for this system; on the other hand, in the prior art, a train is generally taken as a whole, and only one positioning signal receiving terminal is arranged on the train to obtain the TDOA value of the train, and although the measurement precision can be improved by removing data noise through a UKF algorithm, the inventor finds that the precision of the positioning tracking technology of the train is further improved by researching the running characteristics of the train and the UKF algorithm: in fact, in the UKF algorithm, if more observation values can be introduced, the train positioning value obtained by estimating the fusion of the observation values and the estimated values by the UKF algorithm can be closer to the true value, and the positioning tracking precision of the train can be further improved. In the invention, a positioning signal receiving terminal is respectively arranged at the head and tail of a train, and at the same moment, the positioning signal receiving terminals at the head and tail simultaneously receive the downlink information of three base stations managed by the same tracking base station group, and acquire respective TDOA values, and the position information (x 'of the tail terminal can be obtained through a mathematical model and the distance between the two positioning signal receiving terminals and the angle of the train' k ，y′ k ) And position information of the head terminal (x k ，y k ) And converting, namely taking the position information of one of the positioning signal receiving terminals as the positioning information of the whole vehicle, and acquiring two groups of observation data at the moment for the position of the positioning signal receiving terminal, wherein the accuracy of the train positioning tracking estimated value obtained by filtering and denoising the two groups of observation data and the system estimated data through a UKF algorithm is greatly improved. The invention adopts the position of the head terminal as the positioning position of the whole vehicle, and can also adopt the position of the tail terminal as the positioning position of the whole vehicle.
From this, the invention has the following beneficial effects: the method of the invention is used for positioning and tracking the train, and the positioning accuracy can be greatly improved.
Drawings
The drawings of the present invention are described below.
FIG. 1 is a schematic diagram of the structure of the present invention;
fig. 2 is a schematic diagram of an arrangement structure of a base station in an open road section;
fig. 3 is a schematic diagram of an arrangement structure of a base station in a station section;
fig. 4 is a schematic diagram of an arrangement of base stations in a tunnel section.
1, tracking a base station group; 2. a head terminal; 3. a tail end; 11. a first base station; 12. a second base station; 13. and a third base station.
Detailed Description
The invention is further illustrated below with reference to examples.
As shown in fig. 1, a train running line is composed of a plurality of tracking sections, each tracking section is provided with three base stations for tracking the train, and three base stations corresponding to a single tracking section are recorded as a tracking base station group 1 corresponding to the tracking section; the method comprises the steps that a positioning signal receiving terminal device is arranged at the head part and the tail part of a train respectively, the positioning signal receiving terminal device arranged at the head part of the train is marked as a head terminal 2, and the positioning signal receiving terminal device arranged at the tail part of the train is marked as a tail terminal 3; marking a tracking base station group 1 corresponding to a tracking road section where the train is positioned at the moment k as an x tracking base station group 1;
the train positioning tracking method comprises the following steps: the head terminal 2 acquires downlink signals of three base station positioning signals governed by the x tracking base station group 1 at k moment in real time, and the tail terminal 3 acquires downlink signals of three base station positioning signals governed by the x tracking base station group 1 at k moment in real time, and respectively calculates TDOA values corresponding to the head terminal 2 and the tail terminal 3 at k moment according to the downlink signals acquired by the head terminal 2 and the downlink signals acquired by the tail terminal 3; then, according to the obtained TDOA value, a UKF algorithm is adopted to obtain a positioning value (x k ，y k ) The position value (x of the position of the head terminal 2 of the train at the time k k ，y k ) As a positioning value of the train;
the state equation related to the UKF algorithm is as follows: x is X k ＝f(X k-1 )+W k-1 The method comprises the steps of carrying out a first treatment on the surface of the The observation equation related to the UKF algorithm is as follows: z is Z k ＝h(X k )+V k The method comprises the steps of carrying out a first treatment on the surface of the Wherein,,
X k a system state vector at time k, defined as:
wherein x is k And y k The abscissa and the ordinate, v, respectively, of the position of the train head terminal 2 at time k k And alpha k The speed and the angle of the train at the moment k are respectively, wherein the speed is the speed along the running direction of the train, and the angle refers to the degree of an included angle between the running direction of the train and the eastward direction of the coordinate axis;
f(X k-1 ) Is a nonlinear state equation function defined as:
wherein X is k-1 Is the system state vector of the train at the time k-1,
wherein x is k-1 And y k-1 The abscissa and the ordinate, v, respectively, of the position of the train head terminal 2 at time k-1 k-1 And alpha k-1 The speed and the angle of the train at the moment k-1 are respectively; Δt is the time interval between the time of k-1 and the time of k;
W k-1 a system noise vector at time k-1, the W k-1 Zero-mean-compliant white gaussian noise with covariance matrix Q k-1 ，W k-1 The method can be set according to acceleration information in a train motion equation;
Z k a system measurement vector for time k, defined as:
wherein d 2,1 ＝d 2 -d 1 ，d 3,1 ＝d 3 -d 1 ，d′ 2,1 ＝d′ 2 -d′ 1 ，d′ 3,1 ＝d′ 3 -d′ 1 The d is 1 、d 2 And d 3 The distances from the train head terminal 2 to the first base station 11, the second base station 12 and the third base station 13 governed by the x tracking base station group 1 are respectively, and d' 1 、d′ 2 And d' 3 The distances from the tail end terminal 3 of the train to the first base station 11, the second base station 12 and the third base station 13 governed by the x tracking base station group 1 are respectively;
h(X k ) Is a nonlinear observation equation function defined as:
wherein x' k And y' k The abscissa and the ordinate of the position of the tail end terminal 3 of the train at the moment k are respectively; x is x 1 、x 2 And x 3 Respectively tracking the abscissa of the positions of the first base station 11, the second base station 12 and the third base station 13 managed by the base station for x; y is 1 、y 2 And y 3 The x tracking base station is respectively used for tracking the ordinate of the positions of the first base station 11, the second base station 12 and the third base station 13 managed by the base station; wherein x' k ＝x k -Lcosα，y′ k ＝y k -lsinα, said L being the distance from said head terminal 2 to tail terminal 3;
V k for the measurement noise vector at time k, said V k Zero mean-compliant white gaussian noise with covariance matrix R k ，V k The setting can be made according to the measurement accuracy of the 5G-R system TDOA.
Besides the related state equation and observation equation, the specific calculation principle and steps of the UKF algorithm are common processing means in the prior art, and related contents can be obtained from related documents in the prior art by a person skilled in the art. In this embodiment, the steps of calculating the UKF algorithm are briefly described as follows:
(1) Initializing a system state vector estimate X 0 Error covariance matrix P 0 ，k＝1,2,…；
(2) Obtain 2n+1 Sigma sampling points and their corresponding weights, use UT transform, i.e
Wherein,,
representing the ith column of the matrix;
in the formula, subscripts m and c are mean and covariance respectively, and the superscript indicates the sampling point; λ=α 2 (n+kappa) -n is a scaling parameter used to reduce the overall prediction error, where alpha is often a small positive number, kappa is often 0, and beta is often 2;
(3) The state prediction and covariance of the k moment are calculated,
(4) The predicted value obtained according to the step (3)
And covariance->
Step (2) is performed again to retrieve Sigma point +.>
And corresponding weight->
(5) Sigma dot
Substituting the variable values in the model into an observation equation to obtain a predicted observed quantity:
wherein:
(6) Further obtaining a mean value and a covariance matrix of the observed values:
(7) Updating the state and covariance matrix of the system:
in order to make the delay of signal transmission lower and further improve the ranging accuracy, the communication among the base station, the head terminal 2 and the tail terminal 3 adopts a 5G-R (5G for ranging) network system. The 5G-R positioning system is a mobile communication system taking positioning service as one of design targets, compared with the traditional communication system, the introduction of MIMO, ultra-dense network, millimeter wave transmission and D2D communication can further improve the communication performance of the system, and can be used for improving the precision and the application range of wireless positioning and realizing the network full coverage along the railway.
As further optimization, the invention divides the type of the track section of the running line of the train into an open section, a platform section and a tunnel section, and adopts different network coverage modes aiming at the three section types respectively so as to further improve the positioning effect and the precision of the train:
for an open road section, the adopted network coverage mode is DU and CU which are respectively provided with +AAU, namely, AAU units are arranged on the top surface of a base station along a railway, DU are arranged in a machine room of the base station, CU is intensively deployed, and then the CU is connected with a core network in a feedback way. The base station is arranged according to the TDOA principle, and the basic idea of the method is to process three measuring base stations and calculate TDOA data from the acquired downlink positioning reference signals so as to realize the positioning of the receiver. The geometric principle is to determine two single-side hyperbolas by using the TDOA values of the arrival time differences between every two of the three positioning base stations, and the intersection point of the curves is the position of the train, but the TDOA may generate two solutions, namely a 'fuzzy solution', when solving. In order to solve the problem of fuzzy solution, the base stations are required to be reasonably arranged, and the positioning fuzzy area is avoided. By analyzing the influence of the topological structure of the base station on the TDOA positioning accuracy, in the three-base station positioning system, when the triangle formed by the three base stations is an isosceles triangle, the problem of fuzzy solution can be solved. Therefore, in this embodiment, as shown in fig. 2, the three base stations governed by the single tracking base station group 1 on the open road are arranged in the following manner: the first base station 11 is arranged on the right side of the track, the second base station 12 and the third base station 13 are both arranged on the left side of the track, the vertical distances from the second base station 12 and the third base station 13 to the central axis of the track are equal, and the straight line distances from the second base station 12 and the third base station 13 to the first base station 11 are equal. Therefore, the three base stations are distributed in an isosceles triangle, so that the horizontal geometric precision factor of the system is reduced, and the positioning precision of the train is improved.
For a platform section, because the train parking precision requirement of the platform area is higher, if a network coverage mode of an open area is adopted, the positioning precision cannot meet the requirement due to non-line-of-sight propagation of signals. Therefore, aiming at the platform section, the invention adopts a 5G-R pico-base station mode to realize a network full-coverage scheme. As shown in fig. 3, the 5G-R pico-base station is composed of a hub unit (RHub), a Pi Shepin remote unit (pico RRU or pRRU), and a baseband processing unit (BBU). A plurality of prrus within the same 5G-R pico-base station are organized into the same group and are connected with the same RHub, which is connected with a BBU arranged in a communication machine room through an optical cable. pRRU is distributed on two sides of a public area of the platform, is staggered along the track and is in a zigzag shape, so that the network full coverage of 5G-R signals is ensured.
For a tunnel section, the invention adopts a leaky coaxial cable (abbreviated as leaky cable) laying way to carry out network coverage, namely, 5G-R wireless network coverage is realized by adopting a networking mode of '5 GRUU+POI+leaky cable', and as shown in figure 4, leaky cable is covered by adopting a single laying way at one side of a railway. When in the tunnel, the leakage cable can be hung on the wall of the tunnel. The leaky cable has the functions of signal transmission and antenna, and can uniformly radiate and receive the controlled electromagnetic wave energy along the line through controlling the opening of the outer conductor, so that the field intensity is uniformly attenuated without fluctuation, the coverage of the blind area of the electromagnetic field is realized, and the purpose of smooth mobile communication is achieved.
The coverage distance L (unit: m) of the wireless network with source access at both ends of the leaky cable can be calculated by the following formula:
wherein L is 0 、L 1 、L 2 、L 3 The required field intensity in the vehicle, the air coupling loss of the leaky cable, the switching interval and the hundred-meter attenuation of the common leaky cable are respectively, N 1 、N 2 Respectively jumper connector loss, combiner loss, M 1 、M 2 、M 3 、M 4 Respectively the system allowance, the width factor, the vehicle body dielectric loss and the human body loss, P t Is the output power.
Claims (5)
1. A train positioning tracking method based on TDOA is characterized in that:
the train running line is composed of a plurality of tracking road sections, each tracking road section is provided with three base stations for tracking the train, and the three base stations corresponding to the single tracking road section are marked as a tracking base station group corresponding to the tracking road section; the method comprises the steps that a positioning signal receiving terminal device is arranged at the head part and the tail part of a train respectively, the positioning signal receiving terminal device arranged at the head part of the train is marked as a head terminal, and the positioning signal receiving terminal device arranged at the tail part of the train is marked as a tail terminal; marking a tracking base station group corresponding to a tracking road section where the train is positioned at the moment k as an x tracking base station group;
the train positioning tracking method comprises the following steps: the head terminal acquires downlink signals of three base station positioning signals managed by the x tracking base station group at k moment in real time, and simultaneously the tail terminal acquires downlink signals of three base station positioning signals managed by the x tracking base station group at k moment in real time, and respectively calculates TDOA values corresponding to the head terminal and the tail terminal at k moment according to the downlink signals acquired by the head terminal and the downlink signals acquired by the tail terminal; then, according to the obtained TDOA value, a UKF algorithm is adopted to obtain a positioning value (x k ，y k ) The position value (x of the position of the head end of the train at time k k ，y k ) As a positioning value of the train;
the state equation related to the UKF algorithm is as follows: x is X k ＝f(X k-1 )+W k-1 The method comprises the steps of carrying out a first treatment on the surface of the The observation equation related to the UKF algorithm is as follows: z is Z k ＝h(X k )+V k The method comprises the steps of carrying out a first treatment on the surface of the Wherein,,
X k a system state vector at time k, defined as:
wherein x is k And y k The abscissa and the ordinate of the position of the train head terminal at the moment k respectivelyCoordinates, v k And alpha k The speed and the angle of the train at the moment k are respectively;
f(X k-1 ) Is a nonlinear state equation function defined as:
wherein X is k-1 Is the system state vector of the train at the time k-1,
wherein x is k-1 And y k-1 The abscissa and the ordinate, v, of the position of the train head terminal at the moment k-1 respectively k-1 And alpha k-1 The speed and the angle of the train at the moment k-1 are respectively; Δt is the time interval between the time of k-1 and the time of k;
W k-1 the system noise vector is the k-1 moment;
Z k a system measurement vector for time k, defined as:
wherein d 2，1 ＝d 2 -d 1 ，d 3，1 ＝d 3 -d 1 ，d′ 2，1 ＝d′ 2 -d′ 1 ，d′ 3，1 ＝d′ 3 -d′ 1 The d is 1 、d 2 And d 3 The distances from the train head terminal to the first base station, the second base station and the third base station governed by the x tracking base station group are respectively d' 1 、d′ 2 And d' 3 The distance from the tail end terminal of the train to the first base station, the second base station and the third base station governed by the x tracking base station group is respectively;
h(X k ) Is a nonlinear observation equation function defined as:
wherein x' k And y' k The abscissa and the ordinate of the position of the tail end terminal of the train at the moment k are respectively; x is x 1 、x 2 And x 3 Respectively tracking the abscissa of the positions of the first base station, the second base station and the third base station managed by the base station for x; y is 1 、y 2 And y 3 Respectively tracking the ordinate of the positions of the first base station, the second base station and the third base station managed by the base station for the x; wherein x' k ＝x k -Lcosα，y′ k ＝y k -Lsin a, said L being the distance from the head terminal to the tail terminal;
V k is the measured noise vector at time k.
2. The TDOA-based train location tracking method of claim 1, wherein: the communication among the base station, the head terminal and the tail terminal adopts a 5G-R network system.
3. The TDOA-based train location tracking method of claim 2, wherein: the tracking road section comprises an open road section, and three base stations governed by a single tracking base station group of the open road section are arranged in the following manner: the first base station is arranged on the right side of the track, the second base station and the third base station are both arranged on the left side of the track, the vertical distances from the second base station and the third base station to the central axis of the track are equal, and the linear distances from the second base station and the third base station to the first base station are equal.
4. The TDOA-based train location tracking method of claim 2, wherein: the tracking road section comprises a platform road section, and the platform road section adopts a pico-cell mode to realize 5G-R network coverage.
5. The TDOA-based train location tracking method of claim 2, wherein: the tracking road section comprises a tunnel road section, and the 5G-R network coverage is realized by paving a leaky coaxial cable on the tunnel road section.
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Citations (15)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO1994026571A1 (en) * | 1993-05-17 | 1994-11-24 | Hughes Aircraft Company | Train location and control using spread spectrum radio communications |
| JPH09119973A (en) * | 1995-05-31 | 1997-05-06 | General Electric Co <Ge> | Method and system for confirming position of body to be tracked |
| KR20060004321A (en) * | 2004-07-09 | 2006-01-12 | 한국철도기술연구원 | Position location system of the train on the railway by using the phase difference relation |
| WO2012065233A1 (en) * | 2010-11-19 | 2012-05-24 | Commonwealth Scientific And Industrial Research Organisation | Tracking location of mobile devices in a wireless network |
| CN102923167A (en) * | 2012-10-25 | 2013-02-13 | 北京交通大学 | Train tracking and approaching pre-warning system |
| DE102012209311A1 (en) * | 2012-06-01 | 2013-12-05 | Deutsches Zentrum für Luft- und Raumfahrt e.V. | Method for locating position of train in rail topological network, involves multiplying initial weight of hypothesis with all weights of rating obtained by measurements such that hypotheses with high weights is determined |
| CN106143538A (en) * | 2016-07-06 | 2016-11-23 | 兰州交通大学 | A kind of train locating method based on LTE R utilizing double-movement platform |
| CN108931244A (en) * | 2018-07-18 | 2018-12-04 | 兰州交通大学 | Ins error suppressing method and system based on train kinematic constraint |
| CN108981692A (en) * | 2018-06-14 | 2018-12-11 | 兰州晨阳启创信息科技有限公司 | It is a kind of based on inertial navigation/visual odometry train locating method and system |
| CN109229158A (en) * | 2018-09-14 | 2019-01-18 | 北京全路通信信号研究设计院集团有限公司 | Train rapid positioning method and system |
| CA3106029A1 (en) * | 2018-07-17 | 2020-01-23 | Amsted Rail Company, Inc. | System and method for building and managing a train consist |
| CA3074977A1 (en) * | 2019-04-12 | 2020-10-12 | Thales Management & Services Deutschland Gmbh | A method for safely and autonomously determining a position information of a train on a track |
| CN112550375A (en) * | 2020-11-24 | 2021-03-26 | 卡斯柯信号有限公司 | Train conductor identification method and system based on satellite positioning |
| CN113353122A (en) * | 2021-06-25 | 2021-09-07 | 重庆交通大学 | Control method of virtual coupled high-speed train under brake force fault of tracking train |
| CN114056384A (en) * | 2022-01-17 | 2022-02-18 | 矿冶科技集团有限公司 | Safety early warning system for electric locomotive and early warning method thereof |
Family Cites Families (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US9197996B2 (en) * | 2010-11-19 | 2015-11-24 | Commonwealth Scientific And Industrial Research Organisation | Tracking location of mobile device in a wireless network |
| WO2021092149A1 (en) * | 2019-11-06 | 2021-05-14 | Humatics Corporation | Techniques and associated systems and methods for determining train motion characteristics |
| CN111443718B (en) * | 2020-04-24 | 2022-07-26 | 华东交通大学 | High-speed train state feedback prediction control method and system based on prediction error method |
-
2022
- 2022-03-01 CN CN202210196284.0A patent/CN114537477B/en active Active
Patent Citations (15)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO1994026571A1 (en) * | 1993-05-17 | 1994-11-24 | Hughes Aircraft Company | Train location and control using spread spectrum radio communications |
| JPH09119973A (en) * | 1995-05-31 | 1997-05-06 | General Electric Co <Ge> | Method and system for confirming position of body to be tracked |
| KR20060004321A (en) * | 2004-07-09 | 2006-01-12 | 한국철도기술연구원 | Position location system of the train on the railway by using the phase difference relation |
| WO2012065233A1 (en) * | 2010-11-19 | 2012-05-24 | Commonwealth Scientific And Industrial Research Organisation | Tracking location of mobile devices in a wireless network |
| DE102012209311A1 (en) * | 2012-06-01 | 2013-12-05 | Deutsches Zentrum für Luft- und Raumfahrt e.V. | Method for locating position of train in rail topological network, involves multiplying initial weight of hypothesis with all weights of rating obtained by measurements such that hypotheses with high weights is determined |
| CN102923167A (en) * | 2012-10-25 | 2013-02-13 | 北京交通大学 | Train tracking and approaching pre-warning system |
| CN106143538A (en) * | 2016-07-06 | 2016-11-23 | 兰州交通大学 | A kind of train locating method based on LTE R utilizing double-movement platform |
| CN108981692A (en) * | 2018-06-14 | 2018-12-11 | 兰州晨阳启创信息科技有限公司 | It is a kind of based on inertial navigation/visual odometry train locating method and system |
| CA3106029A1 (en) * | 2018-07-17 | 2020-01-23 | Amsted Rail Company, Inc. | System and method for building and managing a train consist |
| CN108931244A (en) * | 2018-07-18 | 2018-12-04 | 兰州交通大学 | Ins error suppressing method and system based on train kinematic constraint |
| CN109229158A (en) * | 2018-09-14 | 2019-01-18 | 北京全路通信信号研究设计院集团有限公司 | Train rapid positioning method and system |
| CA3074977A1 (en) * | 2019-04-12 | 2020-10-12 | Thales Management & Services Deutschland Gmbh | A method for safely and autonomously determining a position information of a train on a track |
| CN112550375A (en) * | 2020-11-24 | 2021-03-26 | 卡斯柯信号有限公司 | Train conductor identification method and system based on satellite positioning |
| CN113353122A (en) * | 2021-06-25 | 2021-09-07 | 重庆交通大学 | Control method of virtual coupled high-speed train under brake force fault of tracking train |
| CN114056384A (en) * | 2022-01-17 | 2022-02-18 | 矿冶科技集团有限公司 | Safety early warning system for electric locomotive and early warning method thereof |
Non-Patent Citations (8)
| Title |
|---|
| A TOA-based localization algorithm with simultaneous NLOS mitigation and synchronization error elimination;Wu, S., Zhang, S., & Huang, D.;《IEEE Sensors Letters》;第3卷(第3期);1-4 * |
| A Weighting Localization Algorithm with LOS and One-Bound NLOS Identification in Multipath Environments;Wu, S. X., Zhang, S. J., Xu, K., & Huang, D. R;《Journal of Information Science & Engineering》;第35卷(第 6期);1-4 * |
| Kalman particle filtering algorithm for symmetric alpha‐stable distribution signals with application to high frequency time difference of arrival geolocation;Xia, N., Wei, W., Li, J., & Zhang, X;《IET Signal Processing》;第10卷(第6期);619-625 * |
| PSO-ICS算法优化的城轨列车自动驾驶***;徐凯;吴磊;杨飞凤;;《铁道科学与工程学报》(第12期);202-210 * |
| TDOA geolocation with the unscented Kalman filter;Savage, C. O., Cramer, R. L., & Schmitt, H. A;《2006 IEEE International Conference on Networking, Sensing and Control》;602-606 * |
| 基于无迹卡尔曼滤波的单站混合定位跟踪算法;刘翔;宋常建;胡磊;钟子发;;《探测与控制学报》;第34卷(第03期);72-74 * |
| 基于非合作定位模型的改进型粒子滤波跟踪算法;滕飞;钟子发;;《探测与控制学报》;第37卷(第04期);73-75 * |
| 多种群分层联合优化的城轨列车ATO研究;徐凯;吴磊;赵梅;;《铁道学报》(第06期);93-99 * |
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