Vehicle sensing method in multi-RIS-assisted OTFS system in Internet of Vehicles environment

Abstract

The invention discloses a vehicle sensing method in a multi-RIS auxiliary OTFS system in an Internet of Vehicles environment, and the method comprises the steps: firstly, building an RIS auxiliary multipath channel model, receiving a signal, and obtaining a DOA of the signal reaching a target vehicle through employing the received signal and an embedded pilot frequency; a projection equation set is established based on the geometric position relation between the RIS and the vehicle, and the motion direction of the target vehicle is solved through the Roucher-Caphelli theorem; constructing a position error vector, defining a target function based on a minimum norm criterion, and estimating the position of a target vehicle by adopting an OTFS channel estimation assisted EWOA algorithm to obtain a position estimation value; and finally, constructing a distance difference error minimization objective function, performing optimization by applying a Chan-EWOA algorithm, and outputting a final position estimation value of the emission source vehicle. According to the method, the vehicle positioning performance is effectively improved under the conditions that no LOS path exists and timing synchronization between nodes is not needed.

Description

Technical Field

[0001] The present invention relates to the field of wireless communication technology, and in particular to a vehicle perception method in a multi-RIS-assisted OTFS system in a vehicle networking environment. Background Art

[0002] With the rapid development of the Internet of Vehicles and communication technology, high-precision and high-reliability vehicle perception technology has become a key factor in the development of the Internet of Vehicles in order to build a safe and reliable Internet of Vehicles environment. Generally, traditional positioning technologies include GPS positioning technology, millimeter-wave radar positioning technology, lidar positioning technology, and positioning technology based on road side units (RSU). These positioning technologies are various and can show good positioning performance advantages in the environment that matches them, but as the Internet of Vehicles environment becomes increasingly complex, they will also have their own limitations. When facing the complex environment of the Internet of Vehicles, such as areas with dense vehicles or tunnels, they often show low reliability due to the lack of LOS (line-of-sight, LOS) path or GPS signal loss. At the same time, some positioning methods require strict synchronization of the clocks of the transmitter and receiver, which will also affect the feasibility of vehicle positioning.

[0003] As a material with controllable electromagnetic wave reflection properties, the intelligent reflective metasurface can regulate the propagation of electromagnetic waves by changing its electromagnetic properties. It can be applied in wireless communication systems, providing a new way to transmit signals and enhance coverage. As an emerging technology, intelligent reflective metasurface (RIS) can regulate the propagation of electromagnetic waves and enhance signal coverage. However, existing technologies may not fully combine RIS with OTFS modulation technology, especially in vehicle perception without LOS and synchronization conditions. Summary of the invention

[0004] In view of the shortcomings of the prior art, the present invention proposes a vehicle perception method in a multi-RIS assisted OTFS system in a vehicle networking environment, which significantly improves the vehicle positioning perception performance and improves the system reliability in the absence of a LOS path and without the need for timing synchronization between nodes.

[0005] In order to solve the above technical problems, the technical solution of the present invention is:

[0006] The steps of the vehicle perception algorithm in a multi-RIS-assisted OTFS system in a connected vehicle environment are as follows:

[0007] Step 1: Establish a RIS-assisted multipath channel model to obtain a specific representation of the received signal

[0008] .

[0009] Step 2: Channel estimation using the received signal and embedded pilots can estimate the integer Doppler shift , according to the Doppler frequency shift of the reflection path in real situation , the DOA of the signal reaching the target vehicle is .

[0010] Step 3: Project according to geometric relationship , using the Rouché-Capelli theorem, we get the relationship , get the target vehicle's moving direction Estimates.

[0011] Step 4: After obtaining the target vehicle's moving direction, based on the position error vector Constructing objective function based on minimum norm criterion , using the EWOA algorithm assisted by OTFS channel estimation to find the estimated value of the target vehicle position .

[0012] Step 4-1: Determine the maximum number of iterations , the number of whales , dimension , search space , in the search space Randomly generated Initial position, initialize the best position of the whale in the current iteration , poor location and the crossover position resulting from the intersection of the worst position and the worse position .

[0013] Step 4-2: Initialization , update the parameters

[0014]

[0015] Step 4-3: Random selection Whales migrate their search strategies and update their positions

[0016] .

[0017] Step 4-4: When and When The whales perform a variety of encirclement and hunting strategies and update their positions

[0018]

[0019] when and When Whales perform priority selection strategies and update their positions

[0020]

[0021] when When Whales perform a full spiral update strategy and update their positions

[0022]

[0023] Step 4-5: Recalculate the hunger of all whales based on the updated position , and update , generating a new crossover solution

[0024] .

[0025] Step 4-6: Update , enter the next iteration. Repeat the above process until , the last generated This is the estimated value of the target vehicle's position. Figure 2 Shows the number of grids in the same Doppler dimension In the following figure, the NRMSE (Normalized Root Mean Squared Error, NRMSE) of the EWOA algorithm assisted by OTFS channel estimation is lower than that of the GWO algorithm, indicating that it has better positioning performance.

[0026] Step 5: After obtaining the position and direction of the target vehicle, we also need to locate the source vehicle. Using the Chan positioning algorithm based on TDOA, we can get the relationship:

[0027] ,

[0028] Find a preliminary estimate of the source vehicle's position.

[0029] Step 6: Construct the objective function based on the minimum distance difference error criterion:

[0030] ,

[0031] in, , the proposed Chan-EWOA optimization algorithm is used to solve the objective function to obtain the final estimate of the source vehicle position .

[0032] Step 6-1: Determine the maximum number of iterations , the number of whales , dimension , using the initial estimate obtained by the Chan localization algorithm to narrow the search space and generate a new search space , randomly generated in the search space Initial position, initialize the best position of the whale in the current iteration , poor location and the crossover position resulting from the intersection of the worst position and the worse position .

[0033] Step 6-2: Initialization , update the parameters

[0034] .

[0035] Step 6-3: Random selection Whales migrate their search strategies and update their positions

[0036] .

[0037] Step 6-4: When and When The whales perform a variety of encirclement and hunting strategies and update their positions

[0038]

[0039] when and When Whales perform priority selection strategies and update their positions

[0040]

[0041] when When The whales use a comprehensive spiral update strategy, which introduces nonlinear time-varying adaptive weights:

[0042] ;

[0043] in, is the nonlinear time-varying adaptive weight, is the current iteration number, is the maximum number of iterations,

[0044] The weight parameters of the spiral update strategy can be adjusted according to the nonlinear adaptive weights at different iteration times, and the position can be updated

[0045] ,

[0046] Step 6-5: Recalculate the hunger of all whales based on the updated position , and update , generating a new crossover solution

[0047] .

[0048] Step 6-6: Update , enter the next iteration. Repeat the above process until , the last generated This is the estimated value of the source vehicle position.

[0049] The present invention also provides a vehicle networking system, which is used to realize the joint estimation of vehicle position and movement direction based on the above-mentioned vehicle perception method.

[0050] The present invention also provides a computer-readable storage medium storing a computer program, which implements the above-mentioned vehicle perception method when executed by a processor.

[0051] The present invention has the following characteristics and beneficial effects:

[0052] By adopting the above technical solution, RIS technology is combined with Internet of Vehicles technology. Based on the virtual node construction mechanism and the easy deployment of RIS, multiple RIS can be flexibly arranged in the Internet of Vehicles environment. In this virtual node scenario consisting of a transmitter vehicle with unknown location and multiple RIS, we have built a system model without LOS path and strict synchronization of the clocks of the transmitter and receiver, which broadens the system application scenarios and makes the model more practical.

[0053] The OTFS system is combined with the Internet of Vehicles technology, and the OTFS modulation technology is used to complete the modulation of the signal. Unlike the traditional spatial spectrum estimation method, OTFS reduces the impact of the time-varying wireless channel caused by the high-speed movement of the vehicle, and can also estimate the DOA in an integrated communication and perception environment. Finally, the simulation results verify the superiority and reliability of the proposed method.

[0054] The traditional Chan positioning algorithm is applied to the EWOA algorithm. By using the initial estimation of the Chan positioning algorithm to reduce the search space of the EWOA algorithm, the cumulative error caused by positioning is effectively reduced, and the positioning perception accuracy of the emitting source vehicle is improved. Finally, the simulation results verify the superiority and reliability of the proposed method. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0056] Figure 1 A schematic diagram of a vehicle perception method model in a multi-RIS assisted OTFS system in a connected vehicle environment according to an embodiment of the present invention;

[0057] Figure 2 This is a comparison chart of positioning performance between the EWOA algorithm assisted by OTFS channel estimation and the GWO algorithm when sensing a target vehicle in an embodiment of the present invention;

[0058] Figure 3 This is a comparison chart of positioning performance among the Chan-EWOA algorithm, the Chan algorithm, and the EWOA algorithm when sensing a transmitting source vehicle in an embodiment of the present invention;

[0059] Figure 4 The figure is a schematic diagram of the positioning performance of the Chan-EWOA algorithm under different numbers of RIS in an embodiment of the present invention. DETAILED DESCRIPTION

[0060] It should be noted that, in the absence of conflict, the embodiments of the present invention and the features in the embodiments may be combined with each other.

[0061] In the description of the present invention, it should be understood that the terms "center", "longitudinal", "lateral", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside" and the like indicate positions or positional relationships based on the positions or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, the terms "first", "second", and the like are only used for descriptive purposes, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, features defined as "first", "second", and the like may explicitly or implicitly include one or more of the features. In the description of the present invention, unless otherwise specified, "multiple" means two or more.

[0062] In the description of the present invention, it should be noted that, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood by specific circumstances.

[0063] Example 1

[0064] This embodiment provides a vehicle perception method in a multi-RIS assisted OTFS system in a connected vehicle environment. The purpose is to construct a virtual node based on the reflection characteristics of RIS in wireless communication, use OTFS channel estimation to obtain the parameters required for vehicle perception, and construct the objective function required for vehicle perception according to the geometric relationship projection and the objective function optimization criterion. Finally, the Chan-EWOA algorithm is proposed to realize vehicle positioning perception in the absence of LOS path and timing synchronization between nodes, and improve the positioning performance of the system in the connected vehicle scenario. The specific steps are as follows:

[0065] First, by placing The location is RISs build virtual nodes, such as Figure 1 As shown, a RIS-assisted multipath channel model is established. The transmitting source vehicle S transmits a set of delay-Doppler information arranged in a grid Information symbols on , it passes After characterizing the wireless channel, at the location The signal received by the target vehicle G can be expressed as:

[0066] .

[0067] in is the sampling time interval, is the sampling frequency interval, and Represent the quantization step size of delay and Doppler respectively. , Respectively represent The complex gain coefficients, delays and Doppler shifts associated with each path, and They represent the corresponding time delay tap and Doppler frequency shift tap respectively. , The variance is Gaussian white noise, , is the Doppler tap and the delay tap is The indicative value of the path that satisfies . Indicates the mod N operation. represents the Dirac function.

[0068] Then, the integer Doppler shift can be estimated by using the received signal and the embedded pilot for channel estimation. , replace the actual Doppler shift in the reflection path with the integer Doppler shift , in order to calculate DOA (Direction of Arrival, DOA). Since the Doppler quantization step size is , then the integer Doppler shift obtained will have an error with the actual Doppler shift .

[0069] When the source vehicle is driving normally, the Doppler of the reflection path can be expressed as ,in is the propagation speed of the signal in the medium, It is The DOA of the paths, is the speed of the target vehicle, It is The center frequency of the signal received by each RIS can be obtained by the spectrum sensing function of the RIS. The target vehicle G can obtain the location of the RIS and the speed of the vehicle through information interaction in the Internet of Vehicles. Therefore, the Doppler frequency shift of the reflection path is ,in is the center frequency of the transmitted signal. At this time, the DOA of the signal reaching the target vehicle G is:

[0070] .

[0071] Then, according to Figure 1 The geometric relationship projection of the vehicle and RIS position shown can obtain the equation system

[0072] ,

[0073] in, is the target vehicle motion direction to be solved.

[0074] Converting it into matrix form, we can get .

[0075] in, , .

[0076] matrix It is expressed as:

[0077] ,

[0078] in:

[0079] ,

[0080] .

[0081] ,

[0082] in, .

[0083] According to the Rouché-Capelli theorem, if and only if The system of equations has a unique solution only when Representation coefficient matrix The augmented matrix of . The coefficient matrix Each column of is linearly independent, so it is straightforward to show that . Augmented matrix The number of columns is , to make ,but Should be In the subspace of , we can get , from which the target vehicle's moving direction can be obtained Estimates.

[0084] After obtaining the target vehicle's moving direction, the position error vector can be obtained according to the geometric relationship projection equation group: ,in,

[0085] ,

[0086] Based on the position error vector Constructing objective function based on minimum norm criterion , the EWOA algorithm assisted by OTFS channel estimation can find the estimated value of the target vehicle position The following is the specific content of the algorithm:

[0087] Determine the maximum number of iterations , the number of whales , dimension , search space , in the search space Randomly generated Initial position, initialize the best position of the whale in the current iteration , poor location and the crossover position resulting from the intersection of the worst position and the worse position .

[0088] initialization , update parameters. Action step length parameters , a selection parameter composed of 0 and 1 randomly , the value range is The random update coefficient , the random probability of selecting different strategies , three randomly selected positions in the crossover solution , a random position in the search space , a random position near the optimal solution .

[0089] Random Selection Whales migrate their search strategies and update their positions

[0090] .

[0091] when and When The whales perform a variety of encirclement and hunting strategies and update their positions

[0092]

[0093] when and When Whales perform priority selection strategies and update their positions

[0094]

[0095] when When The whales are fully spiral updated. In order to improve the efficiency of the algorithm, nonlinear time-varying adaptive weights are added.

[0096] ,

[0097] The algorithm can adjust the control parameters required by the corresponding search strategy according to the nonlinear adaptive weights in different iterations. The comprehensive spiral update strategy is expressed as

[0098] ,

[0099] in, , A random number between 0 and 1.

[0100] Recalculate the hunger of all whales based on the updated position , and update , generating a new crossover solution

[0101] .

[0102] renew , enter the next iteration. Repeat the above process until , the last generated This is the estimated value of the target vehicle's position.

[0103] After obtaining the position and direction of the target vehicle, it is also necessary to locate the source vehicle. Using the reflection path of RIS_1 as a reference, we use the time difference of arrival (TDOA) between other reflection paths and the reflection path of RIS_1. And the estimated position of the target vehicle, the TDOA between different RIS and the source vehicle path can be obtained, which is expressed as

[0104] ,

[0105] in, is the estimated target vehicle and The distance between the RIS, the distance difference between different RIS and the source vehicle path is .

[0106] Using the Chan positioning algorithm based on TDOA, we can get the relationship:

[0107] ,

[0108] in, It can be solved using the root-finding formula, and the parameters It can be expressed as

[0109] ,

[0110] Respectively represent The x-axis coordinate difference and y-axis coordinate difference between the first RIS and the second RIS, .parameter With parameters Similarly, we only need to swap the corresponding x-axis coordinates and y-axis coordinates. Obviously, the position estimate of the source vehicle obtained by the Chan positioning algorithm has errors, so it can only be used as a preliminary estimate of the source vehicle's position.

[0111] Construct the objective function based on the minimum distance difference error criterion:

[0112] ,

[0113] in The proposed Chan-EWOA optimization algorithm is used to solve the objective function to obtain the final estimate of the source vehicle position. . Specific content of Chan-EWOA optimization algorithm:

[0114] Determine the maximum number of iterations , the number of whales , dimension , using the initial estimate obtained by the Chan localization algorithm to narrow the search space and generate a new search space , randomly generated in the search space Initial position, initialize the best position of the whale in the current iteration , poor location and the crossover position resulting from the intersection of the worst position and the worse position .

[0115] initialization , update the parameters

[0116] .

[0117] Random Selection Whales migrate their search strategies and update their positions

[0118] .

[0119] when and When The whales perform a variety of encirclement and hunting strategies and update their positions

[0120]

[0121] when and When Whales perform priority selection strategies and update their positions

[0122]

[0123] when When The whales perform a comprehensive spiral update strategy, and the nonlinear time-varying adaptive weights in the spiral update strategy are:

[0124] ;

[0125] in, is the nonlinear time-varying adaptive weight, is the current iteration number, is the maximum number of iterations,

[0126] The weight parameters of the spiral update strategy can be adjusted according to the nonlinear adaptive weights at different iteration times; and the position is updated

[0127] ,

[0128] Recalculate the hunger of all whales based on the updated position , and update , generating a new crossover solution

[0129] .

[0130] renew , enter the next iteration. Repeat the above process until , the last generated This is the estimated value of the source vehicle position.

[0131] Figure 3 The preliminary estimate obtained by the Chan algorithm using the Chan-EWOA algorithm is demonstrated, which narrows the search space of the EWOA algorithm and improves the vehicle positioning performance. From the simulation results, it can be seen that compared with the other two algorithms, the Chan-EWOA algorithm is less sensitive to TDOA estimation errors. Figure 4 It is shown that under the same TDOA estimation error, as the number of RIS increases, the NRMSE of the Chan-EWOA algorithm gradually decreases, which indicates that the Chan-EWOA algorithm we proposed can enhance the vehicle positioning performance by increasing the number of RIS.

[0132] Example 2

[0133] This embodiment provides a vehicle networking system, including the vehicle perception method disclosed in Example 1, which is used to realize the joint estimation of the vehicle position and movement direction.

[0134] Example 3

[0135] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the vehicle perception method disclosed in Example 1.

[0136] The embodiments of the present invention are described in detail above with reference to the accompanying drawings, but the present invention is not limited to the described embodiments. For those skilled in the art, various changes, modifications, substitutions and variations of these embodiments including components are made without departing from the principles and spirit of the present invention, and still fall within the scope of protection of the present invention.

Claims

1. A vehicle perception method in a multi-RIS assisted OTFS system in a connected vehicle environment, characterized in that: The following steps are involved: Step 1: deploy multiple RIS in the Internet of Vehicles environment and build a multi-RIS-assisted multipath channel model, in which the transmitting source vehicle maps the signal to the delay-Doppler domain grid through orthogonal time-frequency spatial modulation technology and transmits it to the target vehicle through the wireless channel; Step 2: After the target vehicle receives the signal, it uses the embedded pilot to perform channel estimation, obtain the integer Doppler shift, and calculate the signal arrival direction in combination with the RIS position information; Step 3: Based on the geometric position relationship between RIS and the vehicle, a projection equation group is established, and the moving direction of the target vehicle is solved by the Rouché-Capelli theorem; Step 4: construct a position error vector, define the objective function based on the minimum norm criterion, use the EWOA algorithm assisted by OTFS channel estimation to estimate the position of the target vehicle, and obtain the position estimation value; Step 5: Taking the reflection path of a certain RIS as a reference, calculate the arrival time difference of other RIS paths, deduce the distance difference between the source vehicle and the RIS in combination with the target vehicle position estimate, construct the distance difference error minimization objective function, apply the Chan-EWOA algorithm to optimize, and output the final position estimate of the source vehicle.

2. The vehicle perception method according to claim 1, characterized in that: In step 1, the information symbol is defined as: , the OTFS modulation technique arranges information symbols in a delay-Doppler domain grid, and the expression is as follows: ； in, is the sampling frequency interval, and represent the quantization step size of delay and Doppler respectively, and are the grid dimensions in the delay domain and Doppler domain respectively.

3. The vehicle perception method according to claim 1, characterized in that: In step 2, the calculation method of the DOA of the signal reaching the target vehicle is: ； in, is the propagation speed of the signal in the medium, It is The DOA of the paths, is the target vehicle's speed, is the actual Doppler shift, It is The center frequency of the signal received by each RIS, is the center frequency of the transmitted signal.

4. The vehicle perception method according to claim 1, characterized in that: In step 3, the projection equations are established, and the mathematical formula is as follows: ； in, is the target vehicle motion direction to be solved, is the target vehicle position to be solved, It is the location of RIS.

5. The vehicle perception method according to claim 1, characterized in that: The implementation of the Chan-EWOA optimization algorithm in step 5 includes: Step 5-1: Use the Chan positioning algorithm to make a preliminary estimate of the location of the transmitting vehicle and narrow the search space; Step 5-2: Initialize the whale population in the reduced search space, dynamically adjust the position by combining the migration search strategy, encirclement predation strategy, priority rotation strategy and spiral update strategy; and introduce nonlinear time-varying adaptive weights in the spiral update strategy: ； in, is the nonlinear time-varying adaptive weight, is the current iteration number, is the maximum number of iterations, The weight parameters of the spiral update strategy can be adjusted according to the nonlinear adaptive weights at different iteration numbers; Step 5-3: Update the whale position based on the hunger evaluation, and output the optimal solution after iterating to the maximum number of times.

6. The vehicle perception method according to claim 5, characterized in that: In the step 5-2, The update formula of the migration search strategy is: ; when and When The whales perform a rich encirclement and predation strategy and update their positions: ； when and When The whales perform a priority selection strategy and update their positions: ； when When The whales perform a comprehensive spiral update strategy and update their positions: ； in, yes The updated position of the whales, is a random position in the search space, is a random position near the optimal solution, is the random probability of selecting different strategies, is the action step parameter, expressed as , is a random number between 0 and 1, is the rest The updated position of the whales, is the best position for the whale in the current iteration, is a random update coefficient between 0 and 2, are three randomly selected positions in the crossover solution, is the spiral update coefficient, expressed as .

7. The vehicle perception method according to claim 5, characterized in that: In step 5, the distance difference error minimization objective function is defined as: ； in , the Chan-EWOA optimization algorithm is used to solve the objective function to obtain the final estimate of the source vehicle position , in, is the number of RIS used in the scene, is the location of the source vehicle, is the distance difference between different RIS and the source vehicle path.

8. The vehicle perception method according to claim 1, characterized in that: The number of the RIS is no less than 3, and their location information is obtained in real time through Internet of Vehicles interaction.

9. The vehicle perception method according to claim 1, characterized in that: The method is applicable to vehicle networking scenarios without line-of-sight paths and without clock synchronization between transmitters and receivers.

10. A vehicle networking system, characterized in that: The vehicle perception method comprising any one of claims 1 to 9 is used to realize joint estimation of vehicle position and movement direction.

11. A computer-readable storage medium storing a computer program, characterized in that: When the program is executed by a processor, the vehicle perception method described in any one of claims 1 to 9 is implemented.

Images