Array antenna for integrated communication and perception system and angle estimation method for 5G new air interface

Abstract

The present invention belongs to the field of wireless communications, and provides an array antenna of a communication-sensing integrated system and a 5G new air interface angle estimation method, which is used to achieve high-precision, high-resolution, maximum target number and low-complexity angle estimation, thereby meeting the application requirements of ISAC scenarios. The present invention proposes a synaesthesia node transceiver array antenna that supports high-precision angle estimation. Under the condition of the same total number of transceiver antennas, a greater degree of freedom of array angle estimation is achieved, which greatly improves the accuracy, resolution, maximum target number and other performance of angle estimation; under the premise of meeting the same angle estimation performance, the number of antenna array elements required is less, which greatly reduces the cost and volume of the equipment. At the same time, the reference signal of the 5G cellular system is used as an active perception signal, and an improved MUSIC angle estimation method based on virtual array manifold matrix dimensionality reduction is proposed, which achieves low-complexity and high-precision direction perception without affecting the performance of cellular communications.

Description

Technical Field

[0001] The present invention belongs to the field of wireless communications, and specifically provides an array antenna of a communication perception integrated system and a 5G new air interface angle estimation method. Background Art

[0002] The new generation of wireless networks has been regarded as a key driver for many emerging applications, such as smart cities and industries, connected vehicles, and telemedicine. These applications require high-quality wireless communications and high-precision, high-robust environmental perception capabilities. The design of Integrated Sensing and Communication (ISAC) based on the wireless communication technology system enables wireless communication and perception to be integrated into a single system and enhance each other, which has gradually become an important research direction for the sixth generation of mobile communication technology (6G). In the ISAC system, communication devices, as sensors, can receive traditional communication signals and perform angle estimation to achieve target positioning or assist communication. Fast and accurate angle estimation methods are of great significance to the performance improvement of ISAC systems in terms of beamforming, alignment, and channel estimation. At the same time, 5G New Radio (5G NR), as the international standard for the fifth generation of mobile communication technology (5G), has been widely used in various fields. The angle estimation method of the integrated communication and perception system based on 5G NR has attracted more and more attention.

[0003] In terms of array antennas, the transmitting and receiving antennas of existing ISAC systems mostly use uniform array antennas. High-precision and high-resolution angle estimation is required in ISAC scenarios, and the number of targets estimated at the same time is often large. The performance of uniform array angle estimation is related to the number of antenna elements. If the angle estimation performance needs to be improved, the hardware cost and computational complexity will increase. Sparse arrays can achieve sparse perception of incident signals, and their angle estimation performance is no longer limited by the number of antenna elements, which is suitable for ISAC scenario applications.

[0004] In terms of angle estimation, the existing angle estimation methods mainly include subspace-based angle estimation methods, compressed sensing algorithm-based angle estimation methods and signal design-based angle estimation methods; among them, the subspace-based multiple signal classification algorithm (Multiple Signal Classification, MUSIC) requires more antenna array elements and signal processing units to achieve high-precision and high-resolution angle estimation; the angle estimation method based on compressed sensing algorithm has high complexity and is not conducive to high-mobility ISAC scenario applications; in addition, since the angle estimation in the ISAC scenario uses communication signals for estimation, many angle estimation methods based on signal design cannot be used or affect the performance of the communication system. Summary of the invention

[0005] The purpose of the present invention is to provide a communication perception integrated system array antenna and 5G new air interface angle estimation method to address many problems existing in the background technology, so as to achieve high-precision, high-resolution, maximum target number and low-complexity angle estimation based on 5G NR, thereby meeting the application requirements of ISAC scenarios.

[0006] To achieve the above object, the technical solution adopted by the present invention is:

[0007] A communication sensing integrated system array antenna, comprising: a transmitting array antenna and a receiving array antenna; wherein:

[0008] The receiving array antenna and the transmitting array antenna both adopt a two-level nested array, and the basic interval of the two-level nested array is d0;

[0009] Take any array antenna as the reference array and calculate the degree of freedom f of the reference array N ;

[0010] Set the scaling factor D = f N , multiply the spacing between adjacent array elements in the other array antenna by the scaling factor D.

[0011] An angle estimation method for a communication and perception integrated system, wherein the communication and perception integrated system includes an ISAC node and K perception targets, and the transmitting end of the ISAC node adopts a 1TMR mode or an XTMR mode; specifically, the following steps are included:

[0012] Step 1. The receiving end of the ISAC node processes the echo signal to obtain a received signal;

[0013] Step 2. Calculate the covariance matrix Z of the received signal, and vectorize the covariance matrix Z of the received signal into z;

[0014] Step 3. For the preset frequency f p , calculate the covariance matrix of the received signal in the frequency domain: R = z(f p ) H (f p );

[0015] Step 4. Divide the airspace into L grids at equal intervals to obtain the angle domain representation, and then obtain the virtual array manifold matrix A L ; According to the system's differential common array, a uniform array antenna is set to obtain its manifold matrix according to Solve to get the transformation matrix B L ;

[0016] Step 5. According to the transformation matrix B L , converted to the covariance matrix The covariance matrix is ​​smoothed using a spatial smoothing method. Smoothing is performed to obtain the covariance matrix R (bf) ;

[0017] Step 6. For the covariance matrix R (bf) Perform eigenvalue decomposition to obtain the noise subspace E n , and then calculate the spatial spectrum P of the received signal mu ;

[0018] Step 7. According to the spatial spectrum information, the angle estimation values ​​of K perceived targets are obtained.

[0019] Furthermore, in step 1, when the transmitting end of the ISAC node adopts the 1TMR mode, the received signal is y p,q :

[0020] y p,q =Bx p,q +n p,q

[0021] Among them, x p,q is the signal vector, x p,q =[α1,α2,…,α K ] T ; α k is the reflection coefficient of the kth target, λ is the wavelength, σ RCS is the radar cross section, d b,k is the distance from the ISAC node to the kth target, and K is the number of targets;

[0022] B is the array manifold matrix, B = [b(θ1), b(θ2), …, b(θ K )];b(θ k ) is the array steering vector, d t,m is the distance between the mth transmitting antenna and the transmitting reference antenna, d r,n is the distance between the nth receiving antenna and the receiving reference antenna, m∈[1,M], n∈[1,N], θ k is the arrival angle of the kth target, f p is the frequency of the pth subcarrier, c is the speed of light;

[0023] n p,q is the noise vector.

[0024] Furthermore, in step 1, when the transmitter of the ISAC node adopts the XTMR mode, the received signal is y q :

[0025] y q =Bx q +nq

[0026] Among them, x q is the signal vector, x p,q =[α1,α2,…,α K ] T ; α k is the reflection coefficient of the kth target, λ is the wavelength, σ RCS is the radar cross section, d b,k is the distance from the ISAC node to the kth target, and K is the number of targets;

[0027] B is the array manifold matrix, B = [b(θ1), b(θ2), …, b(θ K )];b(θ k ) is the array steering vector, d t,m is the distance between the mth transmitting antenna and the transmitting reference antenna, d r,n is the distance between the nth receiving antenna and the receiving reference antenna, m∈[1,M], n∈[1,N], θ k is the arrival angle of the kth target, f c is the carrier frequency, c is the speed of light;

[0028] N q is the noise matrix.

[0029] Furthermore, in step 4, the angle domain is represented by θ = {θ1, θ2, …, θ L}, virtual array manifold matrix A L for:

[0030] A L =[a(θ1),a(θ2),…,a(θ L )]

[0031] Among them, a(θ l )=exp(-j2πf c d1sin(θ l ) / c),θ l is the angle in the angle domain, l∈[1,L], j is the imaginary unit, f c is the carrier frequency, d1=d0ln(exp(-d2)⊕exp(d2)), d0 is the antenna base spacing, d2=[d t,1 +d r,1 ,d t,1 +d r,2 ,…,d t,M +d r,N ] T , d t,mis the distance between the mth transmitting antenna and the transmitting reference antenna, d r,n is the distance between the nth receiving antenna and the receiving reference antenna, m∈[1,M], n∈[1,N], M is the number of elements of the receiving array antenna on the ISAC node, N is the number of elements of the transmitting array antenna on the ISAC node, and c is the speed of light.

[0032] Furthermore, in step 4, the uniform array antenna manifold matrix for:

[0033]

[0034] in, θ l is the angle in the angle domain, l∈[1,L], j is the imaginary unit, f c is the carrier frequency, d0 is the antenna base spacing, d3=d0(0:V-1) T , V is the number of elements of the uniform array antenna, and c is the speed of light.

[0035] Furthermore, in step 4, the transformation matrix B L Obtained by least squares fitting.

[0036] Furthermore, in step 6, the spatial spectrum P mu for:

[0037]

[0038] in, is the array steering vector of the uniform array antenna.

[0039] Furthermore, in step 7, when the transmitting end of the ISAC node adopts the 1TMR mode, steps 3 to 6 are repeated to calculate the spatial spectrum of P sub-bands, and the average value is calculated as the final spatial spectrum, and the indexes of K peaks in the final spatial spectrum are searched to obtain the angle estimation values ​​of K perceived targets; when the transmitting end of the ISAC node adopts the XTMR mode, the final spatial spectrum P is searched. mu The indices of the K peaks in the image correspond to the angle estimates of the K perceived targets.

[0040] Based on the above technical solution, the beneficial effects of the present invention are:

[0041] The present invention proposes a wireless system integrating active sensing and communication, which can utilize cellular network resources to achieve wireless sensing and reduce resource consumption;

[0042] The present invention proposes a synaesthesia node transceiver array antenna supporting high-precision angle estimation. Under the condition that the total number of transceiver antennas is the same, a greater degree of freedom of array angle estimation is achieved, and the performance of angle estimation, such as accuracy, resolution, and maximum number of targets, is greatly improved. On the other hand, under the premise of meeting the same angle estimation performance, the number of antenna array elements required is less, which greatly reduces the cost and volume of the equipment.

[0043] The present invention uses the reference signals (such as SRS and DMRS) of the 5G cellular system as active sensing signals, and proposes an improved MUSIC angle estimation method based on dimensionality reduction of virtual array manifold matrices. Without affecting the performance of cellular communication (channel estimation, data transmission, etc.), it realizes low-complexity and high-precision direction perception, and it is easy to realize the efficient integration of positioning perception and cellular communication system. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 It is a schematic diagram of the model of the communication perception integrated system in the present invention.

[0045] Figure 2 It is a schematic diagram of the arrangement of the secondary nested array in the present invention.

[0046] Figure 3 It is a schematic diagram of the generation process and position arrangement of the virtual array in the ISAC system of the present invention.

[0047] Figures 4 to 10 This is a simulation result diagram of the angle estimation resolution of Example 1, Example 2, Comparative Example 1 and Comparative Example 2 in the present invention.

[0048] Figure 11 to Figure 13 This is a diagram of the simulation results of the angle estimation target number of Example 1, Example 2, Comparative Example 1 and Comparative Example 2 in the present invention.

[0049] Fig.14 It is a comparison chart of the RMSE curves of Example 1, Example 2, Comparative Example 1 and Comparative Example 2 in the present invention. DETAILED DESCRIPTION

[0050] In order to make the purpose, technical solution and beneficial effects of the present invention more clearly understood, the present invention is further described in detail below with reference to the accompanying drawings and embodiments.

[0051] This embodiment provides an array antenna of a communication perception integrated system and an angle estimation method based on 5G NR. A vehicle network communication perception integrated system based on cellular signals is used as an example for explanation. Figure 1 As shown, it includes: a terminal ISAC node with M transmitting antennas and N receiving antennas, a multi-antenna base station and K sensing targets.

[0052] The location of the terminal ISAC node's transmit and receive antennas meets the requirements of the nested MIMO system. The nested MIMO system is a MIMO system in which both the transmit and receive antennas are nested arrays. The classic two-level nested array arrangement is as follows: Figure 2 As shown, it includes two uniform linear arrays; the first-level array has M1 array elements, and the array element spacing is d1; the second-level array has M2 array elements, and the array element spacing is d2, d2 = (M1 + 1) d1; specifically, S1 = {md1, m = 1, 2, ..., M1}, S2 = {n (M1 + 1) d1, n = 1, 2, ..., M2}, S1 and S2 are the antenna position sets of the first-level array and the second-level array respectively. Taking the transmitting antenna as an example, given the total number of array elements M = M1 + M2, the selection of M1 and M2 will affect the degree of freedom of the array antenna; for the two-level nested array, when M is an even number, set M1 = M2 = M / 2, and the highest degree of freedom (M 2 -2) / 2+M; when M is an odd number, set M1=(M-1) / 2, M2=(M+1) / 2, and the highest degree of freedom (M 2 -2) / 2+M.

[0053] Based on the above settings, the ISAC system generates virtual antennas and their positions are arranged as follows: Figure 3 As shown; for the ISAC system, the antenna signals at the transmitting end are orthogonal to each other, and the receiving end processes the echo signal to generate and co-array, and its normalized position set is D1, as shown Figure 3 Specifically, D1 = u T,m +u R,n , where u T,m is the position of the mth transmitting antenna, u R,n is the position of the nth receiving antenna, m = 1, 2, ..., M, n = 1, 2, ..., N;

[0054] By vectorizing the covariance matrix of the echo signal at the receiving end, the differential covariance matrix can be obtained, and its normalized position set is D2, as shown in Figure 3 Specifically, the elements in set D2 are the result of subtracting the elements in D1, expressed as:

[0055] D2={u T,m +u R,n -u T,m’ -u R,n’ |m,m'=1,2,…,M,n,n'=1,2,…,N}

[0056] The above-mentioned sum co-array and differential co-array are virtual arrays obtained by virtualizing the original arrays.

[0057] In order to obtain the above virtual array, the transmitting antenna and receiving antenna of the terminal ISAC node need to be set as follows: Figure 3 Specifically, the receiving antenna and the transmitting antenna are both set to be two-level nested arrays, the basic interval of the two-level nested array is d0, and any antenna is taken as the reference array to calculate the degree of freedom f of the reference array N ; Set scaling factor D = f N , multiply the intervals between adjacent array elements in the other antenna by the scaling factor D to generate a differential common array (virtual array) that is a uniform array. Figure 3 As shown, in this embodiment, the receiving antenna is used as the reference array, and the degree of freedom of the generated differential common array can reach f = f M ×f N , f M is the degree of freedom of the transmitting antenna. It can be seen that the degree of freedom finally achieved can reach (MN) 2 The order of magnitude can significantly improve the angle estimation accuracy.

[0058] In this embodiment, the ISAC system operates based on the SRS signal in 5G NR. SRS is an OFDM signal, including a ZC (Zadoff–Chu) sequence predefined for each terminal ISAC node, Q OFDM symbols and P subcarriers, and Δf is the subcarrier spacing. SRS has a round-robin mechanism and ZC sequence orthogonal characteristics, which just meet the requirements of the present invention. The SRS antenna round-robin mode is recorded as X-Transmit M-Round (XTMR), that is, X antennas are selected at a time from M (M=1, 2, 3, 4) transmitting antennas for transmitting SRS signals.

[0059] The transmission modes of the transmitter are divided into 1TMR mode and XTMR mode; 1TMR mode: in M ​​consecutive SRS periods, each transmitting antenna sends SRS signals separately in turn; XTMR mode: the NR standard stipulates that each antenna port uses the same resource block and the same basic ZC sequence. The SRS signals sent by each antenna port are distinguished from each other by different cyclic shifts of the basic sequence. The ZC sequence satisfies the shift (not equal to the sequence length) and the sequence after is orthogonal to the previous sequence, so the M transmitting antennas in the system can send SRS signals simultaneously.

[0060] In baseband transmission, s p,q Indicates the frequency domain symbol of the qth OFDM symbol of the SRS signal and the pth subcarrier. When the system adopts the 1TMR mode, the OFDM symbol transmitted by the mth transmitting antenna is s p,q,m ; When using the XTMR mode, the OFDM symbol vector transmitted by the qth OFDM symbol and the mth antenna is s q,m .

[0061] The receiving end processes the frequency domain received signal according to the round-robin mode, specifically:

[0062] For the 1TMR mode: Assume that there are K sensing targets in the environment, and their departure angle or arrival angle is θ k ; In the mth SRS cycle, the signal on the qth OFDM symbol and the pth subcarrier received by the ISAC node is:

[0063]

[0064] Among them, α k is the reflection coefficient of the target, λ is the wavelength, σ RCS is the radar cross section, d b,k is the distance from the ISAC node to the kth target;

[0065] a La (θ k ) is the array steering vector, L a =M or N, d la is the distance from the lath antenna (transmitting antenna or receiving antenna) to the reference antenna, la∈[1,La],[a(θ)] m is the mth term in the array steering vector, f p is the frequency of the pth subcarrier, c is the speed of light;

[0066] h SI is the self-interference vector, n p,q is the noise vector;

[0067] Since the reference signal is known, the reference signal can be subtracted from the received signal point, and the self-interference signal can be weakened by the self-interference elimination technology; after M SRS cycles, all received signals are concatenated to form a column vector y p,q :

[0068]

[0069] Where B is the array manifold matrix, B = [b(θ1), b(θ2), …, b(θ K )],b(θ k ) is the array steering vector, d t,m is the distance between the mth transmitting antenna and the transmitting reference antenna, d r,n is the distance between the nth receiving antenna and the receiving reference antenna, m∈[1,M], n∈[1,N];

[0070] x p,q is the signal vector, x p,q =[α1,α2,…,α K ] T ;

[0071] For XTMR mode: Assume that there are K sensing targets in the environment, and their departure angle or arrival angle is θ k ;The data received by the ISAC node is:

[0072]

[0073] Among them, α k is the reflection coefficient of the target, λ is the wavelength, σ RCS is the radar cross section, d b,k is the distance from the ISAC node to the kth target;

[0074] a La (θ k ) is the array steering vector, L a =M or N, d la is the distance between the lath antenna and the reference antenna, la∈[1,La], f c is the carrier frequency, f c >>P△f, Δf is the subcarrier spacing, P is the number of subcarriers;

[0075] S q is the sending signal matrix, H SI is the self-interference matrix, N q is the noise matrix;

[0076] The qth OFDM symbol signal received by the ISAC node is decoded to:

[0077]

[0078] Where [a(θ)] m is the mth term in the array steering vector;

[0079] The self-interference signal is weakened by the self-interference elimination technology, and then all the obtained signals are concatenated to form a column vector y q :

[0080]

[0081] Where B is the array manifold matrix, B = [b(θ1), b(θ2), …, b(θ K )],b(θ k ) is the array steering vector, d t,m is the distance between the mth transmitting antenna and the transmitting reference antenna, d r,n is the distance between the nth receiving antenna and the receiving reference antenna, m∈[1,M], n∈[1,N];

[0082] x q is the signal vector, x p,q =[α1,α2,…,α K ] T .

[0083] Next, the angle is estimated according to the improved MUSIC angle measurement method based on virtual array manifold matrix dimensionality reduction. Specifically, for the processed received signal y p,q Or receive signal y q , calculate its covariance matrix Z, or Then the covariance matrix Z is vectorized to obtain z = vec[Z], and then the following calculation is performed to complete the angle estimation:

[0084] Calculate the band signal covariance matrix, for a given frequency point f p , the covariance matrix of the calculated signal in the frequency domain is:

[0085] R(f p )=z(f p ) H (f p )

[0086] The spatial domain is divided into L grids according to the spacing of Δθ. The signal directions of K reflected signals are all located on the grid points. L>>K, then the angle domain is expressed as θ={θ1,θ2,…,θ L}, and then obtain the virtual array manifold matrix A L :

[0087] A L =[a(θ1),a(θ2),…,a(θ L )]

[0088] Among them, a(θ l )=exp(-j2πf c d1sin(θ l ) / c), j is the imaginary unit, f c is the carrier frequency, d1 = d0ln(exp(-d2)⊕exp(d2)), d0 is the antenna base spacing (half wavelength), ln represents the natural logarithm, ⊕ is the Kronecker product, d2 = [d t,1 +d r,1 ,d t,1 +d r,2 ,…,d t,M +d r,N ] T , d t,m is the distance between the mth transmitting antenna and the transmitting reference antenna, d r,nis the distance between the nth receiving antenna and the receiving reference antenna, m∈[1,M], n∈[1,N], c is the speed of light;

[0089] According to the ISAC system’s differential common array setting, the required uniform array antenna position is obtained, and its array manifold matrix is ​​obtained.

[0090]

[0091] in, j is the imaginary unit, f c is the carrier frequency, d0 is the antenna base spacing, d3=d0(0:V-1) T , V is the number of differential common array antennas, c is the speed of light;

[0092] The manifold matrix of the required uniform array antenna can be linearly obtained from the obtained virtual array, that is: Therefore, the transformation matrix B can be obtained by least squares fitting: L ; Through this manifold transformation, the dimension of the covariance matrix is ​​changed from becomes Where V is the number of differential co-array antennas, M and N are the number of transmitting and receiving antennas respectively, usually satisfying V < (MN) 2 , thus simplifying the complexity of spatial spectrum calculation;

[0093] Apply transformation matrix B L Get the new covariance matrix

[0094]

[0095] Among them, R S is the transmitted signal power covariance matrix, which is a diagonal matrix, and the elements on the diagonal are the power of the transmitted signal in each direction; σ 2 is the noise power;

[0096] The spatial smoothing method is used (the basic idea is to divide the equidistant linear array into L b The covariance matrix R after forward and backward smoothing is calculated by averaging the covariance matrices of each sub-array. (bf) :

[0097]

[0098] in, is the covariance matrix The first b subarray, l b ∈[1,L b ]; for The conjugate of

[0099] Since the signal subspace and noise subspace E of the system n Orthogonal, that is Therefore, for R (bf) Perform eigenvalue decomposition R (bf) =UΣU H , and obtain the noise subspace E n , and then calculate the spatial spectrum P of the received signal mu :

[0100]

[0101] in, is the steering vector of the uniform array;

[0102] Search and get the spatial spectrum P mu The indices of the K peaks in , then the angle estimates of the K perceived targets are obtained;

[0103] Furthermore, for the 1TMR mode, based on the above calculation, P sub-band signals are processed separately to obtain the spatial spectrum of each sub-band, and the final spatial spectrum is obtained by averaging them, thereby obtaining the angle estimation value; for the XTMR mode, based on the above calculation, the frequency band signal and the spatial spectrum of the frequency band are processed to obtain the angle estimation value.

[0104] The following simulation is used to verify the beneficial effects of the present invention; the parameters are set as follows: the system carrier frequency is 5 GHz, the bandwidth is 15.36 MHz, the subcarrier spacing is 30 kHz, the algorithm grid point spacing is 0.2 degrees, the terminal ISAC node has 3 transmitting antennas (M=3), 4 receiving antennas (N=4), and the three targets are 100 meters away from the ISAC node. The 1TMR and 3TMR modes are used for simulation verification, respectively, as Examples 1 and 2. At the same time, the classic MUSIC algorithm is used as a comparative example. Comparative Example 1 is implemented based on an ISAC system with 7 uniform linear arrays for receiving antennas, and Comparative Example 2 is implemented based on an ISAC system with 28 uniform linear arrays for receiving antennas.

[0105] First, the estimated resolution of the present invention is compared with that of the comparative example. The simulation sets the signal-to-noise ratio to 5 dB. The results are as follows: Figures 4 to 9 As shown in , the dotted line represents the target angle and the solid line is the spatial map; Figure 4 The simulation result diagram of comparative example 1 is shown. The three targets are respectively -37.2 degrees, 5.3 degrees, and 17.3 degrees in front of the ISAC node; Figure 5 The simulation result diagram of comparative example 1 is shown. The three targets are respectively -37.2 degrees, 5.3 degrees, and 18.3 degrees in front of the ISAC node; Figure 6As shown in the simulation result diagram of Example 1 of the present invention, the three targets are respectively -37.2 degrees, 5.3 degrees, and 8.3 degrees in front of the ISAC node; Figure 7 The simulation result diagram of comparative example 2 is shown. The three targets are respectively -37.2 degrees, 5.3 degrees, and 7.8 degrees in front of the ISAC node. Figure 8 The simulation result diagram of comparative example 2 is shown. The three targets are respectively -37.2 degrees, 5.3 degrees, and 8.3 degrees in front of the ISAC node; Fig. 9 The simulation result diagram of Example 2 of the present invention is shown, and the three targets are respectively -37.2 degrees, 5.3 degrees, and 7.8 degrees in front of the ISAC node; Fig.10 The figure shows the simulation result of Example 2 of the present invention. The three targets are respectively -37.2 degrees, 5.3 degrees, and 8.3 degrees in front of the ISAC node. It can be seen from the figure that the estimated resolution of Example 1 is only about 13 degrees, the estimated resolution of Example 1 of the present invention is about 3 degrees, the estimated resolution of Example 2 is about 3 degrees, and the estimated resolution of Example 2 of the present invention is about 3 degrees. Therefore, the present invention greatly improves the angle estimation resolution under the condition that the total number of transmitting and receiving antennas is the same. On the other hand, under the premise of meeting the same angle estimation resolution, the present invention requires fewer antenna array elements, which greatly reduces the equipment cost and volume.

[0106] Secondly, the estimated target numbers of the present invention and the comparative example are compared, and the signal-to-noise ratio is set to 5dB in the simulation. The results are as follows: Figure 11 to Figure 13 As shown in , the dotted line represents the target angle and the solid line is the spatial map; Fig.11 The simulation result diagram of comparative example 1 is shown, and the five targets are evenly arranged at -50 degrees to 50 degrees of the ISAC node; Fig.12 The simulation result diagram of Example 1 of the present invention is shown, and thirteen targets are evenly arranged at -30 degrees to 30 degrees in front of the ISAC node; Fig.13 The simulation result diagram of Example 2 of the present invention is shown. Thirteen targets are evenly arranged at -30 degrees to 30 degrees in front of the ISAC node. As can be seen from the figure, the number of estimated targets of the present invention is greater, and the number of estimated targets exceeds the number of antenna array elements. Theoretically, the number of estimated targets can reach (f M f N -1) pcs.

[0107] Finally, the RMSE (Root Mean Squared Error) of the present invention and the comparative example are compared. Fig.14Shown is a comparison diagram of the RMSE curves of Example 1, Example 2, Comparative Example 1 and Comparative Example 2 of the present invention, setting the signal-to-noise ratio SNR = -20:5:20 (dB), and the three targets are respectively -40 degrees to -30 degrees, 15 degrees to 25 degrees, and 50 degrees to 60 degrees; it can be seen from the figure that compared with Comparative Example 1, the present invention can effectively improve the accuracy of the angle estimation method; compared with Comparative Example 2, the present invention can bring a smaller loss in angle estimation accuracy; specifically, the RMSE of Comparative Example 1 can reach about 8 degrees, the RMSE of the present invention can reach about 0.35 degrees, and the RMSE of Comparative Example 2 can reach about 0.19 degrees.

[0108] In summary, the present invention aims at the angle estimation problem of terminal devices with limited number of antennas in the integrated communication and perception scenario, and proposes a synaesthesia node transceiver array antenna that supports high-precision angle estimation, so that the system degree of freedom reaches the fourth power of the number of transmitting or receiving antenna elements, greatly improving the accuracy, resolution, and maximum number of targets of angle estimation. Further, an improved MUSIC angle estimation method based on virtual array manifold matrix dimensionality reduction is proposed, which can be compatible with the use of uplink reference signals (such as SRS, demodulation reference signals) in cellular signals to achieve direction perception, without affecting cellular communication performance (channel estimation, data transmission, etc.), and realizes low-complexity and high-precision direction perception, which is easy to realize the efficient integration of positioning perception and cellular communication system.

[0109] The above description is only a specific implementation mode of the present invention. Any feature disclosed in this specification, unless otherwise stated, can be replaced by other alternative features that are equivalent or have similar purposes; all the disclosed features, or all the steps in the methods or processes, except for mutually exclusive features and / or steps, can be combined in any way.

Claims

1. A 5G new air interface angle estimation method for a communication perception integrated system, the communication perception integrated system includes an ISAC node and K perception targets, and the transmitting end of the ISAC node adopts a 1TMR mode or an XTMR mode; characterized in that: The following steps are involved: Step 1. The receiving end of the ISAC node processes the echo signal to obtain a received signal; Step 2. Calculate the covariance matrix Z of the received signal, and vectorize the covariance matrix Z of the received signal into z; Step 3. For the preset frequency f p , calculate the covariance matrix of the received signal in the frequency domain: R = z(f p ) H (f p ); Step 4. Divide the airspace into L grids at equal intervals to obtain the angle domain representation, and then obtain the virtual array manifold matrix A L ; According to the system's differential common array, a uniform array antenna is set to obtain its manifold matrix according to Solve to get the transformation matrix B L ; Step 5. According to the transformation matrix B L , converted to the covariance matrix The covariance matrix is ​​smoothed using a spatial smoothing method. Smoothing is performed to obtain the covariance matrix R (bf) ; Step 6. For the covariance matrix R (bf) Perform eigenvalue decomposition to obtain the noise subspace E n , and then calculate the spatial spectrum P of the received signal mu ; Step 7. According to the spatial spectrum information, the angle estimation values ​​of K perceived targets are obtained.

2. According to the 5G new air interface angle estimation method of the communication perception integrated system of claim 1, it is characterized in that: In step 1, when the transmitter of the ISAC node adopts the 1TMR mode, the received signal is y p,q : y p,q =Bx p,q +n p,q Among them, x p,q is the signal vector, x p,q =[α1,α2,…,α K ] T ; α k is the reflection coefficient of the kth perceived target, λ is the wavelength, σ RCS is the radar cross section, d b,k is the distance from the ISAC node to the kth sensing target, and K is the number of targets; B is the array manifold matrix, B = [b(θ1), b(θ2), …, b(θ K )];b(θ k ) is the array steering vector, d t,m is the distance between the mth transmitting antenna and the transmitting reference antenna, d r,n is the distance between the nth receiving antenna and the receiving reference antenna, m∈[1,M], n∈[1,N], θ k is the arrival angle of the kth perceived target, f p is the frequency of the pth subcarrier, c is the speed of light, M is the number of transmitting antennas, and N is the number of receiving antennas; n p,q is the noise vector.

3. According to the 5G new air interface angle estimation method of the communication perception integrated system of claim 1, it is characterized in that: In step 1, when the transmitter of the ISAC node adopts the XTMR mode, the received signal is y q : y q =Bx q +n q Among them, x q is the signal vector, x p,q =[α1,α2,…,α K ] T ; α k is the reflection coefficient of the kth target, λ is the wavelength, σ RCS is the radar cross section, d b,k is the distance from the ISAC node to the kth target, K is the number of targets; B is the array manifold matrix, B = [b(θ1), b(θ2), …, b(θ K )];b(θ k ) is the array steering vector, d t,m is the distance between the mth transmitting antenna and the transmitting reference antenna, d r,n is the distance between the nth receiving antenna and the receiving reference antenna, m∈[1,M], n∈[1,N], θ k is the arrival angle of the kth target, f c is the carrier frequency, c is the speed of light; n q is the noise matrix.

4. According to the 5G new air interface angle estimation method of the communication perception integrated system of claim 1, it is characterized in that: In step 4, the angle domain is represented by θ = {θ1, θ2, …, θ L }, virtual array manifold matrix A L for: A L =[a(θ1),a(θ2),…,a(θ L )] Among them, a(θ l )=exp(-j2πf c d1sin(θ l ) / c),θ l is the angle in the angle domain, l∈[1,L], j is the imaginary unit, f c is the carrier frequency, d1=d0ln(exp(-d2)⊕exp(d2)), d0 is the antenna base spacing, d2=[d t,1 +d r,1 ,d t,1 +d r,2 ,…,d t,M +d r,N ] T , d t,m is the distance between the mth transmitting antenna and the transmitting reference antenna, d r,n is the distance between the nth receiving antenna and the receiving reference antenna, m∈[1,M], n∈[1,N], M is the number of elements of the receiving array antenna on the ISAC node, N is the number of elements of the transmitting array antenna on the ISAC node, and c is the speed of light.

5. According to the 5G new air interface angle estimation method of the communication perception integrated system of claim 1, it is characterized in that: In step 4, the uniform array antenna manifold matrix for: in, θ l is the angle in the angle domain, l∈[1,L], j is the imaginary unit, f c is the carrier frequency, d0 is the antenna base spacing, d3=d0(0:V-1) T , V is the number of elements of the uniform array antenna, and c is the speed of light.

6. The 5G new air interface angle estimation method of the communication perception integrated system according to claim 1 is characterized in that: In step 4, the transformation matrix B L Obtained by least squares fitting.

7. The 5G new air interface angle estimation method of the communication perception integrated system according to claim 1, characterized in that: In step 6, the spatial spectrum P mu for: in, is the array steering vector of the uniform array antenna.

8. The 5G new air interface angle estimation method of the communication perception integrated system according to claim 1, characterized in that: In step 7, when the transmitting end of the ISAC node adopts the 1TMR mode, repeat steps 3 to 6 to calculate the spatial spectrum of P sub-bands, and calculate the average value as the final spatial spectrum, search for the indexes of K peaks in the final spatial spectrum, and obtain the angle estimation values ​​of K perceived targets; when the transmitting end of the ISAC node adopts the XTMR mode, search the final spatial spectrum P mu The indices of the K peaks in the image correspond to the angle estimates of the K perceived targets.

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