Positioning method based on channel frequency response, electronic device, storage medium

Abstract

The application provides a positioning method based on a channel frequency response, an electronic device and a storage medium. The method comprises the following steps: performing conjugate multiplication on each second channel frequency response and a first channel frequency response respectively to obtain a target vector, and subtracting the mean value of multiple values in the target vector from each value in the target vector to obtain a cleaned channel frequency response corresponding to each second channel frequency response; constructing an observation matrix based on multiple cleaned channel frequency responses at the same observation time; determining the angle of arrival of a passive target relative to a second receiver and the channel length of a NLOS channel based on the observation matrix; and determining the target position information of the passive target according to the first position information of a transmitter, the second position information of the second receiver, the angle of arrival and the channel length. The present scheme can analyze the angle of arrival of the passive target relative to the receiver and the channel length of the NLOS channel by means of the noise-reduced channel frequency response, and realizes accurate positioning of the passive target.

Description

Technical Field

[0001] This application relates to the field of wireless positioning technology, and in particular to a positioning method, electronic device, and computer-readable storage medium based on channel frequency response. Background Technology

[0002] In smart home scenarios, passive targets (people or other targets without terminal devices) in the indoor environment change their occupied space as they move, thus affecting the multipath channels of indoor Wi-Fi signals. Based on this principle, Channel State Information (CSI) of Wi-Fi signals can be collected to sense the activity status of passive targets. The multipath transmission channels of Wi-Fi signals include LOS (Line of Sight) channels and NLOS (Non-Line of Sight) channels. Since the activity of passive targets has no impact on the LOS channel, and still objects indoors have no impact on the NLOS channel, data from the NLOS channel can be used to track and locate passive targets. Summary of the Invention

[0003] The purpose of this application is to provide a positioning method, electronic device, and storage medium based on channel frequency response, which can be used to resolve the angle of arrival of a passive target relative to the receiver and the channel length of the NLOS channel by means of the noise-reduced channel frequency response, so as to achieve accurate positioning of the passive target.

[0004] On one hand, this application provides a positioning method based on channel frequency response, applied to a Wi-Fi transceiver system, the Wi-Fi transceiver system including a transmitter, a first receiver, and a second receiver, the first receiver including at least one antenna element, and the second receiver including at least two antenna elements, the method including:

[0005] For the channel frequency response of the Wi-Fi signal corresponding to each antenna element, noise reduction processing is performed to obtain the noise-reduced channel frequency response; wherein, the channel frequency response includes the frequency domain channel estimation values ​​of multiple subcarriers;

[0006] Based on the first adjustment coefficient, the noise-reduced channel frequency response of the first receiver is adjusted to obtain the first channel frequency response;

[0007] Based on the second adjustment coefficient, the frequency responses of at least two noise-reduced channels of the second receiver are adjusted respectively to obtain the second channel frequency response;

[0008] Each second channel frequency response is multiplied by the first channel frequency response by conjugate to obtain a target vector. The average value of multiple values ​​in the target vector is subtracted from each value in the target vector to obtain the cleaned channel frequency response corresponding to each second channel frequency response.

[0009] An observation matrix is ​​constructed based on multiple cleaned channel frequency responses at the same observation time; wherein, the X-axis direction of the observation matrix is ​​the subcarrier index, and the Y-axis direction is the antenna element index.

[0010] In the Y-axis direction of the observation matrix, the angle of arrival of the passive target relative to the second receiver is determined based on the spectral search method;

[0011] In the X-axis direction of the observation matrix, the channel length of the NLOS channel is determined based on the phase offset of multiple subcarriers; wherein, the channel length is the distance from the transmitter to the second receiver;

[0012] The target location information of the passive target is determined based on the first position information of the transmitter, the second position information of the second receiver, the angle of arrival, and the channel length.

[0013] In one embodiment, the step of performing noise reduction processing on the channel frequency response of the Wi-Fi signal corresponding to each antenna element to obtain the noise-reduced channel frequency response includes:

[0014] Perform an inverse Fourier transform on the channel frequency response to obtain the time-domain impulse response, and calculate the covariance matrix corresponding to the time-domain impulse response.

[0015] The covariance matrix is ​​decomposed by ED / SVD to obtain multiple eigenvalues; each eigenvalue corresponds to a channel, and the magnitude of the eigenvalue is positively correlated with the channel strength.

[0016] Based on the aforementioned multiple feature values, the target number of effective channels is determined;

[0017] The frequency domain signal receiving power is determined based on the covariance matrix, the number of targets, the multiple eigenvalues, and the total number of subcarriers in the channel frequency response.

[0018] The maximum value is searched on the diagonal of the covariance matrix. The values ​​on the diagonal are filtered out with the maximum value as the center and a preset filtering window length is used. The average of the remaining values ​​on the diagonal is calculated as the time-domain noise power.

[0019] The time-domain noise power is converted into frequency-domain noise power, and the frequency-domain signal-to-noise ratio is determined using the frequency-domain noise power and the frequency-domain received signal power.

[0020] The filter coefficients are determined based on the frequency domain signal-to-noise ratio and frequency domain correlation.

[0021] Based on the filter coefficients and filter order, the channel frequency response is filtered to obtain the noise-reduced channel frequency response.

[0022] In one embodiment, determining the number of targets for effective channels based on the plurality of feature values ​​includes:

[0023] The multiple feature values ​​are arranged in descending order to obtain a feature value sequence;

[0024] A first sequence and a second sequence are split from the feature value sequence; wherein the first sequence includes all feature values ​​in the feature value sequence except for the first feature value; and the second sequence includes all feature values ​​in the feature value sequence except for the last feature value.

[0025] Calculate the difference sequence between the first sequence and the second sequence, and search for the maximum value in the difference sequence. Based on the index of the maximum value, determine the target number of effective channels.

[0026] In one embodiment, determining the frequency domain signal received power based on the covariance matrix, the number of targets, the plurality of eigenvalues, and the total number of subcarriers in the channel frequency response includes:

[0027] From the plurality of feature values, select the feature value with the largest target quantity as the specified feature value;

[0028] On the diagonal of the covariance matrix, determine multiple values ​​corresponding to multiple specified eigenvalues, and calculate the sum of the values ​​of the multiple values;

[0029] The frequency domain signal receiving power is obtained by dividing the sum of the values ​​by the total number of subcarriers.

[0030] In one embodiment, before determining the filter coefficients based on the frequency domain signal-to-noise ratio and frequency domain correlation, the method further includes:

[0031] Based on the target number and the transmission duration of a single symbol, the delay spread is determined;

[0032] Based on the time-domain spread, the carrier spacing between adjacent subcarriers, and several carrier number spacings, the frequency domain correlation corresponding to each carrier number spacing is calculated.

[0033] In one embodiment, before adjusting the noise-reduced channel frequency response of the first receiver based on a first adjustment coefficient to obtain a first channel frequency response, the method further includes:

[0034] For the denoised channel frequency response at multiple observation times of the first receiver, search for the frequency domain channel estimate with the smallest non-zero value, and use it as the first adjustment coefficient;

[0035] The first adjustment coefficient is amplified according to the preset amplification factor to obtain the second adjustment factor.

[0036] In one embodiment, determining the channel length of the NLOS channel based on the phase offset of multiple subcarriers along the X-axis of the observation matrix includes:

[0037] In the X-axis direction of the observation matrix, the measurement phase of at least one subcarrier is determined respectively;

[0038] The NLOS channel transmission duration is determined based on the measured phase, the subcarrier sequence number, and the carrier spacing between adjacent subcarriers.

[0039] The channel length of the NLOS channel is determined based on the NLOS channel transmission duration and the speed of light.

[0040] In one embodiment, determining the NLOS channel transmission duration based on the measured phase, the subcarrier sequence number of the subcarrier, and the carrier spacing between adjacent subcarriers includes:

[0041] The transmission duration of multiple NLOS channels is determined based on the measured phase of multiple subcarriers, the subcarrier sequence number of multiple subcarriers, and the carrier interval.

[0042] Based on the product of the subcarrier index and the carrier spacing, and the NLOS channel transmission duration corresponding to the multiple subcarriers, a fitting function is determined between the product and the NLOS channel transmission duration.

[0043] The NLOS channel transmission duration corresponding to the maximum product in the fitted function is selected as the NLOS channel transmission duration for calculating the channel length.

[0044] On the other hand, this application provides an electronic device, the electronic device comprising:

[0045] processor;

[0046] Memory used to store processor-executable instructions;

[0047] The processor is configured to execute the aforementioned channel frequency response-based positioning method.

[0048] Furthermore, this application provides a computer-readable storage medium storing a computer program that can be executed by a processor to perform the aforementioned channel frequency response-based positioning method.

[0049] In this application, after denoising the channel frequency response of each antenna element of the Wi-Fi system receiver, the data of the LOS channel can be further cleaned up. Then, an observation matrix is ​​constructed using multiple cleaned channel frequency responses. From the observation matrix, the angle of arrival of the passive target relative to the second receiver and the channel length of the NLOS channel are determined. Based on the angle of arrival and the channel length, and on the basis of the first position information of the transmitter and the second position information of the second receiver, the target position information of the passive target is derived. Attached Figure Description

[0050] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments of this application will be briefly described below.

[0051] Figure 1 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application;

[0052] Figure 2 A schematic flowchart of a positioning method based on channel frequency response provided in an embodiment of this application;

[0053] Figure 3 This is a schematic diagram of a Wi-Fi transceiver system provided in an embodiment of this application;

[0054] Figure 4 A schematic diagram of an observation matrix provided in an embodiment of this application;

[0055] Figure 5 A positioning diagram provided for an embodiment of this application;

[0056] Figure 6 Provided for an embodiment of this application Figure 2 A detailed flowchart of step 210 is shown below;

[0057] Figure 7 A flowchart illustrating a method for determining a target quantity according to an embodiment of this application;

[0058] Figure 8 A flowchart illustrating a method for determining the received power of a frequency domain signal according to an embodiment of this application;

[0059] Figure 9 This is a flowchart illustrating a method for determining frequency domain correlation according to an embodiment of this application.

[0060] Figure 10 Provided for an embodiment of this application Figure 2 A detailed flowchart of step 270 is shown below;

[0061] Figure 11This is a block diagram of a positioning device based on channel frequency response provided in an embodiment of this application. Detailed Implementation

[0062] The technical solutions in the embodiments of this application will now be described with reference to the accompanying drawings.

[0063] Similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, in the description of this application, terms such as "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0064] like Figure 1 As shown, this embodiment provides an electronic device 1, including: at least one processor 11 and a memory 12. Figure 1 Taking a processor 11 as an example, the processor 11 and memory 12 are connected via a bus 10. The memory 12 stores instructions that can be executed by the processor 11. The instructions are executed by the processor 11 to enable the electronic device 1 to perform all or part of the processes of the methods described in the embodiments below. In one embodiment, the electronic device 1 may be a receiver in a Wi-Fi transceiver system or a computing device interfaced with a Wi-Fi transceiver system, used to execute a positioning method based on channel frequency response. For ease of description, the electronic device is referred to as the execution subject below.

[0065] The memory 12 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable red-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk or optical disk.

[0066] This application also provides a computer-readable storage medium storing a computer program that can be executed by a processor 11 to perform the channel frequency response-based positioning method provided in this application.

[0067] See Figure 2This is a flowchart illustrating a channel frequency response-based positioning method according to an embodiment of this application. Figure 2 As shown, the method may include steps 210-280.

[0068] Step 210: Perform noise reduction processing on the channel frequency response of the Wi-Fi signal corresponding to each antenna element to obtain the noise-reduced channel frequency response; wherein, the channel frequency response includes the frequency domain channel estimation values ​​of multiple subcarriers.

[0069] This application relates to a Wi-Fi transceiver system, which includes a transmitter, a first receiver, and a second receiver. The transmitter transmits Wi-Fi signals, and the first and second receivers receive Wi-Fi signals from the transmitter. The first receiver includes at least one antenna element, and the second receiver includes at least two antenna elements. The at least two antenna elements of the second receiver can be a one-dimensional uniform linear array, or other array types, such as a UPA (Uniform Planar Array), a combination of sparse arrays, or a two-dimensional array, etc.; the spacing between adjacent antenna elements in the at least two antenna elements should be greater than half a wavelength.

[0070] In a Wi-Fi transceiver system, the distance between each pair of the transmitter, the first receiver, and the second receiver should exceed several wavelengths and can be configured based on experience. Furthermore, the first position information of the transmitter in the environmental coordinate system and the second position information of the second receiver in the environmental coordinate system need to be determined in advance. This solution requires the use of the first and second position information to track and locate passive targets during subsequent execution.

[0071] See Figure 3 This is a schematic diagram of a Wi-Fi transceiver system provided in an embodiment of this application, as shown below. Figure 3 As shown, the Wi-Fi transceiver system includes a transmitter TX, a first receiver RX1, and a second receiver RX2. The first receiver RX1 has one antenna element, and the second receiver RX2 has three antenna elements.

[0072] During the process of the transmitter transmitting a Wi-Fi signal and the first and second receivers receiving the Wi-Fi signal, for each antenna element of the first and second receivers, the electronic device can use the LS (Least Square) algorithm to perform channel estimation on the pilot signal of the Wi-Fi signal received by that antenna element, thereby obtaining the Channel Frequency Response (CFR). The Channel Frequency Response includes the frequency domain channel estimates of multiple subcarriers.

[0073] For each channel frequency response, the electronic device can perform noise reduction processing to remove noise data, thereby obtaining the noise-reduced channel frequency response.

[0074] Step 220: Based on the first adjustment coefficient, adjust the noise-reduced channel frequency response of the first receiver to obtain the first channel frequency response.

[0075] Step 230: Based on the second adjustment coefficient, adjust the frequency responses of at least two noise-reduced channels of the second receiver respectively to obtain the second channel frequency response.

[0076] For the denoised channel frequency response corresponding to the antenna array element of the first receiver, the electronic device can subtract the first adjustment coefficient from the frequency domain channel estimate of each subcarrier in the denoised channel frequency response to obtain the first channel frequency response. Here, the first channel frequency response is the denoised channel frequency response of the first receiver after adjustment.

[0077] For each antenna element of the second receiver, the electronic device can add a second adjustment coefficient to the frequency domain channel estimate of each subcarrier in the denoised channel frequency response, thereby adjusting to obtain the second channel frequency response corresponding to each antenna element. Here, the second channel frequency response is the adjusted denoised channel frequency domain response of the second receiver.

[0078] In one embodiment, a first adjustment factor and a second adjustment factor may be determined before performing steps 220 and 230.

[0079] The electronic device can acquire the channel frequency response of the first receiver at multiple observation times, and after performing noise reduction processing, it can search for the minimum non-zero frequency domain channel estimate of each subcarrier at each observation time based on the noise-reduced channel frequency response at multiple observation times, and use the searched frequency domain channel estimate as the first adjustment coefficient. Here, the first adjustment coefficient can be denoted as α.

[0080] After obtaining the first adjustment factor, the electronic device can amplify it according to a preset amplification factor to obtain the second adjustment factor. The amplification factor can be configured empirically, and is generally in the thousands or tens of thousands. For example, the amplification factor is 100,000.

[0081] For example, the electronic device can obtain the second adjustment factor using the following formula (1):

[0082] β=σ×α (1)

[0083] Where α is the first adjustment coefficient; β is the second adjustment coefficient; and σ is the amplification coefficient.

[0084] Step 240: Multiply each second channel frequency response by the first channel frequency response using the conjugate multiplication to obtain the target vector, and subtract the mean of multiple values ​​in the target vector from each value in the target vector to obtain the cleaned channel frequency response corresponding to each second channel frequency response.

[0085] The electronic device can multiply each second channel frequency response by the first channel frequency response using a conjugate multiplication to obtain the target vector. The mean of multiple values ​​in the target vector is calculated, and this mean is subtracted from each value in the target vector to obtain the cleaned channel frequency response corresponding to each second channel frequency vector.

[0086] For example, the channel frequency response after cleaning can be obtained by the following formula (2):

[0087]

[0088] in, This indicates the channel frequency response after cleaning; Indicates the frequency response of the first channel; This represents a second channel frequency response.

[0089] By performing the above processing on each second channel frequency response, data from the LOS channel can be eliminated, resulting in a cleaned channel frequency response. The cleaned channel frequency response includes only NLOS channel data, accurately characterizing the impact of passive target movement on the Wi-Fi channel.

[0090] Step 250: Construct an observation matrix based on multiple cleaned channel frequency responses at the same observation time; wherein, the X-axis direction of the observation matrix is ​​the subcarrier index, and the Y-axis direction is the antenna element index.

[0091] The electronic device can construct an observation matrix by taking the cleaned channel frequency responses corresponding to multiple antenna elements of the second receiver at the same time, with the X-axis as the subcarrier index and the Y-axis as the antenna element index. The observation matrix includes multiple frequency domain channel estimates from the cleaned channel frequency responses.

[0092] See Figure 4 This is a schematic diagram of an observation matrix provided in an embodiment of this application, as shown below. Figure 4 As shown, the X-axis of the observation matrix at a single observation time represents the subcarrier index, and the Y-axis represents the antenna index. For example, if the second receiver includes three antenna elements and the channel frequency domain response includes 1024 subcarriers, then the observation matrix includes 3*1024 frequency domain channel estimates.

[0093] Step 260: Determine the angle of arrival of the passive target relative to the second receiver in the Y-axis direction of the observation matrix based on the spectral search method.

[0094] Electronic devices can use any of the spectrum peak search methods, such as MUSIC (multiple signal classification algorithm), ESPRIT (Estimation of Signal Parameters using Rotational Invariance Techniques), or Pisarenko algorithm, to search for the angle of arrival (AOA) of a passive target relative to a second receiver in the Y-axis direction.

[0095] Step 270: Determine the channel length of the NLOS channel based on the phase offset of multiple subcarriers along the X-axis of the observation matrix; where the channel length is the distance from the transmitter to the second receiver.

[0096] The channel length of the NLOS channel is the total distance from the transmitter's Wi-Fi signal to the passive target and from the passive target to the second receiver.

[0097] Electronic devices can determine the phase offset of multiple subcarriers from the X-axis direction of the observation matrix, and then determine the channel length of the NLOS channel based on the phase offset.

[0098] Step 280: Determine the target position information of the passive target based on the first position information of the transmitter, the second position information of the second receiver, the angle of arrival, and the channel length.

[0099] See Figure 5 This is a positioning diagram provided in an embodiment of this application, as shown below. Figure 5 As shown, given the first position information of transmitter TX and the second position information of receiver RX2, the distance between the transmitter and receiver can be determined. The channel length is divided into two segments: the first segment is the distance from the transmitter to the passive target, and the second segment is the distance from the passive target to the second receiver. In this case, a triangle is formed with the transmitter, the second receiver, and the passive target as vertices. Knowing the length of one side, the sum of the lengths of the other two sides, and an angle of the triangle (angle of arrival), the lengths of the other two sides can be calculated, and thus the target position information of the passive target in the environmental coordinate system can be derived.

[0100] By implementing the above measures, after denoising the channel frequency response of each antenna element of the Wi-Fi system receiver, the data of the LOS channel can be further cleaned up. Then, an observation matrix can be constructed using multiple cleaned channel frequency responses. From the observation matrix, the angle of arrival of the passive target relative to the second receiver and the channel length of the NLOS channel can be determined. Based on the angle of arrival and the channel length, and on the basis of the first position information of the transmitter and the second position information of the second receiver, the target position information of the passive target can be derived.

[0101] In one embodiment, see Figure 6 This is provided as an embodiment of the present application. Figure 2 A detailed flowchart of step 210 is shown below. Figure 6 As shown, when performing step 210, steps 211 to 218 can be specifically performed.

[0102] Step 211: Perform an inverse Fourier transform on the channel frequency response to obtain the time-domain impulse response, and calculate the covariance matrix corresponding to the time-domain impulse response.

[0103] For the channel frequency response corresponding to each antenna element, the electronic device can perform an inverse discrete fourier transform (IDFT) on it to obtain the corresponding time-domain impulse response (CIR). The time-domain impulse response includes time-domain channel estimates at multiple sampling points, with each sampling point corresponding to one channel.

[0104] After obtaining the time-domain channel impulse response, the corresponding covariance matrix can be calculated. For example, the covariance matrix can be calculated using the following formula (3):

[0105] R hh =E[h T h * (3)

[0106] Among them, R hh is the covariance matrix; h is the time-domain impulse response; E is the identity matrix.

[0107] Step 212: Perform ED / SVD decomposition on the covariance matrix to obtain multiple eigenvalues; each eigenvalue corresponds to a channel, and the magnitude of the eigenvalue is positively correlated with the channel strength.

[0108] Electronic devices can perform ED (Eigenvalue Decomposition) / SVD (Singular Value Decomposition) decomposition on the covariance matrix to obtain multiple eigenvalues. For example, if the time-domain impulse vector includes time-domain channel estimates for sampling points corresponding to M channels, then ED / SVD decomposition can yield M eigenvalues. Each eigenvalue corresponds to a channel; the larger the eigenvalue, the stronger the channel strength of its corresponding channel.

[0109] Each value on the diagonal of the covariance matrix represents the power of a channel, and each eigenvalue corresponds to a value on the diagonal. In ED / SVD decomposition, the coordinates (row, col) of each eigenvalue in the covariance matrix can be obtained. row represents the row number of the value in the covariance matrix, and col represents the column number of the value in the covariance matrix.

[0110] Step 213: Determine the number of targets in the effective channels based on multiple feature values.

[0111] After obtaining multiple characteristic values, the number of targets for effective channels can be determined. Here, effective channels include LOS channels and NLOS channels.

[0112] In one embodiment, see Figure 7 The above is a flowchart illustrating a method for determining a target quantity according to an embodiment of this application. Figure 7 As shown, the target quantity can be determined through the following steps 710 to 730.

[0113] Step 710: Arrange the multiple feature values ​​in descending order to obtain the feature value sequence.

[0114] After arranging multiple eigenvalues ​​in descending order, the electronic device can obtain a sequence of eigenvalues ​​[λ1, λ2, ..., λ]. M Here, λ1 is the largest eigenvalue, λ M It is the smallest eigenvalue.

[0115] Step 720: Split the first sequence and the second sequence from the feature value sequence; wherein the first sequence includes all feature values ​​in the feature value sequence except for the first feature value; the second sequence includes all feature values ​​in the feature value sequence except for the last feature value.

[0116] From the eigenvalue sequence [λ1, λ2, ..., λ] M In the sequence [λ2, λ3, ..., λ], the second sequence is extracted. M The first sequence [λ1, λ2, ..., λ] M-1Since the eigenvalues ​​in the eigenvalue sequence are arranged from largest to smallest, the eigenvalues ​​at the same position in the first sequence are larger than those in the second sequence.

[0117] Step 730: Calculate the difference sequence of the first sequence and the second sequence, and search for the maximum value in the difference sequence. Based on the index of the maximum value, determine the number of targets for the effective channel.

[0118] An electronic device can subtract the corresponding feature values ​​at the same position in the second sequence from each feature value in the first sequence to obtain a difference sequence. The difference sequence consists of M-1 values, each of which is the difference between the preceding and following feature values ​​in the feature value sequence.

[0119] Since the energy of an effective channel is much greater than that of a noisy channel, the energy difference between the last effective channel and the first noisy channel must be the maximum value in the difference sequence. The electronic device can search for the maximum value in the difference sequence and then determine the target number of effective channels based on the index of this maximum value in the difference sequence. In one embodiment, the electronic device can directly use this index as the target number. Alternatively, the electronic device can increment the index by one and use the sum as the target number.

[0120] Step 214: Determine the frequency domain signal received power based on the covariance matrix, the number of targets, multiple eigenvalues, and the total number of subcarriers in the channel frequency response.

[0121] After obtaining the number of effective channel targets, the electronic device can calculate the frequency domain signal received power based on the covariance matrix, the number of targets, and multiple eigenvalues, combined with the total number of subcarriers in the channel frequency response.

[0122] In one embodiment, see Figure 8 This is a flowchart illustrating a method for determining the received power of a frequency domain signal according to an embodiment of this application. Figure 8 As shown, the method may specifically include the following steps 810 to 830.

[0123] Step 810: Select the feature value with the largest number of targets from multiple feature values ​​as the specified feature value.

[0124] Electronic devices can select a number of target feature values ​​from a list of features arranged from largest to smallest as designated feature values. These designated feature values ​​correspond to the effective channel.

[0125] Step 820: On the diagonal of the covariance matrix, determine the multiple values ​​corresponding to the multiple specified eigenvalues, and calculate the sum of the multiple values.

[0126] The values ​​on the diagonal of the covariance matrix represent the power of each channel. After determining the specified eigenvalues, multiple values ​​corresponding to multiple specified eigenvalues ​​can be determined based on the coordinates (row, col) of the corresponding values ​​in the covariance matrix.

[0127] Electronic devices can calculate the sum of multiple values ​​corresponding to multiple specified feature values.

[0128] Step 830: Divide the sum of the values ​​by the total number of subcarriers to obtain the frequency domain signal received power.

[0129] For example, an electronic device can calculate the frequency domain signal received power using the following formula (4):

[0130]

[0131] Where pow is the received power of the frequency domain signal; L is the number of targets; p i The value corresponding to the i-th specified feature value; K is the total number of subcarriers.

[0132] Step 215: Search for the maximum value on the diagonal of the covariance matrix, filter out the values ​​on the diagonal with the maximum value as the center and the preset filtering window length, and calculate the average of the remaining values ​​on the diagonal as the time-domain noise power.

[0133] The length of the filter window can be configured based on experience; for example, the length of the filter window can be twice the length of the loop prefix.

[0134] The electronic device can search for the maximum value on the diagonal of the covariance matrix; this maximum value represents the power of the LOS channel. Using this maximum value as the center, a filtering window is applied along the diagonal to filter out the power of all valid channels. In this case, the remaining values ​​represent the power of the noisy channel. The electronic device can then calculate the average of the remaining values ​​on the diagonal and use this as the time-domain noise power.

[0135] Step 216: Convert the time-domain noise power to the frequency-domain noise power, and determine the frequency-domain signal-to-noise ratio using the frequency-domain noise power and the frequency-domain received signal power.

[0136] Electronic devices can convert time-domain noise power into frequency-domain noise power using the total number of subcarriers and the total number of sampling points in the time-domain impulse response. For example, the conversion process can be represented by the following formula (5):

[0137]

[0138] in, Frequency domain noise power; denoted as time-domain noise power; M is the total number of sampling points; and K is the total number of subcarriers.

[0139] Electronic devices can calculate the frequency domain signal-to-noise ratio (SNR) by calculating the frequency domain noise power and the frequency domain received signal power. For example, this calculation process can be represented by the following formula (6):

[0140]

[0141] Where, γ FD The signal-to-noise ratio (SNR) is in the frequency domain; pow is the received signal power in the frequency domain. This represents the frequency domain noise power.

[0142] Step 217: Determine the filter coefficients based on the frequency domain signal-to-noise ratio and frequency domain correlation.

[0143] After obtaining the frequency domain signal-to-noise ratio, the filter coefficients can be calculated by combining the frequency domain correlation. For example, this calculation process can be represented by the following formula (7):

[0144]

[0145] Where coeff represents the filter coefficients; γ FD R represents the frequency domain signal-to-noise ratio. HH This represents frequency domain correlation.

[0146] Step 218: Based on the filter coefficients and filter order, filter the channel frequency response to obtain the denoised channel frequency response.

[0147] The filter order can be configured as needed. It should be noted that the filter order must meet the following conditions: Here, tapLen is the filter order, τ is the time delay spread, and Δf is the known carrier spacing between adjacent subcarriers (in Hertz).

[0148] Electronic devices filter the channel frequency response based on the filter coefficients and the filter order, thereby removing noisy channel data and obtaining the denoised channel frequency response.

[0149] The above measures can effectively reduce noise in the channel frequency response of each antenna element.

[0150] In one embodiment, see Figure 9 This is a flowchart illustrating a method for determining frequency domain correlation according to an embodiment of this application. Figure 9 As shown, the method may specifically include the following steps 910 to 930.

[0151] Step 910: Determine the delay spread based on the target number and the transmission duration of a single symbol.

[0152] Electronic devices can multiply the number of targets by the transmission duration of a single conformance to obtain the time-domain extension. For example, this calculation process can be performed using the following formula (8):

[0153] τ=L×t symbol (8)

[0154] Where τ is the time delay spread; L is the number of targets; t symbol The transmission duration of a single symbol already determined in the system.

[0155] Step 920: Based on the time domain spread, the carrier spacing of adjacent subcarriers, and several carrier number spacings, calculate the frequency domain correlation corresponding to each carrier number spacing.

[0156] The carrier spacing is the interval between other subcarriers selected for filtering and the subcarrier itself when filtering any subcarrier. For example, if filtering is performed on the 512th subcarrier, the filtering order is 3, which means the 511th, 513th, 510th, 514th, 509th, and 515th subcarriers will be used. The carrier spacing between the 511th and 512th subcarriers is 1, between the 513th and 512th subcarriers is 1, between the 510th and 512th subcarriers is 2, between the 514th and 512th subcarriers is 2, between the 509th and 512th subcarriers is 3, and between the 515th and 512th subcarriers is 3.

[0157] Each carrier number interval corresponds to a frequency domain correlation. Therefore, the corresponding frequency domain correlation can be calculated using different carrier number intervals.

[0158] For example, the calculation process of frequency domain correlation can be represented by the following formula (9):

[0159]

[0160] Where s represents the carrier number interval; R HH (s) represents the frequency domain correlation corresponding to the carrier number interval s; j is an imaginary number; τ is the time delay spread; Δf is the known carrier interval between adjacent subcarriers.

[0161] After calculating the frequency domain correlation corresponding to each carrier number interval, the filter coefficients corresponding to each carrier number interval can be determined by using multiple frequency domain correlations.

[0162] In one embodiment, see Figure 10 This is provided as an embodiment of the present application. Figure 2 A detailed flowchart of step 270 is shown below. Figure 10 As shown, when executing step 270, steps 271 to 273 can be executed in detail.

[0163] Step 271: Determine the measurement phase of at least one subcarrier in the X-axis direction of the observation matrix.

[0164] The electronic device can determine the measurement phase of at least one subcarrier along the X-axis of the observation matrix. The measurement phase can be expressed as: φ k The measured phase of the k-th subcarrier; D is the total amount of the NLOS channel; j is an imaginary number; Δf is the known carrier spacing between adjacent subcarriers; τ d This represents the NLOS channel transmission duration for the d-th NLOS channel. Here, len d d is the channel length of the d-th NLOS channel; c represents the speed of light.

[0165] Step 272: Determine the NLOS channel transmission duration based on the measured phase, the subcarrier sequence number, and the carrier spacing between adjacent subcarriers.

[0166] Since the measured phase is known, and the subcarrier sequence number and the carrier spacing between adjacent subcarriers are known, the NLOS channel transmission duration can be calculated. Figure 5 For example, there may be multiple NLOS channels between the transmitter TX and the passive target, and between the passive target and the receiver RX2. In fact, these NLOS channels are very close to each other. Therefore, the calculated NLOS channel transmission time can be regarded as the transmission time of any NLOS channel.

[0167] In one embodiment, the electronic device can select the measured phases of multiple subcarriers to determine the NLOS channel transmission duration. In this case, the electronic device can determine multiple NLOS channel transmission durations based on the measured phases of multiple subcarriers, the subcarrier indices, and the carrier spacing. The electronic device can determine one NLOS channel transmission duration separately using the measured phase, subcarrier indices, and carrier spacing of each subcarrier, thereby obtaining multiple NLOS channel transmission durations.

[0168] Since the measured phase can be expressed as: Theoretically, for the same NLOS channel, the measured phase of each subcarrier should be the same (in practice, there may be slight differences). In this case, it can be known that kf Δ With τ d There is a linear relationship between them. Therefore, based on the transmission duration of multiple NLOS channels and the sequence number of multiple subcarriers, a set of values ​​(1*f) can be obtained.Δ ,τ1),(2*f Δ ,τ2),……,(k*f Δ , τ d ).

[0169] Electronic devices can determine a fitting function between the product of the subcarrier indices and carrier spacing of multiple subcarriers and the NLOS channel transmission duration corresponding to the multiple subcarriers.

[0170] The electronic device performs a linear fit on this set of values ​​to obtain the characterization kf Δ With τ d The fitting function for the relationship between them. Furthermore, the maximum product k*f in the fitting function can be obtained. Δ The corresponding NLOS channel transmission duration is selected as the NLOS channel transmission duration for calculating the channel length.

[0171] Step 273: Determine the channel length of the NLOS channel based on the NLOS channel transmission duration and the speed of light.

[0172] Electronic devices can multiply the NLOS channel transmission time by the speed of light to obtain the NLOS channel length.

[0173] Figure 11 This is a block diagram of a positioning device based on channel frequency response according to an embodiment of the present invention, as shown below. Figure 11 As shown, the device may include:

[0174] The noise reduction module 1110 is used to perform noise reduction processing on the channel frequency response of the Wi-Fi signal corresponding to each antenna array element to obtain the noise-reduced channel frequency response; wherein, the channel frequency response includes the frequency domain channel estimation values ​​of multiple subcarriers.

[0175] The first adjustment module 1120 is used to adjust the noise-reduced channel frequency response of the first receiver based on the first adjustment coefficient to obtain the first channel frequency response.

[0176] The second adjustment module 1130 is used to adjust the frequency responses of at least two noise-reduced channels of the second receiver based on the second adjustment coefficient to obtain the second channel frequency response.

[0177] The cleaning module 1140 is used to perform conjugate multiplication of each second channel frequency response with the first channel frequency response to obtain a target vector, and to subtract the mean of multiple values ​​in the target vector from each value in the target vector to obtain the cleaned channel frequency response corresponding to each second channel frequency response.

[0178] The construction module 1150 is used to construct an observation matrix based on multiple cleaned channel frequency responses at the same observation time; wherein, the X-axis direction of the observation matrix is ​​the subcarrier index, and the Y-axis direction is the antenna element index.

[0179] The first determining module 1160 is used to determine the angle of arrival of the passive target relative to the second receiver based on a spectral search method in the Y-axis direction of the observation matrix.

[0180] The second determining module 1170 is used to determine the channel length of the NLOS channel based on the phase offset of multiple subcarriers in the X-axis direction of the observation matrix; wherein the channel length is the distance from the transmitter to the second receiver;

[0181] The third determining module 1180 is used to determine the target position information of the passive target based on the first position information of the transmitter, the second position information of the second receiver, the angle of arrival, and the channel length.

[0182] The specific implementation process of the functions and roles of each module in the above-mentioned device can be found in the implementation process of the corresponding steps in the above-mentioned channel frequency response-based positioning method, and will not be repeated here.

[0183] The apparatuses and methods disclosed in the several embodiments provided in this application can also be implemented in other ways. The apparatus embodiments described above are merely illustrative. For example, the flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of apparatuses, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code, which contains one or more executable instructions for implementing a specified logical function. In some alternative implementations, the functions marked in the blocks may occur in a different order than those marked in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram and / or flowchart, and combinations of blocks in block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0184] In addition, the functional modules in the various embodiments of this application can be integrated together to form an independent part, or each module can exist independently, or two or more modules can be integrated to form an independent part.

[0185] If a function is implemented as a software module and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as a USB flash drive, a portable hard drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

Claims

1. A positioning method based on channel frequency response, applied to a Wi-Fi transceiver system, characterized in that, The Wi-Fi transceiver system includes a transmitter, a first receiver, and a second receiver. The first receiver includes at least one antenna element, and the second receiver includes at least two antenna elements. The method includes: For the channel frequency response of the Wi-Fi signal corresponding to each antenna element, noise reduction processing is performed to obtain the noise-reduced channel frequency response; wherein, the channel frequency response includes the frequency domain channel estimation values ​​of multiple subcarriers; Based on the first adjustment coefficient, the noise-reduced channel frequency response of the first receiver is adjusted to obtain the first channel frequency response; Based on the second adjustment coefficient, the frequency responses of at least two noise-reduced channels of the second receiver are adjusted respectively to obtain the second channel frequency response; Each second channel frequency response is multiplied by the first channel frequency response by conjugate to obtain a target vector. The average of multiple values ​​in the target vector is subtracted from each value in the target vector to obtain the cleaned channel frequency response corresponding to each second channel frequency response. An observation matrix is ​​constructed based on multiple cleaned channel frequency responses at the same observation time; wherein, the X-axis direction of the observation matrix is ​​the subcarrier index, and the Y-axis direction is the antenna element index. In the Y-axis direction of the observation matrix, the angle of arrival of the passive target relative to the second receiver is determined based on the spectral search method; In the X-axis direction of the observation matrix, the channel length of the NLOS channel is determined based on the phase offset of multiple subcarriers; wherein, the channel length is the distance from the transmitter to the second receiver; The target location information of the passive target is determined based on the first position information of the transmitter, the second position information of the second receiver, the angle of arrival, and the channel length. The step of performing noise reduction processing on the channel frequency response of the Wi-Fi signal corresponding to each antenna element to obtain the noise-reduced channel frequency response includes: Perform an inverse Fourier transform on the channel frequency response to obtain the time-domain impulse response, and calculate the covariance matrix corresponding to the time-domain impulse response. The covariance matrix is ​​decomposed by ED / SVD to obtain multiple eigenvalues; each eigenvalue corresponds to a channel, and the magnitude of the eigenvalue is positively correlated with the channel strength. Based on the aforementioned multiple feature values, the target number of effective channels is determined; The frequency domain signal receiving power is determined based on the covariance matrix, the number of targets, the multiple eigenvalues, and the total number of subcarriers in the channel frequency response. The maximum value is searched on the diagonal of the covariance matrix. The values ​​on the diagonal are filtered out with the maximum value as the center and a preset filtering window length. The average of the remaining values ​​on the diagonal is calculated as the time-domain noise power. The time-domain noise power is converted into frequency-domain noise power, and the frequency-domain signal-to-noise ratio is determined using the frequency-domain noise power and the frequency-domain received signal power. The filter coefficients are determined based on the frequency domain signal-to-noise ratio and frequency domain correlation. Based on the filter coefficients and filter order, the channel frequency response is filtered to obtain the noise-reduced channel frequency response.

2. The method according to claim 1, characterized in that, Determining the number of targets for effective channels based on the multiple feature values ​​includes: The multiple feature values ​​are arranged in descending order to obtain a feature value sequence; A first sequence and a second sequence are split from the feature value sequence; wherein the first sequence includes all feature values ​​in the feature value sequence except for the first feature value; and the second sequence includes all feature values ​​in the feature value sequence except for the last feature value. Calculate the difference sequence between the first sequence and the second sequence, and search for the maximum value in the difference sequence. Based on the index of the maximum value, determine the target number of effective channels.

3. The method according to claim 1, characterized in that, Determining the frequency domain signal received power based on the covariance matrix, the number of targets, the plurality of eigenvalues, and the total number of subcarriers in the channel frequency response includes: From the plurality of feature values, select the feature value with the largest target quantity as the specified feature value; On the diagonal of the covariance matrix, determine multiple values ​​corresponding to multiple specified eigenvalues, and calculate the sum of the values ​​of the multiple values; The frequency domain signal receiving power is obtained by dividing the sum of the values ​​by the total number of subcarriers.

4. The method according to claim 1, characterized in that, Before determining the filter coefficients based on the frequency domain signal-to-noise ratio and frequency domain correlation, the method further includes: Based on the target number and the transmission duration of a single symbol, the delay spread is determined; Based on the time-domain spread, the carrier spacing between adjacent subcarriers, and several carrier number spacings, the frequency domain correlation corresponding to each carrier number spacing is calculated.

5. The method according to claim 1, characterized in that, Before adjusting the noise-reduced channel frequency response of the first receiver based on the first adjustment coefficient to obtain the first channel frequency response, the method further includes: For the denoised channel frequency response at multiple observation times of the first receiver, search for the frequency domain channel estimate with the smallest non-zero value, and use it as the first adjustment coefficient; The first adjustment coefficient is amplified according to the preset amplification factor to obtain the second adjustment factor.

6. The method according to claim 1, characterized in that, The determination of the channel length of the NLOS channel based on the phase offset of multiple subcarriers along the X-axis of the observation matrix includes: In the X-axis direction of the observation matrix, the measurement phase of at least one subcarrier is determined respectively; The NLOS channel transmission duration is determined based on the measured phase, the subcarrier sequence number, and the carrier spacing between adjacent subcarriers. The channel length of the NLOS channel is determined based on the NLOS channel transmission duration and the speed of light.

7. The method according to claim 6, characterized in that, The step of determining the NLOS channel transmission duration based on the measured phase, the subcarrier sequence number, and the carrier spacing between adjacent subcarriers includes: The transmission duration of multiple NLOS channels is determined based on the measured phase of multiple subcarriers, the subcarrier sequence number of multiple subcarriers, and the carrier interval. Based on the product of the subcarrier index and the carrier spacing, and the NLOS channel transmission duration corresponding to the multiple subcarriers, a fitting function is determined between the product and the NLOS channel transmission duration. The NLOS channel transmission duration corresponding to the maximum product in the fitted function is selected as the NLOS channel transmission duration for calculating the channel length.

8. An electronic device, characterized in that, The electronic device includes: processor; Memory used to store processor-executable instructions; The processor is configured to execute the channel frequency response-based localization method according to any one of claims 1-7.

9. A computer-readable storage medium, characterized in that, The storage medium stores a computer program that can be executed by a processor to perform the channel frequency response-based positioning method according to any one of claims 1-7.

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