Integrated sensing and communication cooperative transmission method based on space-time block coding OFDM signal

Abstract

Disclosed in the present invention is an integrated sensing and communication (ISAC) cooperative transmission method based on a space-time block coding (STBC) OFDM signal, comprising: establishing an ISAC cooperative transmission system model based on an STBC-OFDM signal, and receiving an integrated signal echo containing state information of an unmanned aerial vehicle while implementing joint transmission to the unmanned aerial vehicle performed by multiple base stations using an STBC-OFDM integrated signal; the unmanned aerial vehicle pre-processing received communication signals to achieve orthogonal separation of s2m-1 and s2m and cooperative transmission diversity gain; processing echo signals received by a first base station and a second base station to acquire observation information of the first base station and the second base station for the unmanned aerial vehicle; implementing multi-base station cooperative tracking and trajectory prediction of the unmanned aerial vehicle by using an extended Kalman filter method; and the base stations uploading observation parameters to a control center by means of fronthaul links, and the control center fusing sensing results of the multiple base stations and performing tracking and prediction on the trajectory of the unmanned aerial vehicle by using the extended Kalman filter method.

Description

Communication-aware integrated cooperative transmission method based on space-time block coded OFDM signals Technical Field

[0001] The present invention relates to a communication-perception-integrated collaborative transmission method based on space-time block coding OFDM signals, and belongs to the field of communication-perception integration. Background Art

[0002] Integrated sensing and communication (ISAC) technology has attracted significant interest from both academia and industry due to its potential to reduce hardware costs and improve spectrum efficiency. By leveraging the transmission signals, equipment, and architecture of existing cellular networks, perceptive mobile networks (PMNs) are expected to go beyond communication-only networks and provide ubiquitous radio sensing capabilities. However, single base stations (BSs) are limited by bandwidth and power, limiting their sensing performance and robustness. An effective approach to address this issue is to leverage collaboration between different BSs, which has been studied in cloud-based radio access networks (C-RANs). L. Pucci et al. studied the performance of dual-base sensing using a 5G NR system. S. Shi et al. proposed an interleaved frame structure to achieve dual-base cooperative sensing. J. Wu et al. studied multi-ISAC node collaboration for target tracking based on extended Kalman filtering (EKF) technology, significantly improving positioning and velocity measurement accuracy. K. Ji et al. verified through hardware testing the improved performance of two-node cooperative sensing through data fusion in an ISAC system using a 5G NR frame structure.

[0003] In communication networks, joint transmission (JT) can significantly improve the throughput of communication system users. Multiple base stations in a C-RAN can collaboratively transmit the same data stream to improve the received signal-to-noise ratio (SNR). Furthermore, to reduce inter-cell interference (ICI), base stations can work together in a distributed manner to transmit coded data streams, such as using space-time block coding (SFBC), effectively reducing the bit error rate (BER). However, most current research on the design of integrated communication and perception collaboration in cellular networks focuses primarily on communication or perception performance, with little research on how to simultaneously achieve joint transmission and collaborative perception. Specifically, the design of integrated communication and perception collaborative transmission systems faces the following challenges: In joint transmission, base stations transmit the same signal, which prevents the perception receiving unit from eliminating interference and generates false targets. While research has focused on the fusion of multi-node single-base observation data, further research is needed on how to fuse multi-node single-base and dual-base observation data.

[0004] Summary of the Invention

[0005] In view of the problems of Cooperative Integrated Sensing and Communication (Co-ISAC) collaborative transmission such as difficulty in eliminating interference and difficulty in fusing multi-node observation data, the main purpose of the present invention is to provide a method for cooperative transmission of Co-ISAC based on space-time block coding OFDM signals. While multiple base stations jointly transmit OFDM integrated signals based on space-time block coding (SFBC) to the UAV, the single-base and dual-base observation information of the UAV is obtained by using the echo of the integrated signal. The base station transmits the observation parameters to the control center through the forward link, and the control center fuses the perception results of multiple base stations and uses the Kalman filtering method to track and predict the trajectory of the UAV, thereby achieving high-precision trajectory tracking.

[0006] The purpose of the present invention is achieved through the following technical solutions.

[0007] The communication perception integrated collaborative transmission method based on space-time block coded OFDM signals disclosed in the present invention includes the following steps:

[0008] Step 1: Establish an ISAC cooperative transmission system model based on STBC-OFDM signals, so that multiple base stations can jointly transmit to the UAV using STBC-OFDM integrated signals while receiving the integrated signal echo containing the UAV status information;

[0009] Step 1.A: Set up the ISAC system's collaborative transmission model;

[0010] The cooperative transmission model includes two ISAC base stations and a UAV hovering above the two base stations; each ISAC base station is equipped with a uniform planar array transmitting antenna and a uniform planar array receiving antenna; the number of transmitting antennas N t =N x ×N y , the number of receiving antennas N r =N t ; N x is the number of horizontal antennas in the array, N y is the number of longitudinal antennas in the array; the receiving antenna is used to receive the echo signal of the downlink signal transmitted by the base station and the scattered signal of the downlink signal transmitted by other base stations; within the mission duration T, the echo signal and scattered signal received by the base station are used for UAV cooperative perception. A mission cycle is divided into N time slots, and the length of each time slot is ΔT = T / N. The base stations are connected through the forward link to share information; the state of the UAV remains unchanged within a time slot, and the state information of the UAV is expressed as x n =[x n ,y n ,z n ,v x,n ,v y,n ,v z,n ] T (1)

[0011] Among them, u n =[x n ,y n ,z n ] T , v n =[v x,n ,v y,n ,v z,n ] T Represent the position coordinates and velocity coordinates respectively. The position coordinates of the two ISAC base stations are expressed as:

[0012] Step 1.B: Based on the cooperative transmission model (1) and (2) constructed in step 1.A, establish the STBC-OFDM transmission signal model;

[0013] In the nth time slot, the mth block data symbol after serial-to-parallel conversion is represented by s m∣n =[s 1,m|n ,…s L,m|n ] T (3)

[0014] Where L is the number of data symbols contained in the data symbol vector, s l,m|n is the lth data symbol;

[0015] Each time slot contains 2M block data symbol vectors, so the data symbol vector contained in the nth time slot is expressed as:

[0016] STBC-OFDM operates on every two consecutive block data symbol vectors to obtain the STBC sub-matrices corresponding to the 2m-1th and 2mth data symbol vectors, and establishes the STBC-OFDM transmission signal model, which is expressed as follows:

[0017] In the 2m-1th OFDM symbol period of the nth time slot, the OFDM modulated s 2m-1∣n and s 2m∣n Transmitted by two ISAC base stations respectively; in the 2mth OFDM symbol period, the OFDM modulated and Transmitted by two ISAC base stations respectively;

[0018] Step 1.C: Based on the ISAC system cooperative transmission model and STBC-OFDM transmission signal model established in steps 1.A and 1.B, the channel between the drone and the two ISAC base stations during the transmission of the mth STBC block in the nth time slot is obtained. The expression is as follows

[0019] Among them, T s is the duration of each symbol of the OFDM signal; ρ0 is the path loss at the reference distance of 1m, G t is the multi-angle antenna gain, represents the distance between base station j and the UAV in the nth time slot, Indicated by The delay generated, represents the Doppler frequency deviation caused by the relative motion between the UAV and the base station j. The calculation formulas of the three are as follows:

[0020] Step 1.D: The channel between the drone and the two base stations established according to step 1.C Omitting the subscript time slot n, the communication signal received by the drone in the 2m-1th and 2mth OFDM symbol periods of the nth time slot after OFDM demodulation is expressed as follows:

[0021] in, Therefore The discrete Fourier transform of is a diagonal matrix with diagonal elements, They represent the DFT expressions of the received noise in the 2m-1 and 2m OFDM symbol periods respectively, and their distribution satisfies

[0022] Step 1.E: Based on the communication signal expression (8) received by the UAV in step 1.D, a scattering path model of the UAV reflected signal is further established. That is, the downlink STBC-OFDM signal sent by the two ISAC base stations is reflected by the UAV to generate four scattering paths, which are the two-way scattering paths from the first base station to the UAV. Scattering path from the second base station to the drone and then to the first base station The second base to the two-way scattering path of the drone Scattering path from the first base station to the drone and then to the second base station The expressions are as follows:

[0023] Where ξ is the radar cross section (RCS), Indicated by and The delay generated, Indicates the Doppler frequency deviation and time delay caused by the relative motion between the drone and base stations i and j Doppler frequency shift The incident angle θ with the horizontal direction of the signal n and the vertical incident angle φ n The expressions are as follows:

[0024] Step 1.F: Based on the scattering path model established in step 1.E, the echo signals received by the first base station and the second base station in the 2m-1th and 2mth OFDM symbol periods of the nth time slot are respectively expressed as follows after OFDM demodulation:

[0025] Where b(θ,φ) represents the steering vector of the base station receiving antenna array, Indicates The DFT result of is a diagonal matrix with diagonal elements; DFT expression of the noise received by base station n in the mth time slot;

[0026] Step 1.G: By establishing the ISAC cooperative transmission system model based on STBC-OFDM signal as shown in formula (5)(6)(8)(9)(11), multiple base stations can use STBC-OFDM integrated signal to transmit cooperatively to the UAV while receiving the integrated signal echo containing the UAV status information.

[0027] Step 2: Based on the ISAC cooperative transmission system model constructed in step 1, the drone pre-processes the received communication signal to achieve s 2m-1 and s 2m Orthogonal separation and cooperative transmission diversity gain.

[0028] Step 2.A: Based on the STBC-OFDM cooperative transmission system model, reconstruct the signal received by the drone into the following expression:

[0029] Step 2.B: Based on the signal reconstructed in Step 2.A, the drone multiplies the received signal by the Hermitian transpose of the channel matrix and performs STBC decoding, obtaining the following results:

[0030] The noise matrix is ​​expressed as follows:

[0031] Step 2.C: According to the operation of steps 2.A and 2.B, realize s 2m-1 and s 2m Orthogonal separation and cooperative transmission diversity gain

[0032] Step 3. Based on the ISAC collaborative transmission system model based on STBC-OFDM signals constructed in step 1, a STBC-OFDM signal perception and processing process adapted to the system is constructed to perform signal processing on the echo signals received by the first base station and the second base station, so as to enable the first base station and the second base station to obtain the observation information of the drone.

[0033] Step 3.A: For the first base station, reconstruct the received signal (12) as follows:

[0034] Step 3.B: Based on the received signal reconstructed in step 3.A, multiply it by the Hermitian transposed matrix to implement STBC decoding. The decoding result is as follows:

[0035] in,

[0036] Step 3.C: Based on the signal obtained in step 3.B, perform angle domain matched filtering on it to obtain Observations In order to obtain the time delay, range Doppler and velocity Doppler of the UAV, point-by-point division of (17) is performed to obtain the following results:

[0037] Among them, G t,b is the angle domain matched filter gain;

[0038] Step 3.D: According to the result obtained in step 3.C All the data obtained in the nth time slot Store in odd-even order, as shown below:

[0039] Step 3.E: Based on the results obtained in step 3.D Performing 2D-DFT operation on it, the first base station obtains the observation result of the drone, which is expressed as follows

[0040] Step 3.F: From step 3.A to step 3.E, the STBC-OFDM signal perception and processing process in the ISAC collaborative transmission system is completed. The second base station obtains the observation information of the second base station on the drone according to the same process as the first base station, thereby realizing the acquisition of the observation information of the drone by the first and second base stations.

[0041] Step 4: Based on the observation information of the UAV by the first and second base stations obtained in step 3, the extended Kalman filter is used to realize multi-base station collaborative tracking and trajectory prediction of the UAV; the base station uploads the observation parameters to the control center through the forward link. The control center integrates the perception results of multiple base stations and uses the extended Kalman filter method to track and predict the trajectory of the UAV, thereby achieving high-precision trajectory tracking.

[0042] Step 4.A: The relationship between the observation parameters of multiple base stations and the drone status is expressed as follows:

[0043] in, and Represent the measurement errors of time delay, Doppler, horizontal angle and vertical angle respectively; and The mean is 0 and the standard deviation is σ τ ,σ D ,σ θ and σ β The standard deviation expressions of time delay, range Doppler, velocity Doppler, horizontal angle and vertical angle are given as follows:

[0044] Γ sis the signal-to-noise ratio at the receiver, T ac =ΔT / 2 represents the coherent accumulation time, θ 3dB and β 3dB represent the horizontal and vertical beam widths respectively, and B is the transmission signal bandwidth.

[0045] Step 4.B: Based on the parameter relationship given in Step 4.A, define the correlation matrix and reconstruct Equation (22) into the following concise form:

[0046] in,

[0047] Then define the observation parameter matrix and measurement error matrix Equation (22) is finally transformed into the following equation: n =g(x n )+w n (25)

[0048] Step 4.C: Based on equation (25) in step 4.B, the UAV state evolution model and prediction mean square error matrix are expressed as follows: n+1∣n =Px n +Z n (26) M n+1∣n =PM n P H (27)

[0049] Among them, P is the state transfer matrix, expressed as

[0050] is the noise term, and its corresponding z n+1 The covariance of is:

[0051] Step 4.D: Based on the UAV state evolution model and prediction mean square error matrix given in step 4.C, the Kalman gain matrix is ​​expressed as follows:

[0052] Where G is the Jacobian matrix of g(·), and G n+1 =G(x n+1∣n );

[0053] Step 4.E: Based on the Kalman gain matrix given in step 4.D, at the n+1th time slot, the state and mean square error matrix of the UAV are expressed as follows: n+1 =x n+1∣n +K n+1 (χ n+1-g(x n+1∣n )) (31) M n+1 =(IK n+1 G n+1 )M n+1∣n (32)

[0054] Step 4.F: Based on the drone state and mean square error matrix obtained in step 4.E, obtain the observation information based on the two ISAC base stations. Use the extended Kalman filter to achieve multi-base station collaborative tracking and trajectory prediction of the drone, obtain the drone's flight information, and thus achieve high-precision trajectory tracking of the drone. Beneficial effects:

[0055] 1. The communication and perception integrated collaborative transmission method based on space-time block coded OFDM signals disclosed in the present invention addresses the problem of serious interference between cells in the ISAC collaborative transmission system, which affects the communication and perception performance. An ISAC collaborative transmission system model based on STBC-OFDM signals is established. Multiple base stations use STBC-OFDM integrated signals to jointly transmit to drones while receiving integrated signal echoes containing drone status information, which is used to further study multi-node observation information acquisition and data fusion.

[0056] 2. This invention discloses a method for integrated communication perception and collaborative transmission based on space-time block coded (STC) OFDM signals. It also proposes an ISAC collaborative transmission system model based on STBC-OFDM signals. It proposes an STBC-OFDM signal perception and processing flow adapted to this system. The echo signals received by the two ISAC base stations are subjected to STBC decoding, symbol storage, angle matched filtering, point-by-point division, and two-dimensional DFT processing, enabling the first and second base stations to acquire single-base and multi-base observation information of the drone, respectively. Because the channel remains fixed during the transmission of two adjacent OFDM signals, the transmission model and signal processing method proposed in this invention are universally applicable.

[0057] 3. The disclosed communication-perception integrated collaborative transmission method based on space-time block coded OFDM signals addresses the issue of multi-node base station observation data fusion in ISAC collaborative transmission systems. By employing extended Kalman filtering technology, this method enables multi-base station collaborative tracking and trajectory prediction for drones. Specifically, base stations transmit observation parameters via a forward link to a control center, where the control center integrates the multi-base station perception results and uses Kalman filtering to track and predict the drone's trajectory, achieving high-precision trajectory tracking. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] 1 is an overall flow chart of a communication perception integrated collaborative transmission method based on space-time block coded OFDM signals and embodiment 1 of the present invention;

[0059] 2 is a flow chart of the communication perception integrated collaborative transmission method based on space-time block coded OFDM signals of the present invention and the establishment of an ISAC collaborative transmission system model based on STBC-OFDM signals, a communication reception signal model, and an echo signal model in Example 1;

[0060] FIG3 is a flow chart of the communication perception integrated cooperative transmission method based on space-time block coded OFDM signals of the present invention and preprocessing of communication signals received by the UAV in Example 1 to achieve joint transmission diversity gain;

[0061] FIG4 is a flowchart of the communication perception integrated cooperative transmission method based on space-time block coded OFDM signals of the present invention and the STBC-OFDM signal perception processing flow adapted to the ISAC cooperative transmission system in Example 1, which realizes the acquisition of single-base and multi-base observation information of the UAV by base stations 1 and 2;

[0062] 5 is a flowchart of the communication perception integrated cooperative transmission method based on space-time block coded OFDM signals of the present invention and the use of extended Kalman filtering technology in Example 1 to achieve multi-base station cooperative tracking and trajectory prediction of drones;

[0063] FIG6 is a diagram showing the variation of the positioning mean square error with the ranging / speed measurement variance after the fusion of base station observation data according to the communication perception integrated cooperative transmission method based on space-time block coded OFDM signals of the present invention and the implementation method in Example 1; FIG6 is a simulation diagram showing the variation of the ranging mean square error with the ranging variance; FIG6 is a simulation diagram showing the variation of the ranging mean square error with the speed measurement variance; FIG6 is a simulation diagram showing the variation of the ranging mean square error with the speed measurement variance;

[0064] Figure 7 is a diagram showing the change of the positioning mean square error with the horizontal angle measurement variance / vertical angle measurement variance after the fusion of base station observation data based on the communication perception integrated collaborative transmission method of space-time block coded OFDM signals of the present invention and the implementation method in Example 1; wherein, Figure a is a simulation result diagram showing the change of the positioning mean square error with the horizontal angle measurement after the fusion of base station observation data; Figure b is a simulation result diagram showing the change of the positioning mean square error with the azimuth angle measurement variance after the fusion of base station observation data. DETAILED DESCRIPTION

[0065] The RIS-assisted backscatter communication perception integrated method of the present invention is described in detail below with reference to the accompanying drawings and specific embodiments.

[0066] Example 1

[0067] 1-5 , this embodiment describes in detail the steps for implementing the communication-aware integrated collaborative transmission method based on space-time block coded OFDM signals according to the present invention.

[0068] This case considers an ISAC cooperative transmission system model based on STBC-OFDM. Multiple base stations jointly transmit to the UAV based on the STBC-OFDM integrated signal to achieve diversity gain. At the same time, the echo signal is used to obtain the single-base and dual-base observation information of the UAV. The multi-node base station observation data is fused at the control center, and the extended Kalman filter is used to realize cooperative tracking and trajectory prediction of the UAV, achieving high-precision trajectory tracking.

[0069] Figure 2 is a flow chart of the communication perception integrated collaborative transmission method based on space-time block coded OFDM signals of the present invention and the establishment of the ISAC collaborative transmission system model based on STBC-OFDM, the communication receiving signal model and the echo signal model in Example 1.

[0070] As can be seen from Figure 2, the present invention addresses the problem of serious interference between base stations in the communication perception integrated cooperative transmission system, which affects the perception performance. By establishing an ISAC cooperative transmission system model based on STBC-OFDM signals, multiple base stations can use STBC-OFDM integrated signals to jointly transmit to drones while receiving integrated signal echoes containing drone status information.

[0071] Step 1.A: Set up the ISAC system's collaborative transmission model;

[0072] The cooperative transmission model includes two ISAC base stations and a UAV hovering above the two base stations; each ISAC base station is equipped with a uniform planar array transmitting antenna and a uniform planar array receiving antenna; the number of transmitting antennas N t =N x ×N y , the number of receiving antennas N r =N t ; N x is the number of horizontal antennas in the array, N y is the number of longitudinal antennas in the array; the receiving antenna is used to receive the echo signal of the downlink signal transmitted by the base station and the scattered signal of the downlink signal transmitted by other base stations; within the mission duration T, the echo signal and scattered signal received by the base station are used for UAV cooperative perception. A mission cycle is divided into N time slots, and the length of each time slot is ΔT = T / N. The base stations are connected through the forward link to share information; the state of the UAV remains unchanged within a time slot, and the state information of the UAV is expressed as x n =[x n ,y n ,z n ,v x,n ,v y,n ,v z,n ] T (1)

[0073] Among them, u n =[x n ,y n ,z n ] T , v n =[v x,n ,v y,n ,v z,n ] T Represent the position coordinates and velocity coordinates respectively. The position coordinates of the two ISAC base stations are expressed as:

[0074] Specifically in this example, N x =N y =8, ΔT=0.05s,

[0075] Step 1.B: Based on the cooperative transmission model (1) and (2) constructed in step 1.A, establish the STBC-OFDM transmission signal model;

[0076] In the nth time slot, the mth block data symbol after serial-to-parallel conversion is represented by s m∣n =[s 1,m|n ,…s L,m|n ] T (3)

[0077] Where L is the number of data symbols contained in the data symbol vector, s l,m|n is the lth data symbol;

[0078] Each time slot contains 2M block data symbol vectors, so the data symbol vector contained in the nth time slot is expressed as:

[0079] STBC-OFDM operates on every two consecutive block data symbol vectors to obtain the STBC sub-matrices corresponding to the 2m-1th and 2mth data symbol vectors, and establishes the STBC-OFDM transmission signal model, which is expressed as follows:

[0080] Therefore, in the 2m-1th OFDM symbol period of the nth time slot, the OFDM modulated s 2m-1∣n and s 2m∣n Transmitted by two ISAC base stations respectively; in the 2mth OFDM symbol period, the OFDM modulated and Transmitted by two ISAC base stations respectively.

[0081] Specifically in this example, 2M=2800.

[0082] Step 1.C: Based on the ISAC system cooperative transmission model and STBC-OFDM transmission signal model established in steps 1.A and 1.B, the channel between the drone and the two ISAC base stations during the transmission of the mth STBC block in the nth time slot is obtained. The expression is as follows

[0083] Among them, T s is the duration of each symbol of the OFDM signal; ρ0 is the path loss at the reference distance of 1m, G t is the multi-angle antenna gain, represents the distance between base station j and the UAV in the nth time slot, Indicated by The delay generated, represents the Doppler frequency deviation caused by the relative motion between the UAV and the base station j. The calculation formulas of the three are as follows:

[0084] Specifically in this example, ρ0=-60dB, G t =G b =21dB.

[0085] Step 1.D: The channel between the drone and the two base stations established according to step 1.C Omitting the subscript time slot n, the communication signal received by the drone in the 2m-1th and 2mth OFDM symbol periods of the nth time slot after OFDM demodulation is expressed as follows:

[0086] in, Therefore The discrete Fourier transform of is a diagonal matrix with diagonal elements, They represent the DFT expressions of the received noise in the 2m-1 and 2m OFDM symbol periods respectively, and their distribution satisfies Specifically in this example, σ 2 =0dB.

[0087] Step 1.E: Based on the communication signal expression (8) received by the UAV in step 1.D, a scattering path model of the UAV reflected signal is further established. That is, the downlink STBC-OFDM signal sent by the two ISAC base stations is reflected by the UAV to generate four scattering paths, which are the two-way scattering paths from the first base station to the UAV. Scattering path from the second base station to the drone and then to the first base station The second base to the two-way scattering path of the drone Scattering path from the first base station to the drone and then to the second base station The expressions are as follows:

[0088] Where ξ is the radar cross section (RCS), Indicated by and The delay generated, Indicates the Doppler frequency deviation and time delay caused by the relative motion between the drone and base stations i and j Doppler frequency shift The incident angle θ with the horizontal direction of the signal n and the vertical incident angle φ n The expressions are as follows:

[0089] Specifically in this example, ξ=10dB

[0090] Step 1.F: Based on the scattering path model established in step 1.E, the echo signals received by the first base station and the second base station in the 2m-1th and 2mth OFDM symbol periods of the nth time slot are respectively expressed as follows after OFDM demodulation:

[0091] Where b(θ,φ) represents the steering vector of the base station receiving antenna array, Indicates The DFT result of is a diagonal matrix with diagonal elements; DFT expression of the noise received by base station n in the mth time slot;

[0092] Step 1.G: By establishing the ISAC cooperative transmission system model based on STBC-OFDM signal as shown in formula (5)(6)(8)(9)(11), multiple base stations can use STBC-OFDM integrated signal to transmit cooperatively to the UAV and receive the integrated signal echo containing the UAV status information;

[0093] At this point, from step 1.A to step 1.G, multiple base stations have achieved joint transmission of STBC-OFDM integrated signals to the UAV while receiving integrated signal echoes containing UAV status information, which can be used to further study multi-node observation information acquisition and data fusion.

[0094] FIG3 is a flow chart of the communication perception integrated cooperative transmission method based on space-time block coded OFDM signals of the present invention and preprocessing of communication signals received by the UAV in Example 1 to achieve joint transmission diversity gain;

[0095] From Figure 3, we can see that in the ISAC cooperative transmission system model constructed based on step 1 of the present invention, the drone pre-processes the received communication signal to achieve s 2m-1 and s 2m Orthogonal separation and cooperative transmission diversity gain;

[0096] Step 2.A: Based on the STBC-OFDM cooperative transmission perception model, reconstruct the signal received by the drone into the following expression:

[0097] Step 2.B: Based on the signal reconstructed in Step 2.A, the drone multiplies the received signal by the Hermitian transpose of the channel matrix and performs STBC decoding, obtaining the following results:

[0098] Among them, the channel matrix is ​​obtained through channel estimation, and the noise matrix is ​​expressed as follows:

[0099] Step 2.C: According to the operation of steps 2.A and 2.B, realize s 2m-1 and s 2m Orthogonal separation and cooperative transmission diversity gain

[0100] So far, from step 2.A to step 2.C, the present embodiment has completed the pre-processing of the communication signal received by the drone using the signal processing method, and achieved s 2m-1 and s 2m orthogonal separation and diversity gain.

[0101] Figure 4 is a flowchart of the communication perception integrated collaborative transmission method based on space-time block coded OFDM signals of the present invention and the perception processing flow of STBC-OFDM adapted to the ISAC collaborative transmission system in Example 1, which realizes the single-base and multi-base observation information acquisition flow of base stations 1 and 2 for drones.

[0102] As can be seen from Figure 4, the present invention proposes an ISAC cooperative transmission system model based on STBC-OFDM signals constructed in step 1, and proposes an STBC-OFDM signal perception and processing flow adapted to the system, which processes the echo signals received by the first base station and the second base station, so that the first base station and the second base station can obtain the observation information of the drone;

[0103] Step 3.A: For base station 1, reconstruct the received signal (12) as follows:

[0104] Step 3.B: Based on the received signal reconstructed in step 3.A, multiply it by the Hermitian transposed matrix to implement STBC decoding. The decoding result is as follows:

[0105] in,

[0106] Step 3.C: Based on the signal obtained in step 3.B, perform angle domain matched filtering on it to obtain Observations Then, in order to obtain the time delay of the single base (double base) and the range and velocity Doppler of the target, point-by-point division of (17) is performed to obtain the following results:

[0107] Among them, G t,b is the angle domain matched filter gain.

[0108] Specifically in this example, G t,b =21dB

[0109] Step 3.D: According to the result obtained in step 3.C All the data obtained in the nth time slot Store in odd-even order, as shown below:

[0110] Step 3.E: Based on the results obtained in step 3.D Performing 2D-DFT operation on it, base station 1 obtains the observation results of single base and dual base, which are expressed as follows

[0111] At this point, from step 3.A to step 3.E, the STBC-OFDM signal perception and processing flow in the ISAC cooperative transmission system is completed. Similarly, the second base station's observation information of the drone can be obtained, enabling the first and second base stations to obtain observation information of the drone.

[0112] Figure 5 is a flow chart of the communication perception integrated collaborative transmission method based on space-time block coded OFDM signals of the present invention and the use of extended Kalman filtering technology in Example 1 to achieve multi-base station collaborative tracking and trajectory prediction of drones.

[0113] As can be seen from Figure 5, based on the observation information of the first and second base stations on the drone obtained in step 3, the extended Kalman filter is used to achieve multi-base station collaborative tracking and trajectory prediction of the drone. That is, the base station uploads the observation parameters to the control center through the forward link. The control center integrates the perception results of multiple base stations and uses the extended Kalman filter method to track and predict the trajectory of the drone, thereby achieving high-precision trajectory tracking.

[0114] Step 4.A: Express the relationship between the observation parameters of multiple base stations and the drone status as follows:

[0115] in, and Represent the measurement errors of time delay, Doppler, horizontal angle and vertical angle respectively; and The mean is 0 and the standard deviation is σ τ ,σ D ,σ θ and σ β The standard deviation expressions of time delay, range Doppler, velocity Doppler, horizontal angle and vertical angle are given as follows:

[0116] Γ s is the signal-to-noise ratio at the receiver, T ac =ΔT / 2 represents the coherent accumulation time, θ 3dB and β 3dB represent the horizontal and vertical beam widths respectively, and B is the transmission signal bandwidth;

[0117] Step 4.B: Based on the parameter relationship given in Step 4.A, define the correlation matrix and reconstruct Equation (22) into the following concise form:

[0118] in,

[0119] Then define the observation parameter matrix and measurement error matrix Equation (22) is finally transformed into the following equation: n =g(x n )+w n (25)

[0120] Step 4.C: Based on equation (25) in step 4.B, the UAV state evolution model and prediction mean square error matrix are expressed as follows: n+1∣n =Px n +z n (26) M n+1∣n =PM n P H (27)

[0121] Among them, P is the state transfer matrix, expressed as

[0122] is the noise term, and its corresponding z n+1 The covariance of is:

[0123] Step 4.D: Based on the UAV state evolution model and prediction mean square error matrix given in step 4.C, the Kalman gain matrix is ​​expressed as follows:

[0124] Where G is the Jacobian matrix of g(·), and G n+1 =G(x n+1∣n ).

[0125] Step 4.E: Based on the Kalman gain matrix given in step 4.D, at the n+1th time slot, the state and mean square error matrix of the drone are expressed as follows: n+1 =x n+1∣n +K n+1 (χ n+1 -g(x n+1∣n )) (31) M n+1 =(IK n+1 G n+1 )M n+1∣n (32)

[0126] At this point, from step 4.A to step 4.E, the observation information based on the two ISAC base stations is completed. The extended Kalman filter is used to implement multi-base station collaborative tracking and trajectory prediction of the UAV, and the UAV's flight information is obtained.

[0127] Based on steps two, three, and four, multi-base station communication perception integrated collaborative transmission based on STBC-OFDM signals was achieved. While the multiple base stations jointly transmitted to the drone based on the STBC-OFDM integrated signal, they also used the echo signals to obtain the drone's single-base and dual-base observation information. The multiple base stations uploaded the observation parameters to the control center via the forward link. The control center then integrated the observation data from multiple base stations and implemented collaborative tracking and trajectory prediction of the drone through an extended Kalman filter, improving trajectory tracking accuracy.

[0128] So far, the process of the communication-awareness integrated collaborative transmission method based on space-time block coded OFDM signals of this embodiment has been completed from step 1 to step 4.

[0129] This implementation has good results:

[0130] FIG6 is a simulation result diagram of the ranging mean square error along with the ranging and speed measurement variance of the communication perception integrated cooperative transmission method based on space-time block coded OFDM signals of the present invention and the implementation method in Example 1.

[0131] In Figure 6(a)(b), the horizontal axis is the ranging variance / speed variance, and the ranging variance and speed variance ranges are 0.2m-2.2m and 0.4m / s-2.4m / s, respectively. The vertical axis is the root mean square error of positioning, in meters. The simulation experiment compares the multi-base cooperative sensing method and the single-base sensing method. It can be seen from Figure 6 that with a smaller ranging variance σ r / Speed ​​measurement variance σ v Corresponding to more precise positioning accuracy, under the same ranging variance / speed measurement variance, the collaborative sensing solution proposed in the present invention has higher positioning accuracy.

[0132] Figure 7 is a simulation result diagram of the change of the horizontal angle measurement and azimuth angle measurement variance of the positioning mean square error after the fusion of base station observation data after the implementation method in Example 1 of the communication perception integrated collaborative transmission method based on space-time block coded OFDM signals of the present invention.

[0133] In Figure 7(a)(b), the horizontal axis is the horizontal angle measurement variance and the azimuth measurement variance, respectively, with a value range of 0.01rad-0.06rad and 0.01rad-0.06rad, and the vertical axis is the positioning root mean square error, in meters. The simulation experiment compares the dual-base cooperative sensing method with the single-base sensing method. As shown in Figure 7, a smaller horizontal angle measurement variance / azimuth measurement variance corresponds to a more accurate positioning accuracy. At the same horizontal angle measurement variance σ θ / Azimuth measurement variance σ β Under this circumstance, the collaborative sensing solution proposed in the present invention has higher positioning accuracy.

[0134] The above specific description further illustrates the purpose, technical solutions and beneficial effects of the invention in detail. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention. Industrial Applicability

[0135] The present invention provides a communication perception integrated cooperative transmission method based on space-time block coding OFDM signals. By establishing an ISAC cooperative transmission system model based on STBC-OFDM signals, multiple base stations can use STBC-OFDM integrated signals to jointly transmit to drones while receiving integrated signal echoes containing drone status information. The drones pre-process the received communication signals to achieve s 2m-1 and s 2mThe system uses a multi-base station collaborative sensing solution to achieve orthogonal separation and collaborative transmission diversity gain; processes the echo signals received by the first and second base stations to obtain observation information of the drone by the first and second base stations; and uses an extended Kalman filter to achieve multi-base station collaborative tracking and trajectory prediction of the drone. The base stations upload the observation parameters to the control center via a forward link. The control center integrates the perception results of multiple base stations and uses the extended Kalman filter method to track and predict the drone's trajectory. The collaborative sensing solution proposed in this invention achieves higher positioning accuracy and has good industrial applicability.

Claims

1. A communication-aware integrated cooperative transmission method based on space-time block coded OFDM signals, characterized in that: The following steps are included: Step 1: Establish an ISAC cooperative transmission system model based on STBC-OFDM signals, so that multiple base stations can jointly transmit to UAVs using STBC-OFDM integrated signals and receive integrated signal echoes containing UAV status information; Step 2: Based on the ISAC cooperative transmission system model constructed in step 1, the drone pre-processes the received communication signal to achieve s 2m-1 and 2m Orthogonal separation and cooperative transmission diversity gain; Step 3: Based on the ISAC cooperative transmission system model based on STBC-OFDM signals constructed in step 1, a STBC-OFDM signal perception and processing flow adapted to the system is constructed to perform signal processing on the echo signals received by the first base station and the second base station, so as to enable the first base station and the second base station to obtain the observation information of the UAV; Step 4: Based on the observation information of the first base station and the second base station on the UAV obtained in step 3, an extended Kalman filter is used to implement multi-base station cooperative tracking and trajectory prediction of the UAV; The base station uploads the observation parameters to the control center through the fronthaul link. The control center integrates the perception results of multiple base stations and uses the extended Kalman filter method to track and predict the trajectory of the UAV, thereby achieving high-precision trajectory tracking.

2. The communication perception integrated cooperative transmission method based on space-time block coded OFDM signals according to claim 1, characterized in that: The implementation method of step one is: Step 1.A: Set the ISAC system's collaborative transmission model; The cooperative transmission model includes two ISAC base stations and a drone hovering over the two base stations; each ISAC base station is equipped with a uniform planar array transmitting antenna and a uniform planar array receiving antenna; the number of transmitting antennas N t =N x ×N y , the number of receiving antennas N r =N t ; N x is the number of lateral antennas in the array, N y is the number of longitudinal antennas in the array; the receiving antenna is used to receive the echo signal of the downlink signal transmitted by the base station and the scattered signal of the downlink signal transmitted by other base stations; within the mission duration T, the echo signal and scattered signal received by the base station are used for UAV cooperative perception. A mission cycle is divided into N time slots, and the duration of each time slot is ΔT = T / N. The base stations are connected through the forward link to share information; the state of the UAV remains unchanged within a time slot, and the state information of the UAV is expressed as x n =[x n ,y n ,z n ,v x,n ,v y,n ,v z,n ] T (1) Among them, u n =[x n ,y n ,z n ] T , v n =[v x,n ,v y,n ,v z,n ] T Respectively represent the position coordinates and velocity coordinates. The position coordinates of the two ISAC base stations are expressed as: Step 1.B: Based on the cooperative transmission model equations (1) and (2) constructed in step 1.A, establish the STBC-OFDM transmission signal model; In the nth time slot, the mth block data symbol after serial-to-parallel conversion is expressed as s m∣n =[s 1,m|n ,…s L,m|n ] T (3) Where L is the number of data symbols contained in the data symbol vector, s l,m|n is the lth data symbol; Each time slot contains 2M block data symbol vectors, so the data symbol vector contained in the nth time slot is expressed as: STBC-OFDM operates on every two consecutive block data symbol vectors to obtain the STBC sub-matrices corresponding to the 2m-1th and 2mth data symbol vectors, and establishes the STBC-OFDM transmission signal model, which is expressed as follows: In the 2m-1th OFDM symbol period of the nth time slot, the OFDM modulated s 2m-1∣n and 2m∣n Transmitted by two ISAC base stations respectively; in the 2mth OFDM symbol period, the OFDM modulated and Transmitted by two ISAC base stations respectively; Step 1.C: According to the ISAC system cooperative transmission model and STBC-OFDM transmission signal model established in steps 1.A and 1.B, the channel between the drone and the two ISAC base stations during the transmission of the mth STBC block in the nth time slot is obtained. The expression is as follows Among them, T s is the duration of each code element of the OFDM signal; ρ0 is the path loss at the reference distance of 1m, G t is the multi-angle antenna gain, represents the distance between base station j and the UAV in the nth time slot, Indicated by The delay generated, represents the Doppler frequency deviation caused by the relative motion between the UAV and the base station j. The calculation formulas of the three are as follows: Step 1.D: The channel between the drone and the two base stations established according to step 1.C Omitting the subscript time slot n, the communication signal received by the drone in the 2m-1th and 2mth OFDM symbol periods of the nth time slot after OFDM demodulation is obtained as follows: in, So The discrete Fourier transform of is a diagonal matrix with diagonal elements, They represent the DFT expressions of the received noise in the 2m-1th and 2mth OFDM symbol periods respectively, and their distribution satisfies Step 1.E: Based on the communication signal expression (8) received by the UAV in step 1.D, a scattering path model of the UAV reflected signal is further established, that is, the downlink STBC-OFDM signal sent by the two ISAC base stations is reflected by the UAV to generate four scattering paths, which are the bidirectional scattering paths from the first base station to the UAV Scattering path from the second base station to the drone and then to the first base station The second base to the two-way scattering path of the drone Scattering path from the first base station to the drone and then to the second base station The expressions are as follows: Where ξ is the radar cross section (RCS), Indicated by and Produced Delay, Represents the Doppler frequency deviation and delay caused by the relative motion between the drone and base stations i and j Doppler frequency deviation The incident angle θ is horizontal to the signal n and the vertical incident angle φ n The expressions are as follows: Step 1.F: Based on the scattering path model established in step 1.E, the echo signals received by the first base station and the second base station in the 2m-1th and 2mth OFDM symbol periods of the nth time slot are respectively expressed as follows after OFDM demodulation: Where b(θ,φ) represents the steering vector of the base station receiving antenna array, Indicates The DFT result of is a diagonal matrix with diagonal elements; DFT expression of the noise received by base station n in the mth time slot; Step 1.G: By establishing the ISAC cooperative transmission system model based on STBC-OFDM signal as shown in formula (5)(6)(8)(9)(11), multiple base stations can use STBC-OFDM integrated signal to cooperatively transmit to the UAV and receive the integrated signal echo containing the UAV status information.

3. The communication perception integrated cooperative transmission method based on space-time block coded OFDM signals according to claim 1 or 2, characterized in that: The implementation method of step 2 is: Step 2.A: Based on the STBC-OFDM cooperative transmission system model, the signal received by the drone is reconstructed into the following expression: Step 2.B: Based on the signal reconstructed in step 2.A, the drone multiplies the received signal with the Hermitian transpose of the channel matrix and performs STBC decoding to obtain the following results: Among them, the noise matrix is ​​expressed as follows: Step 2.C: According to the operation of steps 2.A and 2.B, implement s 2m-1 and 2m Orthogonal separation and cooperative transmission diversity gain 4. The communication perception integrated cooperative transmission method based on space-time block coded OFDM signals according to claim 1, 2 or 3, characterized in that: The implementation method of step three is: Step 3.A: For the first base station, reconstruct the received signal (12) into the following expression: Step 3.B: According to the received signal reconstructed in step 3.A, multiply it with the Hermitian transposed matrix to implement STBC decoding. The decoding result is as follows: in, Step 3.C: According to the signal obtained in step 3.B, perform angle domain matched filtering on it to obtain Observable quantity In order to obtain the time delay, range Doppler and speed Doppler of the UAV, point-by-point division of (17) is performed to obtain the following results: Among them, G t,b is the angle domain matched filter gain; Step 3.D: Based on the results obtained in step 3.C All the data obtained in the nth time slot Store in odd-even order, as shown below: Step 3.E: Based on the results obtained in step 3.D By performing a 2D-DFT operation on it, the first base station obtains the observation result of the drone, which is expressed as follows Step 3.F: From step 3.A to step 3.E, the STBC-OFDM signal perception and processing process in the ISAC collaborative transmission system is completed. The second base station obtains the observation information of the second base station on the drone according to the same process as the first base station, thereby realizing the acquisition of the observation information of the drone by the first base station and the second base station.

5. The communication perception integrated cooperative transmission method based on space-time block coded OFDM signals according to claim 1, 2, 3 or 4, characterized in that: Step 4 is implemented as follows: Step 4.A: The relationship between the observation parameters of multiple base stations and the drone status is expressed as follows: in, and Respectively represent the measurement errors of time delay, Doppler, horizontal angle and vertical angle; and The mean is 0 and the standard deviation is σ τ , σ D , σ θ and σ β The standard deviation expressions of time delay, range Doppler, velocity Doppler, horizontal angle and vertical angle are given as follows: Γ s is the signal-to-noise ratio at the receiver, T ac =ΔT / 2 represents the coherent accumulation time, θ 3dB and β 3dB represent the horizontal and vertical beam widths respectively, and B is the transmission signal bandwidth; Step 4.B: Based on the parameter relationship given in step 4.A, define the correlation matrix and reconstruct equation (22) into the following concise form: in, Then define the observation parameter matrix and measurement error matrix Equation (22) is finally transformed into the following equation: x n =g(x n )+w n (25) Step 4.C: Based on equation (25) in step 4.B, the UAV state evolution model and prediction mean square error matrix are expressed as follows: x n+1∣n =Px n +Z n (26) M n+1∣n =PM n P H (27) Among them, P is the state transfer matrix, expressed as is the noise term, and its corresponding z n+1 The covariance of is: Step 4.D: Based on the UAV state evolution model and predicted mean square error matrix given in step 4.C, the Kalman gain matrix is ​​expressed as follows: where G is the Jacobian matrix of g(·), and G n+1 =G(x n+1∣n ); Step 4.E: Based on the Kalman gain matrix given in step 4.D, the state and mean square error matrix of the drone at the n+1th time slot are expressed as follows: x n+1 =x n+1∣n +K n+1 (x n+1 -g(x n+1∣n )) (31) M n+1 =(I-K n+1 G n+1 )M n+1∣n (32) Step 4.F: Based on the UAV state and mean square error matrix obtained in step 4.E, the observation information based on the two ISAC base stations is obtained, and the extended Kalman filter is used to realize multi-base station collaborative tracking and trajectory prediction of the UAV, and the flight information of the UAV is obtained, thereby realizing high-precision trajectory tracking of the UAV.

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