CN103412682B - Electronic whiteboard experimental system based on infrared ultrasonic co-located and localization method - Google Patents

Abstract

The invention relates to an electronic whiteboard experiment system and a positioning method based on combined infrared and ultrasonic positioning, which belong to the field of wireless positioning. The experimental system mainly includes a signal transmitting module integrating an infrared transmitter and an ultrasonic transmitter, a signal receiving module integrating an infrared receiver and six ultrasonic receivers, a signal processing module and a host computer. The signal pen will generate periodic infrared and ultrasonic signals. The infrared receiver at the receiving end detects the infrared signal. The six ultrasonic receivers start working at the same time. The signal source is obtained by solving the data corrected by the BP neural network. The invention greatly saves the cost of the sensor, has a small volume, can timely and accurately acquire the detection data of the sensor, can be used as an experimental platform for a high-precision algorithm, and provides convenient conditions for research on the effectiveness of the algorithm.

Description

Electronic whiteboard experiment system and positioning method based on infrared and ultrasonic joint positioning

technical field

The invention relates to an electronic whiteboard experiment system and a positioning method based on combined infrared and ultrasonic positioning, and belongs to the field of wireless positioning.

Background technique

The electronic whiteboard is a dry-wipe whiteboard that can be written on the board and can be electronically captured. It integrates a blackboard, multimedia, and a projector. Its functions are far greater than the sum of the three. A high-tech product. The electronic whiteboard is a breakthrough to traditional blackboard writing and multimedia classrooms. It can be used online with projectors and computers, and can easily realize information presentation, editing and other functions by using the data processing function and information transmission function of the computer. The electronic whiteboard teaching platform compares the two teaching methods of the blackboard and the audio-visual platform, retains the interactivity of the blackboard and the richness of the audio-visual education, overcomes the lack of monotony of the blackboard and the unidirectionality of the audio-visual education, and fundamentally solves the problems in the previous teaching mode. The existing problems and deficiencies have truly realized the "interaction between teaching and learning" and realized a high-quality and efficient teaching model.

However, most of the electronic whiteboards on the market are large and highly integrated because they are oriented to customers, and it is difficult to obtain internal sensor information. There is no electronic whiteboard experimental system suitable for experimental research. Therefore, when researchers want to conduct research on the core technology of electronic whiteboards (such as positioning accuracy, recognition accuracy, etc.), they must face the fact that the validity of the research results cannot be verified due to the inability to obtain the sampling information of each sensor integrated in the whiteboard hardware. The problem of feedback information such as the necessary precision, response speed, and accuracy is a major obstacle in research work, and it will inevitably delay the replacement of electronic whiteboards.

At present, the basic positioning algorithms of electronic whiteboards include field strength (RSSI) positioning method, positioning method based on radio wave propagation time (TOA) or time difference (TDOA), positioning method based on radio wave incident angle (AOA) and various hybrid positioning methods. Among them, the positioning method based on TDOA has no strict time synchronization requirements, can be applied to various types of systems, and has low application cost and high positioning accuracy, which has attracted widespread attention. However, due to various disturbances in the environment, the actual measured data often contain errors, resulting in reduced positioning accuracy or even positioning failure. Therefore, it is necessary to study high-precision positioning algorithms.

Contents of the invention

The invention proposes an electronic whiteboard experiment system and a positioning method based on combined infrared and ultrasonic positioning. This experimental system uses the infrared emission sensor and ultrasonic emission sensor to simulate the emission principle of the signal pen to generate infrared and ultrasonic signals. The data is solved to obtain the position information of the signal pen, so as to achieve the purpose of positioning the signal pen. The invention can timely and accurately obtain the detection data of the sensor, can make up for the defects of high integration of current electronic whiteboard products and is not suitable for experimental research, and has the characteristics of small size and low cost; at the same time, it can be used as an experimental platform for high-precision algorithms. It provides convenient conditions for the research on the validity of the verification algorithm. At the same time, using the electronic whiteboard experiment system of the present invention as a hardware platform, a joint positioning algorithm based on BP neural network and Chan algorithm is proposed.

The main idea of the present invention is: when the signal pen presses the switch in the writing area of the electronic whiteboard experiment system to start writing, the signal pen will generate periodic infrared and ultrasonic signals, and the infrared receiving sensor at the receiving end detects the infrared signal (because the distance is short and the infrared The propagation speed is close to the speed of light, and its propagation time is negligible), triggering the DSP interrupt, and the six ultrasonic receiving sensors start working at the same time (considered as instantaneous triggering), respectively detect the time it takes for each to receive the ultrasonic signal, and send it to the signal processing module. The Chan algorithm solves the sampling data corrected by the BP neural network to obtain the position information of the signal source (that is, the signal pen) on the electronic whiteboard experimental system, so as to achieve the purpose of positioning the signal pen.

The specific technical scheme adopted by the present invention is as follows: including a signal transmitting module (referred to as a signal pen), a signal receiving module, a signal processing module, and a host computer software, and is characterized in that: it also includes an infrared emitting sensor, an ultrasonic emitting sensor, an infrared receiving sensor, six One ultrasonic receiving sensor, the infrared emitting sensor and the ultrasonic emitting sensor are fixed in the signal pen, and the periodic infrared signal and ultrasonic signal are sent out by the single-chip microcomputer control, the infrared receiving sensor and six ultrasonic receiving sensors are fixed in the signal receiving module, receiving the infrared signal and Ultrasonic signal, the output end of the signal receiving sensor module is connected with the input end of the signal processing module, the signal processing module adopts DSP as the main control chip, and the output end of the signal processing module is connected with the host computer software.

The positioning algorithm of the above-mentioned electronic whiteboard experimental system includes the following steps:

1) When the switch of the signal transmitting module is pressed, the infrared transmitting sensor and the ultrasonic transmitting sensor send out periodic infrared signals and ultrasonic signals;

2) The infrared receiving sensor at the signal receiving end detects the signal, triggers the six ultrasonic receiving sensors to start working at the same time, and detects the time t _i , i=1, 2, 3, 4, 5, 6 for each receiving the ultrasonic signal;

3) Taking the data of the first ultrasonic receiving sensor as a reference, use the TDOA algorithm to calculate and obtain 5 TDOA values t _i1 , i=2,3,4,5,6;

4) Use the theoretical data without errors to train the BP neural network, and use the trained BP neural network to correct the five TDOA values t _i1 , i=2,3,4,5,6;

5) Using the corrected data, use the Chan algorithm to estimate the position, and obtain the position information of the signal pen on the electronic whiteboard experimental system, so as to achieve the purpose of positioning the signal pen.

The present invention adopts an infrared sensor and an ultrasonic sensor as signal input for obtaining positioning information, which greatly saves the cost of the sensor, reduces the volume at the same time, and can obtain sensor information timely and accurately.

Description of drawings

Figure 1 Schematic diagram of the electronic whiteboard experimental system

Figure 2 The principle block diagram of the electronic whiteboard experiment system

Figure 3 System structure block diagram of the electronic whiteboard experiment system

Figure 4 The software flow chart of the signal emission of the electronic whiteboard experiment system

Figure 5 Schematic diagram of positioning algorithm for electronic whiteboard experiment system

Fig. 6 Principle block diagram of positioning algorithm of electronic whiteboard experiment system

Figure 7. The BP neural network model that corrects the TDOA value

Figure 8 Software flow chart of the positioning algorithm of the electronic whiteboard experiment system

detailed description

The following is a further description of the experimental system in conjunction with the accompanying drawings:

As shown in Fig. 1 and Fig. 2, the hardware structure of the present invention is relatively simple, low in cost, and easy to implement.

In order to reduce the volume of the signal transmitting end, the single-chip microcomputer that controls the pulse waveform in this experimental system is selected as the 8-bit 8-pin single-chip microcomputer PIC12F629 of Microchip Company with a smaller volume. The waveform circuit is mainly generated by the GP0 port and GP1 port of the single-chip microcomputer. Since the resonant circuit in the drive circuit resonates with the 80KHZ frequency component contained in the pulse, the pulse width is directly related to the power of each frequency component contained in the pulse. wide, the greater the power, the shorter the battery life; the pulse width is too narrow, and the 80KHZ signal energy contained in the pulse is small, resulting in insufficient system transmission power. can be driven. The period of the pulse signal is set to 10mS, which ensures that 100 signals can be sent out per second, which fully meets the needs of reception and vision. The GP0 port of the microcontroller PIC12F629 sends out a pulse signal with a pulse width of 400uS and a period of 10mS as the driving signal of the ultrasonic sensor; GP1 port sends out a pulse signal with a pulse width of 200uS and a period of 10mS as the driving signal of the infrared diode. The software implementation process of the PIC microcontroller control process is shown in Figure 4.

The ultrasonic transmitter sensor of this experimental system requires 360-degree omnidirectional transmission and good propagation characteristics in the omnidirectional range. Therefore, this system uses the piezoelectric thin film ultrasonic sensor PT80KHZ-01 produced by the American EMAS sensor company. While meeting the above requirements, the sensor also has the characteristics of high frequency response, good dynamic range, high output voltage, good stability, impact resistance, and not easy to age, which meets the various requirements of this experimental system.

The infrared emitting sensor required by this experimental system also requires 360-degree omnidirectional transmission, but there is no infrared emitting sensor that meets the conditions on the market, so this experimental system uses infrared emitting diode SFH426, its emission angle is 120 degrees, and four SFH426 are used Let it be evenly distributed for a week to achieve 360-degree omnidirectional emission.

The ultrasonic receiving sensor adopts SR80KHZ-01 from EMAS Sensor Company of the United States, which is also a piezoelectric film sensor, which has the advantages of wide range of receiving signals, large direction angle and bandwidth.

The infrared receiving sensor adopts SFH506-38, which is a small infrared remote control system receiving sensor. The PIN diode and preamplifier are installed in the welding shell and encapsulated by epoxy material. The modulated signal output by the receiving sensor can be directly demodulated by the microprocessor. Its main advantage is that it still has reliability in an environment with interference, and the shell has a shielding effect against electromagnetic interference.

The signal processing module of this experimental system is described below in conjunction with Figure 3, including the power management module and the DSP main control circuit. Among them, the infrared receiving sensor is connected with an I/O port of the DSP, and the ultrasonic receiving sensor is connected with the I/O port with the capture function in the DSP, and the ultrasonic propagation time is obtained by using the capture interrupt. The algorithm is realized in DSP, and after solving the sampling data, the position information of the signal pen is output, which is transmitted to the host computer software through the RS232 serial port.

In order to realize the accurate extraction algorithm of ultrasonic time delay, the core chip of the signal processing circuit of this experimental system adopts a TMS320C28X series floating-point DSP controller TMS320F28335 produced by TI Company, which has a high-speed processing capability of 150MHz, which fully meets the requirements of this experimental system. Real-time requirements. Compared with the previous fixed-point DSP, this device has the advantages of high precision, low cost, low power consumption, high performance, high peripheral integration, large data and program storage, and more accurate and fast A/D conversion. TMS320F28335 has a 32-bit floating-point processing unit, 6 DMA channels supporting ADC, McBSP and EMIF, and up to 18 PWM outputs, of which 6 are TI-specific higher-precision PWM outputs, and 12-bit 16-channel ADC. Thanks to its floating point operation unit, users can quickly write control algorithms without spending too much time and energy on dealing with decimal operations. Compared with the previous generation DSC, the average performance is increased by 50%, thus simplifying software development and shortening the development time. cycle and reduce development costs.

The power management module uses an integrated DC-DC transformer circuit, its main function is to convert the battery voltage (12v) into the voltage (5v, 3.3v) required by the DSP control board, infrared receiving sensor, and ultrasonic receiving sensor.

Taking the electronic whiteboard experiment system of the present invention as the hardware platform, a joint positioning algorithm based on BP neural network and Chan algorithm is proposed. The method is as shown in Figures 5-7, with the upper left corner of the writing area as the origin, and the horizontal and vertical directions as the X and Y axes, respectively, to establish a plane Cartesian coordinate system. The specific implementation of the algorithm is:

1) When the switch of the signal pen is pressed, the infrared emission sensor and the ultrasonic emission sensor send out periodic infrared signals and ultrasonic signals;

2) The infrared receiving sensor at the signal receiving end detects that the infrared signal is interrupted, triggers six ultrasonic receiving sensors to start working at the same time, and respectively detects the time t _i , i=1,2,3,4,5, 6;

3) Taking the first ultrasonic receiving sensor as a reference, use the TDOA algorithm to calculate and obtain 5 TDOA values t _i1 =t _i -t ₁ , i=2,3,4,5,6;

4) Carry out data correction with BP neural network, as follows:

a. Design BP neural network: It consists of input layer, hidden layer and output layer. The input layer is composed of 5 TDOA measurement values provided by 6 ultrasonic receiving sensors; the number of neurons in the hidden layer can be obtained by empirical formula, and the transfer function of the hidden layer is selected as a Sigmoid function; the output layer is composed of 5 neurons, using linear Transfer function, the output value is the corrected TDOA value

b. Training BP neural network: the measurement value of the input layer adopts the error-free data obtained by fixed-point measurement; the output value of the output layer is the same as that of the input layer.

c. Use the trained BP neural network to correct the five TDOA values t _i1 in (3), i=2,3,4,5,6

5) Using the corrected distance value, use the Chan algorithm to estimate the position, and obtain the position information of the ultrasonic signal source (that is, the signal pen) on the electronic whiteboard experimental system, so as to achieve the purpose of positioning the signal pen. details as follows:

Let (x, y) be the position of the signal pen to be requested, ( _xi , y _i ) is the known position of the i-th ultrasonic receiving sensor, C is the propagation speed of the ultrasonic wave, R _i represents the signal pen to the i-th ultrasonic receiving sensor The distance of the sensor, R _i1 represents the difference between the distance from the signal pen to the i-th ultrasonic receiving sensor and the distance from the signal pen to the first ultrasonic receiving sensor, then:

${\mathrm{<span\; class="notranslate">\; R</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; c</span>}{\mathrm{<span\; class="notranslate">\; t</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; =</span>{\mathrm{<span\; class="notranslate">\; R</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; R</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; =</span>\sqrt{{<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; x</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; x</span>}<span\; class="notranslate">\; )</span>}^{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; +</span>{<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; the\; y</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; the\; y</span>}<span\; class="notranslate">\; )</span>}^{\mathrm{<span\; class="notranslate">\; 2</span>}}}<span\; class="notranslate">\; -</span>\sqrt{{<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; x</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; x</span>}<span\; class="notranslate">\; )</span>}^{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; +</span>{<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; the\; y</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; the\; y</span>}<span\; class="notranslate">\; )</span>}^{\mathrm{<span\; class="notranslate">\; 2</span>}}}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 2,3,4,5,6</span>}<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; )</span>$

Square both sides to get:

${\mathrm{<span\; class="notranslate">\; R</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}\mathrm{<span\; class="notranslate">\; 1</span>}}^{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; 2</span>}{\mathrm{<span\; class="notranslate">\; R</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}\mathrm{<span\; class="notranslate">\; 1</span>}}{\mathrm{<span\; class="notranslate">\; R</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; =</span>{\mathrm{<span\; class="notranslate">\; K</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 2</span>}{\mathrm{<span\; class="notranslate">\; x</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}\mathrm{<span\; class="notranslate">\; 1</span>}}\mathrm{<span\; class="notranslate">\; x</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 2</span>}{\mathrm{<span\; class="notranslate">\; the\; y</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}\mathrm{<span\; class="notranslate">\; 1</span>}}\mathrm{<span\; class="notranslate">\; the\; y</span>}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; K</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 2</span>}<span\; class="notranslate">\; )</span>$

where x _i1 =x _i -x ₁ , y _i1 =y _i -y ₁ , ,i=2,3,4,5,6

Let Z _a =[x,y,R ₁ ] ^T be an unknown vector, then equation (2) is a ternary linear equation about these three variables, so that the error vector can be obtained:

$\mathrm{<span\; class="notranslate">\; \&psi;</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; h</span>}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; G</span>}}_{\mathrm{<span\; class="notranslate">\; a</span>}}{\mathrm{<span\; class="notranslate">\; Z</span>}}_{\mathrm{<span\; class="notranslate">\; a</span>}}^{\mathrm{<span\; class="notranslate">\; 0</span>}}<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 3</span>}<span\; class="notranslate">\; )</span>$

in, $h=\frac{1}{2}\left[\begin{array}{c}{R}_{\mathrm{2,1}}^2-K_2+K_1\\ R_{\mathrm{3,1}}^2-K_3+K_1\\ ...\\ R_{\mathrm{6,1}}^2-K_6+K_1\end{array}\right]$ , $G_a=\frac{1}{2}\left[\begin{array}{ccc}{x}_{\mathrm{2,1}}& {\mathrm{the\; y}}_{\mathrm{2,1}}& R_{\mathrm{2,1}}\\ x_{\mathrm{3,1}}& {\mathrm{the\; y}}_{\mathrm{3,1}}& R_{\mathrm{3,1}}\\ ...& ...& ...\\ x_{\mathrm{6,1}}& {\mathrm{the\; y}}_{\mathrm{6,1}}& R_{\mathrm{6,1}}\end{array}\right]$

When defining no noise, the expression form of {*} is {*} ⁰ , then:

$t_{i,1}=t_{i,1}^0+{\mathrm{no}}_{i,1}$ , $R_{i,1}=R_{i,1}^0+{\mathrm{Cn}}_{i,1}$

Among them, n _i1 =n _i -n ₁ , and n _i is the systematic error of the detection time of the i-th ultrasonic receiving sensor.

From this, the error vector can be obtained as:

$\mathrm{<span\; class="notranslate">\; \&psi;</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; CB</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; 0.5</span>}{\mathrm{<span\; class="notranslate">\; C</span>}}^{\mathrm{<span\; class="notranslate">\; 2</span>}}\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; \&CircleTimes;</span>\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 4</span>}<span\; class="notranslate">\; )</span>$

in, $B=\mathrm{dig}\{R_2^0,R_3^0,...,R_6^0\}$ , $\mathrm{no}={[{\mathrm{no}}_{\mathrm{2,1}},{\mathrm{no}}_{\mathrm{3,1}},...,{\mathrm{no}}_{\mathrm{6,1}}]}^T$ , Represents the dot product operation, in the actual case , so the term behind the above formula can be ignored, the error vector ψ can be approximated as a random vector that obeys a normal distribution, and the covariance of ψ is:

Ψ=E[ψψ ^T ]=C ² BQB (5)

Among them, Q=E[nn] is the covariance matrix of TDOA, and the approximate solution of formula (4) obtained by using WLS is:

${\mathrm{<span\; class="notranslate">\; Z</span>}}_{\mathrm{<span\; class="notranslate">\; a</span>}}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; arg</span>}\mathrm{<span\; class="notranslate">\; min</span>}<span\; class="notranslate">\; \{</span>{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; h</span>}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; G</span>}}_{\mathrm{<span\; class="notranslate">\; a</span>}}{\mathrm{<span\; class="notranslate">\; Z</span>}}_{\mathrm{<span\; class="notranslate">\; a</span>}}<span\; class="notranslate">\; )</span>}^{\mathrm{<span\; class="notranslate">\; T</span>}}{\mathrm{<span\; class="notranslate">\; \&Psi;</span>}}^{<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; h</span>}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; G</span>}}_{\mathrm{<span\; class="notranslate">\; a</span>}}{\mathrm{<span\; class="notranslate">\; Z</span>}}_{\mathrm{<span\; class="notranslate">\; a</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; \}</span><span\; class="notranslate">\; =</span>{<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; G</span>}}_{\mathrm{<span\; class="notranslate">\; a</span>}}^{\mathrm{<span\; class="notranslate">\; T</span>}}{\mathrm{<span\; class="notranslate">\; \&Psi;</span>}}^{<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\mathrm{<span\; class="notranslate">\; G</span>}}_{\mathrm{<span\; class="notranslate">\; a</span>}}<span\; class="notranslate">\; )</span>}^{<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\mathrm{<span\; class="notranslate">\; G</span>}}_{\mathrm{<span\; class="notranslate">\; a</span>}}^{\mathrm{<span\; class="notranslate">\; T</span>}}{\mathrm{<span\; class="notranslate">\; \&Psi;</span>}}^{<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}}\mathrm{<span\; class="notranslate">\; h</span>}<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 6</span>}<span\; class="notranslate">\; )</span>$

When the distance between the signal pen and the ultrasonic receiving sensor is relatively long, define a R ⁰ to make it compatible with each Approximately, at this time B=R ⁰ I, Z _a can be replaced by:

${\mathrm{<span\; class="notranslate">\; Z</span>}}_{\mathrm{<span\; class="notranslate">\; a</span>}}<span\; class="notranslate">\; =</span>{<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; G</span>}}_{\mathrm{<span\; class="notranslate">\; a</span>}}^{\mathrm{<span\; class="notranslate">\; T</span>}}{\mathrm{<span\; class="notranslate">\; Q</span>}}^{<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\mathrm{<span\; class="notranslate">\; G</span>}}_{\mathrm{<span\; class="notranslate">\; a</span>}}<span\; class="notranslate">\; )</span>}^{<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\mathrm{<span\; class="notranslate">\; G</span>}}_{\mathrm{<span\; class="notranslate">\; a</span>}}^{\mathrm{<span\; class="notranslate">\; T</span>}}{\mathrm{<span\; class="notranslate">\; Q</span>}}^{<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}}\mathrm{<span\; class="notranslate">\; h</span>}<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 7</span>}<span\; class="notranslate">\; )</span>$

When the distance between the signal pen and the ultrasonic receiving sensor is relatively close, the value of B is calculated first, and then the position of the signal pen is obtained by formula (6). So far, the first WLS is over.

Use the first WLS estimate and the constraint relationship for the second WLS:

${\mathrm{<span\; class="notranslate">\; Z</span>}}_{\mathrm{<span\; class="notranslate">\; a</span>}}^{<span\; class="notranslate">\; \&prime;</span>}<span\; class="notranslate">\; =</span>{<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; G</span>}}_{\mathrm{<span\; class="notranslate">\; a</span>}}^{<span\; class="notranslate">\; \&prime;</span>\mathrm{<span\; class="notranslate">\; T</span>}}{\mathrm{<span\; class="notranslate">\; B</span>}}^{<span\; class="notranslate">\; \&prime;</span><span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\mathrm{<span\; class="notranslate">\; G</span>}}_{\mathrm{<span\; class="notranslate">\; a</span>}}^{\mathrm{<span\; class="notranslate">\; T</span>}}{\mathrm{<span\; class="notranslate">\; Q</span>}}^{<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\mathrm{<span\; class="notranslate">\; G</span>}}_{\mathrm{<span\; class="notranslate">\; a</span>}}^{<span\; class="notranslate">\; \&prime;</span>}{\mathrm{<span\; class="notranslate">\; B</span>}}^{<span\; class="notranslate">\; \&prime;</span><span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\mathrm{<span\; class="notranslate">\; G</span>}}_{\mathrm{<span\; class="notranslate">\; a</span>}}^{<span\; class="notranslate">\; \&prime;</span>}<span\; class="notranslate">\; )</span>}^{<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; \&times;</span><span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; G</span>}}_{\mathrm{<span\; class="notranslate">\; a</span>}}^{<span\; class="notranslate">\; \&prime;</span>\mathrm{<span\; class="notranslate">\; T</span>}}{\mathrm{<span\; class="notranslate">\; B</span>}}^{<span\; class="notranslate">\; \&prime;</span><span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\mathrm{<span\; class="notranslate">\; G</span>}}_{\mathrm{<span\; class="notranslate">\; a</span>}}^{\mathrm{<span\; class="notranslate">\; T</span>}}{\mathrm{<span\; class="notranslate">\; Q</span>}}^{<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\mathrm{<span\; class="notranslate">\; G</span>}}_{\mathrm{<span\; class="notranslate">\; a</span>}}^{<span\; class="notranslate">\; \&prime;</span>}{\mathrm{<span\; class="notranslate">\; B</span>}}^{<span\; class="notranslate">\; \&prime;</span><span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; )</span>{\mathrm{<span\; class="notranslate">\; h</span>}}^{<span\; class="notranslate">\; \&prime;</span>}<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 8</span>}<span\; class="notranslate">\; )</span>$

in, $h^{\&prime;}=\left[\begin{array}{c}{(Z_a\left(1\right)-x_1)}^2\\ {(Z_a\left(2\right)-x2)}^2\\ Z_a{\left(3\right)}^2\end{array}\right]$ , $G_a^{\&prime;}=\left[\begin{array}{cc}1& 0\\ 0& 1\\ 1& 1\end{array}\right]$ , $B^{\&prime;}=\left[\begin{array}{ccc}{Z}_a\left(1\right)-x_1& 0& 0\\ 0& Z_a\left(2\right)-{\mathrm{the\; y}}_1& 0\\ 0& 0& Z_a\left(3\right)\end{array}\right]$

Finally, the estimated position of the signal pen is obtained as:

$\mathrm{<span\; class="notranslate">\; Z</span>}<span\; class="notranslate">\; =</span><span\; class="notranslate">\; \&PlusMinus;</span>\sqrt{{\mathrm{<span\; class="notranslate">\; Z</span>}}_{\mathrm{<span\; class="notranslate">\; a</span>}}^{<span\; class="notranslate">\; \&prime;</span>}}<span\; class="notranslate">\; +</span>\left[\begin{array}{c}{\mathrm{<span\; class="notranslate">\; x</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}}\\ {\mathrm{<span\; class="notranslate">\; the\; y</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}}\end{array}\right]<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 9</span>}<span\; class="notranslate">\; )</span>$

In this experimental system, the signal pen only moves in the first quadrant, so the result calculated by formula (9) only takes a positive value, thereby realizing positioning.

Claims (1)

1. A positioning method using an electronic whiteboard experimental system based on infrared and ultrasonic joint positioning, the system includes a signal transmitting module, a signal receiving module, a signal processing module, a host computer, and also includes an infrared transmitter, an ultrasonic transmitter, and an infrared receiver And six ultrasonic receivers, the infrared transmitter and the ultrasonic transmitter are fixed on the signal transmitting module, sending out periodic infrared signals and ultrasonic signals, the infrared receiver and six ultrasonic receivers are fixed on the signal receiving module, receiving the infrared signal and ultrasonic signal, the output end of the signal receiving module is connected to the input end of the signal processing module, and the output end of the signal processing module is connected to the upper computer; It is characterized in that the method comprises the following steps: 1) The infrared transmitter and the ultrasonic transmitter send out periodic infrared signals and ultrasonic signals; 2) The infrared receiver at the receiving end detects the signal, triggers six ultrasonic receivers to start working at the same time, and respectively detects the time t _i , i=1, 2, 3, 4, 5, 6 used for receiving the ultrasonic signal; 3) Taking the data of the first ultrasonic receiving sensor as a reference, use the TDOA algorithm to calculate and obtain 5 TDOA values t _i1 , i=2, 3, 4, 5, 6; 4) Utilize the theoretical data without errors to train the BP neural network, and use the trained BP neural network to correct the five TDOA values t _i1 , i=2, 3, 4, 5, 6; 5) Based on the corrected TDOA value, the Chan algorithm is used to estimate the position, and the position information of the signal pen on the electronic whiteboard experimental system is obtained, so as to achieve the purpose of positioning the signal pen.