Communication perception integrated vehicle positioning method and system based on OTFS modulation

Abstract

The invention relates to the technical field of vehicle positioning, and discloses a communication sensing integrated vehicle positioning method and system based on OTFS modulation, and the method comprises the steps: obtaining a synchronous sensing signal of a target vehicle, carrying out the OTFS modulation of the synchronous sensing signal, so as to obtain a joint modulation signal in a time-frequency domain, time-frequency domain joint analysis is carried out on the joint modulation signal to obtain channel state information of the target vehicle, and the channel state information comprises time delay, Doppler frequency shift and channel gain; generating a distance estimation value of the target vehicle based on the time delay and a preset positioning algorithm; and performing distance correction on the distance estimation value based on the channel gain and the Doppler frequency shift, and determining the spatial position of the target vehicle according to the distance estimation value after distance correction. According to the invention, signal interference in a complex environment can be effectively handled, so that the accuracy of vehicle positioning is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of vehicle positioning, and in particular to a communication and sensing integrated vehicle positioning method and system based on OTFS modulation. Background Art

[0002] In the fields of intelligent transportation and autonomous driving, vehicle positioning technology is crucial. With the development of related technologies, higher requirements have been put forward for the accuracy, real-time performance of vehicle positioning, as well as the spectrum utilization rate and hardware cost of the system.

[0003] Traditional vehicle positioning methods are prone to be affected by multipath fading and Doppler frequency shift in complex environments, such as areas with high-rise buildings in the city or vehicle high-speed driving scenarios, resulting in a decrease in positioning accuracy. At the same time, using communication frequency bands and radar frequency bands separately not only has low spectrum utilization rate, but also increases the complexity and cost of hardware devices. Therefore, how to improve the accuracy of vehicle positioning, increase the spectrum utilization rate, reduce the hardware cost and effectively cope with signal interference in complex environments has become an urgent problem to be solved. Summary of the Invention

[0004] The present invention provides a communication and sensing integrated vehicle positioning method and system based on OTFS modulation, and its main purpose is to solve the problem of low accuracy of vehicle positioning affected by signal interference in complex environments.

[0005] To achieve the above object, a communication and sensing integrated vehicle positioning method based on OTFS modulation provided by the present invention includes: Obtaining a synchronous sensing signal of a target vehicle, wherein the synchronous sensing signal includes: a communication signal and a radar signal; Performing OTFS modulation on the synchronous sensing signal to obtain a joint modulation signal in the time-frequency domain; Performing time-frequency domain joint analysis on the joint modulation signal to obtain the channel state information of the target vehicle, wherein the channel state information includes: delay, Doppler frequency shift and channel gain; Generating a distance estimation value of the target vehicle based on the delay and a preset positioning algorithm, wherein the preset positioning algorithm is:

[0006] Wherein, is the distance difference between the target vehicle and the receiving end and the receiving end ; is the propagation speed of the measurement signal in the medium, is the delay of the signal from the target vehicle to the receiving end ; is the time delay for the signal to reach the receiving end from the target vehicle and is the receiving end identifier is the receiving end identifier; Perform distance correction on the distance estimation value based on the channel gain and the Doppler frequency shift, and determine the spatial position of the target vehicle according to the distance estimation value after distance correction.

[0007] Optionally, the obtaining of the synchronous sensing signal of the target vehicle includes: Synchronously receive the communication signal and the reflected radar signal emitted by the target vehicle based on a multi-band reconfigurable antenna array, where the communication signal uses an OFDM modulation format and the radar signal uses a pulse compression waveform.

[0008] Optionally, the performing of OTFS modulation on the synchronous sensing signal to obtain a joint modulation signal in the time-frequency domain includes: Perform resource region allocation on the synchronous sensing signal based on a preset delay-Doppler domain to obtain the regional signal of the synchronous sensing signal, where the first resource region is allocated to the communication signal and the second resource region is allocated to the radar signal; Perform time-frequency domain grid mapping on the regional signal to obtain a joint modulation signal in the time-frequency domain.

[0009] Optionally, the performing of time-frequency domain grid mapping on the regional signal to obtain a joint modulation signal in the time-frequency domain includes: Generate a Doppler spread estimation value of the regional signal based on the dynamic range of the target vehicle; Generate dynamic grid parameters of the regional signal based on the Doppler spread estimation value, where the dynamic grid parameters include: frequency domain grid resolution, frequency domain grid size, time domain grid resolution, and time domain grid size; Perform time-frequency domain grid mapping on the regional signal based on the dynamic grid parameters to obtain a joint modulation signal in the time-frequency domain.

[0010] Optionally, the calculation formula of the Doppler spread estimation value is as follows:

[0011] where is the Doppler spread estimation value, is the speed of the target vehicle, is the speed of light, is the carrier frequency of the radar transmitted signal, is the acceleration of the target vehicle, is the observation time.

[0012] Optionally, the joint time-frequency domain analysis of the joint modulation signal to obtain the channel state information of the target vehicle includes: Extracting the delay-Doppler domain channel response matrix of the joint modulation signal based on the sparse Bayesian compressive sensing algorithm; Generating the separated multipath components of the joint modulation signal based on the multiple signal classification algorithm and the delay-Doppler domain channel response matrix; Calculating the channel state information of the target vehicle based on the separated multipath variables, where the channel state information is the delay, Doppler shift, and channel gain of each path.

[0013] Optionally, the extracting the delay-Doppler domain channel response matrix of the joint modulation signal based on the sparse Bayesian compressive sensing algorithm includes: Constructing a measurement model of the joint modulation signal, where the measurement model is:

[0014] where, is the measurement vector of the joint modulation signal, is the measurement matrix, is the delay-Doppler domain channel response matrix to be extracted, is the noise vector; Setting prior distributions for the noise vector and the delay-Doppler domain channel response matrix to be extracted based on the sparse Bayesian theory, and calculating the posterior distribution of the delay-Doppler domain channel response matrix to be extracted; Generating the delay-Doppler domain channel response matrix of the joint modulation signal based on the prior distribution and the posterior distribution.

[0015] Optionally, the generating the separated multipath components of the joint modulation signal based on the multiple signal classification algorithm and the delay-Doppler domain channel response matrix includes: Performing eigenvalue decomposition on the delay-Doppler domain channel response matrix based on the multiple signal classification algorithm to divide the signal subspace and the noise subspace; Constructing a spectral function based on the spatial characteristics of the signal subspace and the spatial characteristics of the noise subspace; Searching for the peak position of the spectral function to determine the multipath component parameters of the separated multipath components of the joint modulation signal, Determining the separated multipath components of the joint modulation signal based on the multipath component parameters.

[0016] Optionally, the calculating the channel state information of the target vehicle based on the separated multipath variables includes: Extract the arrival time, frequency offset, and signal amplitude of each path signal in the separated multipath variables; Determine the time delay of each path of the target vehicle based on the arrival time; Determine the Doppler frequency shift of each path based on the frequency offset; Calculate the channel gain of each path based on the signal amplitude, thereby generating the channel state information of the target vehicle.

[0017] To solve the above problems, the present invention also provides a communication and sensing integrated vehicle positioning system based on OTFS modulation. The system includes: A synchronization and sensing signal acquisition module, configured to acquire the synchronization and sensing signal of the target vehicle, where the synchronization and sensing signal includes: a communication signal and a radar signal; A signal OTFS modulation module, configured to perform OTFS modulation on the synchronization and sensing signal to obtain a jointly modulated signal in the time-frequency domain; A signal joint analysis module, configured to perform time-frequency domain joint analysis on the jointly modulated signal to obtain the channel state information of the target vehicle, where the channel state information includes: time delay, Doppler frequency shift, and channel gain; A distance estimation value generation module, configured to generate a distance estimation value of the target vehicle based on the time delay and a preset positioning algorithm, where the preset positioning algorithm is:

[0018] where is the distance difference between the target vehicle and the receiving end and the receiving end is the distance difference, is the propagation speed of the measurement signal in the medium, is the time delay for the signal to reach the receiving end from the target vehicle is the time delay, is the time delay for the signal to reach the receiving end from the target vehicle is the time delay, is the receiving end identifier, is the receiving end identifier; A spatial position determination module, configured to perform distance correction on the distance estimation value based on the channel gain and the Doppler frequency shift, and determine the spatial position of the target vehicle according to the distance estimation value after distance correction.

[0019] The present invention synchronously acquires the communication signal and radar signal of the target vehicle. The two complement each other to make the data more comprehensive. Based on the preset delay-Doppler domain, resource area allocation is performed on the synchronized sensing signal, and the communication signal and radar signal are respectively allocated to different areas, reducing the mutual interference between signals, enabling the receiving end to receive the signals more clearly and accurately. Then, OTFS modulation is performed on them. This modulation can effectively resist the Doppler frequency shift and multipath fading effects caused by high-speed movement and multipath propagation, ensuring accurate signal transmission. The time-frequency domain joint analysis of the jointly modulated signal is carried out to obtain multi-dimensional information such as time delay, Doppler frequency shift, and channel gain, describing the signal propagation characteristics and vehicle state from different aspects. First, a preliminary distance estimate value is generated based on the time delay and a preset algorithm, and the theoretical accuracy of this algorithm is relatively high. Then, the distance estimate value is corrected by comprehensively considering the signal attenuation reflected by the channel gain and the vehicle motion state reflected by the Doppler frequency shift, compensating for the positioning errors caused by factors such as signal attenuation and vehicle motion, thereby improving the positioning accuracy. Therefore, the present invention proposes a communication-sensing integrated vehicle positioning method and system based on OTFS modulation, which can solve the problem of low vehicle positioning accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 It is a schematic flowchart of a communication-sensing integrated vehicle positioning method based on OTFS modulation provided by an embodiment of the present invention; Figure 2 It is a functional module diagram of a communication-sensing integrated vehicle positioning system based on OTFS modulation provided by an embodiment of the present invention; The realization, functional features, and advantages of the object of the present invention will be further described with reference to the embodiments and the accompanying drawings. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0021] It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0022] An embodiment of the present application provides a communication and sensing integrated vehicle positioning method based on OTFS modulation. The execution subject of the communication and sensing integrated vehicle positioning method based on OTFS modulation includes, but is not limited to, at least one of electronic devices such as a server, a terminal, etc. that can be configured to execute the method provided by the embodiment of the present application. In other words, the communication and sensing integrated vehicle positioning method based on OTFS modulation can be executed by software or hardware installed on a terminal device or a server device. The server includes, but is not limited to: a single server, a server cluster, a cloud server, or a cloud server cluster, etc. The server can be an independent server or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, Content Delivery Network (CDN), and big data and artificial intelligence platforms.

[0023] Referring to Figure 1 As shown, it is a schematic flowchart of a communication and sensing integrated vehicle positioning method based on OTFS modulation provided by an embodiment of the present invention. In this embodiment, the communication and sensing integrated vehicle positioning method based on OTFS modulation includes: S1. Obtain a synchronous sensing signal of a target vehicle, where the synchronous sensing signal includes: a communication signal and a radar signal.

[0024] In an embodiment of the present invention, the obtaining of the synchronous sensing signal of the target vehicle includes: Synchronously receive the communication signal transmitted by the target vehicle and the radar signal reflected by it based on a multi-band reconfigurable antenna array, where the communication signal uses an OFDM modulation format and the radar signal uses a pulse compression waveform.

[0025] Specifically, the synchronously receiving the communication signal transmitted by the target vehicle and the radar signal reflected by it based on the multi-band reconfigurable antenna array includes: Configure a multi-band reconfigurable antenna array; Receive the OFDM communication signal transmitted by the target vehicle based on a first sub-array of the multi-band reconfigurable antenna array; Transmit a pulse compression radar signal based on a second sub-array of the multi-band reconfigurable antenna array and receive the reflected radar signal, where the working frequency bands of the communication signal and the radar signal have an overlapping bandwidth of at least 20% within the millimeter wave band.

[0026] Specifically, configuring a multi-band reconfigurable antenna array is a preparatory work for receiving signals. By configuring the antenna array, it can operate within a specific frequency band range and has the ability to receive communication signals and transmit and receive radar signals. For example, the antenna array is set to operate in the millimeter-wave band, and its radiation direction is adjusted to align with the area where the target vehicle is located.

[0027] Specifically, the first sub-array is dedicated to receiving communication signals. The OFDM-modulated communication signals carry relevant information of the vehicle, such as the vehicle's identity identification, driving status, etc. Taking the intelligent transportation system as an example, when a vehicle communicates with a roadside base station, the OFDM communication signals sent by the vehicle may contain information such as its current driving speed and destination. By receiving these signals, the positioning system can obtain some information of the vehicle and provide auxiliary data for subsequent positioning.

[0028] Specifically, the second sub-array is responsible for the transmission and reception of radar signals. After transmitting a pulse compression radar signal, the signal will be reflected when it encounters the target vehicle, and the reflected signal is received by the second sub-array. Radar signals can measure information such as the distance and speed between the vehicle and the antenna. In practical applications, when a vehicle is driving on the road, radar signals can accurately measure the distance between the vehicle and the roadside base station and the vehicle's moving speed, and these information are crucial for positioning the vehicle's location.

[0029] Specifically, the multi-band reconfigurable antenna array is used to synchronously receive communication signals and radar signals, realizing the integration of communication and perception. The communication signals and radar signals have an overlapping bandwidth of at least 20% within the millimeter-wave band, improving the spectrum utilization rate and avoiding spectrum waste caused by separately using the communication band and the radar band. This integrated design reduces the complexity of hardware devices and lowers the cost. By simultaneously obtaining the information of communication signals and radar signals, the vehicle can be more accurately located, improving the accuracy and reliability of the positioning system and providing strong support for applications such as intelligent transportation and autonomous driving.

[0030] S2. Perform OTFS modulation on the synchronous perception signal to obtain a jointly modulated signal in the time-frequency domain.

[0031] In the embodiment of the present invention, the performing OTFS modulation on the synchronous perception signal to obtain a jointly modulated signal in the time-frequency domain includes: Performing resource area allocation on the synchronous perception signal based on a preset delay-Doppler domain to obtain the area signal of the synchronous perception signal, where the first resource area is allocated to the communication signal and the second resource area is allocated to the radar signal; Performing time-frequency domain grid mapping on the area signal to obtain a jointly modulated signal in the time-frequency domain.

[0032] Specifically, OTFS modulation is orthogonal time-frequency-space modulation, which is an efficient modulation technique for time-varying channels. It can effectively resist the effects of multipath fading and Doppler frequency shift, and has obvious advantages in high-speed mobile scenarios. It is widely used in the field of vehicle communication and perception.

[0033] Specifically, communication signals and radar signals have different properties. They are allocated to different regions, such as allocating communication signals to the first resource region and radar signals to the second resource region. For example, in an intelligent transportation system, a roadside base station simultaneously receives signals from multiple vehicles. Through resource region allocation, the communication and radar signals of each vehicle can be separated to avoid signal confusion.

[0034] Specifically, the Doppler spread estimate value is a numerical value that reflects the degree of Doppler frequency shift spread caused by the movement of the target vehicle. When the vehicle moves, the frequencies of the signals it emits and reflects will change, and the Doppler spread estimate value is used to quantify this frequency change range.

[0035] Specifically, the time-frequency domain grid mapping of the regional signal to obtain the joint modulation signal in the time-frequency domain includes: Generating a Doppler spread estimate value of the regional signal based on the dynamic range of the target vehicle; Generating dynamic grid parameters of the regional signal based on the Doppler spread estimate value, where the dynamic grid parameters include: frequency domain grid resolution, frequency domain grid size, time domain grid resolution, and time domain grid size; Performing time-frequency domain grid mapping on the regional signal based on the dynamic grid parameters to obtain the joint modulation signal in the time-frequency domain.

[0036] Specifically, time-frequency domain grid mapping refers to converting the signal after resource region allocation from the delay-Doppler domain to the time-frequency domain according to certain rules, constructing a time-frequency grid, and mapping the energy of the signal to the grid nodes.

[0037] Specifically, according to the generated dynamic grid parameters, the signal after resource region allocation is mapped to the time-frequency domain to form a joint modulation signal. For example, the communication signal and radar signal after resource region allocation are accurately mapped to the corresponding time-frequency grid nodes according to the adjusted frequency domain and time domain grid resolutions and sizes. After mapping, the signal can more clearly show its characteristics in the time-frequency domain, which is convenient for subsequent channel state information analysis and vehicle positioning.

[0038] Generally speaking, resource region allocation is the foundation for separating different types of signals; calculating the Doppler spread estimate value provides a basis for dynamically adjusting the grid parameters; the dynamic grid parameters determine the time-frequency domain grid characteristics and guide the grid mapping; the time-frequency domain grid mapping converts the signal to the time-frequency domain to obtain the joint modulation signal.

[0039] Specifically, in the vehicle networking environment, vehicles are moving at high speeds, and communication and radar signals are vulnerable to Doppler frequency shift and multipath fading. For example, on highways, vehicles move at high speeds and the surrounding environment is complex, making it difficult for traditional modulation techniques to ensure signal stability and accurate positioning.

[0040] Specifically, through resource area allocation, interference between communication signals and radar signals is reduced, and signal quality and processing efficiency are improved. The time-frequency domain grid parameters are dynamically adjusted according to the vehicle's dynamic range for grid mapping, enabling the modulated signal to better adapt to the vehicle's motion state, effectively resisting Doppler frequency shift and multipath fading, improving the accuracy of channel estimation and the reliability of signal transmission, thereby enhancing the accuracy of vehicle positioning and ensuring the stable operation of vehicle communication and positioning functions in the vehicle networking system.

[0041] Specifically, the calculation formula for the Doppler spread estimate is as follows:

[0042] Where, is the Doppler spread estimate, is the speed of the target vehicle, is the speed of light, is the carrier frequency of the radar transmitted signal, is the acceleration of the target vehicle, is the observation time.

[0043] Specifically, assuming the speed of the target vehicle , acceleration , the carrier frequency of the radar transmitted signal , the observation time , the speed of light , substituting into the calculation formula for the Doppler spread estimate gives: .

[0044] Furthermore, by calculating the Doppler spread estimate, the degree of influence of vehicle motion on signal frequency can be understood, providing a basis for subsequent adjustment of grid parameters.

[0045] Specifically, if the Doppler spread estimate is large, it indicates that the frequency change caused by vehicle motion is large, and it is necessary to increase the frequency domain grid resolution and the size of the frequency domain grid to more accurately capture the signal frequency change; at the same time, adjust the time domain grid parameters to adapt to the change of the signal in time. For example, when is large, increase the frequency domain grid resolution, changing from one grid point every to one grid point every , which enables the time-frequency domain grid to better match the signal characteristics under the vehicle's motion state.

[0046] S3. Perform joint time-frequency domain analysis on the joint modulation signal to obtain the channel state information of the target vehicle.

[0047] In the embodiment of the present invention, the performing joint time-frequency domain analysis on the joint modulation signal to obtain the channel state information of the target vehicle includes: Extract the delay-Doppler domain channel response matrix of the joint modulation signal based on the sparse Bayesian compressive sensing algorithm; Generate the separated multipath components of the joint modulation signal based on the multiple signal classification algorithm and the delay-Doppler domain channel response matrix; Calculate the channel state information of the target vehicle based on the separated multipath variables, where the channel state information is the delay, Doppler frequency shift, and channel gain of each path.

[0048] Specifically, regard the separated signals of different paths as individual "small packages", analyze the content in each "small package" to calculate the channel state information, and find information such as the arrival time, frequency change, and signal strength of the signal from each "small package".

[0049] Further, according to the signal arrival time, calculate how long it takes for each path signal to travel from the vehicle to the receiving device, and this time difference is the delay. For example, if one path signal arrives 0.001 seconds later than another path signal, this is their delay difference.

[0050] Further, calculate the Doppler frequency shift according to the frequency change. For example, if the transmitted signal frequency is 100 MHz and it becomes 100.001 MHz when received, then the Doppler frequency shift is 0.001 MHz, and this value can reflect whether the vehicle is approaching or moving away from the receiving device.

[0051] Further, if the transmitted signal is strong and becomes much weaker when received, it indicates that the channel gain is small and the signal attenuates severely during propagation. Through these calculations, the delay, Doppler frequency shift, and channel gain of each path signal are obtained, which are the channel state information of the target vehicle, and these information are very important for accurately determining the vehicle position.

[0052] Specifically, the extracting the delay-Doppler domain channel response matrix of the joint modulation signal based on the sparse Bayesian compressive sensing algorithm includes: Construct the measurement model of the joint modulation signal, where the measurement model is:

[0053] Where, is the measurement vector of the joint modulation signal, is the measurement matrix, is the channel response matrix in the time-delay - Doppler domain to be extracted, is the noise vector; Based on the sparse Bayesian theory, set the prior distribution for the noise vector and the channel response matrix in the time-delay - Doppler domain to be extracted, and calculate the posterior distribution of the channel response matrix in the time-delay - Doppler domain to be extracted; Generate the channel response matrix in the time-delay - Doppler domain of the joint modulation signal based on the prior distribution and the posterior distribution.

[0054] Specifically, when the roadside base station in the intelligent transportation system receives the joint modulation signal sent by the vehicle, the measurement vector is the actually received signal data with noise. The measurement matrix is determined according to the signal sampling method and system characteristics, and it determines how to obtain the measurement values from the original signal. The channel response matrix in the time-delay - Doppler domain to be extracted is the target to be solved, and the noise vector represents various noises mixed in during the receiving process, such as environmental noise, equipment noise, etc.

[0055] Specifically, based on the sparse Bayesian theory, for the noise vector and the channel response matrix set a reasonable prior distribution. Assume that the noise vector obeys the Gaussian distribution, and the elements of the channel response matrix obey the Laplace distribution (utilizing its sparse characteristics). Calculate the posterior distribution of through the Bayesian formula, combine the prior information and the measurement data, and more accurately estimate the channel response matrix. For example, by calculating the posterior distribution through multiple iterations, gradually approach the true channel response matrix.

[0056] Specifically, according to the prior distribution and the posterior distribution, use relevant algorithms (such as maximum a posteriori estimation, etc.) to generate the channel response matrix in the time-delay - Doppler domain of the joint modulation signal. This matrix contains the response information of the signal at different time delays and Doppler frequency shifts, providing basic data for subsequent analysis.

[0057] Specifically, adopting the sparse Bayesian compressive sensing algorithm to extract the channel response matrix can accurately obtain the channel information under the condition of limited data volume and the existence of noise, reduce the data transmission and processing burden, and improve the system efficiency.

[0058] Specifically, generating the separated multipath components of the joint modulation signal based on the multiple signal classification algorithm and the channel response matrix in the time-delay - Doppler domain includes: Based on the multiple signal classification algorithm, perform eigenvalue decomposition on the channel response matrix in the time-delay - Doppler domain to divide the signal subspace and the noise subspace; Construct a spectral function based on the spatial characteristics of the signal subspace and the spatial characteristics of the noise subspace; Search for the peak position of the spectral function to determine the multipath component parameters of the separated multipath components of the joint modulation signal, Determine the separated multipath components of the joint modulation signal based on the multipath component parameters.

[0059] Specifically, use the Multiple Signal Classification (MUSIC) algorithm to perform eigen - decomposition on the obtained time - delay - Doppler domain channel response matrix. For example, assume the channel response matrix is an M×N matrix. After eigen - decomposition, a signal subspace and a noise subspace can be obtained. The signal subspace contains the useful signal components related to the target vehicle, while the noise subspace contains noise and interference components.

[0060] Specifically, construct a spectral function according to the spatial characteristics of the signal subspace and the noise subspace. The form of the spectral function is usually based on the orthogonality of the signal subspace and the noise subspace, and it can reflect the energy distribution of different multipath signals. For example, the common MUSIC spectral function is constructed based on the projection relationship between the signal subspace and the noise subspace.

[0061] Specifically, search for the peak position of the spectral function. The peaks in the spectral function correspond to different multipath components, and the position and amplitude of the peaks represent the parameters of the multipath components (such as time - delay, Doppler frequency shift and other related parameters). By finding these peaks, the multipath component parameters of the separated multipath components of the joint modulation signal can be determined.

[0062] Specifically, as the signal propagates, it will be reflected by roadside buildings, other vehicles, etc. Just like sound has echoes in a valley, these reflected signals are mixed with the directly - arriving signals. The Multiple Signal Classification algorithm is like a "sound discriminator" that separates the signals coming from different paths.

[0063] Specifically, "decompose" the previously obtained channel response matrix into a signal subspace (storing useful vehicle signal components) and a noise subspace (storing interference noise).

[0064] Furthermore, according to the characteristics of the signal subspace and the noise subspace, construct a special "function map" (spectral function). Different positions and "terrain heights" (function values) on this "map" reflect the energy distribution of different multipath signals.

[0065] Specifically, according to the obtained multipath component parameters, separate the signal components of different paths. For example, according to the time - delay and Doppler frequency - shift parameters corresponding to the peaks, extract the signals of each multipath component from the original signal to prepare for subsequent calculation of channel state information.

[0066] Specifically, the multiple signal classification algorithm separates multipath components, can accurately identify signals on different propagation paths, overcome the influence of multipath interference on positioning, and improve positioning accuracy.

[0067] Specifically, calculating the channel state information of the target vehicle based on the separated multipath variables includes: Extracting the arrival time, frequency offset, and signal amplitude of each path signal in the separated multipath variables; Determining the delay of each path of the target vehicle based on the arrival time; Determining the Doppler frequency shift of each path based on the frequency offset; Calculating the channel gain of each path based on the signal amplitude, thereby generating the channel state information of the target vehicle.

[0068] Specifically, extract the arrival time, frequency offset, and signal amplitude of each path signal from the separated multipath variables. For example, by analyzing the signals of each multipath component, obtain the time when it arrives at the receiving end, the frequency offset compared with the transmitting frequency, and the intensity of the signal.

[0069] Specifically, determine the delay of each path of the target vehicle according to the signal arrival time. Assume that the signal is transmitted from the vehicle to the receiving end, and the arrival time of the first path signal is , and the arrival time of the second path signal is . If the signal propagation speed is known as , then the delay of the first path ( is the signal transmission time), and the delay of the second path .

[0070] Specifically, determine the Doppler frequency shift of each path based on the frequency offset. For example, if the transmitting signal frequency is , and the frequency offset of a certain path signal when received is , then the Doppler frequency shift of this path is . According to the principle of the Doppler effect, the Doppler frequency shift is related to the vehicle's moving speed, and the vehicle's moving speed can be calculated by measuring the Doppler frequency shift.

[0071] Specifically, calculate the channel gain of each path based on the signal amplitude. Assume that the transmitting signal amplitude is , and the amplitude of the received signal of a certain path is , then the channel gain of this path . The channel gain reflects the attenuation or enhancement of the signal during propagation, and is of great significance for evaluating signal quality and positioning accuracy.

[0072] Specifically, first, the channel response matrix is extracted through the sparse Bayesian compressive sensing algorithm to obtain the comprehensive information of the signal in the time delay and Doppler frequency shift dimensions; then, the multiple signal classification algorithm is used to process the channel response matrix to separate different multipath components, and the complex mixed signal is decomposed into multiple simple single-path signals; finally, the channel state information is calculated based on the separated multipath components, and the features in the multipath components are converted into specific time delay, Doppler frequency shift, and channel gain parameters, providing directly available data for vehicle positioning. This series of steps are closely linked and gradually analyze the signal in depth to extract key positioning information from the original received signal.

[0073] Specifically, in a complex urban traffic environment, there are a large number of objects such as buildings and other vehicles around the vehicle, and severe multipath effects will occur in signal propagation. For example, in the urban streets with high-rise buildings, when the roadside base station receives the vehicle signal, the signal will reach the base station after multiple reflections and scatterings, resulting in the received signal being a superposition of multiple different path signals.

[0074] Generally speaking, by calculating the channel state information, comprehensively understanding the signal propagation characteristics, the time delay is used to accurately calculate the distance between the vehicle and the base station, the Doppler frequency shift can obtain the vehicle's movement speed and direction, and the channel gain evaluates the signal quality. These information combined can more accurately determine the vehicle's spatial position and movement state, providing reliable data support for applications such as intelligent traffic management and autonomous driving, and ensuring traffic safety and efficient operation.

[0075] S4. Generate a distance estimate value of the target vehicle based on the time delay and a preset positioning algorithm.

[0076] In the embodiment of the present invention, the time delay refers to the time experienced by the signal from being emitted by the target vehicle to reaching the receiving end. It reflects the duration of the signal in the propagation process and is closely related to the distance between the target vehicle and the receiving end.

[0077] Specifically, the preset positioning algorithm is:

[0078] Wherein, is the distance difference between the target vehicle and the receiving end and the receiving end is the distance difference, is the propagation speed of the measurement signal in the medium, is the time delay for the signal to reach the receiving end from the target vehicle is the time delay, is the time delay for the signal to reach the receiving end from the target vehicle is the time delay, and is the receiving end identifier, is the receiving end identifier.

[0079] Specifically, the preset positioning algorithm calculates the distance differences from the target vehicle to each receiving end by using the time delay differences of the signals arriving at different receiving ends.

[0080] Furthermore, the approximate values of the distances between the target vehicle and the receiving ends calculated by the positioning algorithm based on the time delay information are estimates rather than exact values due to various errors in actual measurements.

[0081] Specifically, assume that in an intelligent transportation scenario, there are two roadside signal receiving ends and , which are respectively used to receive the signals emitted by the target vehicle. The propagation speed of the measured signal in the air is known . Through measurement and analysis, the time delay of the signal from the target vehicle to the receiving end , and the time delay of the signal arriving at the receiving end are obtained. Substituting these data into the preset positioning algorithm, we can get: , which means that the distance difference between the target vehicle and the receiving end and the receiving end is approximately 60 meters.

[0082] Specifically, in traffic management, it is necessary to know the position information of vehicles in real time for traffic flow monitoring, traffic signal control, etc. By arranging multiple signal receiving ends along the road, the positioning algorithm can be used to calculate the distance differences from the vehicle to each receiving end, thereby determining the approximate position of the vehicle and achieving precise positioning and tracking of the vehicle.

[0083] S5. Perform distance correction on the distance estimate value based on the channel gain and the Doppler frequency shift, and determine the spatial position of the target vehicle according to the distance estimate value after distance correction.

[0084] In the embodiments of the present invention, the channel gain represents the degree of amplification or attenuation of the signal during transmission. It reflects the propagation characteristics of the signal from the transmitting end to the receiving end and is affected by various factors such as the propagation path, obstacles, and atmospheric conditions. For example, the channel gain may be relatively large when the signal propagates in an open space, while it will become smaller after multiple reflections and diffractions; the Doppler frequency shift is caused by the relative motion between the target vehicle and the receiving end, resulting in a change in the frequency of the received signal relative to the frequency of the transmitted signal. This frequency change amount is the Doppler frequency shift. It is related to the motion speed and direction of the target vehicle and can be used to obtain the motion information of the vehicle.

[0085] Specifically, the channel gain reflects the energy change during signal propagation, and the Doppler shift reflects the motion state of the vehicle. By using these two factors to correct the distance estimation value, the distance estimation can be made closer to the true value. Finally, based on the corrected distance estimation value and the position information of multiple receivers, the spatial position of the target vehicle can be determined.

[0086] Specifically, assume that in an intelligent transportation scenario, there are three roadside base stations acting as receivers 、 and to locate a target vehicle. Through the previous calculations, the distance estimation value from the target vehicle to receiver has been obtained. 。

[0087] Furthermore, through measurement and analysis, the channel gain of the signal received by receiver is obtained. Generally speaking, a channel gain less than 1 indicates signal attenuation, which may mean that the signal propagation path is long or there are obstacles, so the distance estimation value may be on the small side. The influence of the channel gain can be considered through a simple correction coefficient. Assume the correction coefficient 。

[0088] Furthermore, the measured Doppler shift is obtained. The carrier frequency of the radar transmitted signal and the speed of light are known. According to the Doppler shift formula ( is the relative motion speed of the vehicle with respect to the receiver), the relative motion speed of the vehicle with respect to receiver can be calculated. If the vehicle is moving towards receiver , the distance will gradually decrease during signal propagation. Assume that according to the speed and the signal propagation time (calculated based on the previous time delay), a correction amount (the negative sign indicates a decrease in distance) needs to be made to the distance estimation value.

[0089] Furthermore, by comprehensively correcting the channel gain and the Doppler shift, the corrected distance estimation value is obtained. Similarly, the distance estimation values from the target vehicle to receivers and are corrected, and then using the trilateration principle (knowing the position coordinates of the three receivers and the corrected distances from the target vehicle to them), the spatial position of the target vehicle can be determined.

[0090] Specifically, the distance estimation value of the target vehicle is obtained through time delay calculation, but this value has certain errors. The channel gain and Doppler frequency shift reflect additional information about the signal propagation process and the vehicle motion state. Using this information for distance correction can make up for the deficiencies in the previous calculation and obtain a more accurate distance estimation value. Finally, based on the corrected distance estimation value and combined with the position information of multiple receiving ends, the spatial position of the target vehicle can be determined more precisely. The whole process is a process of gradual improvement and refinement.

[0091] In other words, the channel gain and Doppler frequency shift contain important information about signal propagation and vehicle motion. Using this information for distance correction can effectively reduce the positioning errors caused by factors such as signal attenuation and vehicle motion, making the positioning of the target vehicle more accurate.

[0092] As Figure 2 shown, it is a functional module diagram of a communication-sensing integrated vehicle positioning system based on OTFS modulation provided by an embodiment of the present invention.

[0093] The communication-sensing integrated vehicle positioning system 100 based on OTFS modulation according to the present invention can be installed in an electronic device. According to the implemented functions, the communication-sensing integrated vehicle positioning system 100 based on OTFS modulation can include a synchronous sensing signal acquisition module 101, a signal OTFS modulation module 102, a signal joint analysis module 103, a distance estimation value generation module 104, and a spatial position determination module 105. The modules in the present invention can also be referred to as units, which refer to a series of computer program segments that can be executed by a processor of an electronic device and can complete fixed functions, and are stored in the memory of the electronic device.

[0094] In this embodiment, the functions of each module / unit are as follows: The synchronous sensing signal acquisition module 101 is used to acquire the synchronous sensing signal of the target vehicle, where the synchronous sensing signal includes: a communication signal and a radar signal; The signal OTFS modulation module 102 is used to perform OTFS modulation on the synchronous sensing signal to obtain a jointly modulated signal in the time-frequency domain; The signal joint analysis module 103 is used to perform time-frequency domain joint analysis on the jointly modulated signal to obtain the channel state information of the target vehicle, where the channel state information includes: time delay, Doppler frequency shift, and channel gain; The distance estimation value generation module 104 is used to generate the distance estimation value of the target vehicle based on the time delay and a preset positioning algorithm, where the preset positioning algorithm is:

[0095] Among them, is the distance difference between the target vehicle and the receiving end and the receiving end of the distance difference, is the propagation speed of the measurement signal in the medium, is the time delay for the signal to reach the receiving end from the target vehicle of the time delay, is the time delay for the signal to reach the receiving end from the target vehicle of the time delay, is the receiving end identifier, is the receiving end identifier; The spatial position determination module 105 is configured to perform distance correction on the distance estimation value based on the channel gain and the Doppler frequency shift, and determine the spatial position of the target vehicle according to the distance estimation value after the distance correction.

[0096] In several embodiments provided by the present invention, it should be understood that the disclosed methods and systems can be implemented in other ways. For example, the system embodiments described above are merely illustrative. For example, the division of the modules is only a logical function division, and there may be other division methods in actual implementation.

[0097] The modules described as separate components may or may not be physically separated. The components shown as modules may or may not be physical units, that is, they may be located in one place, or they may be distributed to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0098] In addition, in each embodiment of the present invention, the functional modules can be integrated in a processing unit, or each unit can exist physically alone, or two or more units can be integrated in one unit. The above integrated unit can be implemented in the form of hardware, or in the form of a combination of hardware and software functional modules.

[0099] For those skilled in the art, it is obvious that the present invention is not limited to the details of the above-described exemplary embodiments, and without departing from the spirit or basic characteristics of the present invention, the present invention can be implemented in other specific forms.

[0100] The embodiments of the present application can acquire and process relevant data based on artificial intelligence technology. Among them, artificial intelligence is a theory, method, technology and application system that uses a digital computer or a machine controlled by a digital computer to simulate, extend and expand human intelligence, perceive the environment, acquire knowledge and use knowledge to obtain the best results.

[0101] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A communication-sensing integrated vehicle positioning method based on OTFS modulation, characterized in that: The method comprises: Acquire a synchronous perception signal of the target vehicle, wherein the synchronous perception signal includes: a communication signal and a radar signal; Performing OTFS modulation on the synchronization sensing signal to obtain a joint modulation signal in the time-frequency domain; Performing a time-frequency domain joint analysis on the joint modulated signal to obtain channel state information of the target vehicle, wherein the channel state information includes: time delay, Doppler frequency shift and channel gain; The distance estimation value of the target vehicle is generated based on the time delay and a preset positioning algorithm, wherein the preset positioning algorithm is: ， in, The target vehicle to the receiving end and the receiving end The distance difference, It is the speed at which a signal propagates in a medium. is the signal from the target vehicle to the receiving end The delay, is the signal from the target vehicle to the receiving end The delay, is the receiving end identifier, is the receiving end identifier; The distance estimation value is distance-corrected based on the channel gain and the Doppler frequency shift, and the spatial position of the target vehicle is determined according to the distance estimation value after the distance correction.

2. The communication-sensing integrated vehicle positioning method based on OTFS modulation as claimed in claim 1, characterized in that: The step of acquiring the synchronous sensing signal of the target vehicle comprises: The communication signal transmitted by the target vehicle and the reflected radar signal are synchronously received based on a multi-band reconfigurable antenna array, wherein the communication signal adopts an OFDM modulation format and the radar signal adopts a pulse compression waveform.

3. The communication-sensing integrated vehicle positioning method based on OTFS modulation as claimed in claim 1, characterized in that: The performing OTFS modulation on the synchronization sensing signal to obtain a joint modulation signal in the time-frequency domain includes: Allocating resource regions to the synchronous perception signal based on a preset delay-Doppler domain to obtain a region signal of the synchronous perception signal, wherein a first resource region is allocated to the communication signal and a second resource region is allocated to the radar signal; The regional signal is subjected to time-frequency domain grid mapping to obtain a joint modulation signal in the time-frequency domain.

4. The communication-sensing integrated vehicle positioning method based on OTFS modulation as claimed in claim 3, characterized in that: The performing time-frequency domain grid mapping on the regional signal to obtain a joint modulation signal in the time-frequency domain includes: generating a Doppler spread estimate of the area signal based on a dynamic range of the target vehicle; Generating dynamic grid parameters of the regional signal based on the Doppler spread estimate, wherein the dynamic grid parameters include: frequency domain grid resolution, frequency domain grid size, time domain grid resolution and time domain grid size; The regional signal is subjected to time-frequency domain grid mapping based on the dynamic grid parameters to obtain a joint modulation signal in the time-frequency domain.

5. The communication-sensing integrated vehicle positioning method based on OTFS modulation as claimed in claim 4, characterized in that: The calculation formula of the Doppler spread estimate is as follows: ， in, is the Doppler spread estimate, is the speed of the target vehicle, is the speed of light, is the carrier frequency of the radar transmission signal, is the acceleration of the target vehicle, is the observation time.

6. The communication-sensing integrated vehicle positioning method based on OTFS modulation as claimed in claim 1, characterized in that: The performing a time-frequency domain joint analysis on the joint modulation signal to obtain the channel state information of the target vehicle includes: Extracting a delay-Doppler domain channel response matrix of the joint modulated signal based on a sparse Bayesian compressed sensing algorithm; Generate separated multipath components of the joint modulated signal based on a multiple signal classification algorithm and the delay-Doppler domain channel response matrix; The channel state information of the target vehicle is calculated based on the RAP variable, wherein the channel state information includes the time delay, Doppler frequency shift and channel gain of each path.

7. The communication-sensing integrated vehicle positioning method based on OTFS modulation as claimed in claim 6, characterized in that: The extracting the delay-Doppler domain channel response matrix of the joint modulated signal based on the sparse Bayesian compressed sensing algorithm includes: Constructing a measurement model of the joint modulation signal, wherein the measurement model is: ， in, is the measurement vector of the joint modulated signal, is the measurement matrix, is the delay-Doppler domain channel response matrix to be extracted, is the noise vector; Setting a prior distribution for the noise vector and the delay-Doppler domain channel response matrix to be extracted based on sparse Bayesian theory, and calculating a posterior distribution of the delay-Doppler domain channel response matrix to be extracted; A delay-Doppler domain channel response matrix of the jointly modulated signal is generated based on the prior distribution and the posterior distribution.

8. The communication-sensing integrated vehicle positioning method based on OTFS modulation as claimed in claim 6, characterized in that: The generating of the separation multipath components of the joint modulation signal based on the multiple signal classification algorithm and the delay-Doppler domain channel response matrix comprises: Based on a multiple signal classification algorithm, the delay-Doppler domain channel response matrix is ​​eigen-decomposed to divide the signal subspace and the noise subspace; Constructing a spectral function based on the spatial characteristics of the signal subspace and the spatial characteristics of the noise subspace; performing a peak position search on the spectral function to determine multipath component parameters of the separated multipath components of the joint modulation signal, The separated multipath components of the jointly modulated signal are determined based on the multipath component parameters.

9. The communication-sensing integrated vehicle positioning method based on OTFS modulation as claimed in claim 6, characterized in that: The calculating the channel state information of the target vehicle based on the separation multipath variable includes: Extracting the arrival time, frequency offset and signal amplitude of each path signal in the said separation multipath variable; Determining the time delay of each path of the target vehicle based on the arrival time; Determine the Doppler frequency shift of each path based on the frequency offset; The channel gain of each path is calculated based on the signal amplitude, thereby generating the channel state information of the target vehicle.

10. A communication-sensing integrated vehicle positioning system based on OTFS modulation, characterized in that: The system comprises: A synchronous perception signal acquisition module, used to acquire a synchronous perception signal of a target vehicle, wherein the synchronous perception signal includes: a communication signal and a radar signal; A signal OTFS modulation module, used for performing OTFS modulation on the synchronization perception signal to obtain a joint modulation signal in the time-frequency domain; A signal joint analysis module, used for performing a time-frequency domain joint analysis on the joint modulated signal to obtain the channel state information of the target vehicle, wherein the channel state information includes: time delay, Doppler frequency shift and channel gain; A distance estimation value generation module is used to generate a distance estimation value of the target vehicle based on the time delay and a preset positioning algorithm, wherein the preset positioning algorithm is: ， in, The target vehicle to the receiving end and the receiving end The distance difference, It is the speed at which a signal propagates in a medium. is the signal from the target vehicle to the receiving end The delay, is the signal from the target vehicle to the receiving end The delay, is the receiving end identifier, is the receiving end identifier; The spatial position determination module is used to perform distance correction on the distance estimation value based on the channel gain and the Doppler frequency shift, and determine the spatial position of the target vehicle according to the distance estimation value after the distance correction.

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