Multi-path channel enhancement method and system based on AoA filtering in communication and inductance integrated system

Abstract

The invention relates to the field of communication, in particular to a multipath channel enhancement method and system based on AoA filtering in a communication and inductance integrated system. Constructing an uplink communication scene, and generating a transmitting signal, a receiving signal and a channel model based on the uplink communication scene; performing channel estimation on the transmitting signal and the receiving signal based on a least square algorithm to obtain a channel response estimation value; sensing and acquiring AoA information of each communication path, and constructing a channel transfer factor based on the AoA information and a channel model; and performing Kalman filtering processing on the channel response estimation value based on the channel transfer factor, and outputting a filtered channel vector. According to the method, the communication path angle information obtained by sensing is combined with Doppler information, and the channel response estimation value is dynamically optimized by adopting Kalman filtering, so that the estimation precision and real-time performance of the channel state information in the multipath environment are improved.

Description

Technical Field

[0001] The present invention relates to the field of communications, and particularly to a method and system for enhancing a multipath channel based on AoA filtering in a communication and sensing integrated system. Background Art

[0002] The accuracy of channel estimation is particularly evident for the performance of a communication system, especially in a high-speed mobile and complex and changeable wireless environment; in the current era of rapid development of communication technologies, with the increasing advancement of the research and construction of communication networks, the sensing-assisted communication technology has become increasingly crucial.

[0003] Channel estimation technology in the field of wireless communications is of vital importance and is the basis for realizing accurate demodulation and decoding of information. When a signal is transmitted in a wireless channel, it will be affected by various noises and interferences, which will seriously distort the original characteristics of the signal. Therefore, the channel estimation process often generates large errors, and these errors will interfere with the accurate restoration of the original signal by the communication system, thereby seriously affecting the overall performance of the communication system, such as causing a decrease in the data transmission rate and an increase in the bit error rate.

[0004] In a traditional multipath channel estimation system, the multipath effect refers to the fact that when a signal propagates, due to encountering various obstacles, it will undergo reflection, scattering, and refraction, thus forming multiple propagation paths to reach the receiving end; the signals of these different paths are superimposed on each other, making the received signal complex and difficult to process. The consequence of the multipath effect is that it is impossible to maintain good orthogonality between subcarriers. Once the orthogonality between subcarriers is destroyed, the signals will interfere with each other, which will seriously reduce the transmission quality of the signal, is not conducive to the improvement of channel performance, and makes it difficult for the communication system to achieve efficient and stable signal transmission in a multipath environment.

[0005] In addition, accurately estimating the channel state in a multipath environment is relatively complex, which further increases the difficulty of channel estimation. Due to the superposition and interference of multipath signals, it is difficult to obtain the estimated channel state; with the continuous improvement of the communication quality requirements of the communication system, higher requirements are also put forward for the accuracy and real-time performance of channel estimation, which undoubtedly makes the channel estimation task in a multipath environment more arduous. Summary of the Invention

[0006] Aiming at the problems mentioned in the prior art, the present invention proposes a method and system for enhancing a multipath channel based on AoA filtering in a communication and sensing integrated system. By combining the angle information and Doppler information obtained by sensing, the Kalman filter is used to process the initial channel estimation result to reduce noise and enhance the accuracy of the channel state information.

[0007] To achieve the above object, the present invention adopts the following technical solutions: In a first aspect, the present invention proposes a multipath channel enhancement method based on AoA filtering in a communication-sensing integrated system, including the following steps: Construct an uplink communication scenario, and generate a transmitted signal, a received signal, and a channel model based on the uplink communication scenario; Perform channel estimation on the transmitted signal and the received signal based on the least squares algorithm to obtain an estimated value of the channel response; Sense and obtain the AoA information of each communication path, and construct a channel transfer factor based on the AoA information and the channel model; Perform Kalman filtering on the estimated channel response value based on the channel transfer factor, and output the filtered channel vector.

[0008] As a further improvement of the present invention, the transmitted signal is , representing the baseband symbol at the m-th OFDM symbol of the n-th subcarrier, where is the number of subcarriers, is the number of OFDM symbols; The received signal is shown as follows:

[0009] In the formula: represents the number of subcarriers; represents the number of OFDM symbols; represents the channel model of the n -th subcarrier and the m -th OFDM symbol, with a dimension of ; is the transmit power of the signal; is the square root of ; is a noise matrix composed of additive white Gaussian noise of different antenna elements; The channel model has the following expression:

[0010] In the formula: L is the number of multipaths. When represents the direct path, represents the non-direct path, L takes 2; is the duration of the OFDM symbol; is the subcarrier spacing; is the Doppler frequency shift of each communication path between the receiving end and the output end; is the delay of each communication path between the receiving end and the output end; is the fading coefficient of the l -th path of the channel; represents the horizontal angle of arrival of the l th path; represents the receiving antenna steering vector; represents the phase rotation or the propagation phase shift of the wave; represents the summation of the

[0011] As a further improvement of the present invention, the expression of the channel response estimate is as follows:

[0012] where: is the channel response estimate of the th OFDM symbol of the th subcarrier; is the transmitted signal; is the received signal; is the inverse operation; is the Hermitian transpose operation.

[0013] As a further improvement of the present invention, the expression of the channel transfer factor is as follows:

[0014] where: represents the channel transfer factor; is the channel fading coefficient of the LoS path, is the channel fading coefficient of the NLoS path; is the number of antenna elements; represents the numerator of the phase rotation or the propagation phase shift of the wave; represents the angle sine value; represents the angle sine value; represents the spacing between antenna elements; represents the variable name of the concept; represents the wavelength; represents the cumulative addition of the transfer relationship between adjacent arrays of the antenna after multipath superposition from to ;

[0015] As a further improvement of the present invention, the Kalman filtering process is as follows: a. Let the channel response estimate , represents the vector before filtering, and the channel response estimate is filtered to obtain the vector after filtering; b. For the th antenna element, , calculate the current predicted value, and the formula is as follows:

[0016] In the formula: is the filtering result at the previous moment; is the channel transfer factor; the current predicted value; c. Weight and correct the current predicted value according to the Kalman gain, as shown in the following formula:

[0017] In the formula: is the Kalman gain; is the actual value of the channel coefficient at the current moment; is the final predicted filtering result at the current moment; Among them, the Kalman gain is obtained as follows:

[0018] In the formula: is the power of interference plus noise, is the predicted value of the current error covariance; is to perform complex conjugate transpose; is to take the inverse of the sum of; d. Update the error covariance according to the following formula, and the expression is as follows:

[0019] In the formula: is the actual value of the current error covariance; e. Repeat steps b to d until all antenna elements are processed, and output the filtered channel vector.

[0020] As a further improvement of the present invention, decode the filtered channel vector to obtain an information bit stream, and calculate the bit error rate based on the information bit stream to verify the signal performance obtained by this method.

[0021] In the second aspect, the present invention proposes a multipath channel enhancement system based on AoA filtering in a sensing integration system, including: A first construction module, configured to construct an uplink communication scenario, and generate a transmitted signal, a received signal, and a channel model based on the uplink communication scenario; A channel estimation module, configured to perform channel estimation on the transmitted signal and the received signal based on the least squares algorithm to obtain an estimated value of the channel response; A second building block for sensing and obtaining the AoA information of each communication path, and constructing a channel transfer factor based on the AoA information and the channel model; An output module for performing Kalman filtering on the channel response estimate based on the channel transfer factor and outputting a filtered channel vector.

[0022] In a third aspect, the present invention proposes a multipath channel enhancement device based on AoA filtering in a communication and sensing integrated system, including a processor and a memory. When the processor executes the computer program stored in the memory, the multipath channel enhancement method based on AoA filtering in the communication and sensing integrated system as described above is implemented.

[0023] In a fourth aspect, the present invention proposes a computer-readable storage medium for storing a computer program. When the computer program is executed by a processor, the multipath channel enhancement method based on AoA filtering in the communication and sensing integrated system as described above is implemented.

[0024] The present invention has achieved the following technical effects compared with the prior art: By combining the angle information and Doppler information of the communication path obtained by sensing, and using Kalman filtering to dynamically optimize the channel response estimate, the present invention improves the estimation accuracy and real-time performance of the channel state information in a multipath environment. The present invention constructs a channel transfer factor through the communication path angle information, and uses the iterative mechanism of Kalman filtering to dynamically correct the deviation between the predicted value and the observed value of the channel response estimate, thereby effectively suppressing noise interference and enhancing the multipath signal analysis ability, and effectively solving the problems of subcarrier orthogonality destruction and signal distortion caused by multipath effects in traditional systems, so that the data transmission rate and bit error rate performance of the communication system in a high-dynamic complex environment are significantly improved.

[0025] The present invention performs channel estimation through the least squares algorithm, which can quickly generate a channel response estimate, provide basic channel state information in a low-complexity manner, and lay a foundation for the iteration of subsequent Kalman filtering, avoiding excessive consumption of system resources by a complex initialization process; by combining AoA information with the channel model to generate a channel transfer factor, using the channel transfer factor as a Kalman filtering parameter, and processing the channel response estimate by Kalman filtering, it can not only reduce the influence of noise on channel estimation, but also reduce the influence of multipath separation on channel estimation, and improve the accuracy of channel estimation; the present invention verifies the channel estimation effect by decoding the filtered channel vector and calculating the bit error rate, provides a basis for system parameter optimization, and ensures the reliability and scalability of the method in a real scenario. Description of the Drawings

[0026] Figure 1 It is a schematic flowchart of the present invention; Figure 2 Structural diagram of the transmitted signal in the embodiment of the present invention; Figure 3 Schematic diagram of the constructed scenario in the embodiment of the present invention; Figure 4 Simulation result diagram of the comparison between the method of the present invention and other filtering algorithms; Figure 5 Simulation result diagram of the influence of the angle estimation error of the present invention on the performance of the method of the present invention. Detailed implementation manners

[0027] The present invention will be further described in detail below with reference to the drawings and embodiments. It can be understood that the specific embodiments described herein are only used to explain the present invention, rather than limiting the present invention. In addition, it should be noted that for the sake of description, only parts related to the present invention rather than all structures are shown in the drawings.

[0028] As Figure 1 shown, the present invention proposes a multipath channel enhancement method based on AoA filtering in a communication and sensing integrated system, including the following steps: Construct an uplink communication scenario, and generate a transmitted signal, a received signal, and a channel model based on the uplink communication scenario; Perform channel estimation on the transmitted signal and the received signal based on the least squares algorithm to obtain a channel response estimation value; Sense and obtain the AoA (Angle of Arrival) information of each communication path, and construct a channel transfer factor based on the AoA information and the channel model; Perform Kalman filtering on the channel response estimation value based on the channel transfer factor, and output a filtered channel vector.

[0029] The present invention will be further explained below with reference to the drawings and specific embodiments: Step 1. Refer to Figure 3 , which is the uplink communication scenario of this embodiment. In the uplink communication scenario, the base station equipment is a uniformly spaced linear array, and the dimension of the antenna array is , is the number of antenna elements, and the user is a single antenna and is assumed to be a point target.

[0030] Step 2. In the uplink communication scenario of the communication and sensing integrated system in this embodiment, OFDM (Orthogonal Frequency Division Multiplexing) signals are used. As Figure 2 shown, it is the structural diagram of the transmitted signal. The transmitted signal is composed of training symbols and data symbols. The transmitted signal is , indicating the baseband symbol at the m-th OFDM symbol of the n-th subcarrier, where is the number of subcarriers, is the number of OFDM symbols.

[0031] In the embodiment, the received signal corresponding to the nth subcarrier and the mth symbol is as shown in the following formula: (1) In the formula: represents the current number of subcarriers, represents the current number of OFDM symbols, then represents the n th subcarrier's m th OFDM symbol's channel model, with dimension ; is the transmit power of the signal; is the square root of; is the noise matrix composed of additive white Gaussian noise of different antenna elements, and each element follows a complex Gaussian distribution with a mean of 0 and a variance of .

[0032] In this embodiment, considering the time-varying nature and multipath effect of the channel, the channel model expression of the uplink communication channel at the nth subcarrier and the mth OFDM symbol is as follows: (2) In the formula: L is the number of multipaths. When represents the direct (LoS Line-of-Sight) path, represents the non-direct (NLoS Non-Line-of-Sight) path. In this embodiment, it is assumed that there is one LoS path and one NLoS path between the user equipment and the base station, that is L takes 2; is the duration of the OFDM symbol; is the subcarrier spacing; is the Doppler shift of each communication path between the user equipment and the base station; is the delay of each communication path between the user equipment and the base station; is the fading coefficient of the l th path of the channel. In the embodiment, it is assumed to follow a complex Gaussian distribution with a mean of 0 and a variance of ; represents the l th horizontal angle of arrival; represents the receiving antenna steering vector; represents the phase rotation or the propagation phase shift of the wave; represents the ​​Sum over all paths.

[0033] Among them It is specifically expressed as follows: (3) In the formula: represents the antenna spacing; represents the wavelength; represents the l sine value of the horizontal angle of arrival of the th path;

[0034] Step 3: Perform channel estimation on the transmitted signal and the received signal based on the least squares algorithm to obtain the estimated channel response value. The expression of the estimated channel response value is as follows: (4) In the formula: is the estimated channel response value of the th subcarrier of the th OFDM symbol; is the transmitted signal; is the received signal; is to perform an inverse operation; is to perform a Hermitian transpose operation.

[0035] Due to the existence of noise, there is an error in the above channel estimation method, which affects the performance of the communication system. In the integrated communication and sensing system of this embodiment, the AoA of each path is equivalent to and can be obtained through sensing.

[0036] Step 4: In this embodiment, the transfer factor is constructed using the AoA information, and the transfer factor is used as the Kalman filtering factor, which can reduce the influence of noise on the channel estimation result through Kalman filtering. The transfer factor can reflect the relationship between the channel coefficients of different antenna elements.

[0037] Since this embodiment is a two-path scenario with L = 2, the expression of the transfer factor is updated as follows: (5) In the formula: represents the channel transfer factor; is the channel fading coefficient of the LoS path, is the channel fading coefficient of the NLoS path; is the number of antenna elements; represents the phase rotation or the numerator of the wave propagation phase shift; Represents the sine value of the angle ; Represents the sine value of the angle ; Represents the spacing between antenna elements; Represents The variable name of the concept; Represents the accumulation of the transfer relationship between adjacent arrays of each antenna after multipath superposition from to .

[0038] In the embodiment, the LoS path channel fading coefficient and the NLoS path channel fading coefficient are calculated as follows: (6) In the formula: , Are the received signals at the th subcarrier and the th OFDM symbol of the receiving antenna array, and the th subcarrier and the th OFDM symbol of the receiving antenna array, respectively, and , ; Represents the calculation of the vector mean value.

[0039] Where is expressed as follows: (7) In the formula: Represents the first OFDM symbol; Represents the second OFDM symbol; Represents the first subcarrier; Represents the second subcarrier.

[0040] Step 5. In this embodiment, the channel transfer factor is used as the Kalman filtering factor to filter the channel response estimated value .

[0041] First, let represent the vector before filtering, represent the vector after filtering. In the embodiment, , represents the th antenna element. In the embodiment, , indicating that the first antenna element does not undergo Kalman filtering processing.

[0042] Calculate the current predicted value, and the formula is as follows: (8) In the formula: is the filtering result at the previous moment; is the channel transfer factor; is the current predicted value.

[0043] According to the Kalman gain weighted correction is performed on the current predicted value, as shown in the following formula: (9) In the formula: is the Kalman gain; is the actual value of the channel coefficient at the current moment; is the final predicted filtering result at the current moment.

[0044] The Kalman gain can dynamically adjust the weights of the predicted value and the observed value in the state estimation to minimize the estimation error. In the embodiment, the Kalman gain is obtained as follows: (10) In the formula: is the power of interference plus noise, is the predicted value of the current error covariance; is for to perform complex conjugate transpose; is for to take the inverse of the sum of.

[0045] In the embodiment the calculation formula is as follows: (11) In the formula: is the conjugate transpose of.

[0046] In the embodiment, the filtered value is jointly represented by the prior estimated predicted value and the actual value and updated through the Kalman gain. If , then update according to the following formula , let , and repeat the above process.

[0047] (12) In the formula: is the actual value of the current error covariance; If , the iteration ends, and the filtered channel is output.

[0048] Step 6: The bit error rate can measure the accuracy of channel estimation. Decoding the filtered channel vector according to the estimated channel response estimate can recover the information bit stream. Comparing the received information bit stream with the originally transmitted information bit stream and calculating the number of error bits can obtain the bit error rate. The specific calculation formula is as follows:

[0049] The following is the verification of the method of the present invention: Under the MATLAB (Matrix Laboratory) simulation environment, the multi-path channel estimation method based on AoA filtering proposed by the present invention is analyzed and verified.

[0050] During the simulation, the sub-carrier spacing = 480 kHz. The number of OFDM symbols = 32, and the number of sub-carriers = 64. The user transmitting antenna is a single antenna, and the size of the base station receiving antenna is .

[0051] To verify the performance of the method of the present invention, refer to Figure 4 . The figure shows the comparison of the demodulation bit error rate performance of the method of the present invention and various channel estimation methods at different signal-to-noise ratios. The horizontal axis is the signal-to-noise ratio (unit: dB), and the vertical axis is the bit error rate (unit: logarithmic scale). Five methods are shown in the figure for comparison: 1. Initial channel estimation: The least squares method is used to directly obtain the rough CSI (Channel State Information) through the ratio of the received signal to the pilot symbol, without any filtering processing.

[0052] 2. The filtering method of the present invention: The method of the present invention can fuse multi-path angle information through the processing of formula (5) in step four, and can introduce the Kalman filter recursive update mechanism through the processing of formula (6) - formula (12) in step five to realize the dynamic optimization and precise enhancement of the initial CSI estimation.

[0053] 3. LOS information filtering: Extract the antenna direction factor of the direct path as the state transition factor, and only perform one round of Kalman filtering on the direct path.

[0054] 4. NLOS information filtering: Extract the direction information of the non-direct path as the state transition factor, and only perform one round of Kalman filtering on the non-direct path.

[0055] 5. Two-path sequential filtering: The CSI is sequentially filtered by the direction information of the direct path and the non-direct path through two rounds of serial Kalman filtering to further suppress multi-path interference and transient errors.

[0056] It can be seen from Figure 4 Figure 4 that as the signal-to-noise ratio increases, the bit error rates of all methods decrease because the channel estimation accuracy improves with the increase in the signal-to-noise ratio.

[0057] To more intuitively compare the performance differences of different estimation methods in a complex channel, in this embodiment, the signal-to-noise ratio of 0 dB is used as a representative observation point for analysis. From Figure 4 the curve of -1 -1 , it can be seen that the bit error rate of the initial channel estimation is the highest, about 10 -2 -2 , so it shows that the performance of the initial channel estimation is the worst because the influence of noise is not eliminated by filtering; while the bit error rates of the LoS path filtering channel estimation and the NLoS path filtering channel estimation are about 10 -3 -3 and 8×10 -3 -3 respectively, and the bit error rate of the two-path sequential filtering channel estimation is about 4×10 -3 -3 , indicating that even using partial path information filtering can effectively enhance the estimation accuracy. However, since the construction of the transfer factor only utilizes the phase information, the filtering effect improvement is limited; while the bit error rate of the method of the present invention drops to 10 -3 -3 or less, thus maximizing the elimination of the influence of noise, so the performance is the best.

[0058] Referring to Figure 5 Figure 5 , to verify the influence of the perception parameter accuracy on the performance of the proposed algorithm, the influence of the angle error on the system performance is shown, where the abscissa is the signal-to-noise ratio and the ordinate is the bit error rate. It can be seen from the attached figure that as the perception parameter accuracy decreases, the AoA angle error increases and the channel estimation performance also continuously decreases.

[0059] Based on the same inventive concept, the embodiment of the present invention also provides a multipath channel enhancement system based on AoA filtering in a communication and sensing integrated system. Since the principle of solving problems by the multipath channel enhancement system based on AoA filtering in this communication and sensing integrated system is similar to that of the multipath channel enhancement method based on AoA filtering in the foregoing communication and sensing integrated system, the implementation of the multipath channel enhancement system based on AoA filtering in this communication and sensing integrated system can refer to the implementation of the multipath channel enhancement method based on AoA filtering in the communication and sensing integrated system, and the repeated parts will not be elaborated.

[0060] In specific implementation, the multipath channel enhancement system based on AoA filtering in the communication and sensing integrated system provided by the embodiment of the present invention specifically includes: A first construction module, configured to construct an uplink communication scenario, and generate a transmitted signal, a received signal, and a channel model based on the uplink communication scenario; A channel estimation module, configured to perform channel estimation on the transmitted signal and the received signal based on the least squares algorithm to obtain a channel response estimation value; A second construction module, configured to sense and obtain the AoA information of each communication path, and construct a channel transfer factor based on the AoA information and the channel model; An output module, configured to perform Kalman filtering on the estimated channel response value based on the channel transfer factor, and output a filtered channel vector.

[0061] Correspondingly, an embodiment of the present invention further provides a multipath channel enhancement device based on AoA filtering in a communication and sensing integrated system, including a processor and a memory. When the processor executes a computer program stored in the memory, the method for enhancing a multipath channel based on AoA filtering in the communication and sensing integrated system provided by the embodiment of the present invention is implemented.

[0062] For a more specific process of the above method, reference may be made to the corresponding content disclosed in the foregoing embodiments, and details are not described herein again.

[0063] Correspondingly, an embodiment of the present invention further provides a computer-readable storage medium for storing a computer program. When the computer program is executed by a processor, the method for enhancing a multipath channel based on AoA filtering in the communication and sensing integrated system provided by the embodiment of the present invention is implemented.

[0064] In this specification, the embodiments are described in a progressive manner. Each embodiment focuses on the differences from other embodiments. For the same or similar parts among the embodiments, reference may be made to each other. For the systems, devices, and storage media disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the descriptions are relatively simple, and reference may be made to the description of the method part for the relevant parts.

[0065] Those skilled in the art can further realize that the units and algorithm steps of each example described in combination with the embodiments disclosed in this article can be implemented by electronic hardware, computer software, or a combination of the two. To clearly illustrate the interchangeability of hardware and software, the components and steps of each example have been generally described according to their functions in the above description. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Skilled professionals can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of this application.

[0066] The steps of the method or algorithm described in combination with the embodiments disclosed in this article can be directly implemented by hardware, a software module executed by a processor, or a combination of the two. The software module can be placed in a random access memory (RAM), internal memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, removable disk, CD-ROM, or any other form of storage medium well-known in the technical field.

[0067] Finally, it should also be noted that in this text, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article or device comprising a series of elements not only includes those elements, but also includes other elements not expressly listed, or elements inherent to such process, method, article or device. Without further limitation, an element defined by the statement "comprising an..." does not exclude the presence of additional identical elements in the process, method, article or device comprising the said element.

[0068] The above has introduced in detail the multi-path channel enhancement method, system, device and storage medium based on AoA filtering in the synaesthesia integration system provided by the present invention. Specific examples are used in this text to elaborate on the principle and implementation manner of the present invention. The description of the above embodiments is only used to help understand the method and its core idea of the present invention; at the same time, for those of ordinary skill in the art, according to the idea of the present invention, there will be changes in the specific implementation manner and application scope. In summary, the content of this specification should not be construed as a limitation to the present invention.

Claims

1. A method for enhancing multipath channels based on AoA filtering in a synesthesia integration system, characterized in that Including the following steps: Construct an uplink communication scenario, and generate a transmitted signal, a received signal, and a channel model based on the uplink communication scenario; Perform channel estimation on the transmitted signal and the received signal based on the least squares algorithm to obtain an estimated value of the channel response; Sense and obtain the AoA information of each communication path, and construct a channel transfer factor based on the AoA information and the channel model; Perform Kalman filtering on the estimated value of the channel response based on the channel transfer factor, and output the filtered channel vector.

2. The method for enhancing a multipath channel based on AoA filtering in the integrated sensing and communication system according to claim 1, wherein The transmitted signal is , representing the baseband symbol at the m-th OFDM symbol of the n-th subcarrier, where is the number of subcarriers, is the number of OFDM symbols; The received signal is as follows: Wherein: represents the number of subcarriers; represents the number of OFDM symbols; represents the n th subcarrier of the m th OFDM symbol, with a dimension of ; is the transmit power of the signal; is the square root of; is a noise matrix composed of additive white Gaussian noise of different antenna elements; The said channel model The expression is as follows: Wherein: L is the number of multipaths. When represents the direct path, represents the non - direct path, L takes 2; is the duration of the OFDM symbol; is the sub - carrier spacing; is the Doppler frequency shift of each communication path between the receiving end and the output end; is the time delay of each communication path between the receiving end and the output end; is the fading coefficient of the l th path of the channel; represents the l th horizontal angle of arrival; represents the receiving antenna steering vector; represents the phase rotation or the propagation phase shift of the wave; represents the summation over the paths.

3. The multi-path channel enhancement method based on AoA filtering in the integrated sensing and communication system according to claim 1, wherein, The expression of the estimated value of the channel response is as follows: Wherein: is the channel response estimation value of the th sub - carrier for the th OFDM symbol; is the transmitted signal; is the received signal; is to perform an inverse operation on ; is to perform a Hermitian transpose operation on .

4. The multi-path channel enhancement method based on AoA filtering in the integrated sensing and communication system according to claim 1, characterized in that The expression of the channel transfer factor is as follows: In the formula: represents the channel transfer factor; is the channel fading coefficient of the LoS path, is the channel fading coefficient of the NLoS path; is the number of antenna array elements; represents the phase rotation or the numerator of the propagation phase shift of the wave; represents the angle sine value; represents the angle sine value; represents the spacing between antenna array elements; represents the variable name of the concept; represents the wavelength; represents the cumulative addition of the transfer relationship between adjacent arrays of each antenna after multipath superposition from to ​ 5. The multi-path channel enhancement method based on AoA filtering in the synaesthesia integration system according to claim 1, characterized in that The process of the Kalman filtering is as follows: a. Let the channel response estimate value , represent the vector before filtering. Filter the channel response estimate value to obtain the filtered vector ; b. For the th antenna element, , calculate the current predicted value, and the formula is as follows: In the formula: is the filtering result of the previous moment; is the channel transfer factor; is the current predicted value; c. Weight and correct the current predicted value according to the Kalman gain, as shown in the following formula: In the formula: is the Kalman gain; is the actual value of the channel coefficient at the current moment; is the final predicted filtering result at the current moment; where the Kalman gain is obtained as follows: Wherein: is the power of interference plus noise, is the predicted value of the current error covariance; is for to perform complex conjugate transpose; is for to take the inverse of the sum of; d. Update the error covariance according to the following formula, and the expression is as follows: In the formula: is the actual value of the current error covariance; e. Repeat steps b to d until the processing of all antenna array elements is completed, and output the filtered channel vector.

6. The multi-path channel enhancement method based on AoA filtering in the synaesthesia integration system according to claim 1, wherein, Decode the filtered channel vector to obtain an information bit stream, and calculate the bit error rate based on the information bit stream to verify the signal performance obtained by this method.

7. The multipath channel enhancement system based on AoA filtering in the synesthesia integration system is characterized in that Including: A first construction module, configured to construct an uplink communication scenario, and generate a transmitted signal, a received signal, and a channel model based on the uplink communication scenario; A channel estimation module, configured to perform channel estimation on the transmitted signal and the received signal based on the least squares algorithm to obtain an estimated value of the channel response; A second construction module, configured to sense and obtain the AoA information of each communication path, and construct a channel transfer factor based on the AoA information and the channel model; An output module, configured to perform Kalman filtering on the estimated value of the channel response based on the channel transfer factor, and output the filtered channel vector.

8. A multipath channel enhancement device based on AoA filtering in a synesthesia integration system, characterized in that, Including a processor and a memory. Among them, when the processor executes the computer program stored in the memory, the method for enhancing a multipath channel based on AoA filtering in the integrated communication and sensing system according to any one of claims 1 to 6 is implemented.

9. A computer-readable storage medium, characterized in that, For storing a computer program, where when the computer program is executed by a processor, the method for enhancing a multipath channel based on AoA filtering in the integrated communication and sensing system according to any one of claims 1 to 6 is implemented.

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