Biological foreign matter detection method and device based on vehicle-mounted radar automobile wireless charging system

Abstract

The invention discloses a biological foreign matter detection method and device based on a vehicle-mounted radar vehicle wireless charging system, and the device comprises a signal transmitting and receiving part, a signal mixing processing part and a signal processing part, and selects related parameters according to the environment; calculating the number of sampling points to determine the sampling points of the echo, and generating a linear frequency modulation signal through circulation; generating a target echo by the pulse signal, and drawing a time domain waveform; applying mathematical transformation to generate time domain / frequency domain pulse pressure; and through matrix transformation, moving target detection is realized. The method greatly eliminates noise interference, is more accurate in living body detection, improves the safety of the wireless charging system of the electric vehicle, and has high applicability.

Description

Technical Field

[0001] The present invention belongs to the field of foreign body detection for electric vehicle wireless charging systems, and in particular relates to a vehicle-mounted radar living foreign body detection method and system. Background Art

[0002] Wireless charging technology for electric vehicles has the advantages of small footprint, convenience and flexibility, low maintenance cost, and strong interactivity with the power grid. It is expected to become the main charging method for electric vehicles in the future. When electric vehicles are wirelessly charged, the high-frequency magnetic field area generated around the transmitting coil will cause damage to the immune system and internal organs of organisms that stay there for a long time. Therefore, it is necessary to install an organism detection device to detect and identify organisms (including humans and animals) in the safe area so that the charging system can respond in time to reduce the adverse effects of the high-frequency magnetic field on organisms in the area.

[0003] At present, the existing sensor-based living foreign body detection technology is greatly affected by the surrounding environment and requires short-term maintenance. Therefore, the present invention is based on time domain pulse pressure, frequency domain pulse pressure moving target detection (MTD) and other technologies, and uses RCS for model analysis to analyze the characteristics of the detected object and distinguish whether it is a living body, and proposes a vehicle-mounted radar living foreign body detection method.

[0004] In summary, the present invention has important practical significance for detecting living foreign objects in the wireless charging scenario of electric vehicles, improving the safety of wireless charging systems, and reducing the impact of wireless charging on surrounding organisms. Summary of the invention

[0005] Purpose of the invention: In view of the shortcomings of sensor living foreign body detection technology, such as being greatly affected by the surrounding environment and requiring short-cycle maintenance, the present invention provides a biological foreign body detection method for a car wireless charging system based on an on-board radar.

[0006] Technical solution: To achieve the above purpose, the technical solution adopted by the present invention is:

[0007] A biological foreign body detection method for a vehicle wireless charging system based on a vehicle-mounted radar comprises the following steps:

[0008] Step 1: Select relevant parameters according to the environment.

[0009] Step 2, calculate the number of sampling points to determine the sampling points of the echo, and generate a linear frequency modulation signal through a cycle.

[0010] Step 3: Generate target echo from the pulse signal and draw the time domain waveform.

[0011] Step 4: Apply mathematical transformation to generate time domain / frequency domain pulse pressure.

[0012] Step 5: Realize moving target detection through matrix transformation.

[0013] Preferably: the method for cyclically generating a linear frequency modulation signal in step 2:

[0014] By judging the number of sampling points Is it an odd number? If it is an odd number, Add 1 to ensure that the length of the generated pulse compression coefficient is an even number. Next, the pulse compression coefficient of the linear frequency modulation signal is generated by looping . Loop variable The value range is from arrive In the loop, use The function generates complex exponentials representing the phase and frequency of a linear frequency modulated signal.

[0015]

[0016] in, Indicates that each loop variable The corresponding pulse pressure coefficient is stored in The corresponding position of the array, Represents the loop variable, represents the imaginary unit, constructs the complex signal, Represents pi, which is used to adjust the signal phase change. Indicates the bandwidth of the signal, which controls the speed at which the frequency changes. Indicates the time width of the echo, is the sampling rate, represents the initial frequency of the signal, is the sampling rate. Preferably, the number of sampling points in step 2 is calculated from the distance between the target and the radar:

[0017] .

[0018] in, is the number of sampling points, Represents a built-in function that rounds the input value toward zero. is the sampling rate, is the time width of the echo.

[0019] Preferably, in step 3, the method of generating a target echo from a pulse signal and drawing a time domain waveform is as follows: a pulse signal for each target is generated by looping, and the frequency of the signal is adjusted according to the Doppler velocity of the target. The signals of all targets are accumulated, and Gaussian white noise and Rayleigh clutter are added to draw a time domain waveform of the radar echo signal.

[0020] .

[0021] .

[0022] in, represents the clutter in the radar system, represents the function that generates the Rayleigh attenuated signal, is the sampling rate, represents the pulse repetition period, represents the wavelength, represents the number of samples of a pulse signal, Indicates the distance of the radar signal, Indicates the intermediate result of storing the generated clutter signal. Represents a built-in function in MATLAB that creates an array of all zeros of a specified size. Indicates the number of sampling points in one pulse period.

[0023] Preferably, in step 4, the method of using mathematical transformation to generate time domain / frequency domain pulse pressure is as follows: the input signal and pulse pressure coefficient are subjected to Fourier transformation to obtain the signal and pulse pressure coefficient in the frequency domain, and the two are subjected to point multiplication operation in the frequency domain to obtain the frequency domain pulse pressure result. The frequency domain pulse pressure result is subjected to inverse Fourier transformation to obtain the time domain pulse pressure result.

[0024] Preferably: in step 4, the input signal is processed by time domain pulse compression, and the processing result is plotted as a graph for display. The input signal and the filter are processed by convolution operation, and then the amplitude graph of the time domain pulse compression result is plotted. The amplitude graph of the frequency domain pulse compression result and the difference graph of the time domain and frequency domain pulse compression are plotted.

[0025] Preferably, in step 4, the transient points before and after are removed by performing a clipping operation on the time domain pulse pressure result.

[0026] Preferably: in step 5, a method for realizing moving target detection by matrix transformation is as follows: a zero matrix is ​​initialized to store the result of MTD, and each sampling point is looped through. The data of the i-th column in mti is copied to the buffer buff, and the buffer buff is fast Fourier transformed, and the first (PulseNumber-2) values ​​of the fast Fourier transform result are stored in the i-th column of mtd, a distance coordinate vector x is created, which represents the distance corresponding to each sampling point, a Doppler channel coordinate vector y is created, which represents the index of each channel, a new three-dimensional graphics window is created, and the amplitude result of mtd is plotted, wherein the x-axis represents the distance, the y-axis represents the Doppler channel, and the z-axis represents the amplitude value, and a chart is output.

[0027] Preferably, in step 5, the foreign body type is distinguished by multi-radar cross detection, and two-dimensional FTT is used to collect object feature values ​​and compare the foreign body action features within the period.

[0028] According to the existing characteristic values, select single-station RCS or dual-station RCS detection, analyze the echo intensity, analyze the geometry of the foreign object, material properties, and compare them with the data model to finally determine whether it is a living object. In single-station RCS, the frequency domain current and magnetic current are integrated to obtain the far-field electric field and magnetic field distribution, and then the RCS is calculated based on the distribution of the far-field electromagnetic field. Dual-station RCS uses the signal information measured by two radar stations, and calculates the time difference, frequency difference, and amplitude difference of the signals received by the two stations, and then calculates the reflectivity, position, and posture information of the target object.

[0029] Another object of the present invention is to provide a biological foreign body detection system based on a vehicle-mounted radar and a vehicle wireless charging system, which adopts the biological foreign body detection method based on a vehicle-mounted radar and a vehicle wireless charging system, including a signal transmitting and receiving part, a signal mixing processing part and a signal processing part, wherein:

[0030] The signal transmitting and receiving part, the system is responsible for transmitting millimeter wave signals and receiving echo signals to sense the surrounding environment and detect foreign objects.

[0031] The signal mixing processing part performs mixing processing on the received signal, extracts useful information and eliminates noise interference to ensure accurate foreign body detection.

[0032] The signal processing part is responsible for further processing the mixed signal, and uses time domain pulse compression, frequency domain pulse compression and other technologies to detect and analyze foreign objects, so as to achieve effective identification and positioning of objects around the vehicle.

[0033] Compared with the prior art, the present invention has the following beneficial effects:

[0034] 1. The radar is placed in a horizontal cross form near the center of the wheel. Multiple radars work together to effectively reduce detection blind spots.

[0035] 2. When generating linear frequency modulation signals, the complex exponential function exp is used to calculate the phase of each sampling point, which can ensure the precision and accuracy of the generated pulse pressure coefficient.

[0036] 3. Constant False Alarm Rate (CFAR) technology adds noise factors. In actual environments, it can reduce the interference of noise on radar echoes and improve positioning accuracy. At the same time, it can avoid radar echo fluctuations caused by noise and make foreign object characteristics more accurate.

[0037] 4. The use of multi-station RCS effectively adopts a cost-effective detection method based on the general location of the foreign body. According to the echo, its material composition, geometric shape and other characteristics are extracted. Finally, according to mathematical modeling, the characteristic values ​​are compared to eliminate the interference of metal foreign bodies. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 It is a flow chart of positioning and speed measurement.

[0039] Figure 2 It is the time domain waveform of the radar echo signal.

[0040] Figure 3 This is the time domain pulse pressure result diagram.

[0041] Figure 4 This is the MTI result graph.

[0042] Figure 5 This is the MTD result graph.

[0043] Figure 6 It is a graph of CFAR amplitude / distance / speed results.

[0044] Figure 7 This is the CFAR result diagram.

[0045] Figure 8 This is the schematic diagram of CFAR data processing. Fig. 9 Schematic diagram of a biological foreign body detection method for an electric vehicle wireless charging system based on an on-board radar. DETAILED DESCRIPTION

[0046] The present invention is further explained below in conjunction with the accompanying drawings and specific embodiments. It should be understood that these examples are only used to illustrate the present invention and are not used to limit the scope of the present invention. After reading the present invention, various equivalent forms of modifications to the present invention by those skilled in the art all fall within the scope defined by the claims attached to this application.

[0047] The present invention provides a biological foreign body detection method for an electric vehicle wireless charging system based on a vehicle-mounted radar, such as Figure 1-9 As shown, the following steps are included:

[0048] The first step is to locate and measure speed, which includes the following steps:

[0049] Step 1: First input the environment and radar related parameters, such as the speed of light, the detection radar frequency, etc.

[0050] Input relevant parameters according to the required application scenario environment and radar equipment.

[0051] Step 2: Calculate the number of sampling points to determine the sampling points of the echo, and generate a linear frequency modulation signal through a loop:

[0052] The number of sampling points is calculated from the distance between the target and the radar:

[0053]

[0054] (1)

[0055] in, is the number of sampling points, Indicates a built-in function that rounds the input value toward zero. is the sampling rate, is the time width of the echo, represents the pulse compression factor, is the sampling rate, is the time width of the echo.

[0056] By judging Is it an odd number? If it is an odd number, Add 1 to ensure that the length of the generated pulse compression coefficient is an even number. Next, the pulse compression coefficient of the linear frequency modulation signal is generated by looping . Cycle change The value range is from arrive In the loop, use The function generates complex exponentials representing the phase and frequency of a linear frequency modulated signal.

[0057] (2)

[0058] in, Indicates that each loop variable The corresponding pulse pressure coefficient is stored in The corresponding position of the array, Represents the loop variable, represents the imaginary unit, constructs the complex signal, Represents pi, which is used to adjust the signal phase change. Indicates the bandwidth of the signal, which controls the speed at which the frequency changes. Indicates the time width of the echo, is the sampling rate, represents the initial frequency of the signal, is the sampling rate. Finally, by and The function takes conjugate and flip operations respectively to obtain the final pulse pressure coefficient .

[0059] Step 3: Generate target echo from pulse signal and draw time domain waveform:

[0060] Generate a pulse signal for each target through a loop and adjust the frequency of the signal according to the Doppler velocity of the target. Accumulate the signals of all targets and add Gaussian white noise and Rayleigh clutter. Draw the time domain waveform of the radar echo signal.

[0061] (3)

[0062] (4)

[0063] in, represents the clutter in the radar system, represents the function that generates the Rayleigh attenuated signal, is the sampling rate, represents the pulse repetition period, represents the wavelength, represents the number of samples of a pulse signal, Indicates the distance of the radar signal, Indicates the intermediate result of storing the generated clutter signal. Represents a built-in function in MATLAB that creates an array of all zeros of a specified size. Indicates the number of sampling points in one pulse period.

[0064] Step 4: Use mathematical transformation to generate time domain / frequency domain pulse pressure:

[0065] The input signal is processed by time domain pulse compression, and the processing results are plotted as a graph for display. The code processes the input signal and the filter through convolution operation, and then plots the amplitude diagram of the time domain pulse compression result, which is convenient for users to observe and analyze. By Fourier transforming the input signal and the pulse compression coefficient, the signal and pulse compression coefficient in the frequency domain are obtained, and the two are multiplied in the frequency domain to obtain the frequency domain pulse compression result. The frequency domain pulse compression result is inversely Fourier transformed to obtain the time domain pulse compression result. Plot the amplitude diagram of the frequency domain pulse compression result and the difference diagram of the time domain and frequency domain pulse compression. These graphs can show the difference between the frequency domain and time domain pulse compression results, helping users understand the processed signal. By intercepting the time domain pulse compression result, the transient points before and after are removed. This is very meaningful for signal processing in certain application scenarios and can improve the accuracy of subsequent analysis.

[0066] Step 5: Implement moving target detection (MTD) and draw a picture through matrix transformation.

[0067] Initialize a zero matrix to store the results of MTD, loop through each sampling point. Copy the data in the i-th column of mti to the buffer buff, perform a fast Fourier transform (FFT) on the buffer buff, store the first (PulseNumber-2) values ​​of the FFT result in the i-th column of mtd, create a distance coordinate vector x, representing the distance corresponding to each sampling point, with a step size of 15 meters, create a Doppler channel coordinate vector y, representing the index of each channel, create a new three-dimensional graphics window, and plot the amplitude results of mtd, where the x-axis represents the distance, the y-axis represents the Doppler channel, and the z-axis represents the amplitude value, and output the chart.

[0068] The correction of noise effects is mainly achieved by constant false alarm rate detection (CFAR). CFAR, the full name of which is Constant False Alarm Rate Detector, is a common means of radar target detection. Its function is to determine whether a target exists or not. Therefore, the role of the detector becomes to determine whether a signal exists or not in the presence of noise. When we do not know whether the target exists, target detection becomes a decision based on two possible hypotheses. At this time, we call this problem a binary hypothesis testing problem.

[0069] Performance improves with increasing distance from the pdf, or with increasing signal-to-noise ratio. This example illustrates a basic conclusion that detection performance depends on the identification of two hypotheses, or equivalently, the identification of two pdfs.

[0070] The two-dimensional FFT converts the received signal matrix into amplitude and phase information in the frequency domain by performing Fourier transform in the row and column directions, making the frequency components and spatial characteristics of the signal more intuitive and conducive to filtering and denoising operations.

[0071] Multi-station RCS analyzes the approximate location of the foreign object based on the existing detection information. If it is in the cross-detection area of ​​two detection radars, dual-station RCS detection is used. Otherwise, single-station RCS detection is used. By analyzing the echo intensity, the geometric shape and material properties of the foreign object, and comparing them with the data model, it is finally determined whether it is a living object.

[0072] In single-station RCS, the frequency domain current and magnetic current are integrated to obtain the far-field electric and magnetic field distribution, and then the RCS is calculated based on the distribution of the far-field electromagnetic field. Dual-station RCS uses the signal information measured by two radar stations. By calculating the time difference, frequency difference, amplitude difference and other parameters of the signals received by the two stations, the reflectivity, position and attitude information of the target object can be calculated.

[0073] By giving the parameters, the parameters are first processed and the number of sampling points is calculated to obtain a linear frequency modulation signal. Then, a pulse signal and time domain / frequency domain pulse pressure are generated for each foreign object to locate and measure speed, and then the constant false alarm detection (CFAR) is used to correct the noise influence. Through the combination of time domain pulse pressure, frequency domain pulse pressure moving target detection (MTD) and constant false alarm detection (CFAR), the positioning and ranging of objects within 5m are realized, which solves the problem of low measurement accuracy of short-range radar and improves the real-time performance and accuracy in practical applications.

[0074] Another embodiment of the present invention provides a biological foreign body detection system based on a vehicle-mounted radar automobile wireless charging system, which adopts the biological foreign body detection method based on a vehicle-mounted radar automobile wireless charging system, including a signal transmitting and receiving part, a signal mixing processing part and a signal processing part, wherein:

[0075] In the signal transmission and reception part, the system is responsible for transmitting millimeter wave signals and receiving echo signals to sense the surrounding environment and detect foreign objects. The radar is placed in a horizontal cross form near the center of the wheel. The cooperation of multiple radars can effectively reduce the detection blind spot.

[0076] The signal mixing processing part performs mixing processing on the received signal, extracts useful information and eliminates noise interference to ensure accurate foreign object detection. When generating linear frequency modulation signals, the complex exponential function exp is used to calculate the phase of each sampling point, which can ensure the precision and accuracy of the generated pulse pressure coefficient.

[0077] The signal processing part is responsible for further processing the mixed signal, and uses time domain pulse compression, frequency domain pulse compression and other technologies to detect and analyze foreign objects, so as to achieve effective identification and positioning of objects around the vehicle.

[0078] The present invention provides a vehicle-mounted radar live foreign body detection method based on time domain pulse pressure, frequency domain pulse pressure moving target detection (MTD) and other technologies, which can effectively reduce the influence of foreign objects such as metals and lock onto living organisms. The overall part is divided into two parts: foreign body detection and foreign body type differentiation. Foreign body detection uses a vehicle-mounted radar foreign body detection method based on time domain pulse pressure, frequency domain pulse pressure moving target detection (MTD), and constant false alarm detection (CFAR), which specifically includes two parts: positioning speed measurement and correcting noise influence.

[0079] Foreign body detection uses a vehicle-mounted radar foreign body detection method based on time domain pulse pressure, frequency domain pulse pressure moving target detection (MTD), and constant false alarm detection (CFAR). The type of foreign body is distinguished by multi-radar cross detection, and the action characteristics of foreign bodies within the period are compared to determine whether it is a living body. The radar uses millimeter waves to perform time domain pulse compression processing on the signal to achieve high-resolution detection and positioning. The present invention uses RCS to accurately identify living foreign bodies. The radar transmits electromagnetic waves through the antenna to illuminate the target and measures the target's reflection ability to the radar wave to determine the size, shape and material properties of the target, that is, to obtain the target RCS value, and distinguish the type of living body according to the different RCS values. At the same time, the 2DFFT algorithm is used to further obtain the distance and speed. Compared with the prior art, the biological foreign body detection system and method of the electric vehicle wireless charging system of the vehicle-mounted radar proposed in the present invention greatly eliminates noise interference, makes the detection of living bodies more accurate, improves the safety of the electric vehicle wireless charging system, and has strong applicability.

[0080] Constant False Alarm Detection (CFAR) technology adds noise factors. In actual environments, it can reduce the interference of noise on radar echoes and improve positioning accuracy. At the same time, it can avoid radar echo fluctuations caused by noise and more accurately identify foreign object features. Compared with existing technologies, it supports Doppler velocity simulation, noise addition and clutter simulation, effectively reduces the impact of foreign objects such as metal, can adapt to a variety of scenarios, adaptively detect radar echo signals, and extract multi-station RCS to ensure accurate results.

[0081] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A biological foreign body detection method for a car wireless charging system based on a vehicle-mounted radar, characterized in that: The following steps are involved: Step 1, select relevant parameters according to the environment; Step 2, calculating the number of sampling points to determine the sampling points of the echo, and generating a linear frequency modulation signal through a cycle; Step 3, generating a target echo from the pulse signal and drawing a time domain waveform; Step 4, using mathematical transformation to generate time domain / frequency domain pulse pressure; Step 5: Realize moving target detection through matrix transformation.

2. According to claim 1, the biological foreign body detection method based on vehicle-mounted radar automobile wireless charging system is characterized by: The method of looping to generate linear frequency modulation signal in step 2: By judging the number of sampling points Is it an odd number? If it is an odd number, Add 1 to ensure that the length of the generated pulse compression coefficient is an even number; next, generate the pulse compression coefficient of the linear frequency modulation signal through a loop ; Loop variable The value range is from arrive ; In the loop, use The function generates complex exponentials, which represent the phase and frequency of the linear frequency modulation signal respectively; in, Indicates that each loop variable The corresponding pulse pressure coefficient is stored in The corresponding position of the array, Represents the loop variable, represents the imaginary unit, constructs the complex signal, Represents pi, which is used to adjust the signal phase change. Indicates the bandwidth of the signal, which controls the speed at which the frequency changes. Indicates the time width of the echo, is the sampling rate, represents the initial frequency of the signal, is the sampling rate.

3. According to claim 2, the biological foreign body detection method based on vehicle-mounted radar automobile wireless charging system is characterized by: The number of sampling points in step 2 is calculated from the distance between the target and the radar: ； in, is the number of sampling points, Represents a built-in function that rounds the input value toward zero. is the sampling rate, is the time width of the echo.

4. According to claim 3, the biological foreign body detection method based on vehicle-mounted radar automobile wireless charging system is characterized in that: The method for drawing the time domain waveform of the target echo generated by the pulse signal in step 3 is as follows: the pulse signal of each target is generated by looping, and the frequency of the signal is adjusted according to the Doppler velocity of the target; the signals of all targets are accumulated, and Gaussian white noise and Rayleigh clutter are added to draw the time domain waveform of the radar echo signal; ； ； in, represents the clutter in the radar system, represents the function that generates the Rayleigh attenuated signal, is the sampling rate, represents the pulse repetition period, represents the wavelength, represents the number of samples of a pulse signal, Indicates the distance of the radar signal, Indicates the intermediate result of storing the generated clutter signal. Represents a built-in function in MATLAB that creates an array of all zeros of a specified size. Indicates the number of sampling points in one pulse period.

5. According to claim 4, the biological foreign body detection method based on vehicle-mounted radar and vehicle wireless charging system is characterized in that: In step 4, mathematical transformation is used to generate time domain / frequency domain pulse pressure: the input signal and pulse pressure coefficient are Fourier transformed to obtain the signal and pulse pressure coefficient in the frequency domain, and the two are point-multiplied in the frequency domain to obtain the frequency domain pulse pressure result; the frequency domain pulse pressure result is inverse Fourier transformed to obtain the time domain pulse pressure result.

6. The biological foreign body detection method based on vehicle-mounted radar in a vehicle wireless charging system according to claim 5 is characterized in that: In step 4, the input signal is processed by time domain pulse compression, and the processing result is plotted as a graph; the input signal and the filter are processed by convolution operation, and then the amplitude diagram of the time domain pulse compression result is plotted; the amplitude diagram of the frequency domain pulse compression result and the difference diagram of the time domain and frequency domain pulse compression are plotted; the transient points before and after are removed by intercepting the time domain pulse compression result.

7. The biological foreign body detection method based on vehicle-mounted radar in a vehicle wireless charging system according to claim 6 is characterized in that: Correction of noise effects is achieved by constant false alarm detection.

8. The biological foreign body detection method based on vehicle-mounted radar in a vehicle wireless charging system according to claim 7 is characterized in that: In step 5, the method of realizing moving target detection is implemented by matrix transformation: initialize a zero matrix to store the result of MTD, loop through each sampling point; copy the data of the i-th column in mti to the buffer buff, perform fast Fourier transform on the buffer buff, store the first (PulseNumber-2) values ​​of the fast Fourier transform result in the i-th column of mtd, create a distance coordinate vector x, which represents the distance corresponding to each sampling point, create a Doppler channel coordinate vector y, which represents the index of each channel, create a new three-dimensional graphics window, and draw the amplitude result of mtd, where the x-axis represents the distance, the y-axis represents the Doppler channel, and the z-axis represents the amplitude value, and output the chart.

9. The biological foreign body detection method for a vehicle wireless charging system based on a vehicle-mounted radar according to claim 8 is characterized in that: In step 5, the foreign body type is distinguished by multi-radar cross detection, and two-dimensional FTT is used to collect the object feature values ​​and compare the foreign body action characteristics within the cycle; According to the existing characteristic values, single-station RCS or dual-station RCS detection is selected. By analyzing the echo intensity, the geometric shape of the foreign object, and the material properties, and comparing them with the data model, it is finally determined whether it is a living body; in single-station RCS, the frequency domain current and magnetic current are integrated to obtain the far-field electric and magnetic field distribution, and then the RCS is calculated based on the distribution of the far-field electromagnetic field; dual-station RCS uses the signal information measured by two radar stations, and calculates the time difference, frequency difference, and amplitude difference of the signals received by the two stations, and then calculates the reflectivity, position, and posture information of the target object.

10. A biological foreign body detection device for a car wireless charging system based on a vehicle-mounted radar, characterized in that: The biological foreign body detection method based on the vehicle-mounted radar vehicle wireless charging system according to any one of claims 1 to 9 comprises a signal transmitting and receiving part, a signal mixing processing part and a signal processing part, wherein: The signal transmitting and receiving part, the system is responsible for transmitting millimeter wave signals and receiving echo signals to sense the surrounding environment and detect foreign objects; The signal mixing processing part performs mixing processing on the received signal, extracts useful information and eliminates noise interference to ensure accurate foreign body detection; The signal processing part is responsible for further processing the mixed signal, and uses time domain pulse compression, frequency domain pulse compression and other technologies to detect and analyze foreign objects, so as to achieve effective identification and positioning of objects around the vehicle.

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