CN111524367A - Distance measurement fusion method and system, composite traffic flow monitoring device and monitoring system - Google Patents
Distance measurement fusion method and system, composite traffic flow monitoring device and monitoring system Download PDFInfo
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- CN111524367A CN111524367A CN202010638073.9A CN202010638073A CN111524367A CN 111524367 A CN111524367 A CN 111524367A CN 202010638073 A CN202010638073 A CN 202010638073A CN 111524367 A CN111524367 A CN 111524367A
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- G—PHYSICS
- G08—SIGNALLING
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- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/042—Detecting movement of traffic to be counted or controlled using inductive or magnetic detectors
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/04—Detecting movement of traffic to be counted or controlled using optical or ultrasonic detectors
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/052—Detecting movement of traffic to be counted or controlled with provision for determining speed or overspeed
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Abstract
The invention discloses a distance measurement fusion method, which comprises the steps of obtaining distance information collected by all distance measurement sensors at the same moment; calculating a measurement variance estimation value according to the distance information; calculating the optimal weighting factor of each ranging sensor according to the measurement variance estimation value; and calculating an estimation value after the multi-distance information is fused according to the optimal weighting factor and the distance information, and taking the estimation value as final distance information. Meanwhile, a corresponding distance measurement fusion system, a composite traffic flow monitoring device and a monitoring system are disclosed. The invention carries out self-adaptive weighting fusion algorithm judgment based on the acquired distance information, searches the optimal weighting factor corresponding to each sensor in a self-adaptive mode, avoids the problem of signal crosstalk caused by using a single sensor to carry out multiple measurements, and improves the overall distance measurement precision.
Description
Technical Field
The invention relates to a distance measurement fusion method and system, a composite traffic flow monitoring device and a monitoring system, and belongs to the field of intelligent traffic.
Background
The traffic jam problem becomes more and more serious due to the development of the automobile industry and the acceleration of the urbanization process, and the traffic light has important practical significance for improving the urban traffic condition as a means for controlling the traffic flow and improving the road traffic capacity. In the process of controlling the traffic signal lamp, effective acquisition of traffic flow information such as vehicle speed, distance (distance from a sensor to a vehicle) and the like plays an important role, and plays a critical role in adjusting the brightness and darkness of the traffic signal lamp. The problem of signal crosstalk is caused when the single sensor measures distance information for many times, the real-time performance of the system is reduced, and the accuracy of a detection result is influenced.
Disclosure of Invention
The invention provides a distance measurement fusion method and system, a composite traffic flow monitoring device and a monitoring system, which solve the problems disclosed in the background technology.
In order to solve the technical problems, the technical scheme adopted by the invention is as follows:
the distance measurement fusion method comprises the steps of,
acquiring distance information acquired by each distance measuring sensor at the same moment;
calculating a measurement variance estimation value according to the distance information;
calculating the optimal weighting factor of each ranging sensor according to the measurement variance estimation value;
and calculating an estimation value after the multi-distance information is fused according to the optimal weighting factor and the distance information, and taking the estimation value as final distance information.
The measured variance estimate is formulated as,
wherein the content of the first and second substances,
is as followsiDistance measuring sensorkDistance of timeInformation from
The corresponding measured variance estimate is then used to,
is composed of
The auto-covariance function of (a) is,
is composed of
Is measured.
The optimal weighting factor is formulated as,
wherein the content of the first and second substances,
is as followsiThe optimal weighting factor for each of the ranging sensors,
is as followsiDistance measuring sensorkTime and distance information
The corresponding measured variance estimate is then used to,
is as followsjDistance measuring sensorkTime and distance information
The corresponding measured variance estimate is then used to,nin order to measure the number of the sensors,
。
the formula of the estimated value after the multi-distance information fusion is as follows,
wherein the content of the first and second substances,
is composed ofnThe distance information of each distance measuring sensor is fused to form an estimated value,
is as followsiThe optimal weighting factor for each of the ranging sensors,
is as followsiDistance measuring sensorkThe information of the distance of the time of day,nthe number of ranging sensors.
The distance measurement fusion system comprises a distance measurement fusion system,
an acquisition module: acquiring distance information acquired by each distance measuring sensor at the same moment;
a variance estimation module: calculating a measurement variance estimation value according to the distance information;
a weighting factor module: calculating the optimal weighting factor of each ranging sensor according to the measurement variance estimation value;
a fusion estimation module: and calculating an estimation value after the multi-distance information is fused according to the optimal weighting factor and the distance information, and taking the estimation value as final distance information.
The composite traffic flow monitoring device comprises a processor, a geomagnetic sensor, a millimeter wave radar sensor, an infrared sensor and a communication module; the geomagnetic sensor, the millimeter wave radar sensor, the infrared sensor and the communication module are all connected with the processor, and the communication module is externally connected with a flow calculation center;
wherein the content of the first and second substances,
a geomagnetic sensor: acquiring an environmental magnetic field vector in a detection mode;
millimeter wave radar sensor: acquiring dynamic data when a vehicle arrives and distance information between the vehicle and the vehicle in a detection mode;
an infrared sensor: acquiring distance information between the automobile and the automobile in a detection mode;
a processor: acquiring the time of a vehicle passing through the composite vehicle flow monitoring device according to the environmental magnetic field vector, dynamic data when the vehicle arrives and a preset rule, and transmitting the time to a flow calculation center;
according to the distance measurement fusion method, the estimation value obtained after the distance information of the millimeter wave radar sensor and the distance information of the infrared sensor are fused is calculated and is used as the estimation value of the final distance information to be transmitted to the flow calculation center.
The geomagnetic sensor intermittently collects environmental magnetic field vectors in a sleep mode; the processor judges whether the environment magnetic field has disturbance according to the environment magnetic field vector acquired intermittently, responds to the disturbance, sends a mode switching instruction to the geomagnetic sensor, the millimeter wave radar sensor and the infrared sensor, and switches the geomagnetic sensor, the millimeter wave radar sensor and the infrared sensor from the sleep mode to the detection mode.
According to the environmental magnetic field quantity, the dynamic data and the preset rule when the vehicle arrives, the time of the vehicle passing through the composite traffic flow monitoring device is obtained,
calculating the vector variation of the environmental magnetic field according to the vector of the environmental magnetic field;
acquiring a dynamic characteristic value according to dynamic data when a vehicle comes;
and responding to the fact that the environmental magnetic field vector variation is larger than or equal to the first threshold and the dynamic characteristic value is larger than or equal to the second threshold, and then the time when the vehicle passes through the composite vehicle flow monitoring device is obtained.
The system also comprises an activation unit connected with the processor and used for activating the composite traffic flow monitoring device to initialize and start to work.
The monitoring system comprises a flow calculation center and a plurality of composite traffic flow monitoring devices communicated with the flow calculation center;
the flow calculation center: calculating the speed of the vehicle according to the distance between the adjacent composite traffic flow monitoring devices and the time of the vehicle passing through each composite traffic flow monitoring device; and detecting the traffic flow according to the speed of the vehicle and the final distance information of each composite traffic flow monitoring device.
The invention achieves the following beneficial effects: the invention carries out self-adaptive weighting fusion algorithm judgment based on the acquired distance information, searches the optimal weighting factor corresponding to each sensor in a self-adaptive mode, avoids the problem of signal crosstalk caused by using a single sensor to carry out multiple measurements, and improves the overall distance measurement precision.
Drawings
FIG. 1 is a flow chart of a range fusion method;
FIG. 2 is a block diagram of a composite traffic flow monitoring device;
FIG. 3 is a schematic diagram illustrating the switching between sleep mode and detection mode;
FIG. 4 is a schematic diagram of the variation of magnetic field vector;
fig. 5 is a flowchart illustrating the operation of the hybrid traffic flow monitoring device.
Detailed Description
The invention is further described below with reference to the accompanying drawings. The following examples are only for illustrating the technical solutions of the present invention more clearly, and the protection scope of the present invention is not limited thereby.
As shown in fig. 1, the distance measurement fusion method includes the following steps:
step 1, obtaining distance information collected by each distance measuring sensor at the same time.
The distance measuring sensor comprises a millimeter wave radar sensor and an infrared sensor, wherein the millimeter wave radar sensor is used for detecting whether a moving object exists in a measuring range, FMCW frequency modulation continuous waves are adopted to measure the distance information of the vehicle, and the infrared sensor utilizes return signals of infrared rays to identify the distance information of the vehicle.
And 2, calculating a measurement variance estimation value according to the distance information.
The measurement variance estimation value formula is as follows:
wherein the content of the first and second substances,
is as followsiDistance measuring sensorkTime and distance information
The corresponding measured variance estimate is then used to,
is composed of
By auto-covariance function, e.g. using distance-measuring sensors
And distance measuring sensoriPerforming correlation operation to obtain the self-covariance function
，
Is composed of
The average value of (a) of (b),i(ii) a signal of either 1 or 2,
is a millimeter wave radar sensorkThe information of the distance of the time of day,
is an infrared sensorkTime and distance information.
And 3, calculating the optimal weighting factor of each ranging sensor according to the measurement variance estimation value.
The optimal weighting factor is formulated as:
wherein the content of the first and second substances,
is as followsiThe optimal weighting factor for each of the ranging sensors,
is as followsiDistance measuring sensorkTime and distance information
The corresponding measured variance estimate is then used to,
is as followsjDistance measuring sensorkTime and distance information
The corresponding measured variance estimate is then used to,n=2 is the number of ranging sensors,
。
and 4, calculating an estimated value after multi-distance information fusion according to the optimal weighting factor and the distance information, and taking the estimated value as final distance information.
The formula of the estimated value after multi-distance information fusion is as follows:
wherein the content of the first and second substances,
is composed ofnAnd (4) an estimated value obtained after the distance information of each ranging sensor is fused.
According to the method, self-adaptive weighting fusion algorithm judgment is carried out based on the acquired distance information, the optimal weighting factor corresponding to each sensor is searched in a self-adaptive mode, the problem of signal crosstalk caused by multiple measurements by using a single sensor is avoided, and the overall distance measurement precision is improved.
The software system corresponding to the method, namely the ranging fusion system, comprises the following steps:
an acquisition module: acquiring distance information acquired by each distance measuring sensor at the same moment;
a variance estimation module: calculating a measurement variance estimation value according to the distance information;
a weighting factor module: calculating the optimal weighting factor of each ranging sensor according to the measurement variance estimation value;
a fusion estimation module: and calculating an estimation value after the multi-distance information is fused according to the optimal weighting factor and the distance information, and taking the estimation value as final distance information.
As shown in fig. 2, the composite traffic flow monitoring device includes a processor, an activation unit, a geomagnetic sensor, a millimeter wave radar sensor, an infrared sensor, a communication module, and a power supply unit; the activation unit, the geomagnetic sensor, the millimeter wave radar sensor, the infrared sensor and the communication module are all connected with the processor, the communication module is externally connected with a flow calculation center, and the power supply unit supplies power to each power utilization module.
The activation unit adopts a combination mode of a magnet and a magnetic switch and is used for activating the composite vehicle flow monitoring device to initialize and start working.
The geomagnetic sensor, the millimeter wave radar sensor and the infrared sensor jointly form an acquisition unit, the acquisition unit adopts a low-power-consumption mechanism, and a sleep mode and a detection mode exist under the low-power-consumption mechanism. As shown in fig. 3, in the sleep mode, the millimeter wave radar sensor and the infrared sensor do not work, the geomagnetic sensor intermittently collects the environmental magnetic field vector, and transmits the intermittently collected environmental magnetic field vector to the processor, and the processor judges whether the environmental magnetic field has disturbance according to the intermittently collected environmental magnetic field vector, and in response to the disturbance, sends a mode switching instruction to the geomagnetic sensor, the millimeter wave radar sensor and the infrared sensor, and switches the geomagnetic sensor, the millimeter wave radar sensor and the infrared sensor from the sleep mode to the detection mode.
In the detection mode, the geomagnetic sensor collects an environmental magnetic field vector and sends the collected environmental magnetic field vector to the processor, the millimeter wave radar sensor detects whether a moving object exists, if a vehicle comes, dynamic data and distance information between the vehicle and the sensor are collected, and the infrared sensor collects the distance information between the vehicle and the sensor.
The processor acquires the time of the vehicle passing through the composite vehicle flow monitoring device according to the environmental magnetic field vector, the dynamic data when the vehicle arrives and a preset rule, and transmits the time to the flow calculation center; according to the distance measurement fusion method, the estimation value obtained after the distance information of the millimeter wave radar sensor and the distance information of the infrared sensor are fused is calculated, and the estimation value serving as the final distance information is transmitted to the flow calculation center.
The time of a vehicle passing through the composite traffic flow monitoring device is obtained, and the specific process is as follows:
1) calculating the environmental magnetic field vector variation according to the environmental magnetic field vector (see fig. 4);
wherein the content of the first and second substances,
is the amount of vector change of the ambient magnetic field,
respectively, an ambient magnetic field vector at a previous moment and an ambient magnetic field vector at a later moment.
2) And acquiring a dynamic characteristic value according to the dynamic data when the vehicle comes.
3) And responding to the fact that the environmental magnetic field vector variation is larger than or equal to the first threshold and the dynamic characteristic value is larger than or equal to the second threshold, and then the time when the vehicle passes through the composite vehicle flow monitoring device is obtained.
Namely, it is
And is
Wherein, in the step (A),
a first threshold value and a second threshold value respectively,
is a dynamic characteristic value.
The communication module is used for establishing a communication mode between the composite traffic flow detection device and the flow calculation center and wirelessly transmitting a detection result. The communication module adopts a low-power consumption NB-IoT internet of things communication technology, does not depend on a gateway, directly transmits through an operator base station, ensures stable signal transmission, saves the installation and maintenance cost of the gateway, and adopts trigger control to transmit results only when the signal acquisition unit is in a detection mode and detects that a vehicle is moving.
As shown in fig. 5, the operation process of the composite traffic flow monitoring device is as follows:
s1, activating a magnetic switch by a magnet, and initializing;
s2: the geomagnetic sensor enters a sleep mode and intermittently provides an environmental magnetic field vector to the processor;
s3: the processor monitors and analyzes the disturbance condition, if no disturbance exists, the step returns to the step S2, and if the interference of the vehicle is caused, the step enters the step S4;
s4: the processor sends a mode switching instruction to enable the signal acquisition unit to be switched into a detection mode from a sleep mode, and the geomagnetic sensor, the millimeter wave radar sensor and the infrared sensor send acquired detection data to the processor;
s5: the processor acquires the time and the final distance information of the vehicle passing through the composite traffic flow monitoring device;
s6: the processor activates the communication module to transmit the detection result, and returns to step S2 to enter a new round.
The monitoring system comprises a flow calculation center and a plurality of composite traffic flow monitoring devices communicated with the flow calculation center;
the flow calculation center: calculating the speed of the vehicle according to the distance between the adjacent composite traffic flow monitoring devices and the time of the vehicle passing through each composite traffic flow monitoring device
Wherein
The distance between adjacent composite traffic flow monitoring devices,
respectively the time when the vehicle passes through the two composite traffic flow monitoring devices; based on the speed of the vehicle and the final distance information of each composite traffic flow monitoring device, traffic flow detection is performed, such as detecting the distance between vehicles, etc. (the calculation in traffic flow detection is prior art and is not described in detail here).
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The present invention is not limited to the above embodiments, and any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention are included in the scope of the claims of the present invention which are filed as the application.
Claims (10)
1. The distance measurement fusion method is characterized in that: comprises the steps of (a) preparing a mixture of a plurality of raw materials,
acquiring distance information acquired by each distance measuring sensor at the same moment;
calculating a measurement variance estimation value according to the distance information;
calculating the optimal weighting factor of each ranging sensor according to the measurement variance estimation value;
and calculating an estimation value after the multi-distance information is fused according to the optimal weighting factor and the distance information, and taking the estimation value as final distance information.
2. The range-finding fusion method of claim 1, wherein: the measured variance estimate is formulated as,
wherein the content of the first and second substances,
is as followsiDistance measuring sensorkTime and distance information
The corresponding measured variance estimate is then used to,
is composed of
The auto-covariance function of (a) is,
is composed of
Is measured.
3. The range-finding fusion method of claim 1, wherein: the optimal weighting factor is formulated as,
wherein the content of the first and second substances,
is as followsiThe optimal weighting factor for each of the ranging sensors,
is as followsiDistance measuring sensorkTime and distance information
The corresponding measured variance estimate is then used to,
is as followsjDistance measuring sensorkTime and distance information
The corresponding measured variance estimate is then used to,nin order to measure the number of the sensors,
。
4. the range-finding fusion method of claim 1, wherein: the formula of the estimated value after the multi-distance information fusion is as follows,
wherein the content of the first and second substances,
is composed ofnThe distance information of each distance measuring sensor is fused to form an estimated value,
is as followsiThe optimal weighting factor for each of the ranging sensors,
is as followsiDistance measuring sensorkThe information of the distance of the time of day,nthe number of ranging sensors.
5. Range finding fuses system, its characterized in that: comprises the steps of (a) preparing a mixture of a plurality of raw materials,
an acquisition module: acquiring distance information acquired by each distance measuring sensor at the same moment;
a variance estimation module: calculating a measurement variance estimation value according to the distance information;
a weighting factor module: calculating the optimal weighting factor of each ranging sensor according to the measurement variance estimation value;
a fusion estimation module: and calculating an estimation value after the multi-distance information is fused according to the optimal weighting factor and the distance information, and taking the estimation value as final distance information.
6. Compound traffic flow monitoring devices, its characterized in that: the device comprises a processor, a geomagnetic sensor, a millimeter wave radar sensor, an infrared sensor and a communication module; the geomagnetic sensor, the millimeter wave radar sensor, the infrared sensor and the communication module are all connected with the processor, and the communication module is externally connected with a flow calculation center;
wherein the content of the first and second substances,
a geomagnetic sensor: acquiring an environmental magnetic field vector in a detection mode;
millimeter wave radar sensor: acquiring dynamic data when a vehicle arrives and distance information between the vehicle and the vehicle in a detection mode;
an infrared sensor: acquiring distance information between the automobile and the automobile in a detection mode;
a processor: acquiring the time of a vehicle passing through the composite vehicle flow monitoring device according to the environmental magnetic field vector, dynamic data when the vehicle arrives and a preset rule, and transmitting the time to a flow calculation center;
a distance measurement fusion method according to any one of claims 1 to 4, wherein an estimated value obtained by fusing millimeter wave radar sensor distance information and infrared sensor distance information is calculated and transmitted to a flow calculation center as an estimated value of final distance information.
7. A composite traffic flow monitoring device according to claim 6, characterised in that: the geomagnetic sensor intermittently collects environmental magnetic field vectors in a sleep mode; the processor judges whether the environment magnetic field has disturbance according to the environment magnetic field vector acquired intermittently, responds to the disturbance, sends a mode switching instruction to the geomagnetic sensor, the millimeter wave radar sensor and the infrared sensor, and switches the geomagnetic sensor, the millimeter wave radar sensor and the infrared sensor from the sleep mode to the detection mode.
8. A composite traffic flow monitoring device according to claim 6, characterised in that: according to the environmental magnetic field quantity, the dynamic data and the preset rule when the vehicle arrives, the time of the vehicle passing through the composite traffic flow monitoring device is obtained,
calculating the vector variation of the environmental magnetic field according to the vector of the environmental magnetic field;
acquiring a dynamic characteristic value according to dynamic data when a vehicle comes;
and responding to the fact that the environmental magnetic field vector variation is larger than or equal to the first threshold and the dynamic characteristic value is larger than or equal to the second threshold, and then the time when the vehicle passes through the composite vehicle flow monitoring device is obtained.
9. A composite traffic flow monitoring device according to claim 6, characterised in that: the system also comprises an activation unit connected with the processor and used for activating the composite traffic flow monitoring device to initialize and start to work.
10. Monitoring system, its characterized in that: the composite type vehicle flow monitoring device comprises a flow calculation center and a plurality of composite type vehicle flow monitoring devices according to any one of claims 6-9, wherein the composite type vehicle flow monitoring devices are communicated with the flow calculation center;
the flow calculation center: calculating the speed of the vehicle according to the distance between the adjacent composite traffic flow monitoring devices and the time of the vehicle passing through each composite traffic flow monitoring device; and detecting the traffic flow according to the speed of the vehicle and the final distance information of each composite traffic flow monitoring device.
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