Circuit and method for processing wireless sensing

Abstract

The invention relates to a circuit and a method for processing wireless sensing. A processing circuit includes: an estimation circuit configured to generate an estimated phase matrix according to a phase signal; a decomposition circuit for decomposing the estimated phase matrix to generate an eigenvalue matrix and an eigenvector matrix; a first computing circuit configured to perform a long-term averaging of a plurality of feature values in the feature value matrix to generate a plurality of long-term averaged feature values; the second calculation circuit is used for calculating a plurality of difference values of the plurality of long-term average characteristic values and determining an index corresponding to the difference value; a spectrum generation circuit for generating a virtual spectrum according to the index, a plurality of eigenvectors in the eigenvector matrix and a guide vector; and a decision circuit for deciding at least one maximum value and at least one parameter of the virtual spectrum.

Description

Technical Field

[0001] The present invention relates to a circuit and method for a wireless communication system, and in particular to a circuit and method for processing wireless induction. Background Art

[0002] Wireless sensing has the characteristics of device-free recognition and can be applied to indoor human activity recognition, gesture recognition, presence / proximity detection, and breathing monitoring. The range, velocity, and angle of incidence required for wireless sensing can be estimated by digital signal processing. The digital signal processing can be a fast Fourier transform (FFT). However, the resolution of the fast Fourier transform is determined according to the sampling frequency, observation time, and the number of receiving antennas. Increasing the observation time makes it impossible to observe the instantaneous change of the vibration frequency of the sensing target. Increasing the number of receiving antennas increases the hardware cost. Therefore, the present invention proposes a circuit and method for performing wireless sensing without depending on the sampling frequency, observation time, and the number of receiving antennas to avoid the above problems. Summary of the invention

[0003] One of the objectives of the present invention is to provide a circuit and a method thereof for processing a wireless induction to solve the above-mentioned problem.

[0004] The embodiment of the present invention discloses a processing circuit, comprising an estimation circuit, used to generate a phase vector according to a phase signal, and estimate the phase vector to generate an estimated phase matrix; a decomposition circuit, coupled to the estimation circuit, used to decompose the estimated phase matrix to generate an eigenvalue matrix and an eigenvector matrix, wherein the eigenvalue matrix includes a plurality of eigenvalues ​​and the eigenvector matrix includes a plurality of eigenvectors; a first calculation circuit, coupled to the decomposition circuit, used to perform a long-term average of the plurality of eigenvalues ​​to generate a plurality of long-term average eigenvalues; a second calculation circuit, coupled to the first calculation circuit, used to calculate a plurality of differences of the plurality of long-term average eigenvalues, and determine an indicator corresponding to a difference among the plurality of differences; a spectrum generation circuit, coupled to the second calculation circuit, used to generate a virtual spectrum according to the indicator, the plurality of eigenvectors and a steering vector; and a determination circuit, coupled to the spectrum generation circuit, used to determine at least one maximum value of the virtual spectrum and at least one parameter corresponding to the at least one maximum value.

[0005] An embodiment of the present invention also discloses a method, comprising generating a phase vector according to a phase signal; estimating the phase vector to generate an estimated phase matrix; decomposing the estimated phase matrix to generate an eigenvalue matrix and an eigenvector matrix, wherein the eigenvalue matrix includes multiple eigenvalues ​​and the eigenvector matrix includes multiple eigenvectors; performing a long-term average of the multiple eigenvalues ​​to generate multiple long-term average eigenvalues; calculating multiple differences of the multiple long-term average eigenvalues; determining an indicator corresponding to a difference among the multiple differences; generating a virtual spectrum according to the indicator, the multiple eigenvectors and a steering vector; and determining at least one maximum value of the virtual spectrum and at least one parameter corresponding to the at least one maximum value.

[0006] In summary, the present invention provides a circuit and method for wireless sensing. Vibration frequency and incident angle of the sensing target are obtained by super-resolution method (such as multi-signal classification algorithm). Therefore, wireless sensing does not need to be performed according to sampling frequency, observation time and number of receiving antennas. BRIEF DESCRIPTION OF THE DRAWINGS

[0007] Figure 1 FIG. 4 is a schematic diagram of a sensing device according to an embodiment of the present invention.

[0008] Figure 2 is a schematic diagram of a processing circuit according to an embodiment of the present invention.

[0009] Figure 3 FIG. 4 is a schematic diagram of sensing target vibration according to an embodiment of the present invention.

[0010] Figure 4FIG. 4 is a schematic diagram of a phase shift according to an embodiment of the present invention.

[0011] Figure 5 is a schematic diagram of a complex signal according to an embodiment of the present invention.

[0012] Figure 6 FIG. 4 is a schematic diagram of dividing a phase vector into multiple segments according to an embodiment of the present invention.

[0013] Figure 7 FIG. 4 is a schematic diagram of a virtual spectrum according to an embodiment of the present invention.

[0014] Figure 8 FIG. 4 is a schematic diagram of a multi-antenna sensing device according to an embodiment of the present invention.

[0015] Fig. 9 FIG. 4 is a schematic diagram of a multi-antenna sensing device sensing multiple sensing targets according to an embodiment of the present invention.

[0016] Fig.10 FIG. 4 is a flowchart of a process according to an embodiment of the present invention. DETAILED DESCRIPTION

[0017] Figure 1FIG. 1 is a schematic diagram of a sensing device 10 according to an embodiment of the present invention. The sensing device 10 may be composed of a signal generating circuit 100, a transmitting circuit 110, a receiving circuit 120, a low pass filter circuit 130 (e.g., a low pass filter (LPF)), a conversion circuit 140 (e.g., an analog-to-digital converter (ADC)), and a processing circuit 150. The sensing device 10 may be applied to wireless local area networks (WLAN), long term evolution (LTE) systems, LTE-advanced (LTE-A) systems, fifth generation (5G) systems, and other wireless communication systems. The sensing device 10 may support the Institute of Electrical and Electronics Engineers (IEEE) 802.11 standard (e.g., 802.11AX, 802.11be, or subsequent versions thereof). The 802.11 standard may support Orthogonal Frequency Division Multiple Access (OFDMA) or Multi-User Multiple-Input Multiple-Output (MU-MIMO). In one embodiment, the sensing device 10 may be a Frequency Modulated Continuous Wave (FMCW) device.

[0018] exist Figure 1In the embodiment, the signal generating circuit 100 is used to generate a first time domain analog signal sig_time_anal1. The first time domain analog signal sig_time_anal1 is a linear frequency conversion (chirp) signal. The transmitting circuit 110 is coupled to the signal generating circuit 100 and is used to transmit the first time domain analog signal sig_time_anal1. The receiving circuit 120 is used to receive a second time domain analog signal sig_time_anal2. The second time domain analog signal sig_time_anal2 is a reflection signal of the first time domain analog signal sig_time_anal1 (for example, reflected by a sensing target OBJ). The low-pass filtering circuit 130 is coupled to the signal generating circuit 100 and the receiving circuit 120 and is used to perform a mixing process and a low-pass filtering process according to the first time domain analog signal sig_time_anal1 and the second time domain analog signal sig_time_anal2 to generate a third time domain analog signal sig_time_anal3. The conversion circuit 140 is coupled to the low-pass filter circuit 130, and is used to convert the third time-domain analog signal sig_time_anal3 to generate a time-domain digital signal sig_time_dig. The processing circuit 150 is coupled to the conversion circuit 140, and is used to sense (or calculate) at least one parameter P associated with the sensing target OBJ according to the time-domain digital signal sig_time_dig. In one embodiment, the at least one parameter P may be a distance between the sensing target OBJ and the sensing device 10, at least one vibration frequency of the sensing target OBJ, and / or at least one incident angle associated with the sensing target OBJ and the sensing device 10.

[0019] Figure 2 is a schematic diagram of a processing circuit 20 according to an embodiment of the present invention. For example, the processing circuit 20 may be used Figure 1The processing circuit 150 of the present invention is a processing circuit 150. The processing circuit 20 includes an estimation circuit 200, a decomposition circuit 210, a first calculation circuit 220, a second calculation circuit 230, a spectrum generation circuit 240 and a determination circuit 250. In detail, the estimation circuit 200 is used to generate a phase vector according to a phase signal sig_ph, and estimate the phase vector to generate an estimated phase matrix M_ph. The decomposition circuit 210 is coupled to the estimation circuit 200, and is used to decompose the estimated phase matrix M_ph to generate an eigenvalue matrix M_evalue and an eigenvector matrix M_evector. The eigenvalue matrix M_evalue includes a plurality of eigenvalues, and the eigenvector matrix M_evector includes a plurality of eigenvectors. The first calculation circuit 220 is coupled to the decomposition circuit 210, and is used to perform a long-term average of a plurality of eigenvalues ​​to generate a plurality of long-term average eigenvalues ​​λ_LTA. The second calculation circuit 230 is coupled to the first calculation circuit 220, and is used to calculate a plurality of differences of a plurality of long-term average eigenvalues ​​λ_LTA, and to determine an indicator M corresponding to a difference among the plurality of differences. The spectrum generation circuit 240 is coupled to the second calculation circuit 230, and is used to generate a pseudo spectrum PS according to the indicator M, a plurality of eigenvectors, and a steering vector. The determination circuit 250 is coupled to the spectrum generation circuit 240, and is used to determine at least one maximum value of the pseudo spectrum PS and at least one parameter P corresponding to the at least one maximum value.

[0020] In one embodiment, the phase signal sig_ph includes at least one complex index. In one embodiment, at least one complex index is associated with at least one parameter P. In one embodiment, a plurality of eigenvalues ​​correspond to a plurality of eigenvectors, respectively. In one embodiment, one of the plurality of difference values ​​is a difference between two adjacent long-term average eigenvalues ​​in a plurality of long-term average eigenvalues ​​λ_LTA. In one embodiment, the second calculation circuit selects at least one difference value from the plurality of difference values. At least one difference value is greater than a threshold value. Then, the second calculation circuit selects an indicator from at least one indicator corresponding to at least one difference value. In one embodiment, the threshold value is the minimum value of the signal-to-noise ratio (SNR) required by the sensing device 10. In one embodiment, the selected indicator is the maximum value of at least one indicator. In one embodiment, at least one parameter P is at least one vibration frequency or at least one incident angle.

[0021] In one embodiment, the processing circuit 20 further includes a conversion circuit (such as a Fast Fourier Transform (FFT) circuit), a detection circuit, and a third calculation circuit. The conversion circuit may be coupled to Figure 1 The conversion circuit 140 is used to convert a time domain digital signal (eg Figure 1 The detection circuit can be coupled to the conversion circuit to detect a maximum value of the first frequency domain signal to generate a second frequency domain signal. The third calculation circuit can be coupled to the detection circuit and Figure 2 The estimation circuit 200 is used to calculate the phase signal sig_ph of the second frequency domain signal. In one embodiment, the fast Fourier transform circuit can be a range fast Fourier transform (Range-FFT) circuit, a Doppler fast Fourier transform (Doppler-FFT) circuit and / or an angle fast Fourier transform (Angle-FFT) circuit.

[0022] The following embodiments are used to illustrate how the sensing device 10 and the processing circuit 20 sense (or calculate) at least one parameter (at least one vibration frequency or at least one incident angle) associated with the sensing target. First, the signal generating circuit 100 generates a radio frequency transmission signal x T (t) (ie, the first time domain analog signal sig_time_anal1), and the transmitting circuit 110 transmits the RF transmitting signal x T (t). RF transmission signal x T (t) can be expressed as equation (Equation 1):

[0023]

[0024] Among them, A T is the transmission amplitude, f c is the carrier frequency, θ 0 is the initial phase, f T Linear frequency conversion T c is the linear frequency conversion time (N c Linear frequency conversion time T c Can be regarded as a frame time T f , that is, T f =N c T c ), and B is the scanning bandwidth. T (t) After being reflected by a sensing target (eg, sensing target OBJ), the receiving circuit 120 receives the RF receiving signal x R (t) (i.e., the second time domain analog signal sig_time_anal2). The RF receiving signal x R(t) can be expressed as equation (Equation 2):

[0025] x R (t) = x T (t-τ) = A R cos(2πf T (t-τ)+θ 0 +φ) (Formula 2)

[0026] Among them A R is the received amplitude, τ is the delay time ( R is the distance between the sensing target and the sensing device 10, and c is the speed of light), θ 0 is the initial phase, f T is the linear conversion frequency, and φ is the receiving phase ( f c is the carrier frequency, and λ is the signal wavelength). The low-pass filter circuit 130 performs a filtering operation on the RF transmission signal x T (t) and the RF receiving signal x R (t) mixing process and low-pass filtering process to generate an intermediate frequency signal x IF (t) (i.e., the third time domain analog signal sig_time_anal3). Intermediate frequency signal x IF (t) can be expressed as equation (Equation 3):

[0027] x IF (t) = Acos(2πf IF t+φ) (Formula 3)

[0028] Where A is the amplitude, f IF is the intermediate frequency, and φ is the receiving phase. The conversion circuit 140 converts the intermediate frequency signal x IF (t) is converted into a digital signal x(n) (i.e., the time domain digital signal sig_time_dig). The digital signal x(n) can be expressed as equation (Equation 4):

[0029] x(n)=x IF (nT s ) (Formula 4)

[0030] Where T s is the sampling time ( f s is the sampling frequency). The processing circuit 150 (or the processing circuit 20) selects a single linear frequency conversion time digital signal sequence [x(n)x(n+1)…x(n+N′-1)], And through the range fast Fourier transform (Range-FFT), the estimated intermediate frequency is estimated according to the digital signal sequence [x(n)x(n+1)…x(n+N′-1)] Then, the processing circuit 150 (or the processing circuit 20) can calculate the estimated distance by the following equation (Equation 5):

[0031]

[0032] Assume that the sensing target has periodic vibration, such as Figure 3 As shown, where Δd(t) is the displacement caused by the vibration of the sensing target, and T v is the vibration period. Therefore, the distance between the sensing target and the sensing device 10 is R′(t)=R+Δd(t), and the receiving phase φ can be replaced by equation (Equation 6):

[0033]

[0034] Where Δφ(t) is the phase offset caused by the target vibration. The change of phase shift Δφ(t) over time t can be referred to Figure 4 , the horizontal axis is time t, and the vertical axis is phase shift Δφ(t). By observing the continuous N c Linear frequency conversion time T c (i.e. one frame time T f ) in the RF transmission signal x T (t) and the RF receiving signal x R (t), the processing circuit 150 (or the processing circuit 20) may estimate the conversion frequency of the phase shift Δφ(t).

[0035] Through the distance fast Fourier transform, the conversion circuit in the processing circuit 150 (or the processing circuit 20) converts the continuous N c The digital signal x(l,n) is converted into a complex signal Y(l,k) (i.e., the first frequency domain signal). l is the index of the digital signal x(l,n), l=0,1,…,N c -1, and k is the fast Fourier transform index, k=0,1,…,K-1. Figure 5 FIG. 1 is a schematic diagram of a complex signal Y(l,k) according to an embodiment of the present invention, wherein the horizontal axis is the index l of the digital signal x(l,n), and the vertical axis is the fast Fourier transform index k. The detection circuit in the processing circuit 150 (or the processing circuit 20) detects the maximum value Y of the complex signal Y(l,k). max (l,k)(in Figure 5 ) (i.e., the maximum value of the first frequency domain signal), and determining the value corresponding to the maximum value Y max The complex signal Y(l,k′) (in Figure 5The third calculation circuit in the processing circuit 150 (or the processing circuit 20) calculates the phase signal φ(l,k′) (i.e., the phase signal sig_ph) of the complex signal Y(l,k′), as shown in equation (Equation 7):

[0036] φ(l,k′)=tan -1 (Y(l,k′)) (Formula 7)

[0037] The vibration frequency can be inferred from equation (7) by using a fast Fourier transform (e.g., Doppler-FFT). However, the resolution of the fast Fourier transform is limited by the observation time. Increasing the observation time may not detect the instantaneous change of the vibration frequency of the sensing target. In order to avoid increasing the observation time, the phase signal φ(l,k′) can be expressed as a sequence containing multiple complex exponentials as follows:

[0038]

[0039] Among them, {w 0 w 1 …w M-1} is the M vibration frequencies of the sensing target, {a 0 a 1 …a M-1} are the M amplitudes of the sensing target, and M is an unknown positive integer. Through super-resolution methods (such as the Multiple Signal Classification (MUSIC) algorithm), M can be derived to obtain the vibration frequency {w 0 w 1 …w M-1 According to the phase signal φ(l,k′), the estimation circuit 200 generates a phase vector Θ(n,N). The phase vector Θ(n,N) can be expressed as equation (Equation 9):

[0040] Θ(n,N)=[φ(n,k′)φ(n+1,k′)…φ(n+N-1,k′)] T (Formula 9)

[0041] Where N is the length of the phase vector Θ(n,N), N≠N c The estimation circuit 200 divides the phase vector Θ(n,N) of length N into p segments, each segment length is q, such as Figure 6 Therefore, the estimation circuit 200 can generate an estimated phase matrix (i.e., the estimated phase matrix M_ph), as shown in equation (Equation 10):

[0042]

[0043] Wherein, H is the conjugate transpose. The decomposition circuit 210 decomposes the estimated phase matrix As shown in equation (Equation 11):

[0044]

[0045] Among them, λ i The phase matrix The eigenvalues ​​of (i=1,2,…,q,λ 1 >λ 2 >…>λ q )(i.e. multiple eigenvalues), and v i is the eigenvalue corresponding to i eigenvector (i=1,2,…,q) (i.e., multiple eigenvectors). 1 ,λ 2 ,…,λ M} can be regarded as signal energy, {λ M+1 ,λ M+2 ,…,λ q} can be regarded as noise energy, {v 1 ,v 2 ,…,v M} can be regarded as the signal subspace, and {v M+1 ,v M+2 ,…,v q} can be regarded as a noise subspace. In order to reduce the calculation error, the first calculation circuit 220 calculates the eigenvalue λ i Perform long-term averaging to produce the long-term average eigenvalue λ′ i (i.e., multiple long-term average eigenvalues ​​λ_LTA). Long-term average eigenvalue λ′ i It can be expressed as equation (Equation 12):

[0046] λ′ i (n+1)=αλ′ i (n)+(1-α)λ i (n+1) (Formula 12)

[0047] Wherein, i=1, 2, ..., q, α is a forgetting factor (α<1.0). The second calculation circuit 230 calculates two adjacent long-term average eigenvalues ​​λ′ i The difference of 10·log 10 (λ′ i )-10·log 10 (λ′ i+1Among the indicators i whose difference is greater than the minimum noise ratio requirement δ (in dB) of the sensing device 10, the second calculation circuit 230 selects the maximum indicator (i.e., index M), as shown in equation (Equation 13):

[0048]

[0049] Next, according to the maximum value indicator Eigenvector v i and the guide vector s w , the spectrum generating circuit 240 generates a virtual spectrum (ie virtual spectrum PS). It can be expressed as equation (Equation 14):

[0050]

[0051] Among them, s w =[1,e jw ,e j2w ,…,e j(q-1)w ] T , and w is the guessed vibration frequency. If the guide vector s w Belongs to the signal subspace, the guide vector s w Orthogonal to the noise subspace, that is In this case, the virtual spectrum The determination circuit 250 determines the value corresponding to the virtual spectrum Before The maximum vibration frequency It is the vibration frequency of the sensing target. Figure 7 A virtual spectrum according to an embodiment of the present invention Schematic diagram, with the horizontal axis being the vibration frequency w and the vertical axis being the virtual spectrum exist Figure 7 In the virtual spectrum There are 2 maximum values. The vibration frequencies corresponding to the 2 maximum values ​​are 0.2 and 0.32, which are the vibration frequencies of the sensing target.

[0052] Assuming that the vibration frequency range of the sensing target is known, the determination circuit 250 can determine the virtual spectrum within the vibration frequency range. The maximum value and the vibration frequency corresponding to the maximum value To reduce the computational complexity. Vibration frequency It can be expressed as equation (Equation 15):

[0053]

[0054] Among them, {wn,L ,w n,H} is the vibration frequency range of the sensing target, Δw n is the scan resolution, and w n The scanning frequency

[0055] In addition, according to the distances between multiple antennas, the incident angle can be calculated. Figure 8 FIG. 1 is a schematic diagram of a multi-antenna sensing device 80 according to an embodiment of the present invention. The multi-antenna sensing device 80 includes a transmitting circuit 110 and a receiving circuit 120. The transmitting circuit 110 is provided with a transmitting antenna TX for transmitting a radio frequency transmitting signal x T (t). The receiving circuit 120 is provided with receiving antennas RX0-RX3 for receiving the RF receiving signal x R (t). The distance between two adjacent receiving antennas among the receiving antennas RX0 to RX3 is d. The RF receiving signal x R (t) The incident angle for the receiving antennas RX0 to RX3 is θ. The RF received signal x R (t) The distance difference Δd(r) from the sensing target (not shown) to two adjacent receiving antennas among the receiving antennas RX0 to RX3 can be expressed as equation (Equation 16):

[0056] Δd(r)=r·d·sinθ (Equation 16)

[0057] Where r is the index of the receiving antenna RX0~RX3 (r=0,1,2,…,N R -1, N R is the number of receiving antennas). Figure 8 In, N R = 4. The phase offset Δφ(r) of the receiving antennas RX0 to RX3 can be expressed as equation (Equation 17):

[0058]

[0059] Equation (17) can be used to infer the incident angle θ through a fast Fourier transform (e.g., angle fast Fourier transform (Angle-FFT)). However, the resolution of the fast Fourier transform is limited by the number of receiving antennas. In order to reduce the number of antennas (i.e., reduce hardware cost), the phase offset Δφ(r) can be expressed as a sequence containing complex exponentials as follows:

[0060]

[0061] Assume that the received signal is a composite of reflected signals from multiple sensing targets, such as Fig. 9 As shown. Fig. 9 In the middle, the sensing target OBJ 0 ~OBJM-1 and the distance between the multi-antenna sensing device 80 is R, and the corresponding incident angles are θ 0 ~θ M-1 . Sensing target OBJ 0 ~OBJ M-1 The phase shift Δφ(r) can be expressed as a sequence containing multiple complex exponentials as follows:

[0062]

[0063] According to the operation mode of the estimation circuit 200, the decomposition circuit 210, the first calculation circuit 220, the second calculation circuit 230, the spectrum generation circuit 240 and the determination circuit 250, the virtual spectrum Corresponding to the virtual spectrum forward The maximum incident angle OBJ is the sensing target 0 ~OBJ M-1 The incident angle.

[0064] The operation of the processing circuit 20 can be summarized as follows: Fig.10 A process 100 may be used for the processing circuit 150 of FIG. 1. The process 100 includes the following steps:

[0065] Step S1000: Start.

[0066] Step S1002: generating a phase vector according to a phase signal;

[0067] Step S1004: Estimate the phase vector to generate an estimated phase matrix;

[0068] Step S1006: Decomposing the estimated phase matrix to generate an eigenvalue matrix and an eigenvector matrix, wherein the eigenvalue matrix includes a plurality of eigenvalues ​​and the eigenvector matrix includes a plurality of eigenvectors;

[0069] Step S1008: performing a long-term average of the plurality of characteristic values ​​to generate a plurality of long-term average characteristic values;

[0070] Step S1010: Calculate multiple differences of the multiple long-term average characteristic values;

[0071] Step S1012: determining an indicator corresponding to a long-term average characteristic value among the plurality of long-term average characteristic values ​​according to the plurality of difference values;

[0072] Step S1014: generating a virtual spectrum according to the indicator, the plurality of eigenvectors and a steering vector; and

[0073] Step S1016: Determine at least one maximum value of the virtual spectrum and at least one parameter corresponding to the at least one maximum value.

[0074] Step S1018: End.

[0075] The process 100 is used to illustrate the operation of the processing circuit 20 . The detailed description and transformation can be referred to above and will not be repeated here.

[0076] It should be noted that the sensing device 10 (and the signal generating circuit 100, the transmitting circuit 110, the receiving circuit 120, the low-pass filtering circuit 130, the second conversion circuit 140 and the processing circuit 150 therein) and the processing circuit 20 (and the estimating circuit 200, the decomposing circuit 210, the first calculating circuit 220, the second calculating circuit 230, the spectrum generating circuit 240 and the determining circuit 250 therein) can be implemented in many ways. For example, the above devices (circuits) can be integrated into one or more devices (circuits). In addition, the sensing device 10 and the processing device 20 can be implemented by hardware (such as circuits), software, firmware (a combination of hardware devices and computer instructions and data, and the computer instructions and data are read-only software on the hardware devices), electronic systems, or a combination of the above devices, but are not limited thereto.

[0077] In summary, the present invention provides a circuit and method for wireless sensing. Vibration frequency and incident angle of the sensing target are obtained by super-resolution method (such as multi-signal classification algorithm). Therefore, wireless sensing does not need to be performed according to sampling frequency, observation time and number of receiving antennas.

[0078] The above description is only a preferred embodiment of the present invention. All equivalent changes and modifications made according to the scope of the patent application of the present invention should fall within the scope of the present invention.

[0079]

Explanation of symbols

[0080] 10: Sensing device

[0081] 100: Signal generating circuit

[0082] 110: Transmission circuit

[0083] 120: Receiving circuit

[0084] 130: Low-pass filter circuit

[0085] 140: Conversion circuit

[0086] 150: Processing circuit

[0087] OBJ: Sensing target

[0088] sig_time_anal1, sig_time_anal2, sig_time_anal3: time domain analog signal

[0089] sig_time_dig: time domain digital signal

[0090] P: at least one parameter

[0091] 20: Processing circuit

[0092] 200: Estimation circuit

[0093] 210: Decomposing the Circuit

[0094] 220: First calculation circuit

[0095] 230: Second calculation circuit

[0096] 240: Spectrum generation circuit

[0097] 250: Determine the circuit

[0098] sig_ph: phase signal

[0099] M_ph: phase matrix

[0100] M_evalue: eigenvalue matrix

[0101] M_evector: eigenvector matrix

[0102] λ_LTA: long-term average eigenvalue

[0103] M: Indicator

[0104] PS: Virtual Spectrum

[0105] Δd(t): displacement

[0106] T v :Oscillating period

[0107] t: time

[0108] Δφ(t): Phase shift

[0109] k: Fast Fourier transform index

[0110] l: Digital signal indicators

[0111] K: The number of fast Fourier transform indicators N c : Number of digital signal indicators Y max (l,k): maximum value of digital signal Y(l,k′): complex signal Θ(n,N): phase vector

[0112] N: Phase vector length

[0113] q: segment length Virtual spectrum w: oscillation frequency

[0114] x T (t): RF transmission signal x R (t): RF receiving signal TX: transmitting antenna

[0115] RX0~RX3: Receiving antenna

[0116] Δd: distance difference

[0117] d: distance from receiving antenna

[0118] θ,θ 0 ~θ M-1 : Angle of incidence

[0119] OBJ 0 ~OBJ M-1 : Sensing target

[0120] R: Distance

[0121] 100: Process

[0122] S1000, S1002, S1004, S1006, S1008, S1010, S1012, S1014, S1016, S1018: steps.

Claims

1. A processing circuit comprising: an estimation circuit for generating a phase vector according to a phase signal and estimating the phase vector to generate an estimated phase matrix; a decomposition circuit, coupled to the estimation circuit, for decomposing the estimated phase matrix to generate an eigenvalue matrix and an eigenvector matrix, in, The eigenvalue matrix includes a plurality of eigenvalues ​​and the eigenvector matrix includes a plurality of eigenvectors; a first calculation circuit, coupled to the decomposition circuit, for performing a long-term average of the plurality of eigenvalues ​​to generate a plurality of long-term average eigenvalues; a second calculation circuit, coupled to the first calculation circuit, for calculating a plurality of differences of the plurality of long-term average characteristic values, and determining an indicator corresponding to a difference among the plurality of differences; a spectrum generating circuit, coupled to the second calculating circuit, for generating a virtual spectrum according to the indicator, the plurality of eigenvectors and a steering vector; and A determination circuit is coupled to the spectrum generation circuit, and is used to determine at least one maximum value of the virtual spectrum and at least one parameter corresponding to the at least one maximum value.

2. The processing circuit according to claim 1, in, The phase signal includes at least one complex index.

3. The processing circuit according to claim 2, in, The at least one complex exponent is related to the at least one parameter.

4. The processing circuit according to claim 1, in, The multiple eigenvalues ​​correspond to the multiple eigenvectors respectively.

5. The processing circuit according to claim 1, in, One of the plurality of difference values ​​is a difference between two adjacent long-term average eigenvalues ​​in the plurality of long-term average eigenvalues.

6. The processing circuit according to claim 1, in, The step of determining, by the second calculation circuit, the indicator corresponding to the difference value among the plurality of difference values ​​comprises: Selecting at least one difference value from the plurality of difference values, wherein the at least one difference value is greater than a threshold value; and The indicator is selected from at least one indicator corresponding to the at least one difference value.

7. The processing circuit according to claim 6, in, The indicator is a maximum value of the at least one indicator.

8. The processing circuit according to claim 1, in, The at least one parameter is at least one vibration frequency or at least one incident angle.

9. The processing circuit according to claim 1, further comprising: a first conversion circuit, used for converting a time domain digital signal to generate a first frequency domain signal; a detection circuit, coupled to the first conversion circuit, for detecting a maximum value of the first frequency domain signal to generate a second frequency domain signal; and A third calculation circuit is coupled to the detection circuit and the estimation circuit, and is used to calculate the phase signal of the second frequency domain signal.

10. A method for processing wireless induction, comprising: Generate a phase vector according to a phase signal; estimating the phase vector to generate an estimated phase matrix; Decomposing the estimated phase matrix to generate an eigenvalue matrix and an eigenvector matrix, in, The eigenvalue matrix includes a plurality of eigenvalues ​​and the eigenvector matrix includes a plurality of eigenvectors; performing a long-term average of the plurality of eigenvalues ​​to generate a plurality of long-term average eigenvalues; Calculating a plurality of differences of the plurality of long-term average eigenvalues; determining an indicator corresponding to a difference value among the plurality of differences; Generate a virtual spectrum according to the indicator, the plurality of eigenvectors and a steering vector; and At least one maximum value of the virtual spectrum and at least one parameter corresponding to the at least one maximum value are determined.

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