An iterative focusing millimeter wave integrated communication and perception method

Abstract

The present invention discloses an iterative focused millimeter wave integrated communication and perception method, which converts the environmental perception problem into a compressed sensing reconstruction problem, and then realizes the initial rough perception of the environment based on an approximate message passing algorithm. According to a background judgment method, the present invention divides and judges the target object and removes the influence of the background scatterer on the received signal, and finally removes the background scatterer iteratively to obtain a more accurate focused perception result of the target object. Compared with the existing environmental perception reconstruction algorithm, the iterative focused millimeter wave environmental perception algorithm of the present invention significantly improves the accuracy of environmental perception, solves the problem of being unable to accurately perceive a large range of environments due to limited system resources, and provides an efficient environmental perception method for the design of future perception and communication integrated systems.

Description

Technical Field

[0001] The present invention belongs to the technical field of wireless communications, and in particular relates to an iterative focusing millimeter wave integrated communication and sensing method. Background Art

[0002] In the current wireless communication field, the emergence of innovative wireless communication technologies such as ultra-large-scale multiple-input multiple-output (MIMO) technology, intelligent reflective surface (IRS), and wireless artificial intelligence (AI) has provided more possibilities for the design of future wireless communication systems. In the foreseeable future wireless communication application scenarios, technologies such as autonomous driving, intelligent robot navigation, and drone control require not only wireless broadband connections, but also accurate environmental information, including the location, shape, and electromagnetic characteristics of objects in the environment. Therefore, the integrated sensing and communication (ISAC) technology, as a research hotspot for the sixth generation (6G) wireless communication system, aims to achieve environmental perception using wireless communication equipment and basic frameworks.

[0003] In the uplink wireless communication scenario, the user sends a communication signal to the base station for reception. The sent signal is reflected and scattered by objects in the environment, so that the received signal of the base station contains environmental information. A major difficulty in the design of the integrated perception and communication system is how to deal with the large number of potential unknown variables in the environment, so the sparsity of the target environment itself should be utilized. For example, in the urban wireless communication scenario, buildings are sparsely distributed in the block. In addition, in the wireless communication application scenario, the propagation range of electromagnetic waves is very wide, and any environmental scatterers covered by wireless signals will affect the propagation of electromagnetic waves. However, the resources available for environmental perception, such as the number of users, the number of base station receiving antennas, and the number of subcarriers, are limited. Even if the sparsity of environmental information is utilized, environmental perception still faces the problem of high system resource overhead caused by a large number of environmental information variables. At present, the existing millimeter wave communication and perception integration methods do not take into account the impact of limited system resources on the perception algorithm. Under the conditions of limited system resources, it is often only possible to achieve a rough perception of the environment and obtain fuzzy imaging results. It is urgent to use limited resources to focus on specific targets to obtain accurate environmental perception results. In summary, how to use limited system resources to achieve accurate perception of specific targets has high research difficulty and practical significance. Summary of the invention

[0004] In view of the deficiencies of the above-mentioned prior art, the purpose of the present invention is to realize environmental perception by a base station using uplink data sent by multiple users in an uplink wireless communication scenario. The present invention uses the pilot signal or other known data sequence of the existing communication system for perception, which is compatible with the existing communication system to achieve integrated perception and communication. Considering that the system resources used for perception are limited and it is impossible to perceive the entire environment on a large scale, an iterative focused millimeter wave environmental perception method for specific targets is proposed.

[0005] The objective of the present invention is achieved through the following technical solutions:

[0006] An iterative focusing millimeter wave integrated communication and sensing method is provided, the method comprising the following steps:

[0007] S1, in any time slot, using the pilot sequence signal of a certain length sent by all active users in the base station receiving environment to obtain a received signal, wherein the received signal is a signal after the pilot sequence signal is affected by the environment;

[0008] S2, using the received signal in step S1, based on a multi-beam multi-carrier millimeter wave channel model, converting the environmental perception problem of a specific target into a compressed sensing reconstruction problem;

[0009] S3, solving the compressed sensing reconstruction problem in step S2 based on an approximate message passing method to obtain a rough initial result of environmental perception;

[0010] S4, based on the rough initial results of environmental perception, a predetermined area is selected from the overall environment as a focused area of ​​interest, and according to a background judgment method, the target object in the area of ​​interest is divided and judged, and the influence of background scatterers outside the area of ​​interest on the received signal is removed to obtain a received signal corresponding to the target object;

[0011] S5, calculating the environmental perception result based on the received signal corresponding to the target object obtained in step S4;

[0012] S6, repeating steps S4 and S5 in sequence until the algorithm converges to obtain the final environment perception result.

[0013] Furthermore, the step S2 is specifically as follows:

[0014] S21, discretize the environmental information in the received signal in step S1 into pixels, each pixel represents the surrounding size l s × s The environmental information within the small square, let the size of the entire range environment be L s ×W s , then the total number of pixels is N s =L / l s×W / w s ; Each pixel may be empty or contain scatterers; use a scattering coefficient To represent the nth s The scattering coefficient of the small cube where the point cloud point is located. If the inside of the small cube is empty, then Therefore, the environmental information of the entire room can be expressed as

[0015] S22, using a multi-beam multi-carrier millimeter wave channel model, in the nth f On the subcarrier frequency, the base station receiving antenna receives the received signal as follows:

[0016]

[0017]

[0018] in, N c The length of the received signal of the base station RF link is K code elements, is base station N R The beamforming vector of a uniform linear array receiving antenna, δ is the normalized coefficient of the scattering coefficient, and according to the pixel size l s × s The normalization coefficient is used to describe the physical relationship between the electromagnetic wave receiving area and the received power. N u Each user sends a pilot signal with a length of K symbols, and n is noise; Indicates that in the nth f subcarrier frequencies, N u Users to N R receive antennas free space propagation channel; For the nth f Non-line-of-sight channels on subcarriers;

[0019] in, It is expressed as:

[0020]

[0021] in, N u The steering vector of each user, N u The channel gain from each user to the base station;

[0022]

[0023] Among them, j represents the complex number symbol, n R is the receiving antenna number, For nth u The arrival angle of each user, d is the uniform linear array antenna spacing deployed by the base station, is the wavelength; It is expressed as follows:

[0024]

[0025] in, and The nth u The channel amplitude gain and phase shift between each user and the base station;

[0026] In the nth f On the subcarrier frequency, the nth u Users to nth s Free space propagation channel of pixels It is expressed as:

[0027]

[0028] in, and The nth u to the nth user s Channel amplitude gain and phase shift between pixels;

[0029] In the nth f subcarrier frequencies, N s pixels to N R receive antennas free space propagation channel It is expressed as:

[0030]

[0031] in, N s The steering vector of pixels, N s The channel gain from pixels to the base station;

[0032]

[0033] Among them, n R is the receiving antenna number, For nth s The angle of arrival of pixels, It is expressed as follows:

[0034]

[0035] in, and The nth sThe channel amplitude gain and phase shift between pixels and the base station;

[0036] S23, the estimated result of environmental information is expressed as It is expressed as follows:

[0037]

[0038] Where y is the received signal of all subcarriers, w is the beamforming vector of the uniform linear array receiving antenna of all subcarriers, and x is ROI is the environmental information in the area of ​​interest, H NLOS is the non-line-of-sight channel of all subcarriers, H LOS is the line-of-sight channel of all subcarriers, s is the non-line-of-sight channel transmission signal of all subcarriers, and ε is the relaxation variable;

[0039] Since the free space channel coefficients of the direct-line channel can be estimated by numerical models, f The part of the received signal containing unknown environmental information is It is expressed as,

[0040]

[0041] Will combine N f The data of subcarriers is used to transform the iterative focusing environment perception problem of a specific target into the compressed sensing reconstruction problem equation as follows:

[0042]

[0043] Furthermore, the step S3 is specifically as follows:

[0044] S31, first set the initial rough environment perception prior probability, let the environment information distribution be Bernoulli-Gaussian distribution, and its probability density function p x (x|q) is expressed as:

[0045] p x (x|q)=(1-λ)δ(x)+λN(x|θ x ,σ x )

[0046] Among them, x represents the element in the environmental information x, and all parameters are expressed as δ(·) is the impulse function, λ is the sparse coefficient; θ x ∈[0,1] and σ x denote the mean and variance of the environmental information distribution, respectively, and N(·) denotes the standard normal distribution;

[0047] S32, initialization of approximate message passing algorithm parameters, let the input function g in(·), g i ' n (·) and the output function g out (·), g′ out (·) are as follows

[0048]

[0049]

[0050]

[0051]

[0052]

[0053] in, is the input variable, σ w is the noise variance;

[0054] Let the number of iterations be t G =0, residual Sparse vector mean estimation Sparse vector estimation variance

[0055] S33, let M = N c N f K, N c is the number of base stations, K is the number of code elements, N f is the number of subcarriers. For m = 1, 2, ..., M, calculate the variable z m The estimated mean of and variance

[0056]

[0057]

[0058]

[0059] S34, for m = 1, 2, ..., M, calculate the mean of the residuals and variance

[0060]

[0061]

[0062] Among them, y m is the mth element of the received signal;

[0063] S35, for n s=1,2,...,N s ,calculate The observed mean and variance

[0064]

[0065]

[0066] S36, for n s =1,2,…,N s ,calculate The observed mean and variance

[0067]

[0068]

[0069] S37, t G =t G +1, repeat steps S33 to S36 until the convergence condition is reached ε G is the error tolerance;

[0070] S38, sparse variables As the rough initial result of environmental perception of environmental information x.

[0071] Furthermore, the step S4 is specifically as follows:

[0072] S41, selecting a predetermined area from the overall environment as a focused area of ​​interest according to a rough initial result of environmental perception and actual needs, wherein the target object is within the area of ​​interest;

[0073] S42, background scatterers outside the region of interest in the i-th iteration The following are detected:

[0074]

[0075] in, represents the result in the i-th iteration, γ i is the background scatterer detection threshold. As the number of iterations increases, the detection threshold γ i should be lowered;

[0076] S43, removing the background scattering part from the received signal to obtain the received signal of the target object in the i+1th iteration ROI

[0077]

[0078] Where α is a weight variable used to enhance the robustness of the iterative algorithm. As the number of iterations increases, the weight variable α should increase.

[0079] Furthermore, the step S5 is specifically as follows:

[0080] S51, setting the prior probability of environmental information in the iterative focusing process. In the i-th iteration, assuming that the background scatterer obeys the Bernoulli Gaussian distribution, the prior probability formula p(x back )as follows:

[0081]

[0082] where θ back,i and σ back denote the mean and variance of the background environment information distribution, λ is the sparsity coefficient, N(·) denotes the standard normal distribution, x back represents background scatterers;

[0083] The distribution of scatterers in the selected ROI is Gaussian and has no sparsity;

[0084]

[0085] where θ ROI and σ ROI Respectively represent the mean and variance of the ROI environmental information distribution;

[0086] S52, according to the prior probability formula obtained in step S51, setting the prior probability p(x) of the internal and external environmental information of the region of interest in the current (i+1)th iteration round;

[0087] S53, initialization of approximate message passing algorithm parameters, let the input function g in (·), g′ in (·) and the output function g out (·), g′ out (·) are as follows:

[0088]

[0089]

[0090]

[0091]

[0092]

[0093] Let the number of iterations be t G =0, residual Sparse vector mean estimation Sparse vector estimation variance

[0094] S54, let M = N c N f K, for m = 1, 2, ..., M, calculate z m The estimated mean of and variance The details are as follows:

[0095]

[0096]

[0097]

[0098] S55, for m = 1, 2, ..., M, calculate the mean of the residuals and variance The details are as follows:

[0099]

[0100]

[0101] in is the mth element of the received signal obtained in S43;

[0102] S56, for n s =1,2,…,N s ,calculate The observed mean and variance The details are as follows:

[0103]

[0104]

[0105] S57, for n s =1,2,…,N s ,calculate The observed mean and variance The details are as follows:

[0106]

[0107]

[0108] S58,t G =t G+1, repeat steps S54 to S57 until the convergence condition is reached

[0109] S59, the sparse variables estimated in the above steps are As the final environmental perception result of this round of iteration.

[0110] The beneficial effects of the present invention are as follows: in the uplink wireless communication scenario, the present invention proposes a design method for an integrated system of millimeter wave sensing and communication using existing communication equipment, and fully utilizes different system resources based on user-sent data to achieve focused environmental perception. The present invention proposes an iterative focused environmental perception method to solve the problem of low accuracy of large-scale environmental perception due to insufficient system resources. The present invention improves the defect that traditional compressed sensing algorithms cannot focus on a specific range of environmental variables. During the algorithm iteration process, the prior probability of environmental variables is gradually estimated based on the compressed sensing reconstruction results of each step, and an iterative and progressive compressed sensing sparse reconstruction is achieved for specific targets. On the basis of the same system resource overhead, the algorithm of the present invention significantly improves the perception accuracy of specific targets and is superior to existing algorithms. BRIEF DESCRIPTION OF THE DRAWINGS

[0111] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0112] Figure 1 A schematic diagram of a two-dimensional focused millimeter wave environment perception scenario provided by an exemplary embodiment;

[0113] Figure 2 A flowchart of an iterative algorithm provided for an exemplary embodiment;

[0114] Figure 3 A graph showing the relationship between the number of users and the MSE of the environmental perception accuracy when comparing the present invention with other compressed sensing reconstruction algorithms provided by an exemplary embodiment;

[0115] Figure 4 A graph showing the relationship between the number of subcarriers and the MSE of environmental perception accuracy when comparing the present invention with other compressed sensing reconstruction algorithms provided by an exemplary embodiment; DETAILED DESCRIPTION

[0116] In order to better understand the technical solution of the present application, the embodiments of the present application are described in detail below with reference to the accompanying drawings.

[0117] It should be clear that the described embodiments are only part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in the field without creative work are within the scope of protection of the present application.

[0118] The terms used in the embodiments of the present application are only for the purpose of describing specific embodiments, and are not intended to limit the present application. The singular forms "a", "said" and "the" used in the embodiments of the present application and the appended claims are also intended to include plural forms, unless the context clearly indicates other meanings.

[0119] Consider a single-cell uplink communication system where a multi-antenna BS serves multiple single-antenna users. In order to sense a specific target, all users send uplink communication signals to the base station at the same time. Due to the large amount of scattering in the environment, the transmitted signal of each user propagates through multiple paths. Therefore, the signal received by the base station contains rich environmental scattering information, and the purpose is to allow the base station to process the received signal to achieve environmental perception. Figure 1 As shown in Figure 1, considering a simplified two-dimensional scene model, the analysis method for three-dimensional scenes can also be derived. The gray target scatterers are the scatterers that we want to image accurately, and the rest are background scatterers that we don't care about.

[0120] The present invention provides an iterative focusing millimeter wave integrated communication and sensing method, the method comprising the following steps:

[0121] S1, in any time slot, using the pilot sequence signal of a certain length sent by all active users in the base station receiving environment to obtain a received signal, wherein the received signal is a signal after the pilot sequence signal is affected by the environment;

[0122] S2, using the received signal in step S1, based on a multi-beam multi-carrier millimeter wave channel model, converting the environmental perception problem of a specific target into a compressed sensing reconstruction problem;

[0123] In one embodiment, step S2 is specifically as follows:

[0124] S21, discretize the environmental information in the received signal in step S1 into pixels, each pixel represents the surrounding size l s × s The environmental information within the small square, let the size of the entire range environment be L s ×W s , then the total number of pixels is N s =L / l s ×W / w s ; Each pixel may be empty or contain scatterers; use a scattering coefficient To represent the nth s The scattering coefficient of the small cube where the point cloud point is located. If the inside of the small cube is empty, then Therefore, the environmental information of the entire room can be expressed as

[0125] S22, using a multi-beam multi-carrier millimeter wave channel model, in the nth f On the subcarrier frequency, the base station receiving antenna receives the received signal as follows:

[0126]

[0127] in, N c The length of the received signal of the base station RF link is K code elements, is base station N R The beamforming vector of a uniform linear array receiving antenna, δ is the normalized coefficient of the scattering coefficient, and according to the pixel size l s × s The normalization coefficient is used to describe the physical relationship between the electromagnetic wave receiving area and the received power. N u Each user sends a pilot signal with a length of K symbols, and n is noise; Indicates that in the nth f subcarrier frequencies, N u Users to N R receive antennas free space propagation channel; For the nth f Non-line-of-sight channels on subcarriers;

[0128] in, It is expressed as:

[0129]

[0130] in, N u The steering vector of each user, N u The channel gain from each user to the base station;

[0131]

[0132] Among them, j represents the complex number symbol, n R is the receiving antenna number, For nth u The arrival angle of each user, d is the uniform linear array antenna spacing deployed by the base station, is the wavelength; It is expressed as follows:

[0133]

[0134] in, and The nth u The channel amplitude gain and phase shift between each user and the base station;

[0135] In the nth f On the subcarrier frequency, the nth u Users to nth s Free space propagation channel of pixels It is expressed as:

[0136]

[0137] in, and The nth u to the nth user s Channel amplitude gain and phase shift between pixels;

[0138] In the nth f subcarrier frequencies, N s pixels to N R receive antennas free space propagation channel It is expressed as:

[0139]

[0140] in, N s The steering vector of pixels, N s The channel gain from pixels to the base station;

[0141]

[0142] Among them, n R is the receiving antenna number, For nth s The angle of arrival of pixels, It is expressed as follows:

[0143]

[0144] in, and The nth s The channel amplitude gain and phase shift between pixels and the base station;

[0145] S23, the estimated result of environmental information is expressed as It is expressed as follows:

[0146]

[0147] Where y is the received signal of all subcarriers, w is the beamforming vector of the uniform linear array receiving antenna of all subcarriers, and x is ROI is the environmental information in the area of ​​interest, H NLOS is the non-line-of-sight channel of all subcarriers, H LOS is the line-of-sight channel of all subcarriers, s is the non-line-of-sight channel transmission signal of all subcarriers, and ε is the relaxation variable;

[0148] Since the free space channel coefficients of the direct-line channel can be estimated by numerical models, f The part of the received signal containing unknown environmental information is It is expressed as,

[0149]

[0150] Will combine N f The data of subcarriers is used to transform the iterative focusing environment perception problem of a specific target into the compressed sensing reconstruction problem equation as follows:

[0151]

[0152] S3, solving the compressed sensing reconstruction problem in step S2 based on the approximate message passing method to obtain a rough initial result of environmental perception;

[0153] In one embodiment, step S3 is specifically as follows:

[0154] S31, first set the initial rough environment perception prior probability, let the environment information distribution be Bernoulli-Gaussian distribution, and its probability density function p x (x|q) is expressed as:

[0155] p x (x|q)=(1-λ)δ(x)+λN(x|θ x ,σ x )

[0156] Among them, x represents the element in the environmental information x, and all parameters are expressed as δ(·) is the impulse function, λ is the sparse coefficient; θ x ∈[0,1] and σ x denote the mean and variance of the environmental information distribution, respectively, and N(·) denotes the standard normal distribution;

[0157] S32, initialization of approximate message passing algorithm parameters, let the input function g in (·), g′ in (·) and the output function g out (·), g′out (·) are as follows

[0158]

[0159]

[0160]

[0161]

[0162]

[0163] in, is the input variable, σ w is the noise variance;

[0164] Let the number of iterations be t G =0, residual Sparse vector mean estimation Sparse vector estimation variance

[0165] S33, let M = N c N f K, N c is the number of base stations, K is the number of code elements, N f is the number of subcarriers. For m = 1, 2, ..., M, calculate the variable z m The estimated mean of and variance

[0166]

[0167]

[0168]

[0169] S34, for m = 1, 2, ..., M, calculate the mean of the residuals and variance

[0170]

[0171]

[0172] Among them, y m is the mth element of the received signal;

[0173] S35, for n s =1,2,...,N s ,calculate The observed mean and variance

[0174]

[0175]

[0176] S36, for n s =1,2,...,N s ,calculate The observed mean and variance

[0177]

[0178]

[0179] S37, t G =t G +1, repeat steps S33 to S36 until the convergence condition is reached ε G is the error tolerance;

[0180] S38, sparse variables As the rough initial result of environmental perception of environmental information x.

[0181] S4, based on the rough initial results of environmental perception, a predetermined area is selected from the overall environment as a focused area of ​​interest, and according to a background judgment method, the target object in the area of ​​interest is divided and judged, and the influence of background scatterers outside the area of ​​interest on the received signal is removed to obtain a received signal corresponding to the target object;

[0182] In one embodiment, step S4 is specifically as follows:

[0183] S41, selecting a predetermined area from the overall environment as a focused area of ​​interest according to a rough initial result of environmental perception and actual needs, wherein the target object is within the area of ​​interest;

[0184] S42, background scatterers outside the region of interest in the i-th iteration The following are detected:

[0185]

[0186] in, represents the result in the i-th iteration, γ i is the background scatterer detection threshold. As the number of iterations increases, the detection threshold γ i should be lowered;

[0187] S43, removing the background scattering part from the received signal to obtain the received signal of the target object in the i+1th iteration ROI

[0188]

[0189] Where α is a weight variable used to enhance the robustness of the iterative algorithm. As the number of iterations increases, the weight variable α should increase.

[0190] S5, calculating the environmental perception result based on the received signal corresponding to the target object obtained in step S4;

[0191] In one embodiment, step S5 is specifically as follows:

[0192] S51, setting the prior probability of environmental information in the iterative focusing process. In the i-th iteration, assuming that the background scatterer obeys the Bernoulli Gaussian distribution, the prior probability formula p(x back )as follows:

[0193]

[0194] where θ back,i and σ back denote the mean and variance of the background environment information distribution, λ is the sparsity coefficient, N(·) denotes the standard normal distribution, x back represents background scatterers;

[0195] The distribution of scatterers in the selected ROI is Gaussian and has no sparsity;

[0196]

[0197] where θ ROI and σ ROI Respectively represent the mean and variance of the ROI environmental information distribution;

[0198] S52, according to the prior probability formula obtained in step S51, setting the prior probability p(x) of the internal and external environmental information of the region of interest in the current (i+1)th iteration round;

[0199] S53, initialization of approximate message passing algorithm parameters, let the input function g in (·), g′ in (·) and the output function g out (·), g′ out (·) are as follows:

[0200]

[0201]

[0202]

[0203]

[0204]

[0205] Let the number of iterations be t G =0, residual Sparse vector mean estimation Sparse vector estimation variance

[0206] S54, let M = N c N f K, for m = 1, 2, ..., M, calculate z m The estimated mean of and variance The details are as follows:

[0207]

[0208]

[0209]

[0210] S55, for m = 1, 2, ..., M, calculate the mean of the residuals and variance The details are as follows:

[0211]

[0212]

[0213] in is the mth element of the received signal obtained in S43;

[0214] S56, for n s =1,2,...,N s ,calculate The observed mean and variance The details are as follows:

[0215]

[0216]

[0217] S57, for n s =1,2,...,N s ,calculate The observed mean and variance The details are as follows:

[0218]

[0219]

[0220] S58,t G =t G +1, repeat steps S54 to S57 until the convergence condition is reached

[0221] S59, the sparse variables estimated in the above steps are As the final environmental perception result of this round of iteration.

[0222] S6, repeat step S4 and step S5 in order until the algorithm converges, wherein the iterative flow chart is as follows: Figure 2 As shown, the final environment perception result is obtained.

[0223] Through computer simulation, we can see that: Figure 3 and Figure 4 As shown, we compared the imaging effect between the focusing method of the present invention and the large-scale imaging algorithm. Compared with the large-scale imaging, the algorithm of the present invention significantly improves the imaging accuracy of the object in the ROI. Figure 3 It shows that as the number of users increases, the environmental perception effect of the method of the present invention gradually improves and is better than the existing algorithms. Figure 4 It means that as the number of subcarriers increases, the environmental perception effect of the method of the present invention gradually improves and is better than the existing algorithm.

[0224] In the uplink wireless communication scenario, the design method of the millimeter wave sensing and communication system integration system using existing communication equipment fully utilizes different system resources based on user sending data to realize focused environmental perception, converts the environmental perception problem into a compressed sensing reconstruction problem, and then realizes the initial rough perception of the environment based on an approximate message passing algorithm. According to a background judgment method, the present invention divides and judges the target object and removes the influence of the background scatterer on the received signal, and finally removes the background scatterer iteratively to obtain a more accurate focused perception result of the target object. Compared with the existing environmental perception reconstruction algorithm, the proposed iterative focused environmental perception method solves the problem of low accuracy of large-scale environmental perception due to insufficient system resources, improves the defect that the traditional compressed sensing algorithm cannot realize the focus on a specific range of environmental variables, and provides an efficient environmental perception method for the design of future perception and communication integrated systems. In the algorithm iteration process, according to the compressed sensing reconstruction results of each step, the prior probability of the environmental variables is gradually estimated, and an iterative progressive compressed sensing sparse reconstruction is realized for specific targets. On the basis of the same system resource overhead, the algorithm of the present invention significantly improves the perception accuracy of specific targets and is superior to the existing algorithms.

[0225] The above description is merely a preferred embodiment of one or more embodiments of the present specification and is not intended to limit one or more embodiments of the present specification. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of one or more embodiments of the present specification shall be included in the scope of protection of one or more embodiments of the present specification.

Claims

1. An iterative focusing millimeter wave integrated communication and sensing method, characterized in that: The following steps are involved: S1, in any time slot, using the pilot sequence signal of a certain length sent by all active users in the base station receiving environment to obtain a received signal, wherein the received signal is a signal after the pilot sequence signal is affected by the environment; S2, using the received signal in step S1, based on a multi-beam multi-carrier millimeter wave channel model, converting the environmental perception problem of a specific target into a compressed sensing reconstruction problem; S3, solving the compressed sensing reconstruction problem in step S2 based on an approximate message passing method to obtain a rough initial result of environmental perception; S4, based on the rough initial results of environmental perception, a predetermined area is selected from the overall environment as a focused area of ​​interest, and according to a background judgment method, the target object in the area of ​​interest is divided and judged, and the influence of background scatterers outside the area of ​​interest on the received signal is removed to obtain a received signal corresponding to the target object; S5, calculating the environmental perception result based on the received signal corresponding to the target object obtained in step S4; S6, repeating steps S4 and S5 in sequence until the algorithm converges to obtain the final environment perception result.

2. The iterative focusing millimeter wave integrated communication and sensing method according to claim 1, characterized in that: The step S2 is specifically as follows: S21, discretize the environmental information in the received signal in step S1 into pixels, each pixel represents the surrounding size l s × s The environmental information within the small square, let the size of the entire range environment be L s ×W s , then the total number of pixels is N s =L / l s ×W / w s ; Each pixel may be empty or contain scatterers; use a scattering coefficient To represent the nth s The scattering coefficient of the small cube where the point cloud point is located. If the inside of the small cube is empty, then Therefore, the environmental information of the entire room can be expressed as S22, using a multi-beam multi-carrier millimeter wave channel model, in the nth f On the subcarrier frequency, the base station receiving antenna receives the received signal as follows: in, N c The length of the received signal of the base station RF link is K code elements, is base station N R The beamforming vector of a uniform linear array receiving antenna, δ is the normalized coefficient of the scattering coefficient, and according to the pixel size l s × s The normalization coefficient is used to describe the physical relationship between the electromagnetic wave receiving area and the received power. N u Each user sends a pilot signal with a length of K symbols, and n is noise; Indicates that in the nth f subcarrier frequencies, N u Users to N R receive antennas free space propagation channel; For the nth f Non-line-of-sight channels on subcarriers; in, It is expressed as: in, N u The steering vector of each user, N u The channel gain from each user to the base station; Among them, j represents the complex number symbol, n R is the receiving antenna number, For nth u The arrival angle of each user, d is the uniform linear array antenna spacing deployed by the base station, is the wavelength; It is expressed as follows: in, and The nth u The channel amplitude gain and phase shift between each user and the base station; In the nth f On the subcarrier frequency, the nth u Users to nth s Free space propagation channel of pixels It is expressed as: in, and The nth u to the nth user s Channel amplitude gain and phase shift between pixels; In the nth f subcarrier frequencies, N s pixels to N R Free space propagation channel with multiple receiving antennas It is expressed as: in, N s The steering vector of pixels, N s The channel gain from pixels to the base station; Among them, n R is the receiving antenna number, For nth s The angle of arrival of pixels, It is expressed as follows: in, and The nth s The channel amplitude gain and phase shift between pixels and the base station; S23, the estimated result of environmental information is expressed as It is expressed as follows: Where y is the received signal of all subcarriers, w is the beamforming vector of the uniform linear array receiving antenna of all subcarriers, and x is ROI is the environmental information in the area of ​​interest, H NLOS is the non-line-of-sight channel of all subcarriers, H LOS is the line-of-sight channel of all subcarriers, s is the non-line-of-sight channel transmission signal of all subcarriers, and ε is the relaxation variable; Since the free space channel coefficients of the direct-line channel can be estimated by numerical models, f The part of the received signal containing unknown environmental information is It is expressed as, Will combine N f The data of subcarriers is used to transform the iterative focusing environment perception problem of a specific target into the compressed sensing reconstruction problem equation as follows:

3. The iterative focusing millimeter wave integrated communication and sensing method according to claim 2, characterized in that: The step S3 is specifically as follows: S31, first set the initial rough environment perception prior probability, let the environment information distribution be Bernoulli-Gaussian distribution, and its probability density function p x (x|q) is expressed as: p x (x|q)=(1-λ)δ(x)+λN(x|θ x ,σ x ) Among them, x represents the element in the environmental information x, and all parameters are expressed as δ(·) is the impulse function, λ is the sparse coefficient; θ x ∈[0,1] and σ x denote the mean and variance of the environmental information distribution, respectively, and N(·) denotes the standard normal distribution; S32, initialization of approximate message passing algorithm parameters, let the input function g in (·), g′ in (·) and the output function g out (·), g′ out (·) are as follows in, σ v , σ z is the input variable, σ w is the noise variance; Let the number of iterations be t G =0, residual Sparse vector mean estimation Sparse vector estimation variance S33, let M = N c N f K, N c is the number of base stations, K is the number of code elements, N f is the number of subcarriers. For m = 1, 2, ..., M, calculate the variable z m The estimated mean of and variance S34, for m = 1, 2, ..., M, calculate the mean of the residuals and variance Among them, y m is the mth element of the received signal; S35, for n s =1,2,...,N s ,calculate The observed mean and variance S36, for n s =1,2,...,N s ,calculate The observed mean and variance S37, t G =t G +1, repeat steps S33 to S36 until the convergence condition is reached ε G is the error tolerance; S38, sparse variables As the rough initial result of environmental perception of environmental information x.

4. The iterative focusing millimeter wave integrated communication and sensing method according to claim 3, characterized in that: The step S4 is specifically as follows: S41, selecting a predetermined area from the overall environment as a focused area of ​​interest according to a rough initial result of environmental perception and actual needs, wherein the target object is within the area of ​​interest; S42, background scatterers outside the region of interest in the i-th iteration The following are detected: in, represents the result in the i-th iteration, γ i is the background scatterer detection threshold. As the number of iterations increases, the detection threshold γ i should be lowered; S43, removing the background scattering part from the received signal to obtain the received signal of the target object in the i+1th iteration ROI Where α is a weight variable used to enhance the robustness of the iterative algorithm. As the number of iterations increases, the weight variable α should increase.

5. The iterative focusing millimeter wave integrated communication and sensing method according to claim 1, characterized in that: The step S5 is specifically as follows: S51, setting the prior probability of environmental information in the iterative focusing process. In the i-th iteration, assuming that the background scatterer obeys the Bernoulli Gaussian distribution, the prior probability formula p(x back )as follows: where θ back,i and σ back denote the mean and variance of the background environment information distribution, λ is the sparsity coefficient, N(·) denotes the standard normal distribution, x back represents background scatterers; The distribution of scatterers in the selected ROI is Gaussian and has no sparsity; where θ ROI and σ ROI Respectively represent the mean and variance of the ROI environmental information distribution; S52, according to the prior probability formula obtained in step S51, setting the prior probability p(x) of the internal and external environmental information of the region of interest in the current (i+1)th iteration round; S53, initialization of approximate message passing algorithm parameters, let the input function g in (·), g′ in (·) and the output function g out (·), g′ out (·) are as follows: Let the number of iterations be t G =0, residual Sparse vector mean estimation Sparse vector estimation variance S54, let M = N c N f K, for m = 1, 2, ..., M, calculate z m The estimated mean of and variance The details are as follows: S55, for m = 1, 2, ..., M, calculate the mean of the residuals and variance The details are as follows: in is the mth element of the received signal obtained in S43; S56, for n s =1,2,...,N s ,calculate The observed mean and variance The details are as follows: S57, for n s =1,2,...,N s ,calculate The observed mean and variance The details are as follows: S58,t G =t G +1, repeat steps S54 to S57 until the convergence condition is reached S59, the sparse variables estimated in the above steps are As the final environmental perception result of this round of iteration

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