Human body existence sensing method based on dual-source collaborative fusion of millimeter wave radar and infrared pyroelectric sensor

Abstract

The invention discloses a human body existence sensing method based on millimeter wave radar and infrared pyroelectric sensor double-source collaborative fusion, and the method comprises the steps: processing original data collected by a millimeter wave radar sensor receiving end, and obtaining a high-quality distance-speed dimension data matrix; carrying out SVD decomposition, and reconstructing two new data matrixes which are respectively corresponding to a strong target signal and a weak target signal; a human body existence sensing output result of the millimeter wave radar sensor is obtained through the track features and the breathing features; processing a signal of the infrared pyroelectric sensor to obtain a human body existence sensing output result of the infrared pyroelectric sensor; and constructing a heterogenous data matrix according to output results of the two sensors, and performing fusion algorithm processing on the heterogenous data matrix to obtain a fusion result. And finally, the fusion result is stored in a delay memory matrix, and the aging degree is calculated according to the aging factors corresponding to different delay units, so that a final human body existence sensing result is obtained.

Description

Technical Field

[0001] The present invention relates to the fields of millimeter wave radar and infrared pyroelectric sensor fusion application technology and intelligent sensing technology, and in particular to a human presence sensing method based on dual-source collaborative fusion of millimeter wave radar and infrared pyroelectric sensor. Background Art

[0002] With the rapid advancement of science and technology and the continuous evolution of society, the concept of smart life has been deeply rooted in people's hearts, especially in the field of smart home, people have an increasingly high pursuit of intelligent and convenient lifestyles. Under this trend, human presence sensing technology, as a key link in building a smart living environment, has become increasingly important. Although traditional image-based human detection technology is powerful, it faces problems such as high cost, high algorithm complexity, privacy leakage risks, and limited application in complex environments. In contrast, pyroelectric infrared (PIR) sensors have gradually become the darling of indoor human presence detection due to their low cost, low energy consumption, simple installation process, and keen ability to capture human infrared signals. Especially when combined with wireless distributed sensor networks and advanced machine learning algorithms, the application potential of PIR sensors has been greatly expanded, making their deployment in various scenarios more extensive and efficient. However, the raw signal output of pyroelectric sensors is easily affected by changes in heat sources, target movement speed and position, which limits their performance in high-precision human presence detection.

[0003] To overcome this limitation, a more stable and accurate method of human presence detection is needed to make up for the shortcomings of a single sensor. Summary of the invention

[0004] In view of this, the purpose of the present invention is to provide a human presence sensing method based on the dual-source collaborative fusion of millimeter-wave radar and infrared pyroelectric sensor. The method utilizes human presence sensing technology and combines the dual-source collaborative sensing method of millimeter-wave radar sensor and pyroelectric infrared sensor.

[0005] In order to achieve the above object, the present invention provides the following technical solutions:

[0006] The method for sensing human presence based on the dual-source collaborative fusion of millimeter-wave radar and infrared pyroelectric sensor provided by the present invention comprises the following steps:

[0007] S1. Obtain the raw data collected by the receiving end of the millimeter wave radar sensor, and process it to obtain a data matrix in the distance-speed dimension;

[0008] S2, perform SVD decomposition on the range-velocity spectrum data matrix, compare the singular value with the threshold and combine the eigenvalue with the eigenvector to reconstruct two new data matrices, corresponding to the strong target signal and the weak target signal respectively;

[0009] S3, processing the strong target signal and the weak target signal respectively, and obtaining the human presence perception output result of the millimeter wave radar sensor through trajectory characteristics and breathing characteristics;

[0010] S4, obtaining a signal from the infrared pyroelectric sensor and processing it to obtain a voltage signal, and comparing the voltage signal with a preset threshold to obtain a human presence sensing output result of the infrared pyroelectric sensor;

[0011] S5. constructing a heterogeneous data matrix according to the perception output results obtained by the millimeter wave radar sensor and the infrared pyroelectric sensor respectively, and processing the heterogeneous data matrix through a fusion algorithm to obtain a dual-source fusion result;

[0012] S6. The fusion result is stored in the delay memory matrix, and the timeliness is calculated according to the timeliness factors corresponding to different delay units, so as to obtain the final human presence perception result.

[0013] Further, the raw data in step S1 is processed according to the following steps:

[0014] S101, using one-dimensional fast Fourier transform to process the time domain signal of each frame, and convert the signal from the time domain to the frequency domain;

[0015] S102, performing noise removal processing by adopting a dynamic filtering method;

[0016] S103, performing incoherent superposition to accumulate target echo energy;

[0017] S104, using a two-dimensional fast Fourier transform to expand the signal from the one-dimensional frequency domain to the two-dimensional frequency domain, and constructing a range-Doppler domain data matrix containing target range and radial velocity information;

[0018] S105: Eliminate zero-speed targets, that is, eliminate objects without radial velocity.

[0019] Further, the strong target signal in step S2 is processed according to the following steps:

[0020] S301, performing 3DFFT processing on the separated strong target signal to construct a high-dimensional representation of the target in three dimensions of distance, Doppler, and azimuth to form point cloud data;

[0021] S302, performing a clustering operation after obtaining the point cloud data, combining scattered points from the same target together to form independent point clusters;

[0022] S303, using a Kalman filter to process each point cluster, and updating the slant range and azimuth of the observed target;

[0023] S304, using the nearest neighbor rule to associate point clusters between consecutive frames, thereby establishing a target trajectory and achieving target tracking;

[0024] S305. Obtain a strong target showing a coherent trajectory, and determine whether it is a human target based on the coherent trajectory. If so, mark the detection result as "human body exists" and output 1; otherwise, if not, mark it as an interference target, mark it as "human body does not exist", and output 0.

[0025] Further, the weak target signal in step S3 is processed according to the following steps:

[0026] S306, extracting a feature vector from the separated weak target signal to obtain phase data at the range gate where the target is located;

[0027] S307, extracting phase unwrapping, phase difference, smoothing filtering, and breathing value respectively;

[0028] S308, by analyzing the processed phase data, identifying the periodic phase change caused by breathing, and making a judgment in the following manner:

[0029] If breathing features are detected in the weak target signal, indicating that the target has vital signs, it is confirmed to be a human target, and the final human presence detection result is marked as "human presence" and output 1;

[0030] On the contrary, if no breathing feature is detected, it is identified as an interference target and marked as “human body does not exist”, and output is 0.

[0031] Furthermore, in step S4, the signal of the infrared pyroelectric sensor is processed, specifically:

[0032] S401, dividing the space within the field of view of the infrared pyroelectric sensor into multiple independent monitoring sub-areas;

[0033] S402, when a human body or other warm target enters the field of view of the sensor and crosses these monitoring sub-areas, the pyroelectric element inside the sensor obtains a temperature difference signal and converts the temperature difference signal into an electrical signal;

[0034] S403, perform threshold detection and logical judgment, compare the generated voltage signal with the preset threshold, if the voltage signal exceeds the preset threshold, it indicates that there is a moving human target in the detection space, and the human presence detection result is marked as "human body exists", and output 1; conversely, if the voltage does not exceed the preset threshold, it is determined that there is no moving human target in the detection space, and it is marked as "human body does not exist", and output 0.

[0035] Further, the strong target signal and the weak target signal in step S2 are separated and processed in the following manner:

[0036] Perform SVD decomposition on the distance-velocity spectrum data matrix. The mathematical form of SVD is:

[0037] A=UΣV T ;

[0038] Where: U is an m×m orthogonal matrix, whose column vectors are the left singular vectors;

[0039] Σ is an m×n diagonal matrix, and the elements on the diagonal are the singular values, usually arranged in order from large to small;

[0040] V is an n×n orthogonal matrix whose column vectors are right singular vectors;

[0041] The singular values ​​are compared with the threshold to obtain two data matrices corresponding to strong targets and weak targets respectively.

[0042] Furthermore, the step S401 of dividing the space within the field of view of the infrared pyroelectric sensor into a plurality of independent monitoring sub-areas is performed in the following manner:

[0043] By dividing the Fresnel lens into several different areas to monitor different areas in the field of view, each sub-area can be regarded as an independent detection unit that can sense temperature changes independently;

[0044] When a human body or other warm target enters the sensor's field of view and crosses these monitoring sub-areas, a temperature difference is generated, and the pyroelectric element inside the sensor can convert this temperature difference into an electrical signal.

[0045] Furthermore, the threshold value preset in step S4 is calculated according to the following formula:

[0046]

[0047] in:

[0048] X i is the receiving voltage intensity of the i-th unit;

[0049] N is the number of reference units;

[0050] α is the threshold factor, which is usually set according to the required false alarm probability.

[0051] Furthermore, the heterogeneous data matrix in step S5 is constructed in the following manner:

[0052] Obtaining the human presence sensing result R(t) output by the millimeter wave radar sensor and the human presence sensing result I(t) output by the infrared pyroelectric sensor;

[0053] Determine the sampling timestamp according to the sampling frequencies of the two sensors, and align the output results of the millimeter wave radar sensor and the infrared pyroelectric sensor through the timestamp;

[0054] The aligned results are merged into the same matrix to obtain a two-dimensional heterogeneous data matrix, where each row corresponds to a time point and each column corresponds to a sensor.

[0055] Furthermore, the step S5 of obtaining the dual-source fusion result according to the heterogeneous data matrix is ​​specifically performed in the following manner:

[0056] The weighted average method is used to obtain the fusion result S1(t) of the millimeter-wave radar sensor and the infrared pyroelectric sensor by the heterogeneous data matrix, that is,

[0057]

[0058] Among them, F1(t) represents the weighted average fusion result; N represents the number of data points used to calculate the average value; I(t i ) indicates t i The infrared pyroelectric sensor detection result at the time; R(t i ) indicates t i The detection results of the millimeter-wave radar sensor at the time;

[0059] radar result represents the weighted average result obtained by the millimeter-wave radar sensor; PIR result represents the weighted average result obtained by the infrared pyroelectric sensor;

[0060] According to the weighted average method threshold, the fusion result S1(t)=1 is obtained;

[0061] Then the weight distribution method is used to obtain the fusion result S2(t) of the millimeter wave radar sensor and the infrared pyroelectric sensor, that is,

[0062]

[0063] Among them, the trust ratios of millimeter wave radar sensors and infrared pyroelectric sensors are:

[0064] w′ R =0.4 w′ i =0.6(w′ R +w′ I =1);

[0065] According to the threshold setting of the weight distribution method, the fusion result S2(t)=0 is obtained;

[0066] Finally, the fusion result S1(t) and the fusion result S2(t) are logically ORed to obtain the dual-source fusion result S(t), namely:

[0067] S(t)=S1(t)∨S2(t);

[0068] The logical “or” operator ∨ ​​means that if any of the fusion results is 1, the final result is 1; if both are 0, the final result is 0.

[0069] The beneficial effects of the present invention are:

[0070] The present invention provides a method for human presence sensing based on the dual-source collaborative fusion of millimeter-wave radar and infrared pyroelectric sensor. The method utilizes the complementary characteristics of the two sensors. The millimeter-wave radar can work independently of the lighting conditions, while the infrared pyroelectric sensor responds quickly to temperature changes and human movement. Through the data fusion algorithm, the method not only improves the accuracy and robustness of detection, reduces false alarms and missed alarms, but also adapts to various environmental conditions and realizes more complex behavior recognition, such as distinguishing between stationary and moving human bodies. In addition, since clear images are not captured, the method not only protects user privacy, but also achieves efficiency optimization through flexible energy management strategies, providing a more comprehensive, accurate, and privacy-friendly human presence sensing solution, which shows strong application potential in smart homes and fields.

[0071] At the same time, by building a dual-source fusion system that integrates millimeter-wave radar and infrared pyroelectric sensor, and designing a decision-level fusion algorithm, the shortcomings of a single sensor are compensated, and a more stable and accurate human presence perception is achieved. The accuracy and reliability of human presence detection are effectively improved, the false alarm rate is significantly reduced, and strong technical support is provided for the optimization and upgrading of smart home systems.

[0072] Other advantages, objectives and features of the present invention will be described in the following description to some extent, and to some extent, will be obvious to those skilled in the art based on the following examination and study, or can be taught from the practice of the present invention. The objectives and other advantages of the present invention can be realized and obtained through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0073] In order to make the purpose, technical solution and beneficial effects of the present invention clearer, the present invention provides the following drawings for illustration:

[0074] Figure 1 2 is a system architecture diagram of this embodiment.

[0075] Figure 2 This is the algorithm flow chart in this embodiment.

[0076] Figure 3 Schematic diagram of the indoor test scene in this embodiment.

[0077] Figure 4 This is a schematic diagram of monitoring area division in this embodiment.

[0078] Figure 5 This is a schematic diagram of test category classification in this embodiment.

[0079] Figure 6 This is an example diagram of the display interface of the host computer in this embodiment.

[0080] Figure 7 This is an example diagram comparing the transmitted and received signals of the millimeter wave radar sensor in this embodiment.

[0081] Figure 8 This is an example diagram of the RDM thermal force of the strong target signal after decomposition in this embodiment.

[0082] Fig. 9 This is an example diagram of a weak target signal after decomposition in this embodiment.

[0083] Fig.10 This is an example diagram of the strong target trajectory results obtained by the millimeter wave radar sensor in this embodiment.

[0084] Fig.11 This is an example diagram of the weak target breathing result obtained by the millimeter wave radar sensor in this embodiment.

[0085] Fig.12 This is an example diagram of the visible field partitioning of the infrared pyroelectric sensor in this embodiment.

[0086] Fig.13 This is an example diagram of the output signal waveform of the infrared pyroelectric sensor in this embodiment.

[0087] Fig.14 This is an example diagram of the output results of the weighted average method and the weight distribution method in this embodiment.

[0088] Fig.15 This is an example diagram of the output results of the delay memory matrix in this embodiment.

[0089] Fig.16 This is an example diagram of the output result of human presence perception in this embodiment. DETAILED DESCRIPTION

[0090] The present invention is further described below in conjunction with the accompanying drawings and specific embodiments so that those skilled in the art can better understand the present invention and implement it, but the embodiments are not intended to limit the present invention.

[0091] Example 1

[0092] like Figure 1 As shown, Figure 1 This is the system architecture diagram of this embodiment. In actual application, the direction and installation position of the infrared pyroelectric sensor and the millimeter wave radar sensor in the system described in this embodiment can be set in the following manner. In this preferred example, a binary pyroelectric sensor and a 60G millimeter wave radar sensor will be used to send and receive signals.

[0093] The system includes a central processing unit and a millimeter wave radar sensor, an infrared pyroelectric sensor, a human presence buzzer, and a human presence indicator light connected to the central processing unit;

[0094] The central processing unit is provided with a detection control module, an algorithm control module, and an output control module;

[0095] The detection control module is used to collect data from the millimeter wave radar sensor and the infrared pyroelectric sensor, and perform preliminary processing on the data, such as filtering and amplification, to ensure the accuracy and reliability of the data. At the same time, the module is also responsible for coordinating the working status of the two sensors to ensure that they can operate under optimal conditions, such as adjusting the sensitivity or working mode of the sensor;

[0096] The algorithm control module is used to perform advanced data analysis and processing tasks. It uses a specific algorithm to determine whether a human body is present based on the data received from the detection control module. This may involve a variety of technical means such as motion detection, temperature change analysis, and pattern recognition. In addition, this module can also perform more in-depth analysis of the data, such as estimating the location of the person, the direction of movement, etc.

[0097] The output control module is used to control the behavior of the human presence buzzer and the human presence indicator light according to the analysis results of the algorithm control module. For example, when a person is detected, the buzzer can be activated to sound an alarm and the indicator light can be lit to provide a visual prompt;

[0098] The millimeter wave radar sensor is used to emit electromagnetic waves and propagate them to the surrounding environment. When the electromagnetic waves encounter an object (especially a moving human body), they will be reflected back. By analyzing the reflected waveform, the sensor can detect the existence of the object and its relative position, speed and other information. In this system, it is mainly used to detect the movement of the human body;

[0099] The infrared pyroelectric sensor is used to detect infrared radiation emitted by the human body. When a person enters the sensing range of the sensor, since the temperature of the human body is different from the background temperature, the sensor can capture the change in this temperature difference, thereby realizing the detection of the presence of the human body. This sensor is particularly suitable for detecting stationary or slowly moving targets;

[0100] The human presence buzzer is used to sound an alarm when a human body is detected. As one of the alarm mechanisms of the system, it can help users to know whether there is a person in the environment in a timely manner;

[0101] The human presence indicator light is used to light up when a human body is detected, providing an intuitive visual signal. When used in conjunction with a buzzer, it can provide users with more comprehensive warning information;

[0102] like Figure 2 As shown, Figure 2 This is an algorithm processing flow chart of this embodiment, which includes a millimeter wave radar sensor algorithm processing sub-flow, an infrared pyroelectric sensor algorithm processing sub-flow and a decision-level algorithm processing flow.

[0103] The method for sensing human presence based on the dual-source collaborative fusion of millimeter-wave radar and infrared pyroelectric sensor provided in this embodiment includes the following steps:

[0104] S1. Process the raw ADC (Analog-to-Digital Converter) data collected by the radar end to extract and analyze the human presence information, specifically:

[0105] S101 First, a one-dimensional fast Fourier transform (1D FFT) is used to process the time domain signal of each frame, and the signal is converted from the time domain to the frequency domain.

[0106] S102 then implements pulse cancellation technology, which is a dynamic filtering method used to remove the effects of static targets (such as walls, furniture) and background noise (clutter) in the environment. This step is critical for extracting weak dynamic target signals from home environments;

[0107] S103 Secondly, incoherent superposition is performed to enhance weak target signals. This process involves superimposing multiple continuous radar echo signals to accumulate the target echo energy, improve the signal-to-noise ratio, and make weak targets more obvious;

[0108] After completing the above steps, S104 further performs a two-dimensional fast Fourier transform (2D FFT) to expand the signal from the one-dimensional frequency domain to the two-dimensional frequency domain, namely the range-Doppler domain. This helps to construct a complete data matrix containing target range and radial velocity information, which is convenient for subsequent processing;

[0109] S105 Finally, zero-speed target elimination, that is, eliminating objects with no radial velocity. This operation is very critical for detecting moving targets (such as human bodies), because the echo of stationary targets may mask or interfere with the real target signal;

[0110] S2, performing singular value separation on the processed data matrix, corresponding to strong target signals and weak target signals respectively;

[0111] S3, processing the strong target signal and the weak target signal respectively, and obtaining the human presence perception output result of the millimeter wave radar sensor through trajectory characteristics and breathing characteristics;

[0112] Then deal with strong and weak targets separately, specifically;

[0113] Strong target processing flow:

[0114] S301 first performs 3DFFT processing on the separated strong target signal to construct a high-dimensional representation of the target in three dimensions: distance, Doppler, and azimuth, forming point cloud data;

[0115] After obtaining the point cloud, S302 performs a clustering operation to group scattered points from the same target together to form independent point clusters. This step helps to distinguish different targets, especially when multiple targets exist in the radar field of view at the same time;

[0116] S303 then processes each point cluster using a Kalman filter to update the slant range and azimuth of the observed target. The Kalman filter is an effective recursive algorithm for estimating system states that vary over time and can provide stable tracking performance even in a noisy environment.

[0117] After S304, the point clusters between consecutive frames are associated using the nearest neighbor rule, so as to establish a target trajectory and achieve target tracking.

[0118] S305 Finally, if a strong target shows a coherent trajectory, indicating that it has continuous movement in space, then it can be identified as a human target, and the final human presence detection result is marked as "human presence" (output 1); conversely, if there is a lack of trajectory, then it can be identified as an interference target and marked as "human presence" (output 0).

[0119] Weak target processing flow:

[0120] S306 extracts a feature vector from the separated weak target signal to obtain a phase data value at the range gate where the target is located.

[0121] S307 then performs phase unwrapping, phase differentiation, smoothing filtering, breathing value extraction and other processes respectively.

[0122] S308 can identify the periodic phase changes caused by breathing by analyzing the processed phase data. If obvious breathing features are detected in the weak target signal, it indicates that the target has vital signs and can be confirmed as a human target. The final human presence detection result is marked as "human presence" (output 1); conversely, if no breathing features are detected, it can be identified as an interference target and marked as "human absence" (output 0).

[0123] like Figure 3 The fusion sensor of this embodiment is shown as being installed sideways to the monitoring area to ensure maximum coverage. The fusion sensor of this embodiment is installed in an open space, specifically at a fixed point below the ceiling. The sensor position is set to maximize coverage of the area to be monitored. The sensor is oriented to the side facing the monitoring area, which means that its main sensing area points to the space inside the room. The purpose of this arrangement is to ensure that the sensor can effectively capture information from all angles, thereby reducing blind spots and improving the overall monitoring effect.

[0124] like Figure 4 The figure is a top view schematic diagram of the monitoring area division in this embodiment.

[0125] Figure 4 There is a rectangular grid-like plane in the image, representing the specific area that needs to be monitored. For ease of management and analysis, this large area is further divided into multiple smaller sub-areas, each of which is a square cell. At the bottom center of the image, there is a small circle marked, which indicates the actual installation location of the fusion sensor. The sensor is located at the edge of the entire monitoring area, but it is not directly attached to the boundary, but slightly offset. This layout strategy helps to expand the effective coverage of the sensor, enabling it to better monitor activities in the entire area. The coverage of the sensor is fan-shaped, covering most of the grid cells. This means that as long as the target object appears in these cells, it is possible to be detected by the sensor. However, there are also some cells outside the coverage range, and this part of the area may become a blind area and cannot be reached by the current sensor configuration.

[0126] S4, processing the signal of the infrared pyroelectric sensor, specifically:

[0127] S401 first, finely divide the space within the field of view (FOV) of the infrared pyroelectric sensor to form multiple independent monitoring sub-areas;

[0128] S402 Then, when a human body or other warm target enters the sensor's field of view and crosses these monitoring sub-areas, a temperature difference is generated. The pyroelectric element inside the sensor can convert this temperature difference into an electrical signal, usually a voltage change. This conversion is based on the thermoelectric effect, that is, temperature changes cause changes in the carrier concentration in the material, which in turn produces charge accumulation and voltage output;

[0129] S403 finally performs threshold detection and logic judgment. The generated voltage signal will be compared with the preset threshold. If the voltage signal exceeds this threshold, it means that a significant temperature change is detected, indicating that there is a moving human target in the detection space, and the final human presence detection result is marked as "human presence" (output 1); conversely, if the voltage does not exceed the threshold, it can be determined that there is no moving human target in the detection space, and it is marked as "human absence" (output 0).

[0130] S5. construct a heterogeneous data matrix according to the output results of the millimeter wave radar sensor and the infrared pyroelectric sensor; process the constructed heterogeneous data matrix by weighted average method and weight distribution method to obtain a dual-source fusion result;

[0131] S6. According to the dual-source fusion result, the stability is measured by the delayed memory matrix; the stabilized delayed memory matrix is ​​substituted into the time validity calculation formula to obtain the time validity of the current result, thereby obtaining the final judgment result;

[0132] The method provided in this embodiment utilizes the complementary characteristics of the two sensors. The millimeter wave radar can work independently of lighting conditions, while the infrared pyroelectric sensor responds quickly to temperature changes and human movement. Through the data fusion algorithm, this method not only improves the accuracy and robustness of detection, reduces false positives and false negatives, but also adapts to various environmental conditions and realizes more complex behavior recognition, such as distinguishing between stationary and moving human bodies. In addition, since clear images are not captured, this method not only protects user privacy, but also optimizes efficiency through flexible energy management strategies, providing a more comprehensive, accurate, and privacy-friendly human presence perception solution, which shows strong application potential in smart homes and fields.

[0133] Example 2

[0134] Exemplary embodiments of the present invention will be described in more detail below with reference to the accompanying drawings.

[0135] like Figure 5 FIG. 1 is a schematic diagram of a test case in this embodiment. Figure 5A series of test cases are presented in the paper, which are designed to evaluate the performance of the sensor under various conditions. Each test case lists different heights and radii (Rm). These two sets of parameters may be used to define the detection range of the sensor or the size of the test environment. At the same time, different interference sources and normal test items are involved, with the aim of simulating various situations that may occur in the real world to verify the reliability and accuracy of the sensor. These scenarios cover different aspects of static and dynamic, which help to comprehensively evaluate the sensor's ability to respond to different types of actions and environmental changes. By organizing the test cases in this way, researchers can systematically check the performance of sensors in the face of complex and changeable real-world conditions, thereby improving the design and improving the practicality and user experience of the product.

[0136] like Figure 6 Shown is the display interface of the upper computer in this embodiment. This embodiment can identify whether there are human targets in the monitoring area, such as identifying the presence of human beings in normal activities such as people walking, standing, sitting, and squatting, and distinguishing the absence of human beings in interference activities such as fans shaking, curtains shaking, fans blowing green plants, and chairs sliding in, to achieve more stable and accurate human presence perception.

[0137] By processing the measured data of the presence of a human body in a scene, according to the processing steps of this embodiment:

[0138] Step 1: Process the raw data collected by the radar

[0139] First, the transmitting waveform signal and the receiving waveform signal are obtained by millimeter wave radar, mixed and filtered to obtain the intermediate frequency signal, where the time domain waveforms of the transmitting signal and the receiving signal are compared, such as Figure 7 As shown in a), the frequency changes over time as shown in Figure 7 As shown in b). The intermediate frequency signal is processed by FFT to obtain a set of frequency domain complex signals; then, multiple sets of complex matrix signals of different distance units and time units are obtained through slow time data accumulation; then, the echo signals of each sampling point in the frequency domain echo signal are subtracted from the corresponding signals to obtain the frequency domain echo signal after the pulse cancellation filtering of the frequency domain echo signal, so as to remove static clutter; then, the filtered echo signal is processed by incoherent superposition to accumulate the target echo energy and improve the signal-to-noise ratio; after two-dimensional FFT transformation processing, a distance-speed spectrum containing the target distance and speed can be obtained, and finally the zero-speed units in the spectrum where the objects without radial velocity are located are removed to avoid the echo of the stationary target covering or interfering with the real target signal.

[0140] Step 2: Separate strong and weak targets in the distance-velocity spectrum

[0141] Perform SVD decomposition on the distance-velocity spectrum data matrix. The mathematical form of SVD can be expressed as:

[0142] A=UΣV T

[0143] Among them: U is an m×m orthogonal matrix whose column vectors are the left singular vectors of . Σ is an m×n diagonal matrix whose diagonal elements are the singular values ​​of , usually arranged in order from large to small. V is an n×n orthogonal matrix whose column vectors are the right singular vectors of .

[0144] By comparing the singular value with the threshold, we can determine which signals correspond to strong targets and which signals correspond to weak targets. According to the division of strong targets and weak targets, we can select the corresponding column vectors from U and V and combine them with Σ to reconstruct two new data matrices, corresponding to strong targets and weak targets respectively.

[0145] Therefore, after processing in step 1, a distance-velocity spectrum data matrix with a dimension of 256×128 is obtained. This is a two-dimensional array, each row and each column consists of a series of floating-point numbers, containing relevant information about the target. SVD decomposition of the distance-velocity spectrum data matrix obtains a diagonal matrix Σ with a dimension of 256×128. Its main features are as follows: Non-zero elements on the diagonal: These elements are called singular values, which reflect the main energy distribution of the original data. Larger singular values ​​correspond to the most important components in the data, while smaller singular values ​​correspond to minor components or noise.

[0146] Sparsity: Many singular values ​​are observed to be close to zero, which indicates that the original data may have some redundancy or low-rank characteristics. This property allows for effective dimensionality reduction by retaining a few of the largest singular values ​​without losing too much information.

[0147] Energy concentration: Most of the energy is concentrated in the large singular values ​​of the first few rows / columns, while the small singular values ​​in the latter decay rapidly to almost zero. This phenomenon is consistent with the characteristics of many natural signals and image data, that is, important information is often concentrated in a few key features.

[0148] By arranging the singular values ​​in the diagonal matrix from large to small, the maximum singular value can be obtained. 1 / 20 of it is taken as the division threshold. After threshold comparison, two data matrices corresponding to strong targets and weak targets can be obtained respectively.

[0149] like Figure 8 The figure shows an example of the RDM thermal force of the strong target signal after decomposition in this embodiment. Figure 8The relevant data of strong target signals after SVD decomposition is presented in Figure 1. In this example, the horizontal axis represents speed information and the vertical axis represents distance information. The color coding of the chart reflects the energy intensity, and the darker the color, the greater the intensity. It can be seen that there are obvious high-brightness areas under certain time and frequency combinations, which indicates that the signal intensity in these areas is high, which may be the location of the target of interest. In addition, there are some lighter stripes distributed in other places, which may represent background noise or other irrelevant signal components. By comparing the differences between these areas, it is easier to identify which parts contain real useful information.

[0150] like Fig. 9 FIG. 1 is an example diagram of a weak target signal after decomposition in this embodiment. Fig. 9 The relevant data of weak target signals after SVD decomposition are presented in Figure 1. The chart contains three curves, each of which represents the change in signal strength in different time periods. It can be seen that there are some sharp peaks on these curves, which are caused by the instantaneous gain effect caused by the breathing and heartbeat of the weak target; at the same time, there are some smooth fluctuations, which tend to reflect the background noise level. By comparing the similarities and differences between these curves, we can try to find the features that match the expected pattern, and formulate corresponding filtering algorithms to extract the signal components of interest.

[0151] In the strong target processing flow, the separated strong target signals are firstly processed by 3DFFT, so as to construct a high-dimensional representation of the target in three dimensions: distance, Doppler, and azimuth, forming point cloud data.

[0152] After obtaining the point cloud, a clustering operation is performed to group scattered points from the same target together to form independent point clusters. This step helps to distinguish different targets, especially when multiple targets exist in the radar field of view at the same time.

[0153] The point cloud dataset that has been obtained is P = {p1, p2, ..., p n}, where each point p i Contains three coordinate components: p i =(r i ,θ i ,φ i ), where r i represents the distance of the i-th point; θ i represents the azimuth of the i-th point; φ i represents the pitch angle of the i-th point;

[0154] The goal of K-means is to divide these points into K clusters C = {C1, C2, ..., C K}, so that the square error within the cluster is minimized. Cluster center μ K The update rules are as follows:

[0155] Among them, |C K | means C K The number of points in C K represents the kth cluster;

[0156] Then, the Kalman filter is used to process each point cluster and update the observed slant range and azimuth of the target to estimate the system state that changes over time, providing stable tracking performance even in a noisy environment. The state vector of the target that has been obtained is x t , the observation vector is z t , the state transfer matrix is ​​set to A, the observation matrix is ​​set to H, the process noise covariance matrix is ​​set to Q, the observation noise covariance matrix is ​​set to R, and the state update rules are as follows:

[0157]

[0158] in, represents the state estimate at time t, taking into account all measurements up to time t. This is the final estimate, that is, the corrected state estimate; Indicates that at time t, only all previous measurements (i.e., measurements up to time t-1) are considered for state prediction. This is based on the state estimation of the previous step and the prediction of the future state by the system model; K t K represents the Kalman Gain, which determines the influence of the new measurement value on the state estimation. t is a matrix whose calculation depends on the prediction error covariance matrix and the observation matrix H; Z t Represents the value actually measured at time t.

[0159] Then, the nearest neighbor rule is used to associate the point clusters between consecutive frames to establish the target trajectory and achieve target tracking. In the current frame, there is a point cluster C t , we need to find the point cluster C in the previous frame t-1 As its matching object. The matching criterion is usually based on minimizing the distance, that is, finding the nearest point cluster:

[0160]

[0161] Among them, c ti and c′ t-1，i are the coordinate components of the point cluster in the current frame and the previous frame respectively; C t-1 represents the point cluster set in the previous frame, that is, all target positions detected in the previous frame; C′ t-1Indicates that from C t-1 A specific point cluster selected from , that is, the position of a specific target in the previous frame; i represents the index, which is used to traverse each coordinate component in the point cluster;

[0162] Finally, if a strong target shows a coherent trajectory, indicating that it has continuous movement in space, it can be identified as a human target, and the human presence detection result output by the millimeter-wave radar sensor is marked as "human presence" (output 1); conversely, if there is a lack of trajectory, it can be identified as an interference target and marked as "human presence" (output 0).

[0163] like Fig.10 The figure shows an example of a strong target trajectory result obtained by the millimeter wave radar sensor in this embodiment.

[0164] In the weak target processing flow, the feature vector of the separated weak target signal is first extracted to obtain the phase value at the range gate where the target is located.

[0165] Due to the periodicity of the phase value (usually 2π), the phase directly extracted from the signal may be discontinuous. Therefore, phase unwrapping is required to recover the true phase change. The original phase sequence Φ(t) is obtained according to the recursive formula

[0166] Φ c (t) = Φ c (t)+ΔΦ(t), whereΔΦ(t)=Φ(t)-Φ(t-1);

[0167] The phase obtained after phase unwrapping is Φ c (t). It is worth noting that if |ΔΦ(t)|>2π, 2π needs to be added or subtracted to ensure phase continuity. ΔΦ(t) represents the phase difference between adjacent time points.

[0168] Next, we need to calculate the phase Φ after unwrapping c (t) is used for phase difference. Phase difference is used to remove the influence of static background, and is usually achieved by using the phase difference between adjacent sampling points. Phase difference can be expressed as: Δφ(t) = φ c (t)-φ c (t-1).

[0169] In order to further reduce the impact of noise, moving average filtering is usually performed on the phase-differentiated data. The corresponding formula is:

[0170]

[0171] Where N is the window length; Represents the phase difference after filtering, that is, the phase difference value after moving average filtering; represents the phase difference at time point i; t represents the time point, and there are N time points from t-N+1 to t.

[0172] By analyzing the processed phase data, the periodic phase changes caused by breathing can be identified. If obvious breathing features are detected in the weak target signal, it indicates that the target has vital signs and can be confirmed as a human target. The human presence detection result output by the millimeter-wave radar sensor is marked as "human presence" (output 1); conversely, if no breathing features are detected, it can be identified as an interference target and marked as "human absence" (output 0).

[0173] like Fig.11 As shown, Fig.11 This is an example diagram of the weak target breathing result obtained by the millimeter wave radar sensor in this embodiment. Fig.11 The horizontal axis represents time and the vertical axis represents amplitude. The curve shows a periodic fluctuation pattern, which is a typical phase change caused by respiratory movement. Several key features are as follows:

[0174] Periodic fluctuations: The curve shows regular ups and downs, reflecting the expansion and contraction of the chest or abdomen during breathing. Each rise and fall represents a complete breathing cycle.

[0175] Amplitude variations: While the overall trend is cyclical, there are certain periods of time where the amplitude can vary significantly. These variations can be due to different breathing depths or rates.

[0176] Time interval: As can be seen from the figure, the time interval between the two peaks is roughly the same, indicating that the breathing rate is relatively stable.

[0177] By analyzing such phase data, the periodic phase changes caused by breathing can be identified. If obvious breathing features are detected in the weak target signal, it indicates that the target has vital signs and can be confirmed as a human target. The human presence detection result output by the millimeter wave radar sensor is marked as "human presence" (output 1); conversely, if no breathing features are detected, it can be identified as an interference target and marked as "human absence" (output 0).

[0178] Step 3: Process the signal from the infrared pyroelectric sensor

[0179] First of all, the field of view (FOV) of the infrared pyroelectric sensor is the range within which it can effectively detect. In order to improve the detection accuracy and sensitivity, the space of different sensing surfaces in the field of view is usually finely divided to form multiple independent monitoring sub-areas.

[0180] like Fig.12 As shown, Fig.12 This is an example of the visible field of view of an infrared pyroelectric sensor. The area division method is as follows:

[0181] a. For radial sensing area: Divide the field of view into several concentric ring-shaped areas according to the distance from the sensor. For example, Fig.12 The E1, E2 and E3 areas on the left are divided according to different distances from the sensor.

[0182] b. For the tangential sensing area: Divide the field of view into several fan-shaped areas according to the angular direction. For example, Fig.12 Areas A1, A2 and A3 on the right are divided according to angle direction.

[0183] c. Combined division: You can also combine radial and tangential divisions to form a more refined grid area division. This method can improve the spatial resolution while maintaining a certain coverage area.

[0184] The benefits of dividing the area are as follows:

[0185] a. Improve positioning accuracy: By subdividing a large field of view into multiple small areas, the location of the heat source can be more accurately located. This is very important for applications that require precise knowledge of the location of the heat source.

[0186] b. Enhanced responsiveness: Each sub-area has its own sensing element, which means that even a small temperature change can be detected quickly. This is very useful for applications that require high sensitivity.

[0187] c. Reduce false alarm rates: By monitoring each sub-area independently, the system can more effectively distinguish between real targets and background noise or other interference sources, thereby reducing the possibility of false alarms.

[0188] d. Adapt to different scene requirements: According to the requirements of different application scenarios, the system performance can be optimized by adjusting the area division method and size.

[0189] This division can be achieved by dividing the Fresnel lens into several areas; the Fresnel lens is a special optical element that can focus light from different directions to one point. In the infrared pyroelectric sensor, the role of the Fresnel lens is to concentrate the received infrared radiation energy to the sensitive area of ​​the sensor, thereby improving the sensitivity and detection distance of the sensor.

[0190] By designing the shape and structure of the Fresnel lens, the field of view can be divided. Specifically, the Fresnel lens can be divided into several areas, each of which corresponds to a sensing surface of the sensor. In this way, when an object enters a certain area, the corresponding sensing surface will receive more infrared radiation energy, allowing the sensor to monitor different areas in the field of view separately. Each sub-area can be regarded as an independent detection unit that can independently sense temperature changes.

[0191] Then, when a human body or other warm target enters the sensor's field of view and crosses these monitoring sub-areas, a temperature difference is generated. The pyroelectric element inside the sensor can convert this temperature difference into an electrical signal, usually a voltage change. This conversion is based on the thermoelectric effect, that is, temperature changes cause changes in the carrier concentration in the material, resulting in charge accumulation and voltage output;

[0192] Finally, threshold detection and logic judgment are performed. The generated voltage signal will be compared with the preset threshold.

[0193] The preset threshold calculation formula is:

[0194] in:

[0195] X i is the receiving voltage intensity of the i-th unit;

[0196] N is the number of reference cells (excluding the central cell);

[0197] α is the threshold factor, which is usually set according to the required false alarm probability.

[0198] like Fig.13 The figure shows an example of the output voltage signal and threshold waveform of the infrared pyroelectric sensor;

[0199] If the voltage signal exceeds this threshold, it means that a significant temperature change is detected, indicating that there is a moving human target in the detection space, and the final human presence detection result is marked as "human presence" (output 1); conversely, if the voltage does not exceed the threshold, it can be determined that there is no moving human target in the detection space and marked as "human absence" (output 0).

[0200] Step 4: Construct a heterogeneous data matrix based on the output results

[0201] In multi-sensor fusion technology, combining different types of sensor data can help improve the accuracy and robustness of the system. Steps 2 and 3 can respectively obtain the human presence perception result R(t) output by the millimeter wave radar sensor and the human presence perception result I(t) output by the infrared pyroelectric sensor. The result only has the difference between 0 and 1, where 0 represents no one and 1 represents someone. That is:

[0202]

[0203]

[0204] Since the sampling frequencies of the two sensors are different, their timestamps need to be aligned so that the data of the two sensors can be compared and fused at the same time point. If the timestamps of the two sensors do not match completely, linear interpolation can be used to generate data at the missing time points. After aligning the output results of the millimeter-wave radar sensor and the infrared pyroelectric sensor through timestamps and merging them into the same matrix, a two-dimensional heterogeneous data matrix can be obtained, in which each row corresponds to a time point and each column corresponds to a sensor. That is:

[0205]

[0206] Where M represents the final generated two-dimensional heterogeneous data matrix. Each row corresponds to a time point, and each column corresponds to a sensor; R(t n ) represents the time point t n When the output value of the millimeter wave radar sensor is n ) represents the time point t n The output value of the infrared pyroelectric sensor when .

[0207] Step 5: Obtain dual-source fusion results based on heterogeneous data matrix

[0208] For the heterogeneous data matrix obtained in step 4, the weighted average method is first used to obtain the fusion result S1(t) of the millimeter wave radar sensor and the infrared pyroelectric sensor, that is,

[0209]

[0210] Among them, F1(t) represents the weighted average fusion result; N represents the number of data points used to calculate the average value; I(t i ) indicates t i The infrared pyroelectric sensor detection result at the time; R(t i ) indicates t i The detection results of the millimeter-wave radar sensor at the time.

[0211] The radar result represents the weighted average result obtained by the millimeter-wave radar sensor; the PIR result represents the weighted average result obtained by the pyroelectric infrared sensor.

[0212] The weighted average method threshold is set to W1(t) = 0.5. Since F1(t) > W1(t) at this time, the fusion result S1(t) = 1 at this time;

[0213] Then, the fusion result S2(t) of the millimeter-wave radar sensor and the pyroelectric infrared sensor is obtained by the weight allocation method, that is

[0214] Among them, the trust ratios of the millimeter-wave radar sensor and the pyroelectric infrared sensor are respectively:

[0215] w′ R = 0.4w′ i = 0.6(w′ R + w′ I = 1);

[0216] Among them, F1(t) represents the fusion result of the millimeter-wave radar sensor and the pyroelectric infrared sensor obtained by the weighted average method; F2(t) represents the fusion result of the millimeter-wave radar sensor and the pyroelectric infrared sensor obtained by the weight allocation method; w′ R represents the trust weight ratio of the millimeter-wave radar sensor; w′ I represents the trust weight ratio of the pyroelectric infrared sensor;

[0217] The weight allocation method threshold is set to W2(t) = 0.75. Since F2(t) < W2(t) at this time, the fusion result S2(t) = 0 at this time;

[0218] As Fig.14 shown, Fig.14 are the weights and processing results calculated by the weighted average method and the weight allocation method. Fig.14 shows two different weight calculation methods - the weighted average method and the weight allocation method. The two left figures are the results of the weighted average method: the upper figure shows the trend of the weight values of each element in the original input data changing with time; the lower figure is the final output result obtained based on these weight values. The two right figures are the results of the weight allocation method: the upper figure also shows the trend of the weight values of each element in the original input data changing with time, and the lower figure is also the final output result obtained based on these weight values. Generally speaking, this figure presents the importance (represented by numerical values) of each sample point in the entire data set under each method, and also gives a comparison of the overall effects after being processed by the corresponding algorithms.

[0219] Finally, the fusion result S1(t) and the fusion result S2(t) are logically ORed to obtain the dual-source fusion result S(t). That is:

[0220] S(t)=S1(t)∨S2(t);

[0221] The logical “or” operator ∨ ​​means that if any of the fusion results is 1, the final result is 1; if both are 0, the final result is 0.

[0222] Step 6: Output of timeliness determination results

[0223] The fusion result S(t) is stored in a delay memory matrix with a dimension of 1×100 in chronological order. The result in the delay memory matrix is ​​as follows: Fig.15 As shown, Fig.15 This is an example diagram of the output result of the delay memory matrix in this embodiment. Fig.15 An example of the output of a time-delay memory matrix, which is used to store and manage data generated over time, is shown in Figure 1. In this example, the rows of the matrix represent different timestamps, and the columns may represent different data types or sources. The color depth in each cell represents the intensity or importance of the corresponding data type at that moment. Specifically, some continuous strip patterns can be seen in the figure, which means that certain specific types of data continue to appear and remain at a high level for a period of time. On the other hand, there are also blank areas that show that no relevant data has been recorded. This visual presentation makes it easy to identify which time periods have important events or abnormal conditions.

[0224] In addition, the calculation formula of the aging factor corresponding to each delay unit is:

[0225]

[0226] Among them, η i represents the timeliness factor of the ith delay unit, which is used to measure the importance of the data at that time point; t i represents the time step of the ith delay unit, ranging from 1 to 100; n represents the index variable in the summation, ranging from 1 to 100;

[0227] The specific values ​​are shown in the table:

[0228] Table 1 Delay unit and aging factor comparison table

[0229] Delay Unit 1 2 ... 50 ... 99 100 Effectiveness Factor 0.000198 0.000396 ... 0.0099 ... 0.0196 0.0198

[0230] Multiply the fusion result in the delayed memory matrix by the corresponding time factor to get the timeliness, that is:

[0231]

[0232] Γ represents the final timeliness, that is, the comprehensive evaluation of the fusion results after considering the time factor; S(t i ) represents the time point t i The fusion result of

[0233] According to experience, the timeliness judgment threshold is set to TH=0.55.

[0234] After calculating the timeliness, these elements are judged according to the following judgment rules.

[0235] Rule 1: If the timeliness Γ is lower than the threshold value TH, it is considered that there is no human body in the monitoring area.

[0236] Rule 2: If the timeliness Γ is higher than the threshold TH, it is considered that there is a human body in the current monitoring area.

[0237] According to the judgment rule, because Γ>TH, the output result is that there is a human body.

[0238] The output result of human presence in the current monitoring area is as follows Fig.16 As shown, Fig.16 This is an example diagram of the output result of human presence perception in this embodiment.

[0239] It can be seen from the output results that the human presence perception method based on the dual-source collaborative fusion of millimeter-wave radar and infrared pyroelectric sensor provided in this embodiment can quickly and effectively detect the presence of human body in the monitoring area, which verifies the correctness and effectiveness of this embodiment.

[0240] The above-described embodiments are only preferred embodiments for fully illustrating the present invention, and the protection scope of the present invention is not limited thereto. Equivalent substitutions or changes made by those skilled in the art based on the present invention are within the protection scope of the present invention. The protection scope of the present invention shall be subject to the claims.

Claims

1. A human presence sensing method based on the dual-source collaborative fusion of millimeter-wave radar and infrared pyroelectric sensor, characterized in that: The following steps are involved: S1. Obtain the raw data collected by the receiving end of the millimeter wave radar sensor, and process it to obtain a data matrix in the distance-speed dimension; S2, perform SVD decomposition on the range-velocity spectrum data matrix, compare the singular value with the threshold and combine the eigenvalue with the eigenvector to reconstruct two new data matrices, corresponding to the strong target signal and the weak target signal respectively; S3, processing the strong target signal and the weak target signal respectively, and obtaining the human presence perception output result of the millimeter wave radar sensor through trajectory characteristics and breathing characteristics; S4, obtaining a signal from the infrared pyroelectric sensor and processing it to obtain a voltage signal, and comparing the voltage signal with a preset threshold to obtain a human presence sensing output result of the infrared pyroelectric sensor; S5. constructing a heterogeneous data matrix according to the perception output results obtained by the millimeter wave radar sensor and the infrared pyroelectric sensor respectively, and processing the heterogeneous data matrix through a fusion algorithm to obtain a dual-source fusion result; S6. The fusion result is stored in the delay memory matrix, and the timeliness is calculated according to the timeliness factors corresponding to different delay units, so as to obtain the final human presence perception result.

2. The method for sensing human presence based on the dual-source collaborative fusion of millimeter-wave radar and infrared pyroelectric sensor according to claim 1, characterized in that: The raw data in step S1 is processed according to the following steps: S101, using one-dimensional fast Fourier transform to process the time domain signal of each frame, and convert the signal from the time domain to the frequency domain; S102, performing noise removal processing by adopting a dynamic filtering method; S103, performing incoherent superposition to accumulate target echo energy; S104, using a two-dimensional fast Fourier transform to expand the signal from the one-dimensional frequency domain to the two-dimensional frequency domain, and constructing a range-Doppler domain data matrix containing target range and radial velocity information; S105: Eliminate zero-speed targets, that is, eliminate objects without radial velocity.

3. The method for human presence sensing based on the dual-source collaborative fusion of millimeter-wave radar and infrared pyroelectric sensor according to claim 1, characterized in that: The strong target signal in step S2 is processed according to the following steps: S301, performing 3DFFT processing on the separated strong target signal to construct a high-dimensional representation of the target in three dimensions of distance, Doppler, and azimuth to form point cloud data; S302, performing a clustering operation after obtaining the point cloud data, combining scattered points from the same target together to form independent point clusters; S303, using a Kalman filter to process each point cluster, and updating the slant range and azimuth of the observed target; S304, using the nearest neighbor rule to associate point clusters between consecutive frames, thereby establishing a target trajectory and achieving target tracking; S305, obtaining a strong target showing a coherent trajectory, judging whether it is a human target according to the coherent trajectory, and if so, marking the detection result as "human body exists" and outputting 1; On the contrary, if not, it will be marked as an interference target, marked as "human body does not exist", and output 0.

4. The method for human presence sensing based on the dual-source collaborative fusion of millimeter-wave radar and infrared pyroelectric sensor according to claim 1, characterized in that: The weak target signal in step S3 is processed according to the following steps: S306, extracting a feature vector from the separated weak target signal to obtain phase data at the range gate where the target is located; S307, extracting phase unwrapping, phase difference, smoothing filtering, and breathing value respectively; S308, by analyzing the processed phase data, identifying the periodic phase change caused by breathing, and making a judgment in the following manner: If breathing features are detected in the weak target signal, indicating that the target has vital signs, it is confirmed to be a human target, and the final human presence detection result is marked as "human presence" and output 1; On the contrary, if no breathing feature is detected, it is identified as an interference target and marked as "human body does not exist", and output is 0.

5. The method for human presence sensing based on dual-source collaborative fusion of millimeter-wave radar and infrared pyroelectric sensor as claimed in claim 1, characterized in that: The signal of the infrared pyroelectric sensor is processed in step S4, specifically: S401, dividing the space within the field of view of the infrared pyroelectric sensor into multiple independent monitoring sub-areas; S402, when a human body or other warm target enters the field of view of the sensor and crosses these monitoring sub-areas, the pyroelectric element inside the sensor obtains a temperature difference signal and converts the temperature difference signal into an electrical signal; S403, perform threshold detection and logic judgment, compare the generated voltage signal with the preset threshold, if the voltage signal exceeds the preset threshold, it indicates that there is a moving human target in the detection space, and the human presence detection result is marked as "human presence", and output 1; On the contrary, if the voltage does not exceed the preset threshold, it is determined that there is no moving human target in the detection space, and it is marked as "human body does not exist" and the output is 0.

6. The method for human presence sensing based on the dual-source collaborative fusion of millimeter-wave radar and infrared pyroelectric sensor according to claim 1, characterized in that: The strong target signal and the weak target signal in step S2 are separated and processed in the following manner: Perform SVD decomposition on the distance-velocity spectrum data matrix. The mathematical form of SVD is: A=UΣV T ; Where: U is an m×m orthogonal matrix, whose column vectors are the left singular vectors; Σ is an m×n diagonal matrix, and the elements on the diagonal are the singular values, usually arranged in order from large to small; V is an n×n orthogonal matrix whose column vectors are right singular vectors; The singular values ​​are compared with the threshold to obtain two data matrices corresponding to strong targets and weak targets respectively.

7. The method for human presence sensing based on the dual-source collaborative fusion of millimeter-wave radar and infrared pyroelectric sensor as claimed in claim 5, characterized in that: The step S401 of dividing the space within the field of view of the infrared pyroelectric sensor into a plurality of independent monitoring sub-areas is performed in the following manner: By dividing the Fresnel lens into several different areas to monitor different areas in the field of view, each sub-area can be regarded as an independent detection unit that can sense temperature changes independently; When a human body or other warm target enters the sensor's field of view and crosses these monitoring sub-areas, a temperature difference is generated, and the pyroelectric element inside the sensor can convert this temperature difference into an electrical signal.

8. The method for human presence sensing based on the dual-source collaborative fusion of millimeter-wave radar and infrared pyroelectric sensor according to claim 1, characterized in that: The threshold value preset in step S4 is calculated according to the following formula: in: X i is the receiving voltage intensity of the i-th unit; N is the number of reference units; α is the threshold factor, which is usually set according to the required false alarm probability.

9. The method for human presence sensing based on the dual-source collaborative fusion of millimeter-wave radar and infrared pyroelectric sensor according to claim 1, characterized in that: The heterogeneous data matrix in step S5 is constructed in the following manner: Obtaining the human presence sensing result R(t) output by the millimeter wave radar sensor and the human presence sensing result I(t) output by the infrared pyroelectric sensor; Determine the sampling timestamp according to the sampling frequencies of the two sensors, and align the output results of the millimeter wave radar sensor and the infrared pyroelectric sensor through the timestamp; The aligned results are merged into the same matrix to obtain a two-dimensional heterogeneous data matrix, where each row corresponds to a time point and each column corresponds to a sensor.

10. The method for human presence sensing based on the dual-source collaborative fusion of millimeter-wave radar and infrared pyroelectric sensor according to claim 1, characterized in that: The step S5 in which the dual-source fusion result is obtained according to the heterogeneous data matrix is ​​specifically performed in the following manner: The weighted average method is used to obtain the fusion result S1(t) of the millimeter-wave radar sensor and the infrared pyroelectric sensor by the heterogeneous data matrix, that is, Among them, F1(t) represents the weighted average fusion result; N represents the number of data points used to calculate the average value; I(t i ) indicates t i The infrared pyroelectric sensor detection result at the time; R(t i ) indicates t i The detection result of the millimeter-wave radar sensor at the time; radar result represents the weighted average result obtained by the millimeter-wave radar sensor; PIR result represents the weighted average result obtained by the infrared pyroelectric sensor; According to the weighted average method threshold, the fusion result S1(t)=1 is obtained; Then the weight distribution method is used to obtain the fusion result S2(t) of the millimeter wave radar sensor and the infrared pyroelectric sensor, that is, Among them, the trust ratios of millimeter wave radar sensors and infrared pyroelectric sensors are: w′ R =0.4w′ i =0.6(w′ R +w′ I =1); According to the threshold setting of the weight distribution method, the fusion result S2(t)=0 is obtained; Finally, the fusion result S1(t) and the fusion result S2(t) are logically ORed to obtain the dual-source fusion result S(t), namely: S(t)=S1(t)∨S2(t); The logical "or" operator ∨ ​​means that if any of the fusion results is 1, the final result is 1; if both are 0, the final result is 0.

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