CN113253204A - Positioning method, system and device based on pyroelectric infrared sensor - Google Patents
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- G—PHYSICS
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- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S5/00—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations
- G01S5/16—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations using electromagnetic waves other than radio waves
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- G06N3/084—Backpropagation, e.g. using gradient descent
Abstract
The invention provides a positioning method, a system and a device based on a pyroelectric infrared sensor, wherein the method comprises the following steps: dividing the detection area into a first preset number of grids according to a block coding mode; setting a second preset number of pyroelectric infrared sensors, and acquiring pyroelectric infrared information of a detection area based on the pyroelectric infrared sensors; converting the pyroelectric infrared information into a digital voltage signal; and taking the digital voltage signal as the input of a BP neural network training model, taking the code of a grid corresponding to the digital voltage signal as the output to train the BP neural network training model, and obtaining the trained BP neural network training model. The invention discloses a positioning method, a system and a device based on a pyroelectric infrared sensor, which are used for accurately positioning a detection area based on the pyroelectric infrared sensor.
Description
Technical Field
The invention relates to the technical field of communication, in particular to a positioning method, a positioning system and a positioning device based on a pyroelectric infrared sensor.
Background
With the improvement of safety awareness of people in recent years, the demand of human body target positioning for realizing large-scale monitoring and tracking tasks in various application fields is higher and higher. Human body target positioning is mainly divided into two types, namely video detection and pyroelectric infrared sensor detection. Although the video detection has high detection precision, the video detection has high cost, complex operation, large occupied storage amount, easy privacy leakage and large influence of weather change, so the use of the video detection is greatly limited. Compared with the prior art, the pyroelectric infrared sensor has the advantages of low cost, low energy consumption, small volume, high sensitivity, strong anti-interference capability, small storage space, no invasion to privacy of people, and low possibility of being influenced by environmental conditions such as background and illumination. Therefore, the pyroelectric infrared sensor has extremely wide scene application and extremely high market demand in the aspects of automatic control, mode identification, security protection, reconnaissance and the like.
However, there are two existing problems with human body targeting using pyroelectric infrared sensors, one being a hardware problem: most of the prior motion detectors based on the pyroelectric effect are based on a single pyroelectric sensor, but the single pyroelectric sensor has limited detection visual angle and short detection distance, so that the detection area is small, complete information acquisition cannot be realized, the application range is limited, and specific positioning of a human body target cannot be realized; secondly, the software problem: the existing algorithms are based on simple human body target recognition, positioning in a large range can be realized only by judging whether a person is present or not in a certain range, and the relation between a voltage signal acquired by a sensor and a specific position of a human body is difficult to obtain.
Therefore, it is desirable to solve the problem of how to perform positioning detection using a pyroelectric infrared sensor.
Disclosure of Invention
In view of the above-mentioned shortcomings of the prior art, an object of the present invention is to provide a positioning method, system and device based on pyroelectric infrared sensor, which are used to solve the problem of how to perform positioning detection by using pyroelectric infrared sensor in the prior art.
In order to achieve the above and other related objects, the present invention provides a positioning method based on pyroelectric infrared sensor, comprising the following steps: dividing the detection area into a first preset number of grids according to a block coding mode; setting a second preset number of pyroelectric infrared sensors, and acquiring pyroelectric infrared information of a detection area based on the pyroelectric infrared sensors; converting the pyroelectric infrared information into a digital voltage signal; and taking the digital voltage signal as the input of a BP neural network training model, taking the code of the grid corresponding to the digital voltage signal as the expected output to train the BP neural network training model, and obtaining the trained BP neural network training model.
In order to achieve the above object, the present invention further provides a positioning system based on a pyroelectric infrared sensor, comprising: the device comprises a segmentation module, an acquisition module, a conversion module and a training module; the segmentation module is used for dividing the detection area into a first preset number of grids according to a block coding mode; the acquisition module is used for setting a second preset number of pyroelectric infrared sensors and acquiring pyroelectric infrared information of a detection area based on the pyroelectric infrared sensors; the conversion module is used for converting the pyroelectric infrared information into a digital voltage signal; the training module is used for taking the digital voltage signal as the input of a BP neural network training model, taking the code of the grid corresponding to the digital voltage signal as the expected output to train the BP neural network training model, and obtaining the trained BP neural network training model.
In order to achieve the above object, the present invention further provides a positioning device based on a pyroelectric infrared sensor, comprising: a processor and a memory; the memory is used for storing a computer program; the processor is connected with the memory and is used for executing the computer program stored in the memory so as to enable the positioning device based on the pyroelectric infrared sensor to execute any one of the positioning methods based on the pyroelectric infrared sensor.
As described above, the positioning method, system and device based on the pyroelectric infrared sensor of the present invention have the following beneficial effects: the method is used for accurately positioning the detection area based on the pyroelectric infrared sensor.
Drawings
FIG. 1a is a flow chart illustrating a positioning method based on pyroelectric infrared sensor according to an embodiment of the present invention;
FIG. 1b is a schematic diagram of a detection area grid in an embodiment of the pyroelectric infrared sensor-based positioning method of the present invention;
FIG. 1c is a flow chart of a pyroelectric infrared sensor-based positioning method in another embodiment of the present invention;
FIG. 2 is a schematic structural diagram of an embodiment of a pyroelectric infrared sensor-based positioning system of the present invention;
fig. 3 is a schematic structural diagram of an embodiment of a positioning device based on pyroelectric infrared sensors according to the present invention.
Description of the element reference numerals
21 division module
22 acquisition module
23 conversion module
24 training module
31 processor
32 memory
Detailed Description
The embodiments of the present invention are described below with reference to specific embodiments, and other advantages and effects of the present invention will be easily understood by those skilled in the art from the disclosure of the present specification. The invention is capable of other and different embodiments and of being practiced or of being carried out in various ways, and its several details are capable of modification in various respects, all without departing from the spirit and scope of the present invention. It is to be noted that the features in the following embodiments and examples may be combined with each other without conflict.
It should be noted that the drawings provided in the following embodiments are only for illustrating the basic idea of the present invention, so that the components related to the present invention are only shown in the drawings rather than drawn according to the number, shape and size of the components in actual implementation, the type, quantity and proportion of the components in actual implementation can be changed freely, and the layout of the components can be more complicated.
The positioning method, the system and the device based on the pyroelectric infrared sensor are used for accurately positioning the detection area based on the pyroelectric infrared sensor.
As shown in fig. 1a, in an embodiment, the positioning method based on pyroelectric infrared sensor of the present invention includes the following steps:
and step S11, dividing the detection area into a first preset number of grids according to a block coding mode.
Specifically, the dividing the detection region into a first preset number of grids according to a block coding manner includes: and averagely dividing the detection region into c x d regions to obtain a first preset number of grids with the same size, and sequentially coding the c x d regions to obtain region codes. For example, as shown in fig. 1b, by thinning the grid, the detection region is divided into 10 × 10 regions on average, 100 grids with the same size are obtained, the 10 × 10 regions are encoded from 1 to 100, when the human body is in different detection regions, the codes corresponding to the corresponding regions are output, and the recorded region codes are in one-to-one correspondence with the actual physical coordinates, so that the specific positioning of the human body can be realized. When the human body is in different areas, different codes are output, and specific human body positioning can be realized only by simply corresponding the codes to actual physical coordinates.
Specifically, the method further comprises the step of carrying out region coding and labeling on the pyroelectric infrared information according to the collected grids.
And step S12, setting a second preset number of pyroelectric infrared sensors, and acquiring pyroelectric infrared information of the detection area based on the pyroelectric infrared sensors.
Specifically, the setting of the second preset number of pyroelectric infrared sensors includes:
the second preset number is three, and a fresnel lens is arranged on the detection surface of each pyroelectric infrared sensor, so that the detection areas of each pyroelectric infrared sensor are detection areas with the same area and detection angles of 120 degrees, the pyroelectric infrared sensors are sequentially arranged in a circumferential manner, and detection areas with detection angles of 360 degrees and no mutual overlap are formed. The model of the Fresnel lens is 13120F-2, detection within a 360-degree range is achieved, and a detection area is free of blind spots. The number of pyroelectric infrared sensors is increased, 3 pyroelectric infrared sensors are arranged in a combined mode, detection in the 360-degree direction is achieved, a detection area is free of blind spots, and the synergistic effect of the pyroelectric infrared sensors in the multiple number and combined mode is fully exerted.
And step S13, converting the pyroelectric infrared information into a digital voltage signal.
Specifically, the converting the pyroelectric infrared information into a digital voltage signal includes:
and converting the pyroelectric infrared information into a serial digital signal based on an external ADC. The external ADC (ADC-Analog-to-Digital Converter) converts an Analog signal into a Digital signal, which is represented by 0 and 1.
Receiving the serial digital signal based on Pmod AD1 and forwarding the serial digital signal to a shift register; the Pmod AD1 is a two-way ADC acquisition module of a simple Pmod interface, which is a channel analog-to-digital converter.
The serial digital signal is converted into a parallel digital signal based on a shift register. For example, analog voltage signals collected by the pyroelectric infrared sensor are converted into parallel 16-bit digital signals.
The parallel digital signal is converted into a character signal based on the USB-UART and transmitted to the PC. The communication between the FPGA and the PC end can be realized by utilizing a communication mode of a USB-UART (asynchronous serial communication protocol). And the parallel 16-bit digital signals output by the ADC are converted into parallel 10-bit characters through the key module and the serial port sending module, and the parallel 10-bit characters are sent to a serial port debugging tool of the PC. Specifically, a serial port sending module is used for converting a parallel 16-bit digital signal output by the ADC into a parallel 10-bit character signal, storing the parallel 10-bit character signal in a 16-bit system, and sending the parallel 10-bit character signal to a serial port debugging tool of the PC.
Specifically, the method also comprises the step of generating a stable key signal through a key triggering mode. The parallel 16-bit digital signal output by the ADC is converted into a serial 10-bit character signal through a key trigger mode.
Specifically, the method further comprises the steps of monitoring data in real time on the basis of a serial port debugging tool, flexibly adjusting a voltage acquisition system, storing effective samples and facilitating follow-up research.
Specifically, the method also comprises deleting the initial flag bit of the character signal, converting every two characters into a decimal number, generating voltage/time images of all signals according to the time sequence, and deleting the generated error code or noise signal by observing the images. By observing the oscillogram-voltage/time image, the abnormal samples that produce severe bit errors or noise phenomena are deleted.
Specifically, the method further comprises normalizing the digital voltage signal. And scaling the data, and uniformly mapping all the data to an interval [0,1], so that indexes of different units or orders of magnitude can be compared and weighted conveniently.
Specifically, marking the acquired pyroelectric infrared information, and marking the pyroelectric infrared information by using the grid region code of the acquired pyroelectric infrared information as a sample label.
Specifically, the method further comprises the steps of dividing the digital voltage signals into two types, taking one type as training data, accounting for 80% of total data, and training the BP neural network training model to obtain a trained BP neural network model; and the other type of the model is taken as test data, accounts for 20% of the total data, observes the error of the output data and the expected data, and evaluates the performance of the trained BP neural network model.
And step S14, taking the digital voltage signal as the input of a BP neural network training model, taking the code of the grid corresponding to the digital voltage signal as the expected output to train the BP neural network training model, and obtaining the trained BP neural network training model.
Specifically, the method further comprises the steps of setting an input layer, a hidden layer, an output layer node number, a learning rate, a maximum training frequency, an initialization weight w and a threshold b of the BP neural network model.
Conducting digital voltage signals from an input layer to a hidden layer and an output layer by layer, and calculating the output of each hidden layer and the output of the output layer of the BP neural network;
the sample of the input digital voltage signal is x (k) ═ x1(k),x2(k),···xn(k))；
The desired output is y (k) ═ y1(k),y2(k),···yq(k))；
The output of the ith neuron in each hidden layer is:
wherein, w1ihAs weights of the input layer and the hidden layer, b1hA threshold value of each neuron of the hidden layer;
the output of the kth neuron in the output layer is:
wherein, w2hiWeights for hidden and output layers, b2oIs the threshold value of each neuron of the output layer.
Specifically, the method further comprises comparing the actual output with the expected output to obtain an error:
specifically, the method further comprises the steps of reversely propagating from the output layer to the hidden layer and the input layer by layer according to the errors, obtaining the errors of the layers at the same time, and circularly adjusting the weight and the threshold of each neuron until the errors reach a preset error threshold or reach the maximum training times, so that the trained BP neural network training model is obtained. By establishing a BP neural network model, the functional relation between the pyroelectric infrared information acquired by the pyroelectric infrared sensor and the codes of the grids corresponding to the digital voltage signals is obtained, unknown position information can be obtained according to the known input voltage signals, and finally the human body target positioning is realized through the detection of the pyroelectric infrared sensor.
As shown in fig. 1c, the positioning method based on pyroelectric infrared sensor of the present invention comprises the following steps: specifically, the following example performs a summary explanation of all steps.
Dividing the detection area into a first preset number of grids according to a block coding mode; setting a second preset number of pyroelectric infrared sensors, and acquiring pyroelectric infrared information of a detection area based on the pyroelectric infrared sensors; converting the pyroelectric infrared information into a digital voltage signal, and carrying out normalization processing on the digital voltage signal.
Dividing the digital voltage signals into two types, wherein one type is used as training data, accounts for 80% of total data, and training the BP neural network training model to obtain a trained BP neural network model; and the other type of the model is taken as test data, accounts for 20% of the total data, observes the error of the output data and the expected data, and evaluates the performance of the trained BP neural network model.
And taking the digital voltage signal as the input of a BP neural network training model, taking the code of a grid corresponding to the digital voltage signal as the output to train the BP neural network training model, and comparing the actual output with the expected output to obtain an error.
And reversely propagating from the output layer to the hidden layer and the input layer by layer according to the errors, and simultaneously obtaining the errors of each layer, so as to circularly adjust the weight and the threshold of each neuron until the errors reach a preset error threshold or reach the maximum training times, thereby obtaining the trained BP neural network training model.
As shown in fig. 2, in an embodiment, the positioning system based on pyroelectric infrared sensor of the present invention comprises: a segmentation module 21, an acquisition module 22, a conversion module 23 and a training module 24; the segmentation module is used for dividing the detection area into a first preset number of grids according to a block coding mode; the acquisition module is used for setting a second preset number of pyroelectric infrared sensors and acquiring pyroelectric infrared information of a detection area based on the pyroelectric infrared sensors; the conversion module is used for converting the pyroelectric infrared information into a digital voltage signal; the training module is used for taking the digital voltage signal as the input of a BP neural network training model, taking the code of the grid corresponding to the digital voltage signal as the expected output to train the BP neural network training model, and obtaining the trained BP neural network training model.
It should be noted that the structures and principles of the segmentation module 21, the acquisition module 22, the conversion module 23, and the training module 24 correspond to the steps in the positioning method based on the pyroelectric infrared sensor one by one, and therefore, no further description is given here.
It should be noted that the division of the modules of the above system is only a logical division, and the actual implementation may be wholly or partially integrated into one physical entity, or may be physically separated. And these modules can be realized in the form of software called by processing element; or may be implemented entirely in hardware; and part of the modules can be realized in the form of calling software by the processing element, and part of the modules can be realized in the form of hardware. For example, the x module may be a processing element that is set up separately, or may be implemented by being integrated in a chip of the apparatus, or may be stored in a memory of the apparatus in the form of program code, and the function of the x module may be called and executed by a processing element of the apparatus. Other modules are implemented similarly. In addition, all or part of the modules can be integrated together or can be independently realized. The processing element described herein may be an integrated circuit having signal processing capabilities. In implementation, each step of the above method or each module above may be implemented by an integrated logic circuit of hardware in a processor element or an instruction in the form of software.
For example, the above modules may be one or more integrated circuits configured to implement the above methods, such as: one or more Specific Integrated circuits (ASICs), or one or more Microprocessors (MPUs), or one or more Field Programmable Gate Arrays (FPGAs), etc. For another example, when one of the above modules is implemented in the form of a Processing element scheduler code, the Processing element may be a general-purpose processor, such as a Central Processing Unit (CPU) or other processor capable of calling program code. For another example, these modules may be integrated together and implemented in the form of a system-on-a-chip (SOC).
As shown in fig. 3, in an embodiment, the positioning device based on pyroelectric infrared sensor of the present invention comprises: a processor 31 and a memory 32; the memory 32 is for storing a computer program; the processor 31 is connected to the memory 32, and is configured to execute the computer program stored in the memory 32, so as to enable the positioning apparatus based on pyroelectric infrared sensor to execute any one of the positioning methods based on pyroelectric infrared sensor.
Specifically, the memory 32 includes: various media that can store program codes, such as ROM, RAM, magnetic disk, U-disk, memory card, or optical disk.
Preferably, the Processor 31 may be a general-purpose Processor, including a Central Processing Unit (CPU), a Network Processor (NP), and the like; the Integrated Circuit may also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic device, or discrete hardware components.
In summary, the positioning method, system and device based on the pyroelectric infrared sensor of the present invention are used for accurately positioning the detection area based on the pyroelectric infrared sensor. Therefore, the invention effectively overcomes various defects in the prior art and has high industrial utilization value.
The foregoing embodiments are merely illustrative of the principles and utilities of the present invention and are not intended to limit the invention. Any person skilled in the art can modify or change the above-mentioned embodiments without departing from the spirit and scope of the present invention. Accordingly, it is intended that all equivalent modifications or changes which can be made by those skilled in the art without departing from the spirit and technical spirit of the present invention be covered by the claims of the present invention.
Claims (11)
1. A positioning method based on a pyroelectric infrared sensor is characterized by comprising the following steps:
dividing the detection area into a first preset number of grids according to a block coding mode;
setting a second preset number of pyroelectric infrared sensors, and acquiring pyroelectric infrared information of a detection area based on the pyroelectric infrared sensors;
converting the pyroelectric infrared information into a digital voltage signal;
and taking the digital voltage signal as the input of a BP neural network training model, taking the code of the grid corresponding to the digital voltage signal as the expected output to train the BP neural network training model, and obtaining the trained BP neural network training model.
2. The pyroelectric infrared sensor-based positioning method as claimed in claim 1, wherein the dividing of the detection areas into the first preset number of grids in a block coding manner comprises:
and averagely dividing the detection region into c x d regions to obtain a first preset number of grids with the same size, and sequentially coding the c x d regions to obtain region codes.
3. The pyroelectric infrared sensor-based positioning method as claimed in claim 2, further comprising performing area coding labeling on the pyroelectric infrared information according to the collected grids.
4. The pyroelectric infrared sensor-based positioning method according to claim 1, wherein the setting of the second preset number of pyroelectric infrared sensors comprises:
the second preset number is three, and a fresnel lens is arranged on the detection surface of each pyroelectric infrared sensor, so that the detection areas of each pyroelectric infrared sensor are detection areas with the same area and detection angles of 120 degrees, the pyroelectric infrared sensors are sequentially arranged in a circumferential manner, and detection areas with detection angles of 360 degrees and no mutual overlap are formed.
5. The pyroelectric infrared sensor-based positioning method according to claim 1, wherein the converting the pyroelectric infrared information into a digital voltage signal comprises:
converting pyroelectric infrared information into serial digital signals based on an external ADC (analog to digital converter);
receiving the serial digital signal based on Pmod AD1 and forwarding the serial digital signal to a shift register;
converting the serial digital signal into a parallel digital signal based on a shift register;
the parallel digital signal is converted into a character signal based on the USB-UART and transmitted to the PC.
6. The pyroelectric infrared sensor-based positioning method as claimed in claim 5, further comprising deleting the start flag bit of the character signal, converting every two characters into a decimal number, generating voltage/time images of all signals in time sequence, and deleting the generated error code or noise signal by observing the images.
7. The pyroelectric infrared sensor-based positioning method according to claim 1, further comprising setting the number of nodes of an input layer, a hidden layer and an output layer, the learning rate, the maximum training times, the initialization weight w and the threshold b of the BP neural network model;
conducting digital voltage signals from an input layer to a hidden layer and an output layer by layer, and calculating the output of each hidden layer and the output of the output layer of the BP neural network;
the sample of the input digital voltage signal is x (k) ═ x1(k),x2(k),···xn(k))；
The desired output is y (k) ═ y1(k),y2(k),···yq(k))；
The output of the ith neuron in each hidden layer is:
wherein, w1ihAs weights of the input layer and the hidden layer, b1hA threshold value of each neuron of the hidden layer;
the output of the kth neuron in the output layer is:
wherein, w2hiWeights for hidden and output layers, b2oIs the threshold value of each neuron of the output layer.
8. The pyroelectric infrared sensor-based positioning method of claim 1, further comprising comparing the actual output with the expected output to obtain an error:
9. the pyroelectric infrared sensor-based positioning method of claim 8, further comprising reversely propagating from the output layer to the hidden layer and the input layer according to errors layer by layer, and obtaining the errors of each layer, thereby circularly adjusting the weight and the threshold of each neuron until the errors reach a preset error threshold or reach the maximum training times, thereby obtaining the trained BP neural network training model.
10. A positioning system based on pyroelectric infrared sensors, characterized by comprising: the device comprises a segmentation module, an acquisition module, a conversion module and a training module;
the segmentation module is used for dividing the detection area into a first preset number of grids according to a block coding mode;
the acquisition module is used for setting a second preset number of pyroelectric infrared sensors and acquiring pyroelectric infrared information of a detection area based on the pyroelectric infrared sensors;
the conversion module is used for converting the pyroelectric infrared information into a digital voltage signal;
the training module is used for taking the digital voltage signal as the input of a BP neural network training model, taking the code of the grid corresponding to the digital voltage signal as the expected output to train the BP neural network training model, and obtaining the trained BP neural network training model.
11. A positioning device based on a pyroelectric infrared sensor is characterized by comprising: a processor and a memory;
the memory is used for storing a computer program;
the processor is connected with the memory and is used for executing the computer program stored in the memory to enable the positioning device based on the pyroelectric infrared sensor to execute the positioning method based on the pyroelectric infrared sensor of any one of the claims 1 to 9.
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