A self-interference suppression method based on a synesthesia integrated system in complex environments

Abstract

This invention discloses a self-interference suppression method based on a sensing-integrated system in complex environments, comprising: transmitting a downlink sensing signal; receiving the echo signal of the downlink sensing signal and establishing a frequency domain channel matrix based on the echo signal; the frequency domain channel matrix includes a dynamic channel matrix and a static channel matrix; performing a two-dimensional discrete Fourier transform and constant false alarm rate detection on the frequency domain channel matrix to estimate multiple Doppler shifts; the multiple Doppler shifts include a spectral center Doppler shift and at least one spectral leakage Doppler shift; for each Doppler shift, performing multi-level differential filtering operations sequentially to obtain the desired sensing target channel information, effectively solving the spectral leakage problem caused by hardware non-ideals, and accurately identifying and eliminating interference signals.

Description

Technical Field

[0001] This invention belongs to the field of integrated sensing technology, specifically relating to a self-interference suppression method based on an integrated sensing system in complex environments. Background Technology

[0002] Integrated Sensing and Communications (ISAC) systems combine communication and sensing functions on a single platform, achieving synergistic resource utilization and overall system performance improvement. Compared to the traditional approach of deploying communication and independent sensing systems separately, this system can simultaneously complete information transmission and environmental sensing based on a unified wireless signal, avoiding redundant deployment of multiple sets of equipment and significantly reducing hardware costs and system complexity. In terms of communication, the ISAC system improves spectrum utilization efficiency, reduces signal interference, and enhances network coverage and transmission reliability. In terms of sensing, the system utilizes wireless signals generated during communication to detect information such as target location and motion status, eliminating the need for additional dedicated sensors and achieving low-cost, wide-coverage environmental sensing capabilities. In summary, the ISAC system has significant advantages in improving resource utilization efficiency, reducing system deployment costs, and enhancing the synergy between communication and sensing, and has broad application prospects.

[0003] However, integrated sensing systems introduce significant interference, such as self-interference caused by transceiver antenna coupling, self-interference spectrum shifting due to RF link leakage, and static interference in complex environments. This interference has stable power and a concentrated spectrum, reducing target sensing accuracy, especially masking desired targets near the interference source, and potentially even completely obscuring low-speed targets. To improve target sensing accuracy, self-interference suppression methods based on integrated sensing systems in complex environments are needed to suppress interference while preserving the desired target signal.

[0004] In ISAC systems, self-interference cancellation is divided into two types: passive cancellation and active cancellation. Passive cancellation eliminates self-interference through antenna design. Active cancellation operates in the space, radio frequency (RF), and digital domains. In the space domain, coupled self-interference is reduced by combining passive and active beamforming. RF cancellation uses devices such as attenuators and phase shifters. Digital cancellation eliminates both linear and nonlinear interference. Despite the presence of space and RF cancellation, residual self-interference after ADC sampling can hinder signal demodulation, highlighting the importance of digital cancellation in ISAC systems.

[0005] In the digital domain, several algorithms have been proposed to eliminate self-interference while preserving echoes from moving targets. The recursive least squares (RLS) algorithm in the paper "Design and Implementation of Adaptive Anti-jamming Line Frequency Communication Filter Based on RLS Algorithm," 2024 4th International Conference on Electronics, Circuits and Information Engineering (ECIE), Hangzhou, China, 2024, pp. 494-498, achieves adaptive filtering and signal optimization by dynamically adjusting the filter coefficients based on estimates used to continuously update system parameters to minimize the difference between measured and predicted values, thus gradually eliminating self-interference. Furthermore, based on the recursive least squares framework, the paper "Frequency-Domain Hammerstein Self-Interference Canceller for In-BandFull-Duplex OFDM Systems," by K. Komatsu, Y. Miyaji and H. Uehara, 2017 IEEE Wireless Communications and Networking Conference (WCNC), San Francisco, CA, USA, 2017, pp. 1-6, incorporates a parallel Hammerstein model into the recursive least squares estimator (RLS-Hammerstein) to capture the nonlinear behavior of the signal. However, the above method requires prior knowledge of the signal without the desired target, which is difficult in practical sensing scenarios.

[0006] The article B. Jin, X. Ma and Z. Zhang, "Interference-Robust Millimeter-WaveRadar-Based Dynamic Hand Gesture Recognition Using 2-D CNN-TransformerNetworks," in IEEE Internet of Things Journal, vol. 11, no. 2, pp. 2741-2752, 15 Jan. 15, 2024, eliminates noise and environmental influences by averaging multiple measurements (F-mean), thus removing self-interference without requiring known signals of the desired target. The article B. Duan, C. Chen and W. Pan, "Frequency-Domain Differential Interference Cancellation for Full-Duplex OFDMISAC Systems," IEEE Transactions on Vehicular Technology, vol. 73, no. 6, pp. 8615-8631, June 2024, employs frequency-domain differential interference cancellation technology (The frequency... The domain-differential interference cancellation technique (F-DIC) utilizes the invariance of interference to eliminate self-interference and recover the echo of a moving target.

[0007] Existing integrated sensing systems often suffer from self-interference signal leakage in the sensing spectrum due to non-ideal factors in the system hardware, such as frequency shift, nonlinear distortion, and hardware noise, when performing sensing tasks. This leakage not only obscures the desired target near the interference but may also completely mask low-speed targets, significantly reducing the system's environmental sensing accuracy. Furthermore, in complex environments, the superposition of static interference signals and self-interference further exacerbates the difficulty of target detection. Existing interference suppression methods cannot effectively solve the spectrum leakage problem caused by hardware non-idealities, especially when the frequency of the interference signal is close to that of the target signal, making it difficult for the system to accurately identify and eliminate the interference signal. Summary of the Invention

[0008] To address the aforementioned problems in the prior art, this invention provides a self-interference suppression method based on a sensor-integrated system in complex environments.

[0009] The technical problem to be solved by this invention is achieved through the following technical solution: In a first aspect, the present invention provides a self-interference suppression method based on a sensor-integrated system in complex environments, the self-interference suppression method comprising: Transmit downlink sensing signals; The echo signal of the downlink sensing signal is received, and a frequency domain channel matrix is ​​established based on the echo signal; the frequency domain channel matrix includes a dynamic channel matrix and a static channel matrix. A two-dimensional discrete Fourier transform and constant false alarm rate detection are performed on the frequency domain channel matrix to estimate multiple Doppler shifts; the multiple Doppler shifts include a spectral center Doppler shift and at least one spectral leakage Doppler shift; For each Doppler offset, multi-stage differential filtering is performed sequentially to obtain the desired target channel information.

[0010] Secondly, the present invention provides a self-interference suppression device based on a sensing-integrated system in complex environments, the self-interference suppression device comprising: The transmitting unit is used to transmit downlink sensing signals; A module is established to receive the echo signal of the downlink sensing signal and establish a frequency domain channel matrix based on the echo signal; the frequency domain channel matrix includes a dynamic channel matrix and a static channel matrix. The estimation module is used to perform a two-dimensional discrete Fourier transform and constant false alarm rate detection on the frequency domain channel matrix to estimate multiple Doppler shifts; the multiple Doppler shifts include a spectral center Doppler shift and at least one spectral leakage Doppler shift; The differential module is used to perform multi-stage differential filtering operations on each Doppler offset to obtain the desired target channel information.

[0011] Thirdly, the present invention provides an electronic device, including a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus; Memory, used to store computer programs; When a processor executes a computer program stored in memory, it implements the steps described in the self-interference suppression method based on a sensor-integrated system under any of the above-mentioned complex environments.

[0012] Fourthly, the present invention provides a computer-readable storage medium storing a computer program, wherein when the computer program is executed by a processor, it implements the steps of the self-interference suppression method based on a sensor-integrated system under any of the above-mentioned complex environments.

[0013] This invention provides a self-interference suppression method based on a sensing-integrated system in complex environments. The frequency domain channel matrix includes a dynamic channel matrix and a static channel matrix. By performing a two-dimensional discrete Fourier transform and constant false alarm rate (CFAR) detection on the frequency domain channel matrix, multiple self-interference frequency components caused by hardware non-ideals, including spectral center Doppler shift and spectral leakage Doppler shift, can be estimated with high accuracy, providing accurate frequency calibration for subsequent suppression. Based on each estimated Doppler shift, multi-stage differential filtering is performed sequentially, thereby effectively eliminating the corresponding self-interference and static interference components one by one, improving the sensing capability of the sensing-integrated system.

[0014] The present invention will now be described in further detail with reference to the accompanying drawings. Attached Figure Description

[0015] Figure 1 This is a flowchart illustrating a self-interference suppression method based on a synoptic integrated system in a complex environment, provided by an embodiment of the present invention. Figure 2 This is a schematic diagram of the delay-Doppler spectrum corresponding to the first delay unit; Figure 3 This is a schematic diagram of the CA-CFAR testing process; Figure 4 These are schematic diagrams of the amplitude-frequency response of different differential step numbers; Figure 5 This is a schematic diagram of the experimental scenario; Figure 6 This is a diagram comparing different algorithms in terms of self-interference cancellation; Figure 7 These are schematic diagrams illustrating the interference cancellation performance of different algorithms under various interference-to-noise ratios. Figure 8 This is a schematic diagram illustrating the interference cancellation performance of different algorithms under various OFDM symbols; Figure 9 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0016] The present invention will be further described in detail below with reference to specific embodiments, but the implementation of the present invention is not limited thereto.

[0017] To address the ineffectiveness of existing interference suppression methods in resolving spectral leakage caused by hardware non-ideals, particularly when the interference signal and target signal frequencies are close, and the system struggles to accurately identify and eliminate the interference signal, this invention provides a self-interference suppression method based on a sensor-integrated system in complex environments. (See [link to relevant documentation]). Figure 1 , Figure 1This is a flowchart illustrating a self-interference suppression method based on a sensor-integrated system in a complex environment, provided by an embodiment of the present invention. The method specifically includes the following steps: Step S101: Transmit downlink sensing signal.

[0018] In this embodiment of the invention, the integrated sensing system transmits downlink complex orthogonal frequency division multiplexing (OFDM) transmission signals, i.e., downlink sensing signals, including: ; in, Indicates time Downlink sensing signals; Indicates the index of the subcarrier; Indicates the number of subcarriers; Indicates the index of the OFDM symbol; Indicates the number of OFDM symbols; Indicates the first The OFDM symbol, the first Power-normalized modulated data on each subcarrier; Represents the imaginary unit; Indicates the first The frequency of each subcarrier , Indicates the starting frequency. Indicates the subcarrier spacing; Indicates the duration of the entire OFDM symbol; Indicates time ; Represents a rectangle function.

[0019] Step S102: Receive the echo signal of the downlink sensing signal and establish a frequency domain channel matrix based on the echo signal; the frequency domain channel matrix includes a dynamic channel matrix and a static channel matrix.

[0020] In this embodiment of the invention, receiving the echo signal of the downlink sensing signal and establishing a frequency domain channel matrix based on the echo signal includes: Receive the echo signal of the downlink sensing signal; The echo signal is sampled and subjected to discrete Fourier transform to obtain the frequency domain received signal matrix; Channel estimation is performed based on the frequency domain received signal matrix and the modulation data matrix to obtain the frequency domain channel matrix that represents the channel state information.

[0021] In this embodiment of the invention, it is assumed that the number of propagation paths is... So, the first... A propagation path can be represented by complex channel coefficients. Time delay and true Doppler shift To represent it. The echo signal in the time domain can be represented as: ; in, Represents the echo signal in the time domain; This indicates a downlink sensing signal that has undergone a time delay.

[0022] By performing the above-mentioned echo signal in the time domain... , For discrete sampling, the echo signal in the discrete time domain can be represented as: ; in, Represents the echo signal in the discrete time domain; Indicates the index of the time-domain sampling point; Indicates the sampling period; After performing a Discrete Fourier Transform (DFT) on the echo signal in the discrete time domain, the matrix form of the echo signal in the frequency domain, i.e., the frequency domain received signal matrix, is obtained. It can be represented as: ; in, This represents the defined time delay vector. Represents the Doppler phase vector. This represents the phase rotation matrix between subcarriers; It is the unit orthogonal discrete Fourier transform matrix. The conjugate of , which is defined as ; It is a modulated data matrix; Indicates the Hadamard; superscript This represents the conjugate transpose operation of a vector; furthermore, , as well as .

[0023] In this embodiment of the invention, the initial expression for obtaining the frequency domain channel matrix based on the frequency domain received signal matrix and the modulation data matrix is ​​as follows: ; In this embodiment of the invention, for the frequency domain channel matrix exist Several signal transmission paths, these paths are... Dynamic paths and It consists of static paths, among which Frequency domain channel matrix It can be divided into two parts: dynamic channel matrix Includes Doppler offset and static channel matrix It does not include Doppler shift. The ideal expression for the frequency domain channel matrix is: ; When non-ideal factors exist, the frequency domain channel matrix representing the channel state information is: ; in, Indicates the first A static path; Indicates the number of static paths; Indicates the first The static channel matrix corresponding to each static path; Indicates the guide vector; Indicates the true Doppler shift; superscript T Indicates the transpose operation; It is a Doppler shift in the true spectrum center caused by the environment; ( This indicates the leakage frequency shift caused by imperfect hardware factors, which changes with the change of the spectral center, i.e., the true spectral leakage Doppler shift.

[0024] Step S103: Perform two-dimensional discrete Fourier transform and constant false alarm rate detection on the frequency domain channel matrix to estimate multiple Doppler shifts; the multiple Doppler shifts include spectral center Doppler shift and at least one spectral leakage Doppler shift.

[0025] Environmental fluctuations or platform vibrations can cause the self-interference spectrum to deviate from its normal Doppler position. Therefore, accurate estimation of the channel state information spectrum is crucial.

[0026] In this embodiment of the invention, a two-dimensional discrete Fourier transform and constant false alarm rate (CFAR) detection are performed on the frequency domain channel matrix to estimate multiple Doppler offsets, including: A two-dimensional discrete Fourier transform is performed on the frequency domain channel matrix to generate a time-delay-Doppler spectrum; Constant false alarm rate (CFAR) detection is performed along the time delay dimension of the time delay-Doppler spectrum to detect peak values ​​in the time delay-Doppler spectrum; Based on the detected peak values, the corresponding Doppler shift is estimated.

[0027] In this embodiment of the invention, a two-dimensional discrete Fourier transform (2D-DFT) is performed on the frequency domain channel matrix to generate the time delay. Doppler spectrum. Two-dimensional discrete Fourier transform processing obtains the time-delay spectrum by applying the inverse discrete Fourier transform (IDFT) along the frequency dimension, and then applies the discrete Fourier transform (DFT) along the time-delay dimension to generate the time-delay-Doppler spectrum. , can be represented as: ; in, Indicates the latency dimension index. This indicates the frequency dimension index.

[0028] when At this time, a peak will be generated in the time-delay-Doppler spectrum, see [link / reference]. Figure 2 , Figure 2 This is a time-delay-Doppler spectrum diagram corresponding to the first time-delay unit. The peak values ​​are then detected along the time-delay dimension using CA-CFAR (Constant False Alarm Rate Detection), as follows: Figure 3 As shown, Figure 3 This is a schematic diagram of the CA-CFAR detection process. A protection unit is set between the detection unit and the reference unit to prevent the target energy diffusion from affecting the noise estimation. The number of reference units is represented on both sides of the detection unit. A reference cell is used to estimate the power levels of background noise and interference. The formula for calculating the average power of the reference cell is: ; in, Representing the The signal corresponding to each reference unit; This represents the average power of the reference cells, indicating an estimate of the noise and interference near the detection cell in the radar beam obtained by averaging the power of the left and right reference cells. This is achieved by multiplying the average power of the reference cells by a constant. An adaptive detection threshold is generated. If the power of the detection unit exceeds this threshold, a peak is determined to exist at that location. The detected peak location is the Doppler shift that needs to be estimated, and the final estimated Doppler shift at the spectral center is obtained. and spectral leakage Doppler shift , express The estimate, express The estimate.

[0029] It should be noted that the highest peak value is the self-interference spectrum of the channel state information. The remaining peak values ​​are due to spectrum leakage caused by the non-ideal characteristics of the hardware. The algorithm in this paper estimates all peak values ​​and then reconstructs and eliminates them through a multi-level differential self-interference suppression part.

[0030] Step S104: For each Doppler offset, perform multi-level differential filtering operations sequentially to obtain the desired target channel information.

[0031] In step S103, the following has been estimated: and Based on these estimated Doppler shifts, multi-stage differential filtering operations are performed sequentially.

[0032] In this embodiment of the invention, for each Doppler offset, a multi-stage differential filtering operation is sequentially performed to obtain the desired target channel information, including: For each Doppler shift, a corresponding multi-level difference sequence is constructed based on a preset difference level number; By using multi-level differential sequences to perform recursive differential operations on the frequency domain channel matrix, the desired target channel information can be obtained.

[0033] In this embodiment of the invention, based on the expression of the frequency domain channel matrix, it can be known that the first... The OFDM symbol, the first The frequency domain channel state information corresponding to each subcarrier, hereinafter referred to as the non-ideal frequency domain channel state information, is expressed as follows: ; in, It is intended to sense target channel information; This indicates the interference that needs to be suppressed. When there are multiple Doppler offsets, the self-interference will be estimated and eliminated one by one using multi-level differential self-interference suppression for the non-ideal frequency domain channel state information.

[0034] In this embodiment of the invention, the multi-level difference sequence includes: ; ; in, Represents a multi-level difference sequence; Indicates the first The coefficients of a differential sequence; The difference number represents the difference sequence of a multi-level difference sequence; Represents the first element in the frequency domain channel matrix. The subcarrier, the first Frequency domain channel state information corresponding to each OFDM symbol; Indicates the first The subcarrier, the first The guide vector of the OFDM symbol; Indicates Doppler shift; It represents factorial.

[0035] by For example, multi-level difference sequences The expression is: ; ; in, Indicates the first The coefficients of the difference sequence, This represents the difference series number of a multi-level difference sequence. Represents factorial. For example... Figure 4 As shown, Figure 4 This is a schematic diagram of the amplitude-frequency response of different differential steps. The filter will... An interference suppression notch is formed at the location. As the differential level increases, the interference suppression notch widens, and the interference suppression amplitude increases. Different differential levels can be set according to the actual scenario to obtain different interference suppression notches, thereby suppressing interference and retaining the sensing target signal with a frequency close to the interference frequency.

[0036] In this embodiment of the invention, a multi-stage differential filtering operation needs to be performed on each Doppler shift. The following continues with... For example, the following explanation will be given. For ease of understanding, in this embodiment of the invention, the spectrum leakage portion is defined as... Therefore, the expression for the non-ideal frequency domain channel state information can be: ; Difference-based grading For example, the expression for a second-order difference sequence is: ; in , .

[0037] make Let a new recursive sequence formula be defined as:

[0038]

[0039] in, ; The initial value of the recursive sequence is defined as the value of the first iteration. n The differential output value of each subcarrier after differential filtering when processing the first OFDM symbol. For a general term in a recursive sequence, it represents the term in the recursive sequence. n When processing the subcarriers, the data is accumulated from the first OFDM symbol to the second. m The recursive cumulative value obtained after OFDM symbols is equal to the recursive cumulative value of the previous step plus the differential output value of the current step.

[0040] In an embodiment of the present invention, The estimated value, i.e., the channel state matrix after suppressing static and self-interference. The expression is: ; in, It is the first OFDM symbol, the... The residual interference on each subcarrier can be expressed as: ; Furthermore, The estimated value, i.e., the channel state matrix after suppressing static and self-interference. The expression is: ; in, It is the first The OFDM symbol, the first The residual interference on each subcarrier can be expressed as: ; By constructing a multi-level differential sequence for each Doppler shift to be suppressed and performing differential filtering to suppress interference, until all estimated Doppler shifts are suppressed in sequence, the desired sensing target signal is obtained.

[0041] It should be noted that the magnitude of residual interference decreases as the number of OFDMs increases, and therefore can be ignored in practical processing.

[0042] Finally, after the above processing, the static interference and self-interference components in the frequency domain channel matrix are greatly suppressed, leaving mainly the desired dynamic target signal, thus obtaining the desired sensing target channel information and significantly improving the sensing capability. Furthermore, preset differential levels can be set according to the actual scenario to obtain different interference suppression notches, thereby effectively eliminating self-interference caused by non-ideal factors and static interference in complex environments in the integrated sensing system.

[0043] In this embodiment of the invention, the frequency domain channel matrix includes a dynamic channel matrix and a static channel matrix. Performing a two-dimensional discrete Fourier transform and constant false alarm rate (CFAR) detection on the frequency domain channel matrix enables high-precision estimation of multiple self-interference frequency components caused by hardware non-ideals, including spectral center Doppler shift and spectral leakage Doppler shift, providing accurate frequency calibration for subsequent suppression. Based on each estimated Doppler shift, multi-stage differential filtering is sequentially performed, thereby effectively eliminating the corresponding self-interference and static interference components one by one, improving the sensing capability of the integrated sensing system.

[0044] The simulation experiment of a self-interference suppression method based on a synoptic integrated system in a complex environment, provided by an embodiment of the present invention, is as follows: To verify the effectiveness and superiority of a self-interference suppression method based on a sensory integration system in complex environments, addressing the issues of strong self-interference and spectrum leakage caused by non-ideal factors in such systems, a scenario of self-transmitting and self-receiving sensing by the sensory integration system was simulated in a complex indoor setting. Different scenarios under different self-interference conditions were simulated by changing the angle between the transmitting and receiving antennas. Gesture target signals with frequencies close to the interference signals were used as the desired sensing targets. (See [link to relevant documentation]). Figure 5 , Figure 5 This is a schematic diagram of the experimental setup. See Table 1 for experimental parameter settings: Table 1 Experimental Parameter Settings

[0045] Self-interference cancellation processing was performed on the measurement data collected from indoor scenes. The performance of the proposed algorithm was compared with RLS, RLS-Hammerstein, F-DIC, and F-mean under different self-interference intensities. Taking a 60° transmit / receive antenna spacing as an example, the processing results of different algorithms are as follows. See [link / reference] Figure 6 , Figure 6 This is a diagram comparing different algorithms in terms of self-interference cancellation. Figure 6 (a) shows the case where no self-interference cancellation was performed: strong self-interference at the zero Doppler position overwhelmed the target signal. In contrast, Figure 6 In (f), the proposed self-interference cancellation algorithm is applied, which retains only the target signal in the time-Doppler spectrum, which is consistent with the expected result. Figure 6 (b) and Figure 6 (c) in the diagram demonstrates effective self-interference cancellation, but inadvertently weakens the perceived target. In real-world scenarios, the perceived target is a moving target, and pure background noise cannot be obtained before perception. Figure 6 (d) and Figure 6 Figure (e) shows that after self-interference cancellation, the main self-interference at 0 Hz is significantly reduced. However, non-ideal hardware effects result in residual leakage of the self-interference signal, so a considerable self-interference component still exists.

[0046] Furthermore, to verify the effectiveness of different algorithms in suppressing interference, the Interference Reduction Ratio (ICR) is used, i.e.: ; See Figure 7 , Figure 7This diagram illustrates the interference cancellation performance of different algorithms under various interference-to-noise ratios (INR). Changing the angle between the transmit and receive antennas alters the signal-to-interference-plus-noise ratio (SINR). As SINR increases, the ICR of all evaluated algorithms also increases, indicating an overall performance improvement. The algorithm proposed in this invention outperforms other algorithms in terms of ICR. This enhanced performance is attributed to the increasing SINR, which leads to a corresponding increase in leakage interference energy due to hardware limitations, a challenge that the comparative algorithms struggle to adequately address.

[0047] Since the proposed algorithm is related to the number of OFDM symbols processed, its impact on ICR is further analyzed. See [link to relevant documentation]. Figure 8 , Figure 8 This diagram illustrates the interference cancellation performance of different algorithms under various OFDM symbols. As the number of OFDM symbols increases, the interference cancellation ratio (ICR) achieved by all algorithms improves. For each number of OFDM symbols tested, the algorithm proposed in this invention achieves a higher ICR than competing algorithms. This advantage stems from the reduction of residual interference as the number of OFDM symbols increases.

[0048] In this embodiment of the invention, the problem of strong self-interference and spectrum leakage caused by non-ideal factors in integrated sensing systems in complex environments is addressed. This is particularly true when the frequencies of interfering signals and target signals are close, making it difficult for traditional algorithms to identify and eliminate interference while preserving the target signal. This patent employs a joint spectrum estimation and multi-level differential interference suppression method. Compared to other methods, this method can accurately capture frequency shifts caused by hardware non-ideals and sequentially use a multi-level differential approach to eliminate self-interference and static interference components at the estimated interference spectrum positions, preserving the desired target signal and improving the sensing capability of the integrated sensing system.

[0049] Based on the same inventive concept, embodiments of the present invention also provide a self-interference suppression device based on a sensor-integrated system in complex environments, the self-interference suppression device comprising: The transmitting unit is used to transmit downlink sensing signals; A module is established to receive the echo signal of the downlink sensing signal and establish a frequency domain channel matrix based on the echo signal; the frequency domain channel matrix includes a dynamic channel matrix and a static channel matrix. The estimation module is used to perform a two-dimensional discrete Fourier transform and constant false alarm rate detection on the frequency domain channel matrix to estimate multiple Doppler shifts; the multiple Doppler shifts include a spectral center Doppler shift and at least one spectral leakage Doppler shift; The differential module is used to perform multi-stage differential filtering operations on each Doppler offset to obtain the desired target channel information.

[0050] In this embodiment of the invention, the frequency domain channel matrix includes a dynamic channel matrix and a static channel matrix. Performing a two-dimensional discrete Fourier transform and constant false alarm rate (CFAR) detection on the frequency domain channel matrix enables high-precision estimation of multiple self-interference frequency components caused by hardware non-ideals, including spectral center Doppler shift and spectral leakage Doppler shift, providing accurate frequency calibration for subsequent suppression. Based on each estimated Doppler shift, multi-stage differential filtering is sequentially performed, thereby effectively eliminating the corresponding self-interference and static interference components one by one, improving the sensing capability of the integrated sensing system.

[0051] This invention also provides an electronic device, such as... Figure 9 As shown, it includes a processor 901, a communication interface 902, a memory 903, and a communication bus 904, wherein the processor 901, the communication interface 902, and the memory 903 communicate with each other through the communication bus 904. Memory 903 is used to store computer programs; When the processor 901 executes the program stored in the memory 903, it implements the method steps of the self-interference suppression method based on the integrated sensing system under any of the above-mentioned complex environments.

[0052] The communication bus mentioned in the above electronic devices can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. This communication bus can be divided into address bus, data bus, control bus, etc. For ease of representation, only one thick line is used in the diagram, but this does not indicate that there is only one bus or one type of bus.

[0053] The communication interface is used for communication between the aforementioned electronic devices and other devices.

[0054] The memory may include random access memory (RAM) or non-volatile memory (NVM), such as at least one disk storage device. Optionally, the memory may also be at least one storage device located remotely from the aforementioned processor.

[0055] The processors mentioned above can be general-purpose processors, including central processing units (CPUs), network processors (NPs), etc.; they can also be digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0056] The present invention also provides a computer-readable storage medium. A computer program is stored in the computer-readable storage medium, and when executed by a processor, the computer program implements the method steps of the self-interference suppression method based on a sensor-integrated system under any of the aforementioned complex environments.

[0057] Optionally, the computer-readable storage medium may be non-volatile memory (NVM), such as at least one disk storage device.

[0058] Optionally, the aforementioned computer-readable storage medium may also be at least one storage device located remotely from the aforementioned processor.

[0059] In another embodiment of the present invention, a computer program product containing instructions is also provided, which, when run on a computer, causes the computer to execute the steps of the self-interference suppression method based on a sensor-integrated system under any of the above-described complex environments.

[0060] It should be noted that the terms "first," "second," etc., are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present invention. Rather, they are merely examples of apparatuses and methods consistent with some aspects of the invention.

[0061] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Furthermore, those skilled in the art can combine and integrate the different embodiments or examples described in this specification.

[0062] Although the invention has been described herein in conjunction with various embodiments, those skilled in the art will understand and implement other variations of the disclosed embodiments by reviewing the accompanying drawings and the disclosure in carrying out the claimed invention. In the description of the invention, the word "comprising" does not exclude other components or steps, "a" or "an" does not exclude a plurality, and "a plurality" means two or more, unless otherwise explicitly specified. Furthermore, while different embodiments may describe certain measures, this does not mean that these measures cannot be combined to produce good results.

[0063] The method provided in this invention can be applied to electronic devices. Specifically, the electronic device can be a desktop computer, a portable computer, a smart mobile terminal, a server, etc. No limitation is made herein; any electronic device that can implement this invention falls within the protection scope of this invention.

[0064] For the embodiments of the device / electronic device / storage medium, since they are basically similar to the method embodiments, the description is relatively simple, and relevant parts can be referred to in the description of the method embodiments.

[0065] It should be noted that the device, electronic device and storage medium in the embodiments of the present invention are respectively devices, electronic devices and storage media that apply the above-mentioned self-interference suppression method based on a sensor-integrated system in a complex environment. Therefore, all embodiments of the above-mentioned self-interference suppression method based on a sensor-integrated system in a complex environment are applicable to the device, electronic device and storage medium, and can achieve the same or similar beneficial effects.

[0066] The above description, in conjunction with specific preferred embodiments, provides a further detailed explanation of the present invention. It should not be construed that the specific implementation of the present invention is limited to these descriptions. For those skilled in the art, various simple deductions or substitutions can be made without departing from the concept of the present invention, and all such modifications and substitutions should be considered within the scope of protection of the present invention.

Claims

1. A self-interference suppression method based on a synoptic integrated system in complex environments, characterized in that, The self-interference suppression method includes: Transmit downlink sensing signals; The echo signal of the downlink sensing signal is received, and a frequency domain channel matrix is ​​established based on the echo signal; the frequency domain channel matrix includes a dynamic channel matrix and a static channel matrix. Two-dimensional discrete Fourier transform and constant false alarm rate detection are performed on the frequency domain channel matrix to estimate multiple Doppler shifts; the multiple Doppler shifts include spectral center Doppler shift and at least one spectral leakage Doppler shift; For each Doppler offset, multi-stage differential filtering is performed sequentially to obtain the desired target channel information.

2. The self-interference suppression method according to claim 1, characterized in that, Receiving the echo signal of the downlink sensing signal and establishing a frequency domain channel matrix based on the echo signal includes: Receive the echo signal of the downlink sensing signal; The echo signal is sampled and subjected to discrete Fourier transform to obtain the frequency domain received signal matrix; Channel estimation is performed based on the frequency domain received signal matrix and the modulation data matrix to obtain a frequency domain channel matrix that characterizes the channel state information.

3. The self-interference suppression method according to claim 1, characterized in that, A two-dimensional discrete Fourier transform and constant false alarm rate (CFAR) detection are performed on the frequency domain channel matrix to estimate multiple Doppler shifts, including: Perform a two-dimensional discrete Fourier transform on the frequency domain channel matrix to generate a time-delay-Doppler spectrum; Constant false alarm rate (CFAR) detection is performed along the time delay dimension of the time delay-Doppler spectrum to detect peak values ​​in the time delay-Doppler spectrum; Based on the detected peak values, the corresponding Doppler shift is estimated.

4. The self-interference suppression method according to claim 1, characterized in that, For each Doppler offset, a multi-stage differential filtering operation is performed sequentially to obtain the desired target channel information, including: For each Doppler shift, a corresponding multi-level difference sequence is constructed based on a preset difference level number; The frequency domain channel matrix is ​​recursively differentially processed using a multi-level differential sequence to obtain the desired target channel information.

5. The self-interference suppression method according to claim 4, characterized in that, The multi-level difference sequence includes: ； ； in, Represents a multi-level difference sequence; Indicates the first The coefficients of a differential sequence; The difference number represents the difference sequence of a multi-level difference sequence; Represents the first element in the frequency domain channel matrix. The subcarrier, the first Frequency domain channel state information corresponding to each OFDM symbol; Indicates the first The subcarrier, the first The steering vector of each OFDM symbol; Indicates Doppler shift; It represents factorial.

6. The self-interference suppression method according to claim 1, characterized in that, The frequency domain channel matrix includes: ； in, This represents the frequency domain channel matrix; This represents the dynamic channel matrix; Indicates the first A static path; Indicates the number of static paths; Indicates the first The static channel matrix corresponding to each static path; Indicates the guide vector; This indicates the true Doppler offset.

7. A self-interference suppression device based on a sensor-integrated system in complex environments, characterized in that, The self-interference suppression device includes: The transmitting unit is used to transmit downlink sensing signals; A module is established to receive the echo signal of the downlink sensing signal and establish a frequency domain channel matrix based on the echo signal; the frequency domain channel matrix includes a dynamic channel matrix and a static channel matrix. The estimation module is used to perform two-dimensional discrete Fourier transform and constant false alarm rate detection on the frequency domain channel matrix to estimate multiple Doppler shifts; the multiple Doppler shifts include spectral center Doppler shift and at least one spectral leakage Doppler shift; The differential module is used to perform multi-stage differential filtering operations on each Doppler offset to obtain the desired target channel information.

8. The self-interference suppression device according to claim 7, characterized in that, The establishment module is specifically used to receive the echo signal of the downlink sensing signal; sample and perform discrete Fourier transform on the echo signal to obtain a frequency domain received signal matrix; and perform channel estimation based on the frequency domain received signal matrix and the modulation data matrix to obtain a frequency domain channel matrix characterizing channel state information.

9. An electronic device, characterized in that, It includes a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus; Memory, used to store computer programs; A processor, when executing a computer program stored in a memory, implements the self-interference suppression method based on a sensor-integrated system in a complex environment as described in any one of claims 1-6.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, which, when executed by a processor, implements the self-interference suppression method based on a sensor-integrated system in a complex environment as described in any one of claims 1-6.

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