A method and device for integrated sensing and communication based on OAM mode driving

Abstract

This invention discloses an integrated sensing method and apparatus based on OAM mode-driven sensing, comprising: generating an OFDM communication signal conforming to communication standards without customized sensing adjustments; determining the OAM mode topology charge number according to detection requirements; feeding the OFDM signal into a uniform circular array, and applying a calculated array element feed phase offset to make the radiated signal carry a specific OAM spiral phase wavefront; receiving the echo signal and retaining the phase characteristics; calculating the target azimuth angle using OAM mode matching or spectral analysis techniques and extracting subcarrier phase change data; and combining the above information to calculate the target radial velocity, micro-Doppler, and range information. Key steps include array calibration, signal conversion and allocation, phase offset calculation and application, echo reception preprocessing, and mode matching analysis, achieving synergy between communication and sensing functions without requiring additional spectrum or dedicated sensors, thus improving sensing accuracy and system adaptability.

Description

Technical Field

[0001] This invention relates to the field of communication and sensing management technology, and in particular to a method and apparatus for integrated sensing based on OAM mode driving. Background Technology

[0002] With the deep integration of communication and sensing technologies, integrated communication and sensing has become a core direction for improving spectrum resource utilization and expanding the functions of wireless systems, with wide application needs in scenarios such as intelligent transportation, industrial monitoring, and low-altitude security. Traditional communication systems only focus on optimizing data transmission efficiency, and sensing functions require separate deployment of dedicated sensors, resulting in hardware redundancy and wasted spectrum resources. Orbital angular momentum (OAM), as an inherent property of electromagnetic waves, has orthogonal characteristics in its different modes, enabling multimodal parallel transmission and sensing within the same frequency band, providing a new technical path for integrated communication and sensing. Integrated communication and sensing technology based on OAM mode-driven transmission, by loading a specific OAM spiral phase wavefront into the communication signal, allows the signal to simultaneously carry user data and sensing information without additional spectrum resources, enabling the coordinated realization of communication and sensing functions and meeting the needs of multifunctional, high-efficiency wireless systems in complex scenarios.

[0003] Existing integrated sensing technology has two significant drawbacks: First, most solutions require specialized customized processing of communication signals, leading to a trade-off between communication and sensing performance. This makes it difficult to simultaneously ensure the reliability of data transmission and the accuracy of sensing results. Furthermore, the complex signal processing flow increases the difficulty of system implementation and hardware overhead. Second, existing technologies have limitations in improving angular resolution and distinguishing multiple targets. Traditional array signal processing methods rely on differences in signal amplitude or time delay to achieve target perception. When facing multi-target scenarios in complex environments, they are easily affected by interference signals and cannot effectively extract the precise orientation and motion information of the target. Moreover, the sensing range and angular resolution are difficult to adjust flexibly according to actual needs, resulting in poor adaptability. Summary of the Invention

[0004] In order to overcome the shortcomings and deficiencies of the existing technology, the present invention provides a method and device for integrated sensing based on OAM mode driving.

[0005] The technical solution adopted in this invention is a sensing integration method based on OAM mode driving, comprising the following steps: S1, generating a standard OFDM communication signal carrying random user data through a base station baseband unit. The subcarrier, pilot, and cyclic prefix parameters of this signal all follow the communication standard without any customized adjustments for sensing functions; S2, determining one or more OAM mode topology charges for the sensing task based on preset detection range and angular resolution requirements; S3, feeding the OFDM signal generated in S1 into an N-element uniform circular array, and determining the OAM mode topology charges determined in S2. S4. Calculate the feed phase offset corresponding to each array element and apply it to the corresponding array element so that the radiated signal carries the calibrated OAM spiral phase wavefront; S5. Receive the echo signal formed by environmental reflection through a uniform circular array, and perform array reception processing on the echo signal to retain phase characteristics; S6. Use OAM mode matching or mode spectrum analysis technology to solve the target azimuth information from the phase characteristics of the echo signal, and extract subcarrier phase change data by combining the frequency domain characteristics of the OFDM signal; S7. Based on the azimuth information and subcarrier phase change data obtained in S5, jointly solve the target's radial velocity, micro-Doppler, and range information.

[0006] Furthermore, the element feed phase offset in S3 is calculated using the following formula: ,in, The topological charge number of the OAM mode determined for S2, Let n be the azimuth angle of the nth array element on the circular ring. This is the phase calibration compensation amount for the array elements. Let n be the amplitude attenuation coefficient of the nth array element. Let be the phase noise coefficient of the nth array element.

[0007] Furthermore, the feature extraction formula for OAM modal spectrum analysis in S5 is as follows: ,in, This represents the OAM modal spectral power value. The echo received signal at the k-th subcarrier and the m-th symbol time is... For signal observation duration, For OAM modal topology charge number, Let be the phase angle corresponding to the k-th subcarrier and the m-th symbol at time t. The subcarrier-symbol dimension weighting coefficient.

[0008] Furthermore, the formula for calculating the radial velocity in S6 is: ,in, For the target radial velocity, The speed of electromagnetic wave propagation. For carrier frequency, For symbol period, for The cross-correlation value of the signals at time 10:00. For Doppler frequency shift, These represent the time window lengths for cross-correlation calculations.

[0009] Furthermore, the formula for calculating the azimuth angle in S5 is as follows: ,in, For the estimated target azimuth, Let n be the complex form of the received signal of the nth array element. These represent the operations of taking the real part and the imaginary part, respectively. Let n be the noise power of the nth element. The azimuth deviation compensation value for array installation.

[0010] Furthermore, the distance calculation formula in S6 is as follows: ,in, For the target distance, The speed of electromagnetic wave propagation. For subcarrier spacing, Let be the phase change of the s-th subcarrier. The weighting coefficient for the s-th subcarrier is... The total number of subcarriers participating in the distance calculation, round This indicates the rounding operation. This is the distance calibration compensation amount.

[0011] Further, S3 includes the following sub-steps: S31, assigning RF channel numbers and position coordinates to each element of the uniform circular array, clarifying the relative angular relationship and physical position parameters of each element on the ring; S32, converting the OFDM baseband signal generated in S1 into an RF signal through a digital-to-analog converter module, and distributing it to each RF input port of the uniform circular array after power amplification; S33, calculating the required feed phase offset for each element based on the OAM mode topology charge determined in S2 and the azimuth parameters of each element through a phase calculation module; S34, precisely applying the calculated phase offset to the RF signal of the corresponding element through the phase-adjustable module of the RF front end, so that the radiated signal of each element forms a preset OAM spiral phase wavefront.

[0012] Further, step S4 includes the following sub-steps: S41, each element of the uniform circular array synchronously receives the echo signal reflected from the target in the environment, converts the received signal into an electrical signal, and transmits it to the low-noise amplification module; S42, the amplified echo electrical signal is filtered to remove out-of-band interference signals and noise components, and retains the frequency components of the effective echo signal; S43, the filtered analog echo signal is converted into a digital signal through the analog-to-digital conversion module to ensure that the signal sampling rate meets the requirements of subsequent processing; S44, the digitized echo signal is stored in the buffer module according to the element number and the receiving time order to provide data support for subsequent phase feature extraction and modal analysis.

[0013] Further, S5 includes the following sub-steps: S51, reading the digitized echo signals of each array element from the buffer module, and constructing a mode matching matrix according to the OAM mode topological charge sequence; S52, performing conjugate multiplication operation on the constructed mode matching matrix and the echo signal matrix to obtain the matching output result corresponding to each OAM mode; S53, performing spectral analysis processing on the matching output result to identify the OAM mode topological charge corresponding to the energy peak; S54, based on the identified OAM mode topological charge, extracting the phase features of the echo signal corresponding to the mode, and establishing a mapping relationship between the phase features and the target azimuth angle.

[0014] An OAM-modal driven integrated sensing method and apparatus are disclosed. The apparatus, applied to an OAM-modal driven integrated sensing method, includes: an OFDM random communication signal generation module for generating OFDM baseband signals carrying random user data and conforming to communication standards, without any customized sensing design; its output is connected to the signal input of an OAM-UCA integrated transmitter array; an OAM modal parameter configuration and phase calculation module for determining the OAM modal topology charge number according to sensing task requirements and calculating the feed phase offset of each array element; its output is connected to the phase control terminal of the OAM-UCA integrated transmitter array; and an OAM-UCA integrated transmitter array module consisting of N phase / amplitude tunable transmitters. The array elements of the frequency front-end form a circular structure, receiving communication signals and applying phase shift, radiating beams carrying OAM modes. The receiving end is connected to the sensing signal receiving and preprocessing module. The sensing signal receiving and preprocessing module is used to receive echo signals and perform low-noise amplification, filtering, analog-to-digital conversion, and storage. Its output end is connected to the OAM mode feature extraction module. The OAM mode feature extraction module uses mode matching or spectral analysis techniques to extract echo phase features, calculate the target azimuth angle, and its output end is connected to the multi-dimensional sensing information fusion and calculation module. The multi-dimensional sensing information fusion and calculation module receives azimuth angle information and subcarrier phase change data, jointly calculates the target radial velocity, micro-Doppler, and range information, and completes the sensing data output.

[0015] Beneficial Effects: This invention proposes an integrated sensing method and device based on OAM mode-driven sensing. It eliminates the need for customized adjustments to the communication signal, directly utilizing standard OFDM signals to carry the OAM spiral phase wavefront. This ensures that communication parameters meet industry standards and achieves coordinated operation of communication and sensing functions through modal orthogonality. It completely solves the problems of mutual constraints between communication and sensing performance and complex system implementation in traditional solutions. Furthermore, it eliminates the need for dedicated sensing sensors, reducing hardware redundancy and spectrum resource waste. Regarding improved sensing performance, through uniform circular array and OAM mode matching and spectral analysis techniques, precise array element phase calibration and multi-dimensional feature extraction significantly enhance angular resolution and target discrimination capabilities, effectively resisting interference signals. It can accurately calculate target azimuth, distance, radial velocity, and micro-Doppler information, overcoming the limitations of insufficient sensing accuracy in complex environments with traditional array signal processing. Moreover, the OAM mode topology charge can be flexibly configured according to detection requirements, enabling dynamic adjustment of sensing range and resolution, significantly improving system adaptability and meeting the multi-functional application needs of different scenarios. Attached Figure Description

[0016] Figure 1 This is a flowchart illustrating the overall process of the method of the present invention.

[0017] Figure 2 This is a flowchart of method step S3 of the present invention;

[0018] Figure 3 This is a flowchart of method step S4 of the present invention;

[0019] Figure 4 This is a flowchart of step S5 of the method of the present invention;

[0020] Figure 5 This is a diagram showing the system unit composition of the present invention. Detailed Implementation

[0021] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. The application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0022] like Figure 1 As shown, a synesthetic integration method based on OAM mode driving includes the following steps:

[0023] S1 generates a standard OFDM communication signal carrying random user data through the base station baseband unit. The subcarrier, pilot, and cyclic prefix parameters of this signal all follow the communication standard and have not been customized for the sensing function.

[0024] Specifically, the S1 implementation process involves generating standard OFDM communication signals through the base station baseband unit. This process strictly adheres to the technical specifications of mainstream communication standards, without introducing any customized adjustments for sensing functions, ensuring the signal has universal communication compatibility. In practice, the base station baseband unit first acquires randomly distributed user data, including various types of service information such as voice, text, and images. The data transmission rate is set to an adjustable range of 10Mbps to 1Gbps to adapt to different communication scenarios. During the signal generation phase, the number of subcarriers is explicitly set to 64 to 2048, with a standard subcarrier spacing of 15kHz or 30kHz. Pilot signals are evenly distributed at a density of one pilot inserted every 12 subcarriers, and the pilot sequence uses a Zadoff-Chu sequence design to ensure the accuracy of channel estimation. The cyclic prefix length is configured to 0.8 microseconds, 1.6 microseconds, or 3.2 microseconds based on channel delay characteristics, effectively resisting inter-symbol interference. During the generation process, the baseband unit completes data modulation, encoding, and interleaving operations through a digital signal processing chip. The modulation method supports adaptive adjustment of QPSK, 16QAM, 64QAM, etc., and the encoding rate adopts multiple options such as 1 / 2, 2 / 3, 3 / 4. The interleaving depth is set to a dimension that matches the number of subcarriers. The bandwidth of the final output OFDM baseband signal can be flexibly configured between 1.4MHz and 20MHz to meet different communication bandwidth requirements and lay the foundation for standard communication signals for subsequent loading of OAM modes.

[0025] S2, based on the preset detection range and angular resolution requirements, determine one or more OAM modal topology charges for the sensing task;

[0026] Specifically, the core of S2 implementation is to scientifically determine the OAM modal topology charge number for the sensing task based on the needs of the actual detection scenario. This process directly determines the detection range and angular resolution of the sensing system. In practice, the technical specifications of the detection range are first clarified, setting the azimuth detection range to omnidirectional coverage from 0 degrees to 360 degrees or directional coverage from 30 degrees to 150 degrees, the distance detection range to 10 meters to 500 meters, and the angular resolution to be controlled between 0.5 degrees and 5 degrees. These parameters need to be configured specifically according to different application scenarios such as intelligent transportation, industrial monitoring, and low-altitude security. Then, the topology charge number is calculated and filtered using the system's preset modal configuration algorithm, combined with the above detection parameters. The value range of the OAM modal topology charge number is set to an integer between -10 and +10, with each topology charge number corresponding to a specific spiral phase wavefront characteristic. When the detection range is large and the angular resolution requirement is low, topology charge numbers with smaller absolute values ​​such as ±1 or ±2 can be selected. In this case, the signal coverage is wide but the focusing is weak. When high-precision angular resolution is required, topology charge numbers with larger absolute values ​​such as ±5 to ±10 are selected to improve the sensitivity to phase changes and thus improve the resolution accuracy. It also supports the configuration of single or multiple topology charge numbers. When combining multiple modes, it adopts mode time-division multiplexing or space-division multiplexing, transmitting signals with 2 to 8 different topology charge numbers in parallel to achieve simultaneous detection of multiple targets. The determination of the topology charge number needs to be verified through system pre-simulation to ensure that under the set detection parameters, the signal propagation loss is controlled within an acceptable range and the inter-modal interference is below -30dB, ensuring the effectiveness and reliability of the sensing signal.

[0027] S3, feed the OFDM signal generated in S1 into a uniform circular array of N elements. Based on the OAM mode topology charge determined in S2, calculate the feed phase offset corresponding to each element and apply it to the corresponding element so that the radiated signal carries the calibrated OAM spiral phase wavefront.

[0028] Specifically, the implementation of S3 involves converting the standard OFDM signal generated by S1 into a radiated signal carrying a specific OAM spiral phase wavefront. This relies heavily on the precise phase control of an N-element uniform circular array. In practice, the hardware parameters of the uniform circular array are first defined. The number of elements, N, is set to 8 to 64, evenly distributed on a ring with a radius of 0.5 to 2 wavelengths. The elements employ omnidirectional antenna units, operating in the 2GHz to 6GHz common communication frequency bands. Each element is equipped with an independent RF front-end module, supporting independent phase and amplitude control. The OFDM signal is fed in through a baseband signal distribution unit, synchronously transmitting the baseband signal output from S1 to the RF front-end of each element. The transmission delay is controlled within 10 nanoseconds to ensure the time synchronization of signals from each element. The phase offset calculation strictly adheres to the OAM mode topology charge determined by S2, combined with the azimuth parameters of each array element on the ring. Real-time calculations are performed using a dedicated phase calculation chip. The azimuth angles increase sequentially according to the array element number, and the azimuth angle difference between adjacent elements is 360 degrees divided by the number of elements N. During the calculation, an element phase calibration compensation is introduced, obtained through prior array calibration testing. The calibration compensation error for each element is controlled within ±0.5 degrees. The phase application process is achieved through a digital phase shifter on the RF front end. The phase shifter has a phase adjustment accuracy of 0.1 degrees and an adjustment range of 0 to 360 degrees, accurately responding to the calculated phase offset. During implementation, the system monitors the output signal phase of each element in real time and corrects the phase deviation to the allowable range through a feedback control mechanism. Ultimately, the signal radiated by the entire array forms an OAM signal with a specific spiral phase wavefront. This signal maintains modal orthogonality during spatial propagation, laying the foundation for subsequent extraction of sensing echoes.

[0029] S4, receives the echo signal formed by environmental reflection through a uniform circular array, and performs array reception processing on the echo signal to preserve phase characteristics;

[0030] Specifically, the S4 implementation process uses a uniform circular array to receive and preprocess environmental echo signals. The key is to preserve the phase characteristics of the echo signals, providing high-quality data for subsequent OAM mode analysis. In practice, the array's synchronous reception mechanism is first activated. Each array element simultaneously captures the echo signals reflected from targets in the environment within a preset reception time slot. The reception time slots are strictly synchronized with the frame structure of the transmitted signals, with synchronization errors controlled within 1 nanosecond, ensuring time consistency of the received signals from multiple array elements. The array elements convert the received electromagnetic wave signals into weak electrical signals. This conversion process is completed by the low-noise amplifier front-end built into the array element. The amplifier's noise figure is controlled below 1.5dB, and the gain is adjustable from 20dB to 60dB, adaptively adjusting according to the echo signal strength to avoid signal saturation or insufficient signal-to-noise ratio. The amplified electrical signal is then filtered using a bandpass filter. The filter's center frequency matches the carrier frequency of the transmitted signal, and its bandwidth is 1.2 times that of the transmitted signal. This effectively filters out out-of-band interference and environmental noise, with in-band ripple not exceeding 0.5dB and out-of-band rejection greater than 60dB. The filtered analog signal is then converted to a digital signal via an analog-to-digital converter (ADC). The ADC's sampling rate is set to 5 times the transmitted signal bandwidth, with a 16-bit sampling precision to ensure complete preservation of the signal's phase and amplitude information. Clock jitter during sampling is controlled to within 10 picoseconds to reduce sampling distortion. The digitized echo signal is stored in a high-speed cache module according to the array element number and reception time. The cache module has a storage capacity of at least 1GB and a read / write rate of 10Gbps, supporting real-time data writing and subsequent fast retrieval. During storage, the system performs integrity verification on the data, using a checksum mechanism to ensure that no data is lost or erroneous. At the same time, it records key information such as the array element number and timestamp corresponding to each data, providing an index for subsequent joint processing of multi-array data. The total latency of the entire receiving and processing process is controlled within 10 microseconds, meeting the requirements of real-time sensing.

[0031] S5 employs OAM mode matching or mode spectrum analysis techniques to calculate the target azimuth information from the phase characteristics of the echo signal, and extracts subcarrier phase change data by combining the frequency domain characteristics of the OFDM signal.

[0032] Specifically, the S5 implementation process involves extracting key sensing features from the echo signal, calculating the target azimuth angle using OAM mode matching or mode spectrum analysis techniques, and extracting subcarrier phase change data to provide core input for multi-dimensional sensing information calculation. In practice, the digitized echo signals of each array element are first read from the buffer module. A mode matching matrix is ​​constructed according to the OAM mode topological charge sequence, with the matrix dimension matching the number of array elements, modes, and signal length. Matrix elements are generated using preset mode basis functions, with the orthogonality error controlled below -40dB. If mode matching technology is used, the constructed mode matching matrix is ​​multiplied conjugately with the echo signal matrix. This operation is performed by a dedicated digital signal processing chip with a processing speed of at least 1000 MIPS, yielding the matching output results for each OAM mode. A peak detection algorithm identifies the mode with the highest energy, and the topological charge corresponding to this mode has a fixed mapping relationship with the target azimuth angle. The target azimuth angle is calculated based on this, with the azimuth angle calculation error controlled within ±0.3 degrees. If modal spectrum analysis is used, the echo signal is integrated and its power spectrum is estimated. The integration time is consistent with the observation time of the transmitted signal. Power spectrum estimation uses a periodogram method combined with a window function. The Hanning window is selected to reduce spectral leakage, and the frequency resolution of the spectrum estimation is 1 / 10 of the subcarrier interval of the transmitted signal. Simultaneously with azimuth calculation, the frequency domain characteristics of the OFDM signal are extracted. The time-domain echo signal is converted to the frequency domain using a Fast Fourier Transform (FFT). The number of points in the FFT is consistent with the number of subcarriers, and the spectral offset error during the transformation process is controlled within 0.1 subcarrier intervals. For each subcarrier, its phase change data at different symbol times is calculated with a calculation accuracy of 0.1 degrees. Abnormal phase values ​​are eliminated using a sliding window method, with the window length set to 5 symbol periods to ensure that the extracted phase change data accurately reflects the signal changes caused by target motion. Finally, the target azimuth information and the phase change sequence of each subcarrier are output, providing data support for subsequent range and velocity calculations.

[0033] S6, based on the azimuth information and subcarrier phase change data obtained in S5, jointly calculates the target's radial velocity, micro-Doppler, and range information.

[0034] Specifically, the implementation of S6 is based on the azimuth information and subcarrier phase change data acquired by S5. Through multi-dimensional information fusion, it achieves accurate calculation of target radial velocity, micro-Doppler, and distance information, completing the closed loop of integrated sensing function. In specific implementation, the target distance is first calculated using the subcarrier phase change data. Based on the phase delay principle of electromagnetic wave propagation, the round-trip distance of the signal propagation is calculated by calculating the phase difference between different subcarriers and combining the subcarrier spacing parameters. A subcarrier weighting coefficient is introduced in the calculation process. This coefficient is adaptively allocated according to the signal-to-noise ratio of the subcarriers. The higher the signal-to-noise ratio, the greater the weight of the subcarrier. The weight value ranges from 0.1 to 1.0. By weighted summation, the influence of noise on the distance calculation is reduced. The accuracy of the distance calculation is controlled within 0.5 meters, and the calculation range covers a preset detection range of 10 meters to 500 meters. For radial velocity calculation, the Doppler frequency shift information is obtained by calculating the phase change rate through the subcarrier phase change trend at consecutive symbol times. Combined with the carrier frequency and electromagnetic wave propagation speed, the radial velocity of the target is calculated. A cross-correlation algorithm is used to improve the stability of the velocity estimation during the calculation process. The cross-correlation time window length is set to 10 symbol periods, which can effectively suppress random noise interference. The radial velocity calculation accuracy is 0.1 m / s, and the calculation range is -50 m / s to 50 m / s, covering the motion velocity range of common targets. For the calculation of micro-Doppler information, high-frequency fluctuation components in the phase change data are analyzed. Wavelet transform is used to perform multi-scale decomposition of the phase sequence, with a decomposition level of 5 layers. This effectively separates the Doppler contribution from the overall target motion and micro-motion. The micro-Doppler frequency calculation accuracy is 1 Hz, which can identify the target's vibration, rotation, and other micro-motion characteristics. The entire solution process is achieved through a multi-dimensional information fusion algorithm. The algorithm uses azimuth information as a constraint to eliminate false target information that does not conform to the azimuth range, thereby improving the reliability of the solution results. During the fusion process, Kalman filtering is used for data smoothing. The state equation and observation equation of the filter are constructed based on the target motion model. The filter gain is adaptively adjusted according to the signal-to-noise ratio. Finally, the verified target radial velocity, micro-Doppler, and distance information are output. The total solution delay is controlled within 20 microseconds, which meets the requirements of real-time perception application scenarios.

[0035] Preferably, the element feed phase offset in S3 is calculated using the following formula: ,in, The topological charge number of the OAM mode determined for S2, Let n be the azimuth angle of the nth array element on the circular ring. This is the phase calibration compensation amount for the array elements. Let n be the amplitude attenuation coefficient of the nth array element. Let be the phase noise coefficient of the nth array element.

[0036] Specifically, the S3 array element feed phase offset calculation process uses a multi-parameter fusion calculation method to ensure the accuracy of the phase offset application, providing reliable support for the formation of the OAM spiral phase wavefront. During implementation, the value range and configuration standards of each parameter are first clarified. The OAM mode topology charge number is selected from integers from -10 to +10, which directly determines the rotation direction and density of the spiral phase. The azimuth angle of the nth array element on the ring is evenly distributed according to the number of array elements. When the number of array elements is 8 to 64, the azimuth angle difference between adjacent array elements is between 5.625 degrees and 45 degrees. The array element phase calibration compensation is obtained through previous array calibration tests, and the calibration compensation value of each array element is controlled within ±0.5 degrees to compensate for phase deviations caused by differences in array element hardware. The amplitude attenuation coefficient of the nth array element ranges from 0.8 to 1.0, and the phase noise figure ranges from 0.01 to 0.1. Both parameters are dynamically updated by real-time monitoring of the array element's operating status. The calculation process is performed by a dedicated phase calculation chip with a processing speed of no less than 1000 MIPS, ensuring that the phase offset of all array elements is calculated within the signal transmission time slot. This calculation method comprehensively considers multiple factors such as OAM mode characteristics, array element physical location, hardware calibration compensation, and array element operating status, effectively reducing the impact of amplitude attenuation and phase noise on phase offset accuracy. This ensures that the phase difference of the radiated signals from each array element meets the preset requirements, guaranteeing that the formed OAM spiral phase wavefront has good orthogonality and stability, laying the foundation for accurate reception and analysis of subsequent sensing echoes.

[0037] Preferably, the feature extraction formula for OAM modal spectrum analysis in S5 is: ,in, This represents the power value of the OAM modal spectrum. The echo received signal at the k-th subcarrier and the m-th symbol time is... For signal observation duration, For OAM modal topology charge number, Let be the phase angle corresponding to the k-th subcarrier and the m-th symbol at time t. The subcarrier-symbol dimension weighting coefficient.

[0038] Specifically, the feature extraction method for OAM modal spectrum analysis in step S5 is implemented by strengthening the OAM modal features corresponding to the target through multi-dimensional signal accumulation and weighted calculation, thereby improving the accuracy of modal identification. During implementation, relevant technical parameters are first set: the number of subcarriers K ranges from 64 to 2048, the number of symbols M ranges from 16 to 128, the signal observation duration T ranges from 10 microseconds to 100 microseconds, and the subcarrier-symbol dimension weighting coefficient γ is dynamically allocated based on the subcarrier signal-to-noise ratio and symbol quality, with a value range of 0.1 to 1.0. Dimensions with higher signal-to-noise ratios and better symbol quality receive higher weights. During calculation, time-domain integration is performed on the echo received signal at each subcarrier and each symbol time. The integration interval is strictly limited to the observation duration T. During integration, a window function is used to reduce signal leakage; either a Hanning window or a Blackman window is selected. The integral result is then multiplied by the corresponding phase factor using complex numbers, and the square of the modulus of the result is taken. Finally, a double summation operation is performed along the subcarrier and symbol dimensions to obtain the spectral power value corresponding to each OAM mode. This feature extraction method enhances the target signal energy through multi-dimensional accumulation, highlights the effective signal components through weighted operations, and suppresses the influence of noise and interference. This results in a clear peak in the spectral power value of the target's corresponding OAM mode, facilitating rapid and accurate identification of the target's modal topological charge number and providing precise feature input for subsequent target azimuth angle calculation.

[0039] Preferably, the radial velocity calculation formula in S6 is: ,in, For the target radial velocity, The speed of electromagnetic wave propagation. For carrier frequency, For symbol period, for The cross-correlation value of the signals at time 10:00. For Doppler frequency shift, These represent the time window lengths for cross-correlation calculations.

[0040] Specifically, the radial velocity calculation method in step S6 combines cross-correlation calculations with phase analysis to achieve high-precision estimation of the target radial velocity. During implementation, key technical parameters are set: the carrier frequency f ranges from 2 GHz to 6 GHz, the symbol period T_s ranges from 7.6 microseconds to 66.67 microseconds, and the time window lengths P and Q for cross-correlation calculations are both set to 5 to 20 symbol periods to ensure sufficient coverage of signal variation periods to capture Doppler shift characteristics. The calculation process first performs windowing on the continuously received signal, dividing it into time windows corresponding to P and Q. The cross-correlation value R of the signal within the two time windows is calculated. The cross-correlation calculation uses a sliding window method with a sliding step size of one symbol period to ensure no signal variation information is missed. Then, the cross-correlation value is multiplied by the phase factor corresponding to the Doppler shift using a complex number. The Doppler shift f_d in the phase factor is initially estimated, and then iteratively optimized to gradually approximate the true value. The results of multiplication are summed, and the total phase change is obtained through phase calculation. Finally, the radial velocity of the target is calculated by combining the electromagnetic wave propagation speed c with a fixed proportional relationship. This solution method enhances the temporal correlation of the signal through cross-correlation operations, suppresses random noise interference, and improves the phase estimation accuracy through multi-time window accumulation. The radial velocity calculation error is controlled within 0.1 m / s, which can accurately capture the radial motion state of the target and meet the high-precision requirements for target velocity perception in different scenarios.

[0041] Preferably, the azimuth angle calculation formula in S5 is: ,in, For the estimated target azimuth, Let n be the complex form of the received signal of the nth array element. These represent the operations of taking the real part and the imaginary part, respectively. Let n be the noise power of the nth element. The azimuth deviation compensation value for array installation.

[0042] Specifically, the azimuth calculation process in step S5 improves the accuracy and stability of azimuth estimation through multi-element signal fusion and noise compensation mechanisms. During implementation, the complex form of the received signal from each element is first obtained. This complex signal includes the amplitude and phase information of the signal. The noise power σ_n² of each element is obtained through preliminary calibration tests, with a value ranging from -120dBm to -90dBm. The array installation azimuth offset compensation value θ_offset is determined through array installation calibration, with a value within ±1 degree, used to compensate for azimuth deviations caused by installation errors. During the calculation, the real and imaginary parts of the received signal from each element are extracted. The ratio of the imaginary part to a term including the square of the real part, the square of the imaginary part, and the square root of the noise power is calculated. Then, the azimuth component corresponding to each element is obtained through arcsine operation. Subsequently, the azimuth components of all elements are arithmetically averaged to obtain a preliminary azimuth estimate. Finally, the array installation azimuth offset compensation value is superimposed to obtain the final target azimuth estimate. This solution method makes full use of the signal information of multiple array elements, reduces the noise and error of individual array elements through averaging, and introduces noise power and installation deviation compensation to further improve the solution accuracy, so that the azimuth angle estimation error is controlled within ±0.3 degrees. It can accurately locate the spatial azimuth of the target and provide a reliable azimuth reference for subsequent multi-dimensional sensing information solution.

[0043] Preferably, the distance calculation formula in S6 is: ,in, For the target distance, The speed of electromagnetic wave propagation. For subcarrier spacing, Let be the phase change of the s-th subcarrier. The weighting coefficient for the s-th subcarrier is... The total number of subcarriers participating in the distance calculation, round This indicates the rounding operation. This is the distance calibration compensation amount.

[0044] Specifically, the implementation scheme for distance calculation in step S6 achieves accurate measurement of the target distance through subcarrier phase change accumulation and calibration compensation. During implementation, core technical parameters are set: the electromagnetic wave propagation speed *c* adopts a fixed standard value; the subcarrier spacing *Δf* is 15kHz or 30kHz; the total number of subcarriers *S* involved in the distance calculation ranges from 32 to 1024; the weighting coefficient *w_s* of the *s*-th subcarrier is dynamically configured based on the subcarrier's phase stability and signal-to-noise ratio, ranging from 0.5 to 1.0; and the distance calibration compensation *d_cal* is obtained through distance calibration experiments, ranging from 0 to 1 meter, used to compensate for system errors during signal propagation. The calculation process first extracts the phase change *Δφ_s* of each subcarrier at different symbol times, with the calculation accuracy controlled within 0.1 degrees. Then, the phase change of each subcarrier is multiplied by its corresponding weighting coefficient, and all products are summed. The summation result is then divided by π and rounded to obtain the integer form of the accumulated phase change coefficient. By combining the electromagnetic wave propagation speed and subcarrier spacing, a preliminary target distance value is calculated using a fixed proportional relationship. Finally, the distance calibration compensation is subtracted to obtain the final target distance measurement result. This solution method improves the anti-interference capability of distance estimation through multi-subcarrier phase information fusion, uses weighted operations to highlight the contribution of high-quality subcarriers, and introduces calibration compensation to correct system errors, keeping the distance calculation accuracy within 0.5 meters. This enables precise acquisition of target spatial distance information and improves the multi-dimensional sensing data system.

[0045] Preferred, such as Figure 2 As shown, step S3 includes the following sub-steps: S31, assigning RF channel numbers and position coordinates to each element of the uniform circular array, clarifying the relative angular relationship and physical position parameters of each element on the ring; S32, converting the OFDM baseband signal generated in S1 into an RF signal through a digital-to-analog converter module, and distributing it to each RF input port of the uniform circular array after power amplification; S33, calculating the required feed phase offset for each element based on the OAM mode topology charge determined in S2 and the azimuth parameters of each element through a phase calculation module; S34, precisely applying the calculated phase offset to the RF signal of the corresponding element through the phase-adjustable module of the RF front end, so that the radiated signal of each element forms a preset OAM spiral phase wavefront.

[0046] Specifically, step S3 is broken down into smaller steps to clarify the complete implementation process of OAM mode loading, ensuring accurate phase offset application and stable spiral phase wavefront formation. During implementation, step S31 is executed first to number the RF channels and calibrate the position coordinates of the array elements in a uniform circular array. The number of array elements ranges from 8 to 64, and channel numbering from 1 to N is completed in a clockwise or counterclockwise order. The three-dimensional coordinates of each array element are determined using laser ranging and angle measurement equipment, clarifying the relative angular difference and absolute physical position of each element on the ring. The coordinate calibration error is controlled within 0.1 mm, providing accurate position parameters for subsequent phase calculations. Next, in step S32, the OFDM baseband signal generated in S1 is input to the digital-to-analog converter module. This module has a sampling rate five times the bandwidth of the transmitted signal and a conversion accuracy of 16 bits. After converting the digital baseband signal into an analog RF signal, it is sent to the power amplifier module. The amplifier gain is adjustable from 20dB to 60dB, and the output power is controlled between 10dBm and 43dBm. The RF signal after power amplification is evenly distributed to each RF input port of the uniform circular array through a power divider. The port isolation of the power divider is greater than 30dB, ensuring that the signal power consistency error at each port does not exceed ±0.5dB. Then, step S33 is executed. Based on the OAM mode topology charge determined in S2 and combined with the azimuth parameters of each array element calibrated in S31, the feed phase offset is solved through a dedicated phase calculation module. The calculation module has a processing speed of no less than 1000MIPS. At the same time, the array element amplitude attenuation coefficient and phase noise figure are introduced for error compensation to ensure that the phase offset calculation accuracy reaches 0.1 degrees. Finally, S34 is implemented, in which the calculated phase offset is applied to the radio frequency signal of the corresponding array element through the digital phase shifter of the radio frequency front end. The phase shifter response time is less than 1 microsecond, and the phase adjustment range covers 0 degrees to 360 degrees. The phase application effect of each array element is monitored through a real-time feedback control mechanism, and the phase deviation is corrected to within ±0.2 degrees. This ensures that the signals radiated by all array elements are spatially superimposed to form a preset OAM spiral phase wavefront, thus guaranteeing the coordinated realization of communication data transmission and sensing detection.

[0047] Preferred, such as Figure 3 As shown, step S4 includes the following sub-steps: S41, each element of the uniform circular array synchronously receives the echo signal reflected from the target in the environment, converts the received signal into an electrical signal, and transmits it to the low-noise amplification module; S42, the amplified echo electrical signal is filtered to remove out-of-band interference signals and noise components, and retains the frequency components of the effective echo signal; S43, the filtered analog echo signal is converted into a digital signal through the analog-to-digital conversion module to ensure that the signal sampling rate meets the requirements of subsequent processing; S44, the digitized echo signal is stored in the buffer module according to the element number and the receiving time order to provide data support for subsequent phase feature extraction and modal analysis.

[0048] Specifically, the echo signal reception and processing flow in step S4 preserves the phase characteristics of the echo signal through four orderly operations, providing high-quality data for subsequent modal analysis and target calculation. First, step S41 is executed, where all elements of the uniform circular array start receiving under the trigger of the synchronization control signal, with the synchronization error controlled within 1 nanosecond. Each element converts the electromagnetic wave echo signal reflected by the target in the environment into a weak electrical signal. The conversion process is achieved through the built-in transceiver switch of the array element, with a switching time of less than 10 nanoseconds, ensuring no interference between the receiving and transmitting time slots. The converted electrical signal is transmitted to the low-noise amplification module through a low-loss transmission line, with the transmission line loss controlled within 0.5dB. Next, in step S42, the low-noise amplification module amplifies the weak electrical signal. The module's noise figure does not exceed 1.5dB, and the gain is adaptively adjusted according to the signal strength, amplifying the signal amplitude to the optimal input range of the analog-to-digital converter (ADC). The signal is then fed into a bandpass filter for filtering. The filter's center frequency is the same as the transmitted signal carrier frequency, its bandwidth is 1.2 times the transmitted signal bandwidth, its in-band ripple does not exceed 0.5dB, and its out-of-band rejection is greater than 60dB, effectively filtering out out-of-band interference signals and environmental noise such as power frequency interference and external electromagnetic radiation. Then, in step S43, the filtered analog echo signal enters the ADC. This module's sampling rate is set to 5 times the transmitted signal bandwidth, its sampling precision is 16 bits, and its sampling clock jitter is controlled within 10 picoseconds to avoid sampling distortion. The analog signal is converted to a digital signal, and oversampling technology is used during the conversion to improve the signal-to-noise ratio (SNR), with an improvement of at least 3dB. Finally, S44 is executed. The digitized echo signal is stored in the high-speed cache module according to the array element number and the reception time. The cache module has a storage capacity of no less than 1GB and a read / write rate of 10Gbps, supporting real-time writing and fast reading. During storage, array element number, timestamp, signal strength and other index information are added to each data block. Data integrity verification is achieved through cyclic redundancy check code to ensure that no data is lost or erroneous, providing structured and high-quality raw data support for subsequent OAM mode feature extraction and phase analysis.

[0049] Preferred, such as Figure 4 As shown, S5 includes the following sub-steps: S51, read the digitized echo signals of each array element from the buffer module, and construct a mode matching matrix according to the OAM mode topology charge number sequence; S52, perform conjugate multiplication operation on the constructed mode matching matrix and the echo signal matrix to obtain the matching output result corresponding to each OAM mode; S53, perform spectral analysis processing on the matching output result to identify the OAM mode topology charge number corresponding to the energy peak; S54, based on the identified OAM mode topology charge number, extract the phase features of the echo signal corresponding to the mode, and establish the mapping relationship between the phase features and the target azimuth angle.

[0050] Specifically, the feature extraction and azimuth angle calculation process in step S5 is clearly defined in steps, achieving accurate target azimuth angle calculation and subcarrier phase change data extraction through four collaborative operations. First, step S51 is executed to read the digitized echo signal from the high-speed cache module according to the array element number and time sequence. The signal reading rate matches the cache module's write rate to ensure continuous data flow without interruption. Based on the OAM mode topology charge sequence determined in step S2, a mode matching matrix is ​​constructed. The matrix dimension is consistent with the number of array elements, the number of modes, and the signal length. The matrix elements are generated based on orthogonal mode basis functions, and the orthogonality error of the basis functions is controlled below -40dB. The matrix construction process is completed by a dedicated digital signal processing chip, with a processing delay of no more than 5 microseconds. Next, in S52, the constructed mode matching matrix and the echo signal matrix are multiplied by conjugate. This operation uses a parallel processing architecture with a processing speed of at least 2000 MIPS. Matrix multiplication achieves matched filtering between the echo signal and each OAM mode, highlighting the modal components corresponding to the target signal and suppressing the influence of noise and interference modes. The resulting matched output is stored in complex form, preserving the amplitude and phase information of the signal. Then, in S53, the matched output is subjected to spectral analysis. A periodogram method combined with a Hanning window is used to suppress spectral leakage. The window function sidelobe attenuation is greater than 40 dB, and the frequency resolution of the spectral analysis is 1 / 10 of the subcarrier spacing of the transmitted signal. A peak detection algorithm identifies the topological charge number of the OAM mode corresponding to the energy peak. The peak detection threshold is dynamically set based on the noise power to ensure that the target mode is not missed and there are no false peaks. Finally, S54 is executed. Based on the identified target OAM mode topological charge number, the phase features of the echo signal corresponding to that mode are extracted. The phase feature extraction accuracy is controlled within 0.1 degrees. The mapping relationship between the phase features and the target azimuth angle is established through statistical analysis. The mapping relationship is determined through previous system calibration experiments, and the calibration error is controlled within ±0.3 degrees. At the same time, combined with the frequency domain characteristics of the OFDM signal, the time domain echo signal is converted to the frequency domain through fast Fourier transform. The phase change data of each subcarrier at different symbol times are extracted. The sampling interval of the phase change data is consistent with the symbol period, providing core feature input for the joint calculation of target radial velocity, distance and other information.

[0051] like Figure 5As shown, a sensing integration method and apparatus based on OAM mode-driven sensing is disclosed. This apparatus is applied to an OAM mode-driven sensing integration method and includes: an OFDM random communication signal generation module, used to generate OFDM baseband signals carrying random user data and conforming to communication standards, without any customized sensing design; its output is connected to the signal input of an OAM-UCA integrated transmitter array; an OAM mode parameter configuration and phase calculation module, used to determine the OAM mode topology charge number according to sensing task requirements and calculate the feed phase offset of each array element; its output is connected to the phase control terminal of the OAM-UCA integrated transmitter array; and an OAM-UCA integrated transmitter array module, consisting of N phase / amplitude controllable... The array elements of the radio frequency front-end form a circular structure, receive communication signals and apply phase shift, radiating a beam carrying the OAM mode. The receiver is connected to the sensing signal receiving and preprocessing module. The sensing signal receiving and preprocessing module receives the echo signal and performs low-noise amplification, filtering, analog-to-digital conversion, and storage. Its output is connected to the OAM mode feature extraction module. The OAM mode feature extraction module uses mode matching or spectral analysis techniques to extract echo phase features, calculate the target azimuth angle, and its output is connected to the multi-dimensional sensing information fusion and calculation module. The multi-dimensional sensing information fusion and calculation module receives azimuth angle information and subcarrier phase change data, jointly calculates the target radial velocity, micro-Doppler, and range information, and completes the sensing data output.

[0052] An OAM-driven integrated sensing method and device achieves efficient coordination of communication and sensing functions. It eliminates the need for customized modifications to standard communication signals, directly generating signals based on existing communication standards and loading specific spiral phase wavefronts. This ensures both the compliance and reliability of data transmission, and through orthogonality, allows the signal to simultaneously carry communication data and sensing information, completely avoiding the performance constraints between the two in traditional solutions. Furthermore, relying on a dedicated array structure and phase control technology, it eliminates the need for additional sensing sensors, significantly reducing hardware redundancy and spectrum usage, simplifying the system architecture and signal processing flow, lowering hardware costs and implementation complexity, and significantly improving spectrum resource utilization.

[0053] In terms of perception performance optimization, this technology effectively overcomes the shortcomings of traditional technologies, such as insufficient angular resolution and weak multi-target discrimination, through flexible configuration of modal parameters and precise phase calibration and feature extraction. Using specific arrays and modal analysis techniques, it can accurately extract multi-dimensional features of targets from echo signals, resist the influence of interference signals, and achieve high-precision calculation of target location, distance, and motion status. Simultaneously, core parameters can be flexibly adjusted according to detection range and resolution requirements, achieving dynamic adaptation of perception performance. This solves the problems of traditional technologies, such as difficulty in flexibly adjusting perception range and resolution and poor adaptability to complex environments, meeting the multi-functional application needs of different scenarios and demonstrating stronger environmental adaptability and practical value.

[0054] In the description of this invention, it should be noted that, unless otherwise explicitly specified and limited, the terms "set," "install," "connect," "link," and "fix" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal communication between two components. Those skilled in the art will understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0055] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various equivalent changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A sensory integration method based on OAM mode driving, characterized in that, Includes the following steps: S1 generates a standard OFDM communication signal carrying random user data through the base station baseband unit. The subcarrier, pilot, and cyclic prefix parameters of this signal all follow the communication standard and have not been customized for the sensing function. S2, based on the preset detection range and angular resolution requirements, determine one or more OAM modal topology charges for the sensing task; S3, feed the OFDM signal generated in S1 into a uniform circular array of N elements. Based on the OAM mode topology charge determined in S2, calculate the feed phase offset corresponding to each element and apply it to the corresponding element so that the radiated signal carries the calibrated OAM spiral phase wavefront. S4, receives the echo signal formed by environmental reflection through a uniform circular array, and performs array reception processing on the echo signal to preserve phase characteristics; S5 employs OAM mode matching or mode spectrum analysis techniques to calculate the target azimuth information from the phase characteristics of the echo signal, and extracts subcarrier phase change data by combining the frequency domain characteristics of the OFDM signal. S6, based on the azimuth information and subcarrier phase change data obtained in S5, jointly calculates the target's radial velocity, micro-Doppler, and range information.

2. The sensor integration method based on OAM mode driving according to claim 1, characterized in that, The phase offset of the array element feed in S3 is calculated by the following formula: ,in, The topological charge number of the OAM mode determined for S2, Let n be the azimuth angle of the nth array element on the circular ring. This is the phase calibration compensation amount for the array elements. Let n be the amplitude attenuation coefficient of the nth array element. Let be the phase noise coefficient of the nth array element.

3. The sensor integration method based on OAM mode driving according to claim 1, characterized in that, The feature extraction formula for OAM modal spectrum analysis in S5 is as follows: ,in, This represents the OAM modal spectral power value. The echo received signal at the k-th subcarrier and the m-th symbol time is... For signal observation duration, For OAM modal topology charge number, Let be the phase angle corresponding to the k-th subcarrier and the m-th symbol at time t. The subcarrier-symbol dimension weighting coefficient.

4. The sensor integration method based on OAM mode driving according to claim 1, characterized in that, The formula for calculating the radial velocity in S6 is as follows: ,in, For the target radial velocity, The speed of electromagnetic wave propagation. For carrier frequency, For symbol period, for The cross-correlation value of the signals at time 10:

00. For Doppler frequency shift, These represent the time window lengths for cross-correlation calculations.

5. The sensor integration method based on OAM mode driving according to claim 1, characterized in that, The formula for calculating the azimuth angle in S5 is as follows: ,in, For the estimated target azimuth, Let n be the complex form of the received signal of the nth array element. These represent the operations of taking the real part and the imaginary part, respectively. Let n be the noise power of the nth element. The azimuth deviation compensation value for array installation.

6. The sensor integration method based on OAM mode driving according to claim 1, characterized in that, The distance calculation formula in S6 is as follows: ,in, For the target distance, The speed of electromagnetic wave propagation. For subcarrier spacing, Let be the phase change of the s-th subcarrier. The weighting coefficient for the s-th subcarrier is... The total number of subcarriers participating in the distance calculation, round This indicates the rounding operation. This is the distance calibration compensation amount.

7. The sensor integration method based on OAM mode driving according to claim 1, characterized in that, S3 includes the following sub-steps: S31, assigning RF channel numbers and position coordinates to each element of the uniform circular array, clarifying the relative angular relationship and physical position parameters of each element on the ring; S32, converting the OFDM baseband signal generated in S1 into an RF signal through a digital-to-analog converter module, and distributing it to each RF input port of the uniform circular array after power amplification; S33, calculating the required feed phase offset for each element using a phase calculation module based on the OAM mode topology charge determined in S2 and the azimuth parameters of each element; S34, precisely applying the calculated phase offset to the RF signal of the corresponding element through the phase-adjustable module of the RF front end, so that the radiated signal of each element forms a preset OAM spiral phase wavefront.

8. The sensor integration method based on OAM mode driving according to claim 1, characterized in that, S4 includes the following sub-steps: S41, each element of the uniform circular array synchronously receives the echo signal reflected from the target in the environment, converts the received signal into an electrical signal and transmits it to the low-noise amplification module; S42, the amplified echo electrical signal is filtered to remove out-of-band interference signals and noise components, and retains the frequency components of the effective echo signal; S43, the filtered analog echo signal is converted into a digital signal through the analog-to-digital conversion module to ensure that the signal sampling rate meets the requirements of subsequent processing; S44, the digitized echo signal is stored in the buffer module according to the array element number and the receiving time order to provide data support for subsequent phase feature extraction and modal analysis.

9. The sensor integration method based on OAM mode driving according to claim 1, characterized in that, S5 includes the following sub-steps: S51, read the digital echo signals of each array element from the cache module, and construct a mode matching matrix according to the OAM mode topological charge number sequence; S52, perform conjugate multiplication operation on the constructed mode matching matrix and the echo signal matrix to obtain the matching output result corresponding to each OAM mode; S53, perform spectral analysis processing on the matching output result to identify the OAM mode topological charge number corresponding to the energy peak; S54, based on the identified OAM mode topological charge number, extract the phase features of the echo signal corresponding to the mode, and establish the mapping relationship between the phase features and the target azimuth angle.

10. A sensor-integrated device based on OAM mode driving, characterized in that, This device is applied to the OAM mode-driven integrated sensing method described in claim 1, comprising: an OFDM random communication signal generation module, used to generate OFDM baseband signals carrying random user data and conforming to communication standards, without any customized sensing design, and whose output is connected to the signal input of the OAM-UCA integrated transmitter array; an OAM mode parameter configuration and phase calculation module, used to determine the OAM mode topology charge number according to the sensing task requirements, calculate the feed phase offset of each array element, and whose output is connected to the phase control terminal of the OAM-UCA integrated transmitter array; and an OAM-UCA integrated transmitter array module, consisting of N array elements with phase / amplitude adjustable RF front-ends. The system is structured in a ring, receiving communication signals and applying phase shift, radiating a beam carrying OAM modes. The receiver is connected to the sensing signal receiving and preprocessing module. The sensing signal receiving and preprocessing module receives echo signals and performs low-noise amplification, filtering, analog-to-digital conversion, and storage. Its output is connected to the OAM mode feature extraction module. The OAM mode feature extraction module uses mode matching or spectral analysis techniques to extract echo phase features and calculate the target azimuth angle. Its output is connected to the multi-dimensional sensing information fusion and calculation module. The multi-dimensional sensing information fusion and calculation module receives azimuth angle information and subcarrier phase change data, jointly calculates the target radial velocity, micro-Doppler, and range information, and completes the sensing data output.

Images