CN111098890B - Train collision avoidance method and device based on millimeter wave radar - Google Patents
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B61—RAILWAYS
- B61L—GUIDING RAILWAY TRAFFIC; ENSURING THE SAFETY OF RAILWAY TRAFFIC
- B61L23/00—Control, warning or like safety means along the route or between vehicles or trains
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B61—RAILWAYS
- B61L—GUIDING RAILWAY TRAFFIC; ENSURING THE SAFETY OF RAILWAY TRAFFIC
- B61L23/00—Control, warning or like safety means along the route or between vehicles or trains
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Abstract
The invention relates to a train anti-collision method and a train anti-collision device based on a millimeter wave radar, which belong to the technical field of radars and solve the anti-collision problem of subway trains, wherein the method comprises the steps of preprocessing radar echo signals reflected in front of a subway locomotive head to obtain target echo signals; classifying the target echo signals by adopting a trained SVM model, and filtering false target echo signals to obtain real target echo signals; and calculating the relative distance and speed between the subway locomotive head and the target object according to the real target echo signal, judging whether the relative distance and speed exceed an anti-collision threshold value, and performing train anti-collision early warning if the relative distance and speed exceed the anti-collision threshold value. The invention can solve the problem of receiving and processing radar signals in the special environment of the subway tunnel and give early warning to obstacles in front of the subway train; interference signals and false targets are removed, and the influence of false alarms on the advancing of the train is avoided; besides, trackside equipment is not required to be arranged, the cost is low, and the maintenance is convenient.
Description
Technical Field
The invention relates to the technical field of radars, in particular to a train collision avoidance method and device based on a millimeter wave radar.
Background
The existing distance measuring and positioning technology for subway trains mainly depends on an Automatic Train Protection (ATP) subsystem, an Automatic Train Operation (ATO) subsystem and an Automatic Train Supervision (ATS) subsystem in a Communication Based Train Control System (CBTC) System.
Current CBTC systems are expensive, requiring many active beacons to be placed, which means high fitment and maintenance costs; and the system communication is closed, and the emergent obstacles outside the system can not be identified and measured and can not send out early warning.
In actual operation, the CBTC system inevitably has some faults. When the ATP system meets the condition that the ATP system needs to be cut off, the safety guarantee of the train mainly depends on train dispatching at a station, observation of a driver and execution of degradation regulations, a safety protection means at an equipment level is lacked, risks exist, and a rear-end collision event of the train under the condition that the ATP is cut off easily occurs.
Disclosure of Invention
In view of the above analysis, the present invention aims to provide a train collision avoidance method and apparatus based on millimeter wave radar, which solves the collision avoidance problem of subway trains.
The purpose of the invention is mainly realized by the following technical scheme:
the invention discloses a train collision avoidance method based on a millimeter wave radar, which comprises the following steps:
preprocessing a radar echo signal reflected in front of a subway locomotive head to obtain a target echo signal;
classifying the target echo signals by adopting a trained SVM model, and filtering false target echo signals to obtain real target echo signals;
and calculating the relative distance and speed between the subway locomotive head and the target object according to the real target echo signal, judging whether the relative distance and speed exceed an anti-collision threshold value, and if so, performing train anti-collision early warning.
Further, the pretreatment method comprises the following steps:
performing data primary screening on the radar echo signals, and eliminating transverse interference signals;
estimating a clutter power value of the position where the target is located;
and comparing the clutter power value serving as a threshold value with the power of the radar echo signal after preliminary screening, and rejecting a target unit only comprising the clutter signal in the radar echo signal to obtain a target echo signal.
Further, in the data preliminary screening, a transverse distance threshold value is set, and radar echo signals with transverse distances exceeding the transverse distance threshold value are removed.
Further, the lateral distance threshold is matched with the width of the subway tunnel.
Further, the estimated clutter power of the position where the target is located is an arithmetic average of the powers of all target units included in the current echo signal.
Further, the training method of the SVM model comprises the following steps:
constructing a training data set, wherein the training data is a radar echo signal;
constructing an SVM classifier;
preprocessing a training data set to obtain a target echo signal;
processing a target echo signal, and extracting a characteristic vector group; sticking a data label of a false target in the target echo signal to be +1, and sticking a data label of a real obstacle to be-1;
and sending the extracted feature vector group and the corresponding data label as input into an SVM trainer for model training.
Further, the SVM classifier adopts a linear kernel function.
Further, the training data set includes radar return signals collected while the train is running without an obstacle, and radar return signals collected while a specific obstacle is placed in front of the train travel.
Further, the set of feature vectors includes range, velocity, and target echo power data for the target.
The invention also discloses a train anti-collision device based on the millimeter wave radar, which comprises a radar transmitter, a radar receiver and an anti-collision processor;
the radar transmitter is arranged at the locomotive of the train locomotive and used for transmitting radar signals to the front of the locomotive;
the radar receiver is arranged at the locomotive of the locomotive and used for receiving radar echo signals reflected in front of the locomotive;
the anti-collision processor is respectively connected with the radar transmitter and the radar receiver, and processes the radar echo signal by adopting the train anti-collision method, finds out obstacles and performs train anti-collision early warning.
The invention has the following beneficial effects:
the method solves the problem of receiving and processing the radar signals in the special environment of the subway tunnel, eliminates interference signals and false targets, and avoids false alarm from influencing train advancing.
The invention does not need to arrange trackside equipment, has low cost and convenient maintenance, can measure the distance between the subway train and the object in front, makes early warning and prevents the train from colliding with obstacles.
Drawings
The drawings are only for purposes of illustrating particular embodiments and are not to be construed as limiting the invention, wherein like reference numerals are used to designate like parts throughout.
Fig. 1 is a flowchart of a train collision avoidance method according to a first embodiment;
FIG. 2 is a flowchart illustrating a preprocessing method according to one embodiment of the present invention;
FIG. 3 is a flowchart of a training method of an SVM model according to the first embodiment;
fig. 4 is a connection schematic diagram of the train anti-collision device in the second embodiment;
fig. 5 is a schematic diagram of train deployment of the train collision avoidance device in the second embodiment.
Detailed Description
The preferred embodiments of the present invention will now be described in detail with reference to the accompanying drawings, which form a part hereof, and which together with the embodiments of the invention serve to explain the principles of the invention.
The first embodiment,
The embodiment discloses a train collision avoidance method based on millimeter wave radar, as shown in fig. 1, comprising the following steps:
s1, preprocessing a radar echo signal reflected in front of a locomotive of a subway locomotive to obtain a target echo signal;
s2, classifying the target echo signals by adopting a trained SVM model, and filtering false target echo signals to obtain real target echo signals;
and S3, calculating the relative distance and speed between the subway locomotive head and the target object according to the real target echo signal, judging whether the relative distance and speed exceed an anti-collision threshold value, and performing train anti-collision early warning if the relative distance and speed exceed the anti-collision threshold value.
As shown in fig. 2, the preprocessing method in step S1 includes:
s101, performing data primary screening on the radar echo signals, and eliminating transverse interference signals;
specifically, in the data preliminary screening, a transverse distance threshold value is set, and radar echo signals with transverse distances exceeding the transverse distance threshold value are rejected.
Preferably, the transverse distance threshold is matched with the width of the subway tunnel, for example, when the width of the subway tunnel is greater than 5 meters, a millimeter wave radar is installed with the center of the train as the origin, and the transverse distance threshold is set to be 2.5 meters, so that radar signals reflected by the wall of the subway tunnel with the abscissa greater than 2.5 meters can be eliminated.
S102, estimating clutter power of a position where a target is located;
the millimeter wave radar is used in a subway tunnel environment, the environment is complex, a radar echo signal comprises a plurality of target units, and the target units comprise target units only containing clutter and target units containing both clutter and target information; estimating clutter power in an echo signal to judge a target unit containing target information, namely estimating the clutter power of a position where a target is located; and when the train travels in the tunnel, the environment is time-varying, so the clutter power is also time-varying.
Specifically, the power of all target units included in the current echo signal is arithmetically averaged to be used as the clutter power of the position where the target is located. Since the arithmetic average is the arithmetic average of the target unit with higher power, which contains both clutter and target information, and the target unit with lower power, which contains only clutter, the clutter power can be used as a power threshold to distinguish whether the target unit contains target information.
And S103, comparing the clutter power value serving as a threshold with the power of the preliminarily screened radar echo signal, and eliminating a target unit only comprising the clutter signal in the radar echo signal to obtain a target echo signal.
Specifically, if the power value of a target unit in the radar echo signal is greater than a threshold value, it is indicated that the target unit of the radar echo contains not only clutter but also target information, and at this time, the amplitude of the target echo signal in the target unit is the square root of the power value; if the power value is smaller than the threshold value, the unit only contains the clutter signals, and the amplitude value of the target unit is set to be 0.
The SVM model adopted in step S2 is a trained SVM model, and as shown in fig. 3, the training method includes the following steps:
1) Constructing a training data set, wherein the training data is a radar echo signal;
specifically, the constructed training data set includes radar return signals collected when the train is running without an obstacle, and radar return signals collected when a specific obstacle is placed in front of the travel of the train.
2) Constructing an SVM classifier;
specifically, the SVM classifier adopts a linear kernel function.
For a given training sample set D = { (x) 1 ,y 1 ),(x 2 ,y 2 ),…,(x m ,y m )},y i ∈{-1,+1}；
The classification learning of the embodiment finds a partition hyperplane in the sample space based on the training set D, and separates samples of different classes. In sample space, the hyperplane can be partitioned by the following linear equation w T x + b =0 to describe: wherein ω = (ω) 1 ；ω 2 ；…；ω d ) The direction of the hyperplane is determined for the normal vector, and the distance between the hyperplane and the origin is determined for the displacement term b. The hypothesis hyperplane (ω, b) can correctly classify the training samples, i.e., for (x) i ,y i ) E.g. D, if y i = 1, then there is ω T x i +b>0; if y i If = 1, then there is ω T x i +b<0. Order to
When the following conditions are met:
the dividing hyperplane with the maximum interval can be found for classification processing.
3) Preprocessing a training data set to obtain a target echo signal;
specifically, the preprocessing method in step S1 is adopted to process the training data set to obtain the target echo signal included in the training data set.
4) Processing a target echo signal, and extracting a characteristic vector group; sticking a data label of a false target in the target echo signal to be +1, and sticking a data label of a real obstacle to be-1;
specifically, the extracted feature vector group x i Including the relative distance, velocity, and target echo power data of the target.
Wherein, LFMCW (Linear Frequency Modulated Continuous Wave) method can be adopted to extract the distance of the target,
specifically, triangular transformation is performed according to the calculated continuously transmitted millimeter waves to obtain the radar transmission signal and the radar receiving signal, except for a lag time Δ t, the characteristics are the same, and the relation between the lag time Δ t and the relative distance R is as follows: Δ T =2R/c, where c denotes the speed of light, Δ F is the frequency difference of the mixing output, T is the radar scan period, Δ F is the signal bandwidth, so the relative distance is:
the difference of the relative distances measured continuously can obtain the relative speed of the target;
the target echo power data is obtained according to the method in the preprocessing.
5) Extracting the feature vector group x i And corresponding data tag y i Sending the input into an SVM trainer to carry out model training to obtainTrained model parameter normal vector ω = (ω =) 1 ；ω 2 ；…；ω d ) And a displacement term b.
Classifying the real-time acquired target echo signals by the SVM model trained by the method, and filtering false target echo signals to obtain real target echo signals;
and then calculating the relative distance and speed between the subway locomotive head and the target object according to the real target echo signal, judging whether the relative distance and speed exceed an anti-collision threshold value, and if so, performing train anti-collision early warning.
The anti-collision threshold value can be a relative distance threshold value or/and a relative speed threshold value, and when the relative distance between the locomotive head of the train and the obstacle measured by the radar is smaller than the distance threshold value or/and the relative speed is larger than the relative speed threshold value, an early warning is sent out.
Listing that the operation has certain speed requirement, so that the relative distance reflects the time of collision with the target object; meanwhile, because the millimeter wave radar has a certain detection distance, the time of collision with the target object can be judged according to the relative speed; therefore, the condition that the collision occurs in the preset time can be effectively warned by setting the relative distance threshold value or/and the relative speed threshold value.
Compared with the prior art, the train collision avoidance method based on the millimeter wave radar can solve the problem of receiving and processing radar signals in the special environment of the subway tunnel, and can give early warning to obstacles in front of the subway train; interference signals and false targets are removed, and the influence of false alarms on the advancing of the train is avoided; besides, trackside equipment is not required to be arranged, the cost is low, and the maintenance is convenient.
Example II,
The embodiment also discloses a train anti-collision device based on the millimeter wave radar, which comprises a radar transmitter, a radar receiver and an anti-collision processor, as shown in fig. 4;
the radar transmitter is arranged at the locomotive of the train locomotive and used for transmitting millimeter wave radar signals to the front of the locomotive;
the radar receiver is arranged at the locomotive of the locomotive and used for receiving radar echo signals reflected in front of the locomotive;
the anti-collision processor is respectively connected with the radar transmitter and the radar receiver, and the train anti-collision method of the embodiment I is adopted to process the radar echo signals, find out obstacles and start train anti-collision measures.
The anti-collision processor can adopt a structure of a millimeter wave radar ranging processing module and an upper computer, the millimeter wave radar ranging processing module is connected with the radar receiver, receives radar echo signals and directly transmits the radar echo signals to the upper computer for processing, the relative distance and the relative speed between the radar and a barrier are calculated through echoes, and a train anti-collision measure is started; or the millimeter wave radar ranging processing module locally completes calculation and then uploads the related data to the upper computer. The connection between the millimeter wave radar ranging processing module and the upper computer can be wired, and data and instruction interaction can also be carried out in a wireless communication mode.
As shown in fig. 5, a train deployment schematic diagram of a train collision avoidance device is shown, wherein a train runs in a tunnel along a track direction, a millimeter wave radar transmitter located at a train head is installed at the train head and transmits a millimeter wave beam forward, the millimeter wave beam is reflected by a barrier to form an echo, the millimeter wave radar receives the echo, a millimeter wave radar ranging processing module analyzes and processes the echo, a false target is removed, a real target is calculated, the relative distance, the relative speed and the relative angle between the millimeter wave radar and a target object are calculated, results are packaged and sent to an upper computer through a CAN bus, and the upper computer sends out an early warning if the relative distance is too small or the relative speed is too large.
Compared with the prior art, the beneficial effects of the train anticollision device based on millimeter wave radar that this embodiment provided are the same basically with the beneficial effects that embodiment one provided, and it is not repeated here.
The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present invention are included in the scope of the present invention.
Claims (2)
1. A train collision avoidance method based on millimeter wave radar is characterized by comprising the following steps:
preprocessing a radar echo signal reflected in front of a subway locomotive head to obtain a target echo signal;
the pretreatment method comprises the following steps:
performing data primary screening on the radar echo signals, and eliminating transverse interference signals;
in the data preliminary screening, setting a transverse distance threshold value, and rejecting radar echo signals of which the transverse distance exceeds the transverse distance threshold value;
the transverse distance threshold is matched with the width of the subway tunnel;
estimating a clutter power value of the position where the target is located;
the radar echo signal comprises a plurality of target units, wherein the target units comprise target units only containing clutter and target units containing both the clutter and target information; when the clutter power of the position where the target is located is estimated, the arithmetic mean value of the power of all target units included in the current echo signal is taken as the clutter power of the position where the target is located;
comparing the clutter power value serving as a threshold value with the power of the radar echo signal after preliminary screening, and eliminating target units only comprising clutter signals in the radar echo signal to obtain a target echo signal;
if the power value of the target unit in the radar echo signal is larger than the threshold value, the target unit of the radar echo is indicated to contain not only the clutter but also target information, and the amplitude of the target echo signal in the target unit is the square root of the power value; if the power value is smaller than the threshold value, the unit only contains the clutter signals, and the amplitude value of the target unit is set to be 0; the target information comprises real obstacle information and false target information;
classifying the target echo signals by adopting a trained SVM model, and filtering false target echo signals to obtain real target echo signals;
the SVM model training method comprises the following steps:
1) Constructing a training data set, wherein the training data is a radar echo signal;
the constructed training data set comprises radar echo signals collected when a train runs under the condition that no obstacle exists, and radar echo signals collected when a specific obstacle is placed in front of the running train;
2) Constructing an SVM classifier;
the SVM classifier adopts a linear kernel function;
d = { (x) for a given training sample set 1 ,y 1 ),(x 2 ,y 2 ),…,(x m ,y m )},y i ∈{-1,+1}；
The classification learning finds a division hyperplane in a sample space based on a training set D, and samples of different classes are separated; in sample space, the hyperplane can be partitioned by the following linear equation w T x + b =0 to describe: wherein ω = (ω) 1 ；ω 2 ；…；ω d ) The direction of the hyperplane is determined for the normal vector, the distance between the hyperplane and the origin is determined for the displacement item b, and obviously, the division of the hyperplane can be determined by the normal vector and the displacement; the hypothesis hyperplane (ω, b) can correctly classify the training samples, i.e., for (x) i ,y i ) E is D, if y i = 1, then there is ω T x i +b>0; if y i If = 1, then there is ω T x i +b<0. Order to
When the following conditions are met:
the division hyperplane with the maximum interval can be found for classification processing;
3) Preprocessing a training data set to obtain a target echo signal;
specifically, the preprocessing method is adopted to process the training data set to obtain a target echo signal included in the training data set;
4) Processing a target echo signal, and extracting a characteristic vector group; the data label of the false target in the target echo signal is pasted as +1, and the data label of the real barrier is pasted as-1;
the extracted feature vector group comprises the relative distance and speed of the target and target echo power data;
specifically, triangular transformation is performed according to the calculated continuously transmitted millimeter waves to obtain radar transmission signals and radar reception signals, except for a lag time Δ t, the characteristics are the same, and the relation between the lag time Δ t and the relative distance R is as follows: Δ T =2R/c, where c denotes the speed of light, Δ F is the frequency difference of the mixing output, T is the radar scan period, Δ F is the signal bandwidth, so the relative distance is:
continuously measuring the difference of the relative distances to obtain the relative speed of the target;
obtaining target echo power data according to a method in preprocessing;
5) The extracted feature vector group and the corresponding data labels are used as input and sent to an SVM trainer for model training; obtaining a trained model parameter normal vector omega = (omega) 1 ；ω 2 ；…；ω d ) And a displacement term b;
classifying the target echo signals acquired in real time by adopting a trained SVM model, and filtering false target echo signals to obtain real target echo signals;
calculating the relative distance and speed between the subway locomotive head and the target object according to the real target echo signal, judging whether the relative distance and speed exceed an anti-collision threshold value, and if so, performing train anti-collision early warning;
the anti-collision threshold value is a relative distance threshold value and a relative speed threshold value, and when the relative distance between the locomotive head of the train and the obstacle measured by the radar is smaller than the distance threshold value and the relative speed is larger than the relative speed threshold value, an early warning is sent out.
2. A train anti-collision device based on a millimeter wave radar is characterized by comprising a radar transmitter, a radar receiver and an anti-collision processor;
the radar transmitter is arranged at the locomotive of the train locomotive and used for transmitting radar signals to the front of the locomotive;
the radar receiver is arranged at the locomotive of the locomotive and used for receiving radar echo signals reflected in front of the locomotive;
the anti-collision processor is respectively connected with the radar transmitter and the radar receiver, and the train anti-collision method according to claim 1 is adopted to process the radar echo signals, find obstacles and perform train anti-collision early warning.
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