Adaptive low-complexity-based millimeter wave radar human perception detection method and system

Abstract

The application discloses a kind of millimeter wave radar human perception detection method and system based on adaptive low operation complexity, the method first utilizes to obtain echo data, carries out discrete fourier transform, generates corresponding distance spectrum;Multiple distance spectra are accumulated in amplitude, to obtain the distance spectrum after accumulation.Calculate the cross-correlation coefficient between current frame and previous frame distance spectrum, pearson correlation coefficient and structural similarity index, and through adaptive linear combination method, generate comprehensive decision coefficient, according to the comprehensive decision coefficient to human existence is detected and judged.This method can dynamically adapt to human detection needs in different scenarios by combining various correlation analysis techniques, has lower computational complexity and higher robustness, especially suitable for human perception detection tasks in low-power application scenarios, so that the human perception detection capability of millimeter wave radar in low-power applications has been significantly improved, while maintaining high detection accuracy and reliability.

Description

TECHNICAL FIELD

[0001] The present application relates to the fields of radar signal processing, millimeter wave technology and human perception technology, in particular to a millimeter wave radar human perception detection method and system based on adaptive low operation complexity. BACKGROUND

[0002] Existing millimeter wave radar technology has been widely applied in automatic driving, security monitoring and smart home fields, and its main advantages are strong penetration ability, good environmental adaptability and high resolution. However, the traditional 24GHz millimeter wave radar usually relies on complex signal processing algorithms such as fast Fourier transform (FFT) and high-order statistical filtering in human body detection. Although these algorithms can provide higher accuracy, they are accompanied by higher computational complexity and power consumption, which limits their application in low-power scenarios.

[0003] In smart home and Internet of Things devices, since the devices are mostly battery-powered or run on platforms with limited power consumption, traditional high-complexity algorithms are difficult to meet the power consumption requirements while ensuring detection accuracy. In addition, static clutter (such as stationary objects such as furniture or walls) has a greater impact on human body detection results. The existing methods have a large amount of calculation when removing clutter, making it difficult to meet the real-time human perception requirements. Many existing methods have a trade-off between detection accuracy and computational complexity, making it difficult to achieve efficient human detection under low-power conditions. This technical limitation poses a challenge to the widespread application of intelligent perception systems, and there is an urgent need to develop a solution that can balance low power consumption and efficient detection. SUMMARY

[0004] Therefore, the purpose of the present application is to provide a millimeter wave radar human perception detection method based on adaptive low operation complexity. This method is suitable for low-power 24GHz millimeter wave radar human perception detection methods, combining adaptive algorithms and low operation complexity signal processing techniques to achieve efficient human detection and perception on low-power hardware platforms. This method is particularly suitable for intelligent monitoring, security devices and Internet of Things (IoT) intelligent perception systems in low-power scenarios.

[0005] To achieve the above purpose, the present application provides the following technical solutions:

[0006] The millimeter wave radar human perception detection method based on adaptive low operation complexity provided by the present application includes the following steps:

[0007] Obtain the echo data of multiple chirp signals using a single-channel millimeter wave radar, and perform discrete Fourier transform on the echo data to generate the corresponding range profile;

[0008] Divide and process the range profile of different range units;

[0009] The multiple range spectra after the static clutter removal processing are subjected to amplitude accumulation, so as to obtain the accumulated range spectrum;

[0010] The cross-correlation coefficient, the Pearson correlation coefficient and the structural similarity index between the current frame and the previous frame range spectrum are calculated, and an adaptive linear combination method is used to generate a comprehensive decision coefficient;

[0011] The human body existence is detected and judged according to the comprehensive decision coefficient.

[0012] Further, the cross-correlation coefficient ρ, the Pearson correlation coefficient p and the structural similarity index SSIM are respectively calculated according to the following formula:

[0013]

[0014] Wherein, μ F and are the mean values of the current frame range spectrum F[n] and F1[n];

[0015] and are the variances of the signals F[n] and F1[n];

[0016] is the covariance between the signals F[n] and F1[n];

[0017] C1 and C2 are preset constants.

[0018] Further, the comprehensive decision coefficient is calculated according to the following formula:

[0019] ∈(t)=a(t)*ρ+b(t)*p+c(t)*SSIM

[0020] Wherein, ∈(t) represents the comprehensive decision coefficient; a(t), b(t), c(t) represent the adaptive weight distribution of different correlation coefficients; ρ represents the cross-correlation coefficient; p represents the Pearson correlation coefficient; SSIM represents the structural similarity index.

[0021] Further, the adaptive weight distribution of different correlation coefficients is calculated according to the following formula:

[0022]

[0023] c(t)=1―a(t)―b(t)

[0024] Wherein, a(t), b(t), c(t) respectively represent the adaptive weight distribution, N(t) represents the current frame noise mean value; M(t) represents the target corresponding amplitude value.

[0025] Further, the frequency and distance in the distance spectrum are calculated according to the following formula:

[0026]

[0027] Wherein, f s represents the sampling rate of the radar echo signal ADC, c represents the speed of light, T represents the frequency sweeping time constant of one chirp signal, n represents the n th position in the distance spectrum, B represents the frequency sweeping bandwidth of one chirp signal, N FFT represents the number of discrete Fourier transform points.

[0028] Further, the division of the region is carried out according to the following manner:

[0029] The detection region is divided into two regions according to the distance, and the two regions include a presence sensing region and a motion sensing region;

[0030] The presence sensing region is used for detecting static and moving human bodies through radar;

[0031] The motion sensing region is used for detecting the motion state of the human body through radar.

[0032] Further, the detection and judgment of the human body presence according to the comprehensive decision coefficient are carried out according to the following steps:

[0033] According to the calculated comprehensive decision coefficient ∈(t), whether the human body exists in the detection region is judged through a preset threshold value; when ∈(t) exceeds the preset threshold value, it is considered that the human body exists, and otherwise it is judged that no human body exists.

[0034] The millimeter wave radar human body sensing detection system based on adaptive low operation complexity provided by the application comprises a memory, a processor and a computer program stored in the memory and capable of running on the processor, and the processor implements the above-mentioned method when executing the program.

[0035] The application has the following beneficial effects:

[0036] The adaptive low-complexity-based millimeter wave radar human perception detection method provided by the application is suitable for low-power millimeter wave radars and has the characteristics of adaptability and low computational complexity. First, the method uses a single-channel millimeter wave radar to obtain echo data of multiple Chirp signals and performs discrete Fourier transform (DFT) on the echo data to generate corresponding range bin spectra. Then, the range spectra of different distance units are regionally divided and processed to remove static clutter interference. Next, the amplitudes of the multiple range spectra after the static clutter removal processing are accumulated to obtain the accumulated range spectrum. On this basis, the cross-correlation coefficient, the Pearson correlation coefficient and the structural similarity index (SSIM) between the current frame and the previous frame range spectrum are calculated, and an adaptive linear combination method is used to generate a comprehensive decision coefficient. Finally, the presence of a human body is detected and judged according to the comprehensive decision coefficient. The method can dynamically adapt to human body detection requirements in different scenarios by combining multiple correlation analysis techniques, has low computational complexity and high robustness, and is particularly suitable for human perception detection tasks in low-power application scenarios.

[0037] The method has low computational complexity: the adaptive linear combination reduces the amount of complex calculation, and the accumulation and correlation analysis improve the detection accuracy. High robustness: the method can adaptively adjust parameters to adapt to human perception detection in various complex environments, reducing false positives and false negatives. Low-power design: the algorithm is optimized to run efficiently on low-power hardware platforms, making it particularly suitable for smart home and Internet of Things scenarios. The human perception detection capability of the millimeter wave radar in low-power applications has been significantly improved while maintaining high detection accuracy and reliability.

[0038] Other advantages, objects, and features of the present application will be apparent to those skilled in the art from the following specification, which is to be taken in conjunction with the accompanying drawings, wherein said drawings depict the principles of the present application by way of example only, and the present application can be practiced in various forms, so as not to be limited to any one or several embodiments described herein. The embodiments described herein are to be considered in a descriptive sense only and not for purposes of limitation. BRIEF DESCRIPTION OF DRAWINGS

[0039] In order to make the purposes, technical solutions and beneficial effects of the present application clearer, the present application provides the following drawings for explanation:

[0040] Figure 1 The system architecture diagram of the present embodiment.

[0041] Figure 2 The radar installation and region division schematic diagram in the present embodiment.

[0042] Figure 3 The method flowchart in the present embodiment.

[0043] Figure 4 The time-domain data in this embodiment and the corresponding distance spectrum.

[0044] Figure 5 The distance spectrum distribution in different space scenes in this embodiment.

[0045] Figure 6 The change process of the distance spectrum and the three correlation coefficients from no one to someone.

[0046] Figure 7 The change process of the decision coefficient from no one to someone in this embodiment.

[0047] Figure 8 The detection result display of the upper computer in this embodiment.

[0048] Figure 9 The output result of the human body presence perception. DETAILED DESCRIPTION

[0049] The present application will be further described below in conjunction with the drawings and specific embodiments, so that those skilled in the art can better understand the present application and implement it, but the embodiments are not limiting the present application.

[0050] Embodiment 1

[0051] As shown in Figure 1 The method provided by the present embodiment has the characteristics of adaptability and low computational complexity. By combining various correlation analysis techniques, the present method can dynamically adapt to human detection requirements in different scenarios, has low computational complexity and high robustness, and is particularly suitable for human perception detection tasks in low-power application scenarios.

[0052] The method provided by the present embodiment includes the following steps:

[0053] S1, using a single-channel millimeter wave radar to emit a plurality of chirp signals and receive the reflected echo signals thereof;

[0054] S2, performing discrete Fourier transform (DFT) on the received echo signals to generate a plurality of distance spectra of distance units;

[0055] S3, performing static clutter filtering on the distance spectrum of each distance unit to obtain a distance spectrum with clutter removed;

[0056] S4, performing amplitude accumulation on the plurality of distance spectra with clutter removed to generate a cumulative distance spectrum F(t) of the current frame;

[0057] S5, record the distance spectrum F(t-T) of the last frame, and calculate the cross-correlation coefficient (p), the Pearson correlation coefficient (p) and the structural similarity index (SSIM) of the current frame and the last frame;

[0058] S6, according to the change of different correlation coefficients, adaptively distribute weights a(t), b(t), c(t), wherein:

[0059]

[0060] c(t) = 1-a(t)-b(t);

[0061] Wherein, N(t) represents the current frame noise mean; M(t) represents the amplitude value corresponding to the target;

[0062] S7, generate a comprehensive decision coefficient ∈(t) by linear combination of the above coefficients, the calculation formula is as follows:

[0063] ∈(t) = a(t)*p + b(t)*p + c(t)*SSIM;

[0064] S8, determine whether there is a human body in the target area according to the comprehensive decision coefficient ∈(t).

[0065] The cross-correlation coefficient p in the embodiment is used to measure the time sequence similarity of the current frame and the last frame; the Pearson correlation coefficient p is used to quantify the linear correlation between the current frame and the last frame; the structural similarity index SSIM is used to describe the similarity of the current frame and the last frame in visual structure;

[0066] In the adaptive weight distribution process in the embodiment, the target function value based on multiple correlation levels N(t) is used to update the weights a(t), b(t) and c(t).

[0067] The cumulative processing of the distance spectrum in the embodiment includes weighting and accumulating the echo signal amplitude values of each distance unit to reduce the influence of signal noise.

[0068] The time filtering processing of the decision coefficient a(t) of multiple frames in the embodiment is used to smooth the detection result and enhance the stability of human body detection.

[0069] The method in the embodiment can be applied to low-power 24GHz millimeter wave radar, which is used for real-time detection of human body existence in the target area, has the characteristics of low computational complexity and high robustness, and is particularly suitable for smart home, security monitoring and energy saving control scenes.

[0070] Embodiment 2

[0071] This embodiment provides a human detection method for low-power 24GHz millimeter-wave radar, aiming to solve the problems of high computational complexity and high power consumption in existing technologies. By effectively processing the millimeter-wave radar echo signal and combining it with an adaptive algorithm, this method achieves high-precision human detection while filtering out static clutter, and maintains low computational complexity, making it suitable for low-power scenarios. Specifically, this method includes the following steps:

[0072] S1. Transmit multiple chirp signals through a single-channel millimeter-wave radar and receive their echo signals;

[0073] S2. Perform a discrete Fourier transform on the received echo signal to generate a rangebin spectrum for multiple range cells;

[0074] S3. Divide the signal into two regions (existence sensing region and motion sensing region) according to different distances. Perform static clutter filtering on the distance spectrum in the motion sensing region to obtain the clutter-free distance spectrum. For the signal in the existence sensing region, directly calculate its distance spectrum.

[0075] S4. Accumulate the range spectra of multiple chirp signal echoes in the two regions to obtain the accumulated current frame range spectrum F(t);

[0076] S5. Record the distance spectrum F(t-T) of the previous frame, denoted as F1(t), and calculate the cross-correlation coefficient (ρ), Pearson correlation coefficient (p), and structural similarity index (SSIM) between the current frame and the previous frame.

[0077] S6. Adaptively assign weights a(t), b(t), and c(t) according to the levels of different correlation coefficients, where:

[0078]

[0079] c(t) = 1 - a(t) - b(t)

[0080] S7. The comprehensive decision coefficient ∩(t) is generated by linearly combining the above coefficients. The calculation formula is as follows:

[0081] ∈(t)=a(t)*ρ+b(t)*p+c(t)*SSIM

[0082] S8. Determine whether a human body exists within the target area based on the comprehensive decision coefficient ∈(t).

[0083] Example 3

[0084] In order to better understand the human perception detection method suitable for low-power 24GHz millimeter wave radar provided by the present application, further description will be made in combination with specific embodiments. In the embodiments, the 24GHz low-power AT24MP1T1RS32A chip developed by the domestic company Jiankong (Shanghai) Intelligent Technology Co., Ltd. is adopted, which contains a Cortex-M0+ processor kernel and a radio frequency transceiver. The system structure is shown in Figure 1 In the present example, the radar installation height is 1.8 meters, the pitch angle is 10°, the horizontal field of view (FOV) is ±60°, and the coverage range is 5 meters. The detection area is divided into two sub-areas: the presence perception area (0.5m~2m) and the motion perception area (2m~5m).

[0085] For the presence perception area, the radar needs to detect stationary and moving human bodies;

[0086] For the motion perception area, only the motion state of the human body needs to be detected.

[0087] Figure 2 The schematic diagram of radar installation and detection is shown, Figure 3 The calculation process of the present method is shown.

[0088] The processing steps of the present method are shown as follows:

[0089] Step 1: Millimeter wave radar signal acquisition and preprocessing

[0090] In this step, a 24GHz single-channel millimeter wave radar is used for data acquisition. The radar transmits multiple chirp signals and receives the echo signals reflected by the human body. The frequency of each chirp signal changes linearly, which supports accurate distance measurement. The received echo signals are processed by mixing to generate intermediate frequency signals. Then, an analog-to-digital converter (ADC) samples the intermediate frequency signals to obtain discrete time domain signal data.

[0091] Figure 4 The time domain echo graph of multiple chirp signals is shown in (a).

[0092] The received time domain signal is converted to the frequency domain by discrete Fourier transform (DFT) to generate a range bin spectrum containing multiple distance units. Each frequency component corresponds to the reflection distance and intensity characteristics of the target in space, representing the reflection characteristics of the human target. Through Fourier transform, the frequency characteristics of the echo signal can be effectively extracted, providing a basis for subsequent clutter filtering and feature extraction. The relationship between the frequency units in the range bin spectrum and the echo distance units of the target is shown in the following formula:

[0093]

[0094] where R represents the distance of the target; f s represents the sampling rate of the radar echo signal ADC, c represents the speed of light, T represents the frequency sweeping time constant of one chirp signal, n represents the nth position in the range profile, B represents the frequency sweeping bandwidth of one chirp signal, N FFT represents the number of discrete Fourier transform points.

[0095] According to the above formula, the frequency unit in the range profile can be converted into its corresponding distance unit value. Figure 4 (b) in FIG. 6 shows the range profile amplitude of multiple chirp signals.

[0096] Step 2: static clutter filtering and range profile accumulation

[0097] In the preferred example, different distance units are divided into two intervals:

[0098] There are a presence sensing area (0.5m~2m) and a motion sensing area (2m~5m). The division of the two areas has the advantage that different areas allow the system to adjust the sensitivity and algorithm according to the needs, optimize resource allocation, and reduce false positives and false negatives. In addition, the division of the areas makes the system better adapt to actual application scenarios, such as automatic control of devices in smart home or detection of personnel movement in security monitoring. Through such division, the system can optimize the sensitivity and algorithm efficiency, make more accurate detection decisions according to the signal characteristics at different distances, and improve the overall detection performance and resource utilization efficiency.

[0099] For the motion sensing area, the frequency domain echo signal of each distance unit is filtered for clutter, and the average value of each sampling point is subtracted to obtain the frequency domain echo signal after removing static clutter. Static clutter is usually generated by fixed objects (such as walls, furniture), and filtering these signals helps to enhance the dynamic reflection signal of the human target.

[0100] For the presence sensing area, all echo signals are retained, including those of stationary and moving targets.

[0101] The multiple range profiles after removing static clutter are accumulated to enhance the reflection signal strength and suppress noise. Accumulation processing can generate stable detection results in a short time, improving the robustness of detection.

[0102] Figure 5 FIG. 8 shows the accumulated range profile distribution in different environments.

[0103] Step 3: correlation analysis

[0104] Based on the characteristics that the spectral amplitude of the stationary target remains unchanged in the time dimension, the method detects the presence of human beings by analyzing the distance spectrum correlation between adjacent frames. In this step, the radar system calculates the correlation coefficient (Correlation Coefficient, ρ), Pearson correlation coefficient (Pearson Correlation Coefficient, p) and structural similarity index (SSIM) between the current frame (F[n]) and the previous frame (F1[n]), respectively from the signal similarity, amplitude change and structural similarity three angles to evaluate the frame change. The calculation method is as follows:

[0105]

[0106] Where, μ F and are the mean of the current frame distance spectrum F[n] and F1[n];

[0107] and are the variance of the signal F[n] and F1[n];

[0108] is the covariance between the signals F[n] and F1[n];

[0109] C1 and C2 are small constants introduced to avoid zero denominator;

[0110] N represents the sampling length of the signal;

[0111] F[i] represents the signal value of the i-th position of the current frame;

[0112] F1[i] represents the signal value of the i-th position of the previous frame;

[0113] F represents the current frame signal; F1 represents the previous frame signal;

[0114] Figure 6 The change process of the distance spectrum and the three correlation coefficients from no one to human is shown.

[0115] Step 4: Adaptive linear combination of correlation coefficients

[0116] The complexity of human perception detection is due to the diversity of the environment and the change of the target. Different scenes (such as open environment or environment with obstacles) may depend on different correlation indicators.

[0117] Therefore, the method adopts an adaptive linear combination strategy to weight and combine the three correlation coefficients to generate a comprehensive decision coefficient. The specific combination formula is as follows:

[0118] e(t) = a(t) * p + b(t) * p + c(t) * SSIM;

[0119] where p, p and SSIM represent the cross-correlation coefficient, the Pearson correlation coefficient and the structural similarity index, respectively, and a(t), b(t) and c(t) are adaptive weights.

[0120] The update function of the weight is an improvement based on the Sigmoid function, whose value depends on the noise level N(t) and the motion amplitude M(t) to dynamically allocate different correlation coefficient weights, thereby improving the detection performance of the radar system in different environments. The update process is as follows:

[0121]

[0122] c(t) = 1 - a(t) - b(t);

[0123] If N(t) is large, it means that the echo signal is disturbed by strong noise, at which time the system should rely more on features with strong anti-interference ability, such as the cross-correlation coefficient. Therefore,

[0124] a(t) will be small, and the weight of the cross-correlation coefficient will increase. If M(t) is large, it means that the target is moving obviously, at which time the system can rely more on features that can capture dynamic changes, such as SSIM.

[0125] At this time, b(t) becomes small, and the weight of SSIM increases, so that the system can better detect moving targets.

[0126] c(t) provides a margin for adjusting the weight in the system, ensuring that the system does not rely completely on a single feature, but combines multiple correlation analysis, thereby improving the detection ability in complex environments.

[0127] Step 5: Human presence perception

[0128] According to the calculated comprehensive decision coefficient e(t), by setting a suitable threshold, it is determined whether a human body exists in the detection area. When e(t) exceeds the preset threshold, it is considered that a human body exists, otherwise it is determined that no human body exists.

[0129] Figure 7 The changes of the decision coefficient in different time periods are shown from no human to human. The decision rule for e(t) is as follows:

[0130] Region Decision rule Output result Presence awareness (0.5m~2m) ∈(t)>0.97 Determine no one Motion awareness (0.5m~2m) ∈(t)>0.95 Determine no one

[0131] The human perception decision result can be used in intelligent security monitoring, intelligent home control and other scenarios. The system triggers relevant actions according to the detection result, such as alarm, turning on / off the light, starting the camera, etc.

[0132] The output result of the human body sensing is as shown in Figure 8 and Figure 9 The low-power consumption millimeter wave radar human body sensing detection method of the present application can effectively reduce the computational complexity while maintaining high detection accuracy under low-power consumption conditions through the above specific embodiments. This method is particularly suitable for low-power consumption scenarios such as smart home and security monitoring, and has good robustness and adaptability.

[0133] The low-power consumption millimeter wave radar in this embodiment uses a low-power consumption (21-25) GHz millimeter wave radar, preferably a low-power consumption 24 GHz millimeter wave radar.

[0134] The above-described embodiments are only preferred embodiments for fully illustrating the present application, and the protection scope of the present application is not limited thereto. Any equivalent substitutions or transformations made by those skilled in the art based on the present application are within the protection scope of the present application. The protection scope of the present application is subject to the claims.

Claims

1. A method for human perception detection based on adaptive low computational complexity for millimeter wave radar, characterized in that: The method comprises the following steps: Obtaining echo data of multiple chirp signals by using a single-channel millimeter wave radar, and performing discrete Fourier transform on the echo data to generate corresponding distance spectra; Respectively performing regional division and processing on the distance spectra of different distance units; and performing amplitude accumulation on the multiple distance spectra to obtain accumulated distance spectra; Calculating cross-correlation coefficients, Pearson correlation coefficients and structural similarity indexes between the current frame and the previous frame distance spectra, and generating a comprehensive decision coefficient by a linear combination method; Detecting and judging the existence of a human body according to the comprehensive decision coefficient.

2. The adaptive low-computational complexity based millimeter wave radar human perception detection method of claim 1, wherein: the cross-correlation coefficient , the Pearson correlation coefficient and the structural similarity index SSIM are calculated from, respectively, according to the following equations: wherein, and are the mean values of the current frame distance spectrum and the previous frame distance spectrum respectively. and are respectively and variances of is and covariance between and is a preset constant; distance spectrum representing the i-th position of the current frame; distance profile of the i-th location of the previous frame.

3. The adaptive low-computational complexity based millimeter wave radar human perception detection method of claim 1, wherein: The comprehensive decision coefficient is calculated according to the following formula: wherein, denotes a comprehensive decision coefficient; denotes that weights are assigned adaptively at the level of different correlation coefficients; denotes a cross-correlation coefficient; denotes a Pearson correlation coefficient SSIM denotes a structural similarity index.

4. The adaptive low-computational complexity based millimeter wave radar human perception detection method of claim 3, wherein: The horizontal adaptive weight distribution of the different correlation coefficients is calculated according to the following formula: wherein, respectively represent adaptively assigning weights, represents a current frame noise mean value; represents a target corresponding amplitude value.

5. The adaptive low-computational complexity based millimeter wave radar human perception detection method of claim 1, wherein: The relationship between the frequency units in the distance spectrum and the echo distance units of a target is calculated according to the following formula: where R represents the distance of the target, represents the sampling rate of the radar echo signal ADC, represents the speed of light, represents the sweep time of a chirp signal, represents the n-th position in the range profile, represents the sweep bandwidth of a chirp signal, represents the number of points of the discrete Fourier transform.

6. The adaptive low-computational complexity based millimeter wave radar human perception detection method of claim 1, wherein: The regional division is performed in the following manner: The detection region is divided into two regions according to distance, and the two regions include an existence perception region and a motion perception region; The existence perception region is used for detecting stationary and moving human bodies by using a radar; The motion perception region is used for detecting the motion state of a human body by using a radar.

7. The adaptive low-computational complexity based millimeter wave radar human perception detection method of claim 1, wherein: The detection and judgment of the existence of a human body according to the comprehensive decision coefficient are performed in the following steps: According to the calculated comprehensive decision coefficient , whether a human body exists in the detection area is determined by a preset threshold value; when the preset threshold value is exceeded, it is considered that a human body exists, and otherwise it is determined that no human body exists.

8. An adaptive low-complexity based millimeter wave radar human perception detection system comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, The processor implements the method of any one of claims 1 to 7 when executing the program.

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