Non-contact motion information demodulation method and system

Abstract

The application discloses a kind of non-contact motion information demodulation method and system, and the digital quadrature baseband signal containing target motion information is obtained by sampling;Each sampling point in the digital quadrature baseband signal includes I route sampling value and Q route sampling value;Calibrate direct current bias by subtracting the average value of two digital quadrature baseband signals I, Q;The I route sampling value and Q route sampling value of every two adjacent sampling points in the digital quadrature baseband signal after calibration are crossed and multiplied to obtain positive and negative polarity;According to the approximation between the circular arc and chord of the two-dimensional coordinates of adjacent sampling points, the chord length of each chord vector is superimposed using polarity representation increase or decrease, to obtain phase reconstruction result, and the target motion information is recovered based on phase reconstruction result.The application can improve the robustness, real-time performance and accuracy of motion information demodulation.

Description

Technical Field

[0001] This invention belongs to the field of motion detection technology, specifically relating to a non-contact motion information demodulation method and system. Background Technology

[0002] Single-frequency continuous wave (CW) radar is a simple, low-cost, and highly sensitive non-contact detection tool that has been widely used in recent years for detecting minute movements. In vital sign detection, CW radar has been used to observe respiratory movements in animals, rescue trapped victims after major earthquakes, detect obstructive sleep apnea in patients, or monitor human heart rate variability (HRV) for clinical analysis. Advanced integration of radar systems has enabled the development of compact, portable devices based on Doppler radar sensor chips for non-contact monitoring applications. Increasing the operating frequency is a common method to improve radar detection sensitivity; therefore, much research has focused on millimeter-wave radar sensing. By emitting electromagnetic waves to illuminate the target, CW radar receives the echo signal scattered by the target's motion. Digital down-conversion yields a quadrature baseband signal, which is then demodulated to obtain minute motion information of the target, such as… Figure 1 The diagram shown is a block diagram of the CW radar.

[0003] Due to hardware limitations and clutter signals scattered by stationary targets in the surrounding environment, a DC bias is typically introduced into the baseband signal. Therefore, an orthogonal baseband signal can be modeled as follows:

[0004]

[0005] Where d0 represents the fixed distance between the object and the radar, and x(t) represents the time-varying displacement of the object. It is residual phase noise, which can be ignored. DC I and DC Q This is the DC bias component, primarily originating from hardware defects and static objects in the surrounding environment, and is typically a constant. For complex motions, the object's displacement is usually not a simple time-varying displacement x(t), which may introduce a dynamically changing DC bias. λ is the wavelength of the emitted electromagnetic wave. A I and A Q It represents the amplitude of the two orthogonal signals.

[0006] The motion signal x(t) can be recovered in the following way:

[0007]

[0008] To recover the aforementioned motion signal, the phase difference between adjacent sampling points needs to be calculated and superimposed. Advanced semiconductor technology significantly alleviates the problem of phase and amplitude imbalance. When I and Q are matched, the amplitudes of the two orthogonal signals are approximately equal; therefore, the phase difference within a single sampling interval can be expressed as:

[0009]

[0010] For n≥2, the phase differences within the sampling interval are superimposed to obtain:

[0011]

[0012] Where N is the number of sampling points.

[0013] The motion signal to be recovered is represented as follows:

[0014]

[0015] As can be seen, in order to recover the motion signal mentioned above, it is necessary to first perform DC bias calibration on the two orthogonal I and Q signals to remove DC bias. I and DC Q When I and Q are matched, the baseband signal will appear in the constellation diagram as (DC) I DC Q A circle with center ).

[0016] Therefore, circle center estimation is a common method for calibrating DC bias; least squares and gradient descent are commonly used circle fitting methods, and Kalman filters can be used to dynamically find the circle center to remove DC bias. The accuracy of existing demodulation schemes is highly dependent on accurate circle center estimation; inaccurate estimation introduces errors into the calculation, and these errors accumulate, causing severe distortion. Furthermore, circle fitting based on optimization methods typically requires high computational costs, limiting the application of traditional methods in most situations. Especially in the detection of vital signs, complex chest wall movements generate dynamically changing DC biases, leading to severe distortion and difficulties in real-time detection in existing demodulation methods. Summary of the Invention

[0017] The technical problem to be solved by the present invention is to provide a non-contact motion information demodulation method and system to address the shortcomings of the prior art, thereby solving the technical problems of severe distortion and difficulty in real-time detection in existing demodulation methods.

[0018] The present invention adopts the following technical solution:

[0019] A non-contact motion information demodulation method includes the following steps:

[0020] S1. A digital orthogonal baseband signal containing target motion information is obtained by sampling. Each sampling point in the digital orthogonal baseband signal includes I-channel sampled value and Q-channel sampled value.

[0021] S2. Calculate the average value of the I-channel and Q-channel sampled values ​​obtained in step S1, and subtract the corresponding average value from each I-channel and Q-channel sampled value to calibrate the DC bias.

[0022] S3. Perform a cross-multiplication operation on the I-path and I-path sampled values ​​obtained in step S2, and calculate the polarity of each cumulative term;

[0023] S4. Using the I-path sampling values ​​and I-path sampling values ​​of the sampling points obtained in step S1, construct the chord vector based on the arc-chord approximation principle to calculate the chord length and characterize the phase difference;

[0024] S5. Using the polarity obtained in step S3, the chord lengths of each chord vector obtained in step S4 are superimposed to obtain the chord length superposition result. Based on the chord length superposition result, phase reconstruction is performed to recover the target motion information.

[0025] Specifically, in step S1, the target motion information includes the target's heartbeat information, pulse information, or blood flow information.

[0026] Furthermore, the digital orthogonal baseband signal containing target motion information includes a set of continuous sampling points (I[1], Q[1]), (I[2], Q[2]), ... (I[N], Q[N]), where I[1], I[2], ... I[N] are the I-channel sampling values ​​of each sampling point, Q[1], Q[2], ... Q[N] are the Q-channel sampling values ​​of each sampling point, and N represents the number of sampling points.

[0027] Specifically, in step S3, the process of extracting positive and negative polarities is as follows:

[0028]

[0029] Where I'[n], Q'[n] represent the nth sampling point of the baseband signal after removing DC from the mean, || represents the modulus operation, ω(n) is the angular frequency, p[n] is the polarity of the chord length corresponding to the phase angle between the nth and (n-1)th sampling points with ±1 values, and represents the increase or decrease of the Doppler phase generated by the target motion, respectively.

[0030] Furthermore, the angular frequency ω(n) is:

[0031]

[0032] Specifically, in step S4, the arc length corresponding to the phase angle between adjacent sampling points is approximated as the chord length, and the chord length is used to characterize the phase difference.

[0033] Furthermore, the phase difference Δθ[n] between adjacent I / Q sampling points is expressed as:

[0034]

[0035] Where R represents the radius of the circle corresponding to the current I / Q sampling point, l represents the arc length corresponding to the phase angle, I[n] is the I-channel sampling value of the nth sampling point in the digital quadrature baseband signal, and Q[n] is the Q-channel sampling value of the nth sampling point in the digital quadrature baseband signal.

[0036] Specifically, in step S5, the displacement motion x(n) of the target obtained after phase reconstruction is expressed as:

[0037]

[0038] Where λ represents the wavelength of the single-frequency electromagnetic wave signal emitted towards the target for motion detection, and the digital quadrature baseband signal is obtained by receiving, down-converting, and sampling the single-frequency electromagnetic wave signal returned from the target; p(k) represents the polarity of the chord length corresponding to the phase angle between the k-th sampling point and the (k-1)-th sampling point with a value of ±1, x(n) represents the target motion information to be recovered, θ[n] represents the result of superimposing the chord lengths of each chord vector, R represents the radius of the circle corresponding to the current I / Q sampling point, Q[k] is the Q-path sampling value of the k-th sampling point in the digital quadrature baseband signal, and I[k] represents the I-path sampling value of the k-th sampling point in the digital quadrature baseband signal.

[0039] Furthermore, the result θ[n] obtained by superimposing the chord lengths of each chord vector is:

[0040]

[0041] Where I[k] is the I-channel sampled value of the k-th sampling point in the digital quadrature baseband signal, Q[k] is the Q-channel sampled value of the k-th sampling point in the digital quadrature baseband signal, and n is the number of sampling points.

[0042] Secondly, embodiments of the present invention provide a non-contact motion information demodulation system, comprising:

[0043] The acquisition module obtains a digital orthogonal baseband signal containing target motion information through sampling. Each sampling point in the digital orthogonal baseband signal includes I-channel sampled values ​​and Q-channel sampled values.

[0044] The calibration module calculates the average value of the I-channel and Q-channel sampled values ​​obtained by the acquisition module, and subtracts the corresponding average value from each I-channel and Q-channel sampled value to calibrate the DC bias.

[0045] The cross-multiplication module performs a cross-multiplication operation on the I-path and Q-path sampled values ​​obtained from the calibration module to calculate the polarity of each cumulative term;

[0046] The approximation module uses the I-channel and Q-channel sampled values ​​of the sampling points obtained by the acquisition module as the two-dimensional coordinates of the sampling points, and constructs a chord vector based on the arc-chord approximation principle to calculate the chord length and characterize the phase difference.

[0047] The demodulation module uses the polarity obtained by the cross module to superimpose the chord lengths of each chord vector obtained by the approximation module to obtain the chord length superposition result, and performs phase reconstruction based on the chord length superposition result to recover the target motion information.

[0048] Compared with the prior art, the present invention has at least the following beneficial effects:

[0049] A non-contact motion information demodulation method accurately calculates the phase angle accumulation direction, i.e., the positive and negative polarities, in the next step through calibration. Compared with the traditional circle fitting calibration method, it does not require a complex optimization process, greatly reducing computational costs. The method uses a subtractive averaging operation to calibrate the DC biased digital quadrature baseband signal I′ / Q′ to calculate the phase angle change rate, i.e., the angular frequency, from which the positive and negative polarities are extracted. After DC compensation, the polarity corresponding to the phase angle between adjacent sampling points can be represented by the angular frequency. Since only positive and negative polarities are involved, the square term does not need to be considered. Furthermore, because the phase calculation and phase accumulation processes are separated, the phase calculation process is completely immune to DC bias; therefore, the averaging method can guarantee accurate determination of the positive and negative polarities of the phase accumulation direction. After a cross-multiplication operation in the digital domain, the positive and negative polarities are obtained to characterize the phase change direction of the I / Q sampling points of the time-series quadrature signal. Using the I-channel and Q-channel sample values ​​as the two-dimensional coordinates of the sampling points, the directed line connecting every two adjacent sampling points in the digital quadrature baseband signal forms a chord vector. The magnitude of this chord vector is calculated to characterize the phase difference angle. The reason why the vector constructed based on adjacent sampling points is named a chord vector is because in the constellation diagram of digital quadrature baseband signals, adjacent sampling points located at the center of the circle represent DC bias (DC). I DC Q On an arc, the line connecting the two endpoints of the arc is a chord. Based on the arc-chord approximation principle, the arc length corresponding to the phase difference angle between adjacent digital orthogonal baseband signal I / Q sample values ​​is approximated as the chord length, and the chord length is used to characterize the phase difference value. Phase reconstruction is performed using the discrete chord length and corresponding polarity used to characterize the phase difference angle to recover the original motion information. Based on the above calculation process, the displacement motion information of the target is measured non-contactly. For vital signs such as breathing and heartbeat, respiratory rate and heart rate can be obtained through further signal processing. Compared with traditional methods, the above calculation process does not involve any complex mathematical operations, saving computational costs and enabling real-time processing on most processors.

[0050] Furthermore, the target's heartbeat, pulse, or blood flow will cause displacement of the chest cavity or body surface. By measuring the displacement of the chest cavity or body surface, information about the target's heartbeat, pulse, or blood flow can be obtained.

[0051] Furthermore, during the measurement process, the target's displacement motion information is converted into phase angle changes of orthogonal signals. By using the I / Q sampling points of two digital orthogonal baseband signals, the phase angle used to acquire the target motion information can be uniquely determined. A continuous set of sampling points can obtain continuous phase angle changes; by reconstructing these phase changes, the target's displacement motion is measured.

[0052] Furthermore, to recover the original motion, these discrete chord lengths used to characterize the phase difference need to be accumulated to reconstruct the original phase. The phase accumulation or subtraction is determined by calculating the positive or negative polarity corresponding to each chord length. This polarity is used to solve the phase discontinuity problem, enabling sensing of motion of arbitrary amplitude. After DC compensation, the polarity of the phase angle between adjacent sampling points can be represented by angular frequency. Since only positive and negative polarities are involved, the square term does not need to be considered. Moreover, because the phase calculation and phase accumulation processes are separated, the phase calculation process is completely immune to DC bias; therefore, the averaging method can guarantee accurate positive and negative values ​​for determining the direction of phase accumulation, i.e., polarity. After a cross-multiplication operation in the digital domain, the positive and negative polarities are obtained to characterize the phase change direction of the I / Q sampling points of the time-series quadrature signal.

[0053] Furthermore, regarding the change in phase angle, in the time domain, we obtain the rate of change of angle by calculating the angle derivative, i.e., the angular frequency. After discretization, in the digital domain, the angular frequency ω(n) can be obtained by cross-multiplying the I / Q sampling points of adjacent digital quadrature baseband signals.

[0054] Furthermore, advancements in integrated circuit technology have enabled increased signal sampling rates, significantly reducing the cost of millimeter-wave radar. For 60GHz millimeter-wave radar, when measuring minute target displacements, even a movement of only 1.25mm will produce an arc of a full circle in the constellation diagram. The arc-chord approximation phase representation algorithm can fully meet the accuracy requirements of sensing. Based on the arc-chord approximation principle, the arc length corresponding to the phase difference angle between adjacent digital quadrature baseband signal I / Q sample values ​​is approximated as the chord length, and the chord length is used to represent the phase difference. The chord length between adjacent I / Q sample points is independent of the circle center; therefore, using the chord length to represent the phase difference angle completely eliminates DC bias while meeting accuracy requirements. This gives the method excellent robustness and high accuracy.

[0055] Furthermore, by calculating the chord length to represent the phase difference angle, and using it for phase reconstruction, the motion information of the target can be obtained.

[0056] Furthermore, the displacement motion information of the target will be measured non-contactly; for vital signs such as breathing and heartbeat, respiratory rate and heart rate can be obtained through further signal processing; compared with the traditional method, the above calculation process does not involve any complex mathematical operations, saving computing costs and can be processed in real time on most processors.

[0057] Furthermore, by utilizing the phase difference angle and corresponding polarity between adjacent digital quadrature signal I / Q sampling points, the phase changes caused by target motion can be reconstructed. This phase reconstruction method innovatively separates the phase calculation and phase reconstruction processes, proposing a method that combines the determination of the phase accumulation direction using positive and negative polarities with the phase difference calculation representing the phase using chord length. Compared with traditional phase demodulation methods such as DDM and MDACM, it does not require complex circle fitting operations to calibrate the DC bias, saving computational costs and enabling real-time signal processing. The separation of the phase calculation and phase reconstruction processes helps improve the robustness of the algorithm.

[0058] It is understandable that the beneficial effects of the second aspect mentioned above can be found in the relevant descriptions in the first aspect mentioned above, and will not be repeated here.

[0059] In summary, this invention innovatively separates the phase calculation and phase reconstruction processes, proposing a method that combines phase difference calculation using positive and negative polarities to determine the phase accumulation direction and chord length to characterize the phase. Compared with traditional phase demodulation methods, it does not require complex circle fitting operations to calibrate the DC bias. The chord length between adjacent sampling points is independent of the circle center, therefore the phase calculation process of this invention is completely immune to DC bias, significantly improving sensing accuracy. The separation of the phase calculation and phase reconstruction processes helps to improve the robustness of demodulation.

[0060] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0061] Figure 1 A schematic diagram of the CW radar;

[0062] Figure 2 A simulation diagram of triangular motion with DC offset;

[0063] Figure 3 The simulation diagram of sinusoidal motion non-contact sensing with added DC is shown, where (a) is the time domain waveform of the target displacement motion and (b) is the spectrum of the target displacement motion.

[0064] Figure 4Figure 1 shows the experimental results of the small pendulum motion sensing experiment. (a) is a constellation diagram of the I / Q sampling points of the digital orthogonal baseband signal of the small pendulum displacement motion, (b) is a magnified view of the time-domain details of the small pendulum displacement motion, and (c) is the time-domain waveform and spectrum of the small pendulum displacement motion.

[0065] Figure 5 The results of the vital sign signal detection experiment are shown in the figure. (a) is a constellation diagram of the I / Q sampling points of the digital orthogonal baseband signal for vital sign signal detection, and (b) is the time-domain waveform and spectrum of the vital sign movement of the subject.

[0066] Figure 6 To obtain the polarity diagram for the approximate phase angle calculation of the arc chord and the DC compensation using the averaging method;

[0067] Figure 7 This is a schematic diagram of the process of the present invention. Detailed Implementation

[0068] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0069] In the description of this invention, it should be understood that the terms "comprising" and "including" indicate the presence of the described features, integrals, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or collections thereof.

[0070] It should also be understood that the terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the invention. As used in this specification and the appended claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms unless the context clearly indicates otherwise.

[0071] It should also be further understood that the term "and / or" as used in this specification and the appended claims refers to any combination and all possible combinations of one or more of the associated listed items, and includes such combinations. For example, A and / or B can represent three cases: A alone, A and B simultaneously, and B alone. Additionally, the character " / " in this invention generally indicates that the preceding and following objects have an "or" relationship.

[0072] It should be understood that although terms such as first, second, third, etc., may be used in the embodiments of the present invention to describe the preset range, these preset ranges should not be limited to these terms. These terms are only used to distinguish the preset ranges from one another. For example, without departing from the scope of the embodiments of the present invention, the first preset range may also be referred to as the second preset range, and similarly, the second preset range may also be referred to as the first preset range.

[0073] Depending on the context, the word "if" as used here can be interpreted as "when," "when," "in response to determination," or "in response to detection." Similarly, depending on the context, the phrase "if determination" or "if detection (of the stated condition or event)" can be interpreted as "when determination," "in response to determination," "when detection (of the stated condition or event)," or "in response to detection (of the stated condition or event)."

[0074] The accompanying drawings illustrate various structural schematic diagrams according to embodiments disclosed in this invention. These drawings are not to scale, and some details have been enlarged for clarity, and some details may have been omitted. The shapes of the various regions and layers shown in the drawings, as well as their relative sizes and positional relationships, are merely exemplary and may deviate from reality due to manufacturing tolerances or technical limitations. Furthermore, those skilled in the art can design regions / layers with different shapes, sizes, and relative positions as needed.

[0075] This invention provides a non-contact motion information demodulation method. It obtains a digital orthogonal baseband signal containing target motion information through sampling. Each sampling point in the digital orthogonal baseband signal includes an I-channel sample value and a Q-channel sample value. The DC bias is calibrated by subtracting the average value of the I and Q channels of the two digital orthogonal baseband signals. A cross-multiplication operation is performed on the I-channel and Q-channel sample values ​​of every two adjacent sampling points in the calibrated digital orthogonal baseband signal to obtain the sign bit. Based on the approximation of the arcs and chords between the two-dimensional coordinates of adjacent sampling points, the chord lengths of each chord vector are superimposed using the sign of the sign bit to obtain an arc length superposition result. The target motion information is then recovered based on the arc length superposition result. This invention improves the robustness, real-time performance, and accuracy of motion information demodulation.

[0076] Please see Figure 6 The I / Q sampling points of a digital quadrature baseband signal are represented as a circle on a constellation diagram. Within one continuous segment, the trajectory of the I / Q signal is shown by the line connecting the red dots in the figure, with the center position representing the DC component. I DC Q When calculating the required polarity for accumulation, DC calibration is necessary. Traditional calibration methods based on circle fitting rely on complex optimization processes and require high computational costs.

[0077] The phase calculation process based on the arc-chord approximation principle proposed in this invention approximates the arc length arc[n] corresponding to the phase difference angle Δθ[n] as the chord length chord[n], which is used to characterize the phase difference angle Δθ[n]. To recover the original motion, these discrete chord lengths used to characterize the phase difference need to be accumulated to reconstruct the original phase. By calculating the positive and negative polarities corresponding to each chord length, the accumulation or subtraction of the phase is determined. This polarity is used to solve the phase discontinuity problem and realize the sensing of motion of arbitrary amplitude. After averaging, the obtained DC estimation point is shown as the green dot in the figure. At this time, the phase difference angle corresponding to the green dot is Δθ'[n] in the figure. Although it is significantly different from the true phase difference angle Δθ[n] in value, after the cross-multiplication operation in the digital domain, we can obtain the same positive and negative polarities as the true phase angle Δθ[n], which is used to characterize the rotation direction of our time-series orthogonal sampling data point I / Q trajectory, that is, the accumulation direction of the phase angle. Compared with traditional circle fitting calibration methods, the averaging operation requires only a small amount of computational cost, which ensures the method's real-time processing capability.

[0078] The I / Q sampling point values ​​of the digital orthogonal baseband signal after DC compensation using the averaging method are expressed as follows:

[0079] I′[]=I[n]-mean(I)Q′[]=Q[n]-mean(Q)

[0080] Where I[n] and Q[n] represent the nth sampling point of the digital quadrature baseband signal I / Q; I and Q represent the N sampling point values ​​of the digital quadrature baseband signal I / Q within this continuous segment, n = 1, 2, 3, ..., N;

[0081] The process of extracting the positive and negative polarity p[n] by the cross-multiplication operation can be represented as:

[0082]

[0083] Where I'[n], Q'[n] represent the nth sampling point of the baseband signal after removing DC from the mean, and || represents the modulo operation.

[0084] Please see Figure 7 The present invention provides a non-contact motion information demodulation method, comprising the following steps:

[0085] S1. Obtain a digital orthogonal baseband signal containing target motion information through sampling; each sampling point in the digital orthogonal baseband signal includes I-channel sampled values ​​and Q-channel sampled values.

[0086] The target motion information includes the target's heartbeat, pulse, or blood flow information, and is not limited to biological motion information. For example, it can also be information about the subtle motion of a periodic pendulum ball.

[0087] The digital quadrature baseband signal obtained by sampling includes a set of continuous sampling points, denoted by (I[1], Q[1]), (I[2], Q[2]), ... (I[N], Q[N]); where I[1], I[2], ... I[N] are the I-channel sampling values ​​of each sampling point, Q[1], Q[2], ... Q[N] are the Q-channel sampling values ​​of each sampling point, and N represents the number of sampling points.

[0088] S2. Calculate the average value of the I-channel and Q-channel sampled values ​​at the sampling points, and subtract this average value from each I-channel and Q-channel sampled value to calibrate the DC bias.

[0089] S3. Perform cross-multiplication based on the I-path and Q-path sampled values ​​to calculate the polarity of each cumulative term;

[0090] Specifically, by calculating the positive or negative polarity corresponding to each chord length, the phase accumulation or subtraction is determined. This polarity is used to solve the phase discontinuity problem and realize the sensing of motion of arbitrary amplitude. As shown in equations (1) and (2), after DC compensation, the polarity corresponding to the phase angle between adjacent sampling points can be represented by the angular frequency. In the analog domain, the angular frequency ω(t) of the signal is calculated by the following formula:

[0091]

[0092] After converting it to the digital domain, the angular frequency ω(n) is calculated as follows:

[0093]

[0094] Since only positive and negative polarities are involved, the square term does not need to be considered. Furthermore, because the phase calculation and phase accumulation processes are separate, and the phase calculation process is completely immune to DC bias, the averaging method can guarantee an accurate determination of the positive and negative polarities of the phase accumulation direction. Figure 6 As shown, the DC estimated by the averaging method is represented by the green dots in the figure. Clearly, the DC estimate is not precise, but for the phase angle Δθ[n], after DC compensation using the averaging method, the difference angle Δθ'[n] used to obtain the polarity is obtained. Although there is a significant numerical inequality, after a cross-multiplication operation in the digital domain, the polarity obtained is the same as the true phase angle Δθ[n], used to characterize the rotation direction of the I / Q trajectory of the time-series orthogonal sampling data points.

[0095] The process of DC compensation using the averaging method is expressed as follows:

[0096] I′=I-mean(I) (4)

[0097] Q′=Q-mean(Q) (5)

[0098] From (4) and (5), the process of extracting positive and negative polarities can be represented as:

[0099]

[0100] Where I'[n], Q'[n] represent the nth sampling point of the baseband signal after removing DC from the mean, and || represents the modulo operation.

[0101] S4. Using the I-channel and Q-channel sampled values ​​of the sampling points as the two-dimensional coordinates of the sampling points, construct a chord vector based on the arc-chord approximation principle to calculate the chord length and characterize the phase difference.

[0102] Specifically, based on the arc-chord approximation principle, the arc length corresponding to the phase angle between adjacent sampling points is approximated as the chord length, and the chord length is used to characterize the phase difference.

[0103] The phase difference between adjacent I / Q sampling points is expressed as:

[0104]

[0105] Where R represents the radius of the circle corresponding to the current I / Q sampling point, which is related to the amplitude of the I / Q signal; l represents the arc length corresponding to the phase angle.

[0106] The change in chord length between adjacent sampling points is independent of the center of the circle. Therefore, the phase calculation process of this method is completely immune to dynamic DC bias, which greatly improves the measurement accuracy.

[0107] S5. The chord lengths of each chord vector are superimposed using polarity to obtain the chord length superposition result, and the phase reconstruction is performed based on the chord length superposition result to recover the target motion information.

[0108] Specifically, the phase change is reconstructed by superimposing the chord lengths that characterize the phase angle and their corresponding positive and negative polarities between adjacent sampling points from the previous step:

[0109]

[0110] Where θ[n] represents the result of superimposing the chord lengths of each chord vector, which is also the phase reconstruction result.

[0111] The displacement motion x(n) of the target obtained after phase reconstruction is expressed as:

[0112]

[0113] Where λ represents the wavelength of the single-frequency electromagnetic wave signal emitted towards the target for motion detection, and the digital quadrature baseband signal is obtained by receiving, down-converting, and sampling the single-frequency electromagnetic wave signal returned from the target; p(k) represents the polarity of the chord length corresponding to the phase angle between the k-th and (k-1)-th sampling points with values ​​of ±1, respectively representing the increase or decrease of the Doppler phase generated by the target motion; x(n) represents the target motion information to be recovered. In calculating the normalized motion of the target, the constant term... It can be approximated as 1.

[0114] In another embodiment of the present invention, a non-contact motion information demodulation system is provided. This system can be used to implement the above-mentioned non-contact motion information demodulation method. Specifically, the non-contact motion information demodulation system includes an acquisition module, a calibration module, a cross module, an approximation module, and a demodulation module.

[0115] The acquisition module obtains a digital orthogonal baseband signal containing target motion information by sampling. Each sampling point in the digital orthogonal baseband signal includes I-channel sampling value and Q-channel sampling value.

[0116] The calibration module calculates the average value of the I-channel and Q-channel sampled values ​​obtained by the acquisition module, and subtracts the corresponding average value from each I-channel and Q-channel sampled value to calibrate the DC bias.

[0117] The cross-multiplication module performs a cross-multiplication operation on the I-path and Q-path sampled values ​​obtained from the calibration module to calculate the polarity of each cumulative term;

[0118] The approximation module uses the I-channel and Q-channel sampled values ​​of the sampling points obtained by the acquisition module as the two-dimensional coordinates of the sampling points, and constructs a chord vector based on the arc-chord approximation principle to calculate the chord length and characterize the phase difference.

[0119] The demodulation module uses the polarity obtained by the cross module to superimpose the chord lengths of each chord vector obtained by the approximation module to obtain the chord length superposition result, and performs phase reconstruction based on the chord length superposition result to recover the target motion information.

[0120] In another embodiment of the present invention, a terminal device is provided, comprising a processor and a memory. The memory stores a computer program, the computer program including program instructions, and the processor executes the program instructions stored in the computer storage medium. The processor may be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. It is the computing and control core of the terminal, suitable for implementing one or more instructions, specifically suitable for loading and executing one or more instructions to achieve a corresponding method flow or corresponding function. The processor described in this embodiment of the present invention can be used for the operation of a non-contact motion information demodulation method, including:

[0121] A digital orthogonal baseband signal containing target motion information is obtained through sampling. Each sampling point in the digital orthogonal baseband signal includes I-channel and Q-channel sample values. The average value of the I-channel and Q-channel sample values ​​at the sampling point is calculated, and the DC bias is calibrated by subtracting the corresponding average value from each I-channel and Q-channel sample value. A cross-multiplication operation is performed on the I-channel and Q-channel sample values ​​to calculate the polarity of each cumulative term. Using the I-channel and Q-channel sample values ​​of the sampling point as the two-dimensional coordinates of the sampling point, a chord vector is constructed based on the arc-chord approximation principle to calculate the chord length representing the phase difference. The chord lengths of each chord vector are superimposed using the polarity to obtain the chord length superposition result. Phase reconstruction is performed based on the chord length superposition result to recover the target motion information.

[0122] In another embodiment of the present invention, a storage medium is also provided, specifically a computer-readable storage medium (memory). This computer-readable storage medium is a memory device in a terminal device used to store programs and data. It is understood that the computer-readable storage medium here can include both the built-in storage medium in the terminal device and extended storage media supported by the terminal device. The computer-readable storage medium provides storage space that stores the terminal's operating system. Furthermore, this storage space also stores one or more instructions suitable for loading and execution by a processor. These instructions can be one or more computer programs (including program code). It should be noted that the computer-readable storage medium here can be high-speed RAM or non-volatile memory, such as at least one disk storage device.

[0123] One or more instructions stored in a computer-readable storage medium can be loaded and executed by a processor to implement the corresponding steps of the non-contact motion information demodulation method in the above embodiments; one or more instructions in the computer-readable storage medium are loaded and executed by the processor to perform the following steps:

[0124] A digital orthogonal baseband signal containing target motion information is obtained through sampling. Each sampling point in the digital orthogonal baseband signal includes I-channel and Q-channel sample values. The average value of the I-channel and Q-channel sample values ​​at the sampling point is calculated, and the DC bias is calibrated by subtracting the corresponding average value from each I-channel and Q-channel sample value. A cross-multiplication operation is performed on the I-channel and Q-channel sample values ​​to calculate the polarity of each cumulative term. Using the I-channel and Q-channel sample values ​​of the sampling point as the two-dimensional coordinates of the sampling point, a chord vector is constructed based on the arc-chord approximation principle to calculate the chord length representing the phase difference. The chord lengths of each chord vector are superimposed using the polarity to obtain the chord length superposition result. Phase reconstruction is performed based on the chord length superposition result to recover the target motion information.

[0125] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0126] Please seeFigure 2 In human vital sign signal detection, the random swaying of the human body often causes dynamic movement of the center of the circle. In this case, DAM and MDACM may experience errors or even distortion due to the difficulty in accurately estimating the DC bias for center compensation. This paper simulates the dynamic movement of the center of the circle by using a time-varying DC bias in MATLAB, selecting a triangular wave that clearly reflects details for simulation. To eliminate noise effects, the signal-to-noise ratio of the I / Q signals is set to 30 dB. The triangular motion amplitude is set to 3 mm, and the sampling rate is 100 Hz. The constellation diagram of the motion is shown below. Figure 2 As shown, the I / Q trajectory is distorted due to the dynamic movement of the center. The center of the circle is estimated using the least squares algorithm on this signal; the estimated center is shown as the green dot in the figure. Figure 2 As shown, the center estimation is not accurate, causing distortion in the demodulation results of DAM and MDACM, resulting in a sinusoidal waveform. However, the method of this invention has good accuracy because the phase angle calculation process is independent of the center position, and the demodulation results do not produce any distortion due to the low requirement for center estimation. The normalized MSE of the demodulation results of the above methods were calculated respectively, as shown in the table.

[0127]

[0128] The above simulations verified that the method of the present invention is immune to dynamic DC bias, effectively improving the sensing accuracy. Through adaptive segmentation, it is expected to achieve temporal detail recovery of vital signs signals.

[0129] In MATLAB, a sinusoidal motion with an amplitude of 6 mm and a frequency of 1.3 Hz is simulated over 10 seconds, and a linear motion with a slope of 0.005 mm / s is added on top of it. The ideal normalized motion x(t) curve is as follows: Figure 2 As shown by the dark curve, the signal sampling rate is 100Hz. By changing the signal-to-noise ratio of the baseband I / Q signals, simulations verified the robustness of the algorithm and its immunity to phase discontinuity problems. The moving constellation diagram is shown below. Figure 2 The simulation considers two different DC bias conditions: a fixed DC bias caused by hardware and environmental factors, resulting in a shift in the center of the circle; and a DC bias caused by random factors during the measurement process, such as temperature and random vibrations, leading to a dynamic shift in the center of the circle.

[0130] To further illustrate the algorithm's low precision requirement for center estimation, only a subtraction operation was used to compensate for the fixed DC bias during demodulation. The proposed method was compared with three other methods: DAM and MDACM. The results are as follows: Figure 3As shown in the figure, the method of this invention exhibits good measurement accuracy under various signal-to-noise ratios. Furthermore, as shown in the magnified spectral section of the figure, the method of this invention better recovers time-domain details; the spectrum remains undistorted after Fourier transform, while DDM and MDACM show partial distortion. The normalized mean square error (MSE) of the demodulation results of several methods was calculated, and the results are shown in the figure. Under various signal-to-noise ratios, the method of this invention has the smallest MSE. Simultaneously, the demodulation of large-amplitude motion also demonstrates the immunity of the phase discontinuity problem of the method of this invention.

[0131] The experiment used TI's mmwavcas-rf-evm and mmwcas-dsp-evm, both equipped with a 77GHz AWR2243 radar chip, to sense the motion of a small pendulum located 1 meter from the radar. The experimental scenario is shown in the figure. When the pendulum moves freely, it will generate a standard sine wave with a fixed frequency of 1.33Hz due to the interaction of forces. The constellation diagram of the motion is shown below. Figure 4 As shown, demodulation was performed using DDACM, MDACM, and the method of this invention, and the results are as follows. Figure 4 As shown in Figure c, the DAM algorithm incorrectly generates an additional linear motion, while the MDACM algorithm produces local distortion due to dynamic DC bias. In contrast, the sensing result of the method of this invention not only has an accurate sine waveform, but also, as shown by the yellow envelope in the figure, accurately senses the attenuation of the pendulum's free motion amplitude. Further experiments verify the high accuracy of the method of this invention.

[0132] The above-mentioned equipment was used to measure human vital signs. The subject sat 50cm away from the radar, and the measurement scenario was as follows: Figure 5 As shown. Vital signs were collected for 10 seconds; the constellation diagram of the signals is shown below. Figure 5 As shown in (b), the subject's chest movement was sensed using DDACM, MDACM, and the method of this invention. The respiratory rate and heart rate were obtained after spectral analysis. Although the subject's swaying caused a large deviation in the estimation of the center of the I / Q trajectory (shown as a green dot in the figure), rendering DDACM and MDACM inoperable, the method of this invention could still accurately measure the subject's respiratory rate and heart rate with considerable resolution. Experiments verified the high robustness and accuracy of the method of this invention to DC compensation, and it holds promise for real-time detection of vital signs.

[0133] In summary, this invention presents a non-contact motion information demodulation method and system that innovatively separates the phase calculation and phase reconstruction processes. It proposes a method combining phase difference calculation using positive and negative polarities to determine the phase accumulation direction and chord length to characterize the phase. Compared to traditional phase demodulation methods such as DAM and MDACM, it eliminates the need for complex circle fitting operations to calibrate the DC bias. Since the chord length between adjacent sampling points is independent of the circle center, the phase calculation process of this invention is completely immune to DC bias, significantly improving the accuracy of the method. The separation of the phase calculation and phase reconstruction processes contributes to improved algorithm robustness.

[0134] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is merely an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this application. The specific working process of the units and modules in the above system can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0135] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0136] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed in this invention can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0137] In the embodiments provided by this invention, it should be understood that the disclosed devices / terminals and methods can be implemented in other ways. For example, the device / terminal embodiments described above are merely illustrative. For instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0138] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0139] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0140] If the integrated module / unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc. It should be noted that the content included in the computer-readable medium can be appropriately added or removed according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electrical carrier signals and telecommunication signals.

[0141] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0142] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0143] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0144] The above content is only for illustrating the technical concept of the present invention and should not be construed as limiting the scope of protection of the present invention. Any modifications made to the technical solution based on the technical concept proposed in this invention shall fall within the scope of protection of the claims of this invention.

Claims

1. A non-contact motion information demodulation method, characterized in that, Includes the following steps: S1. Obtain a digital orthogonal baseband signal containing target motion information through sampling. Each sampling point in the digital orthogonal baseband signal includes... I Road sample value and Q Road sample value; S2, Calculate the sampling points obtained in step S1 I Luhe Q The average value of each road sample value, so that each I Luhe Q The DC bias is calibrated by subtracting the corresponding average value from the sampled value. S3, the result obtained in step S2 I Luhe Q The process of performing cross-multiplication on the sampled values, calculating the polarity of each cumulative term, and extracting the positive and negative polarities is as follows: in, , This indicates the mean after removing DC. road, The nth sampling point of the roadbed signal, , After removing DC from the mean road, Roadbed signal One sampling point, This represents the modulo operation. Angular frequency, The polarity of the chord length corresponding to the phase angle between the nth sampling point and the (n-1)th sampling point with a value of ±1 represents the increase or decrease of the Doppler phase generated by the target motion, respectively. S4. The sampling points obtained in step S1 I Road sample value and Q The path sampling value is the two-dimensional coordinate of the sampling point. Based on the arc-chord approximation principle, a chord vector is constructed to calculate the chord length, which represents the phase difference. S5. Using the polarity obtained in step S3, the chord lengths of each chord vector obtained in step S4 are superimposed to obtain the chord length superposition result. Based on the chord length superposition result, phase reconstruction is performed to recover the target motion information.

2. The non-contact motion information demodulation method according to claim 1, characterized in that, In step S1, the target motion information includes the target's heartbeat information, pulse information, or blood flow information.

3. The non-contact motion information demodulation method according to claim 2, characterized in that, The digital quadrature baseband signal containing target motion information consists of a set of continuous sampling points ( I [1], Q [1]), ( I [2], Q [2]), …( I [ n ], Q [ n ]), I [1], I [2], … I [ N [For each sampling point] I Road sample value, Q [1], Q [2], … Q [ N [For each sampling point] Q Road sample value, N The number of sampling points. n This represents the nth sampling point, 1 ≤ n ≤ N .

4. The non-contact motion information demodulation method according to claim 1, characterized in that, In step S4, the arc length corresponding to the phase angle between adjacent sampling points is approximated as the chord length, and the chord length is used to characterize the phase difference.

5. The non-contact motion information demodulation method according to claim 4, characterized in that, Phase difference between adjacent I / Q sampling points Represented as: in, The radius of the circle corresponding to the current I / Q sampling point is represented by l, and the arc length corresponding to the phase angle is represented by l. The nth sampling point in the digital quadrature baseband signal I Road sample value, The nth sampling point in the digital quadrature baseband signal Q Road sample value, The first in the digital quadrature baseband signal sampling points Road sample value, The first in the digital quadrature baseband signal sampling points Road sample value.

6. The non-contact motion information demodulation method according to claim 1, characterized in that, In step S5, the displacement motion of the target is obtained through phase reconstruction. x ( n ) is represented as: in, This refers to the wavelength of a single-frequency electromagnetic wave signal emitted towards a target for motion detection. The digital quadrature baseband signal is obtained by receiving, down-converting, and sampling the single-frequency electromagnetic wave signal returned from the target. Indicates having The polarity of the chord length corresponding to the phase angle between the k-th and (k-1)-th sampling points of the numerical value. x ( n () indicates the target motion information to be recovered. This represents the result of superimposing the chord lengths of the individual chord vectors. This represents the radius of the circle corresponding to the current I / Q sampling point. The first in the digital quadrature baseband signal k sampling points Q Road sample value, I [ k ] represents the first in a digital quadrature baseband signal. k sampling points I Road sample value, The first in the digital quadrature baseband signal sampling points Road sample value, The first in the digital quadrature baseband signal sampling points Road sample value.

7. The non-contact motion information demodulation method according to claim 6, characterized in that, The result of superimposing the chord lengths of each chord vector. for: in, The first in the digital quadrature baseband signal k sampling points I Road sample value, Q [ k [The first character in the digital quadrature baseband signal is] the [th character]. k sampling points Q Road sample value.

8. A non-contact motion information demodulation system, characterized in that, include: The acquisition module obtains a digital orthogonal baseband signal containing target motion information through sampling. Each sampling point in the digital orthogonal baseband signal includes... I Road sample value and Q Road sample value; The calibration module calculates the sampling points obtained by the acquisition module. I Luhe Q The average value of each road sample value, so that each I Luhe Q The DC bias is calibrated by subtracting the corresponding average value from the sampled value. The cross module, obtained from the calibration module I Luhe Q The process of performing cross-multiplication on the sampled values, calculating the polarity of each cumulative term, and extracting the positive and negative polarities is as follows: in, , This indicates the mean after removing DC. road, The nth sampling point of the roadbed signal, , After removing DC from the mean road, Roadbed signal One sampling point, This represents the modulo operation. Angular frequency, The polarity of the chord length corresponding to the phase angle between the nth sampling point and the (n-1)th sampling point with a value of ±1 represents the increase or decrease of the Doppler phase generated by the target motion, respectively. The approximation module uses the sampling points obtained by the acquisition module. I Road sample value and Q The path sampling value is the two-dimensional coordinate of the sampling point. Based on the arc-chord approximation principle, a chord vector is constructed to calculate the chord length, which represents the phase difference. The demodulation module uses the polarity obtained by the cross module to superimpose the chord lengths of each chord vector obtained by the approximation module to obtain the chord length superposition result, and performs phase reconstruction based on the chord length superposition result to recover the target motion information.

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