CN203198644U - Novel automobile tire pressure wireless monitoring system - Google Patents

Abstract

The utility model discloses a new type of wireless monitoring system for automobile tire pressure, which consists of several SAW sensors 1, a wireless transceiver switch 2, a wireless transmitter 3, a wireless receiver 4, ADSP5, an antenna 6, and a display device 7 , Alarm device 8 and so on. The utility model realizes the passiveization of the sensor itself, and its service life is not limited by the battery at all; it can realize the miniaturization of the sensor, and the influence on the dynamic balance of the wheel is completely negligible; the wireless transceiver system adopts a single-chip application circuit, so that the transceiver The device is small in size and easy to install; weak signal processing is realized on the ADSP chip to improve the reliability of the entire monitoring system.

Description

A New Type of Wireless Monitoring System for Automobile Tire Pressure

technical field

The tire pressure monitoring system (TPMS) is mainly used for real-time tire pressure automatic monitoring and alarm, so that the driver can take corresponding measures to avoid accidents and ensure the safe driving of the car. The utility model relates to a novel wireless monitoring system for automobile tires. the

Background technique

The existing TPMS directly measures the pressure signal through the tire pressure sensor on the wheel, processes it through the microprocessor, and sends it to the receiving end in the cab by the radio frequency chip, thereby displaying the result for the driver's reference. However, this method needs to install a lithium battery in the tire as the power supply of the wireless transmission module, which not only affects the dynamic balance of the tire, but also has a limited battery life, making it inconvenient to replace it inside the tire. the

Utility model content

The purpose of the utility model is to overcome the deficiencies in the prior art and provide a passive method. By installing the surface acoustic wave (SAW) sensor on the wheel to detect the pressure of the tire, it overcomes the shortcomings of the existing technology, and at the same time makes full use of the advantages of the ADSP processor in signal processing, and processes the received weak signal with a neural network algorithm to improve the system. accuracy and reliability. the

The utility model is realized through the following schemes. the

A novel wireless monitoring system for automobile tire pressure of the utility model is shown in Fig. 1 . Its composition and connection relationship: several SAW sensors (1) installed on the wheel, wireless transceiver switch (2), wireless transmitter (3), wireless receiver (4), ADSP (5), antenna (6) , a display device (7), an alarm device (8); the pressure of the tire is detected by several SAW sensors (1) installed on the wheel; the microprocessor ADSP (5) controls the wireless transceiver switch (2), the wireless transmitter ( 3) It is connected with the wireless receiver (4) to realize the wireless passive reading of the tire pressure; the ADSP (5) is connected with the display (7) and the alarm (8) to realize the real-time display of the change curve of the tire pressure and abnormal alarm . the

1. The composition of SAW sensor 1 is as shown in Figure 2, interdigital transducer (9), substrate (10), coding reflection grid (11), reading reflection grid (12), reed tube (13), coding magnet (14). the

2. Wireless transmitter (3) The application of a single-chip wireless transmitter circuit is shown in Figure 3. The MAX7044, 300-450MHz ASK transmitter circuit is selected. MAX7044 is a VHF/UHF transmitter chip based on a crystal oscillator PLL. the

3. The wireless transceiver (4) uses a single-chip wireless receiving circuit, as shown in FIG. 4 . Select MAX7033 and MAX7044 in Figure 3 to form a wireless data transceiver system. MAX7033 is a low-power CMOS superheterodyne receiver chip that receives ASK signals in the frequency range of 300-450MHz. the

Compared with the prior art, the utility model has the following advantages:

1. Realize the passiveization of the sensor itself, and its life is not limited by the battery at all, avoiding the trouble of replacing the battery and disassembling the tire. the

2. The miniaturization of the sensor can be realized, and the influence on the dynamic balance of the wheel is completely negligible. the

3. The wireless transceiver system adopts a single-chip application circuit, which makes the transceiver device small in size and easy to install. the

4. Realize the neural network algorithm on the ADSP chip to process weak signals, improving the reliability of the entire monitoring system. the

Description of drawings

Figure 1 is a composition diagram of a new type of wireless monitoring system for automobile tires. the

Figure 2 SAW sensor structure diagram. the

Figure 3 schematic diagram of the wireless transmitter circuit. the

Figure 4 Wireless receiver circuit schematic. the

Figure 5 Multi-layer forward BP neural network structure diagram

Figure 6 Block diagram of adaptive filter based on neural network

Among them: SAW sensor (1), wireless transceiver switch (2), wireless transmitter (3), wireless receiver (4), ADSP (5), antenna (6), display device (7), alarm device (8 ), interdigital transducer (9), base body (10), coding reflection grid (11), reading reflection grid (12), reed pipe (13), coding magnet (14) and so on. the

Detailed ways

Below in conjunction with accompanying drawing, the utility model is further described:

1. Principle of SAW sensor 1

The interdigital transducer shown in Figure 2 is used for the excitation and detection of surface acoustic waves. It converts the radio signal received by the antenna into SAW, and at the same time detects the reflected SAW into electromagnetic waves and transmits them through the antenna. The center frequency of the transmitted radio frequency pulse signal is 433MHz. The bandwidth of the frequency response function is -3dB. A split-finger transducer is used as the reflection grid. the

2. The working principle of wireless transmitter 3

As shown in Figure 3, MAX7044 transmits OOK/ASK data in the frequency range of 300-450MHz, the data rate reaches 100Kb/s, the output power is +13dBm (50Ω load), and the power supply voltage is +2.1-+3.6V. The current consumption is only 7.7mA at 2.7V, the standby voltage current consumption is 130nA, the clock output frequency f _XTAL =16Hz, and the operating temperature range is -40°C-+125°C. The schematic diagram of the MAX7044 application circuit is shown in Figure 3. In Fig. 3, C31, C32, C36, L31, L33, and Y31 have different values for different operating frequencies.

3. The working principle of wireless receiver 4

As shown in Figure 4, the RF input signal range of the MAX7033 chip receiver is -120--114dBm, the maximum data is 86Kb/s, the working voltage is 3.3V or 5.0V, the startup time is 250us, the current consumption is 6.88mA, and the power consumption is low. Mode current consumption <3.5uA, when f(RF)=433MHz, the crystal oscillator frequency is 4.7547MHz. In Figure 4, C49, L41, L42 and Y41 have different values for different operating frequencies. the

4. Selection of ADSP chip

ADSP5 chooses one of the ADSP21XX series chips, and the specific technical parameters take ADSP2181 as an example. Like other DSP chips of ADI, the ADSP2181 is also based on the Harvard structure, that is, a parallel structure based on a separate parallel bus. This structure uses a separate instruction bus and data bus, so that fetching instructions and fetching data and once arithmetic Logical operations can be performed simultaneously, so it can perform a multiply-add operation in a single cycle. ADSP2181 is a high-performance 16-bit fixed-point digital signal processor launched by Analog Devices in the second half of 1994. It has a high-speed operation capability of 33MIPS (there is a version of 40MIPS now) and a flexible and convenient user interface, and is one of the most advanced fixed-point DSP products in the world today. the

ADSP2181 performance indicators: completely separated on-chip program and data bus structure---Harvard Architecture, 33MIPS single-cycle instruction system, single-cycle instruction jump, multifunction instructions (Multifunction Instructions) support 80k on-chip memory (On-chip Memory), including 16k x24bit program memory and 16k*16bit data memory, program memory can be used to store both program and data, separate arithmetic logic unit (ALU), multiplier/accumulator (MAC ), barrel shifter Barrel Shifter), two sets of completely independent data access address generators (DAG), programmable 16-bit internal clock, 16-bit internal DMA port (IDMA) supports high-speed on-chip memory access, Separate memory address space and I/O address space with programmable strobe, programmable wait state, 6 external interrupts, and 13 programmable flag bits can be used for flexible signal indication and data communication. At the same time, the neural network algorithm is used for weak signal processing, see the appendix for details. the

5. Monitoring the working process of the system

Reflection occurs when a surface acoustic wave encounters a surface with an impedance discontinuity. Therefore, a wireless SAW short-distance and low-power passive sensor in a single-port working mode can be manufactured by using a plurality of coded reflective grids (9). The radio frequency inquiry pulse transmitted by the wireless transmitter (3) is received by the antenna (6) on the surface acoustic wave device, and the interdigital transducer (7) connected to the antenna (6) converts the electrical signal into a surface acoustic wave (SAW) . The reflective grid located on the sound track partially reflects the sound wave, and the reflected echo is converted by the IDT piezoelectrically and then emitted through the antenna (6). The coded reflective grid (12) has different reflective degrees (reflection coefficients) to sound waves under normal and abnormal tire pressure conditions, so that the echo signal contains a modulated signal with a numerical value. The wireless receiver (4) receives the signal to realize the wireless passive reading of the tire pressure, and the display (8) displays the change curve of the tire pressure in real time. (9) Make sound and light. the

Appendix

Weak signal detection method based on neural network

Neural network refers to an algorithm that simulates the neural structure of organisms and the way they process information to perform calculations. The neural networks used for weak signal detection mainly include BP neural network, RBF neural network and GRNN neural network. the

BP neural network provides a new method for adaptive nonlinear filtering because of its advantages of high parallelism, self-learning, self-adaptation, and nonlinear mapping. The multi-layer BP neural network uses the Sigmoid function as its activation function, which can realize nonlinear function approximation with arbitrary precision, so the nonlinear adaptive filter composed of BP neural network can detect weak signals submerged by noise. the

1 Principle of Adaptive Noise Cancellation

Adaptive filtering theory is an optimal filtering method developed on the basis of linear filtering such as Wiener filtering and Kalman filtering. The principle of adaptive noise cancellation does not need to know the statistical characteristics of interference noise in advance, it can adjust its working state to the best state in the process of successive iterations, and it is effective for suppressing both broadband noise and narrowband noise. The characteristic of adaptive filtering is that the transfer function of the filter often changes with the change of the input signal, that is, the characteristic of nonlinear mapping between the input and output of the filter. Compared with many shortcomings and limitations of linear filters, adaptive filtering can achieve better performance than linear processing in many occasions. the

The core of adaptive noise cancellation is the adaptive filter. Adaptive filtering is to use an adaptive algorithm to adjust the filter parameters so that the filter output is close to the noise superimposed in the output signal of the sensor 1, so that the output of the canceller is close to the measured signal. Adaptive filtering can use the minimum mean square error criterion as its optimal criterion, that is, the mean square value of the output e(k) of the canceller reaches the minimum. Let's analyze this, the output of the canceller is:

e(k)=y(k)-z(k)=s(k)+n(k)-z(k)

Its mean square value is

E[e ² (k)]=E[(s(k)+n(k)-z(k)) ² ]

＝E[s ² (k)+n ² (k)+z ² (k)-2n(k)z(k)+2s(k)n(k)-2s(k)z(k)]

=E[s ² (k)]+E[(n(k)-z(k)) ² ]+2E[s(k)n(k)]-2E[s(k)z(k)]

If the interference noise n(k) is not correlated with the measured signal s(k), then the output x(k) of the sensor 2 is not correlated with s(k), and the output z(k) obtained by filtering x(k) ) and s(k) are also independent of each other, then:

E[s(k)n(k)]＝0

E[s(k)z(k)]＝0

Putting it into the above formula, we get:

E[e ² (k)]=E[s ² (k)]+E[(n(k)-z(k)) ² ]

=R _s (0)+E[(n(k)-z(k)) ² ]

In the formula, R _s (0) is the average power of the signal. It can be seen that when z(k) tends to n(k), E[e ² (k)] reaches the minimum value, that is, it can be extracted from the noise at this time There is a useful signal.

2 Adaptive filter design based on BP neural network

Artificial Neural Network (ANN) is a neural network artificially constructed on the basis of human understanding of the brain's neural network to achieve certain functions. It is a theoretical mathematical model of the human brain's neural network, based on imitating the An information processing system based on the structure and function of the neural network. The artificial neural network is a complex network connected by a large number of simple components, which is highly nonlinear, capable of performing complex logic operations and realizing a system of nonlinear relationships. Fig. 5 is a typical structure diagram of BP neural network. the

The learning process of BP neural network consists of two parts, namely forward propagation and back propagation. During forward propagation, the input information is processed by the hidden layer and then transmitted to the output layer, and the state of neurons in each layer only affects the state of neurons in the next layer. If the output result of the output layer exceeds the expected value range, it will be transferred to backpropagation, and the error information will be returned along the original neuron connection path. During the return process, the connection weights of neurons in each layer are modified one by one. This process is iterated continuously, and finally the signal error is within the allowable range. the

Fig. 6 is a functional block diagram of an adaptive filter based on a neural network. Usually the adaptive filter is composed of two parts, namely the filter part and the adaptive algorithm part, and the adaptive algorithm part is used to adjust the filtering parameters. The dotted box in Figure 6 is the filter part. The process of adaptive filtering is to continuously adjust the parameters of the filter through the neural network algorithm to achieve the optimal filtering effect. the

Claims (5)

1. a novel vehicle tire pressure wireless monitor system is characterized in that: by the pressure that is installed in several SAW sensors (1) detection tires on the wheel; Microprocessor ADSP (5) control wireless receiving and dispatching change-over swith (2), wireless launcher (3) and wireless receiver (4) are realized the wireless and passive of tire pressure is read; ADSP (5) is connected with telltale (7), annunciator (8), realizes that the change curve of vehicle tire pressure shows and abnormal alarm in real time.

2. vehicle tire pressure wireless monitor system according to claim 1, it is characterized in that: described passive SAW sensor (2) comprises interdigital transducer (9), matrix (10), coding reflecting grating (11), reading reflecting grating (12), reed capsule (13), encoder magnet (14).

3. vehicle tire pressure wireless monitor system according to claim 1, it is characterized in that: described wireless launcher (3) comprises MAX7044 radiofrequency launcher chip and interface circuit thereof.

4. vehicle tire pressure wireless monitor system according to claim 1, it is characterized in that: described wireless receiver (4) comprises MAX7033 radio frequency receiver chip and interface circuit thereof.

5. vehicle tire pressure wireless monitor system according to claim 1 is characterized in that: the core microprocessors employing ADSP2181 chip of described ADSP (5).

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