Vehicle Sensing Method in a Multi-RIS Assisted OTFS System in the Internet of Vehicles Environment

Abstract

The present invention discloses a vehicle perception method in a multi-RIS-assisted OTFS system in a vehicle networking environment. First, a multi-path channel model assisted by RIS is established, and the DOA of the signal arriving at the target vehicle is obtained by using the received signal and the embedded pilot; a projection equation set is established based on the geometric position relationship between the RIS and the vehicle, and the moving direction of the target vehicle is solved by the Rouché-Capelli theorem; a position error vector is constructed, a target function is defined based on the minimum norm criterion, and the EWOA algorithm assisted by OTFS channel estimation is used to estimate the position of the target vehicle to obtain a position estimation value; finally, a target function for minimizing the distance difference error is constructed and optimized by the Chan-EWOA algorithm, and the final position estimation value of the transmitting source vehicle is output. This method effectively improves the vehicle positioning performance in the absence of LOS paths and without the need for timing synchronization between nodes.

Description

Technical Field

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

[0002] With the rapid development of vehicle networking and communication technologies, in order to construct a safe and reliable vehicle networking environment, high-precision and high-reliability vehicle sensing technologies have become key factors in the development of vehicle networking. Generally, traditional positioning technologies include GPS positioning technology, millimeter-wave radar positioning technology, lidar positioning technology, and positioning technology based on roadside units (RSUs), etc. These positioning technologies are various and can exhibit good positioning performance advantages in environments matching themselves. However, with the increasing complexity of the vehicle networking environment, they also have their respective limitations. Facing the complex environment in vehicle networking, such as areas with dense vehicles or tunnels, they often show low reliability due to the lack of LOS (line-of-sight, LOS) paths or GPS signal loss. At the same time, the requirement of strict clock synchronization between the transceiver ends for some positioning methods will also affect the feasibility of vehicle positioning.

[0003] Intelligent reflecting metasurface, as a material with controllable electromagnetic wave reflection characteristics, can regulate the propagation of electromagnetic waves by changing its electromagnetic characteristics. It can be applied in wireless communication systems, providing a new way for signal transmission and enhancing the coverage range. Intelligent reflecting metasurface (RIS), as an emerging technology, can regulate the propagation of electromagnetic waves and enhance signal coverage. However, the existing technologies may not fully combine RIS with OTFS modulation technology, especially for vehicle sensing under non-LOS and non-synchronization conditions. Summary of the Invention

[0004] Aiming at the deficiencies of the existing technology, the present invention proposes a vehicle sensing method in a multi-RIS-assisted OTFS system in a vehicle networking environment, which significantly improves the vehicle positioning and sensing performance and enhances the system reliability without LOS paths and without inter-node timing synchronization.

[0005] To solve the above technical problems, the technical solution of the present invention is as follows:

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

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

[0008] .

[0009] Step 2: Channel estimation is performed using the received signal and embedded pilots, and the integer Doppler shift can be estimated. According to the Doppler shift of the reflection path in the real situation , the DOA at which the signal arrives at the target vehicle is obtained as .

[0010] Step 3: Projection is obtained according to the geometric relationship , and the relational expression is obtained using the Rouché - Capelli theorem , and the estimated value of the moving direction of the target vehicle is obtained.

[0011] Step 4: After obtaining the moving direction of the target vehicle, based on the norm - minimum criterion of the position error vector, the objective function is constructed, and the estimated value of the target vehicle position is found using the EWOA algorithm assisted by OTFS channel estimation .

[0012] Step 4 - 1: Determine the maximum number of iterations , the number of whales , the dimension , the search space , in the search space , randomly generate initial positions, initialize the best position of the whales in the current iteration , the worse position and the crossover position generated by crossing the worst position and the worse position .

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

[0014]

[0015] Step 4 - 3: Randomly select whales for the migration search strategy and update the position

[0016] .

[0017] Step 4 - 4: When and , select the remaining whales for the rich encircling predation strategy and update the position

[0018]

[0019] When and , select the remaining The whales perform a priority selection strategy and update their positions

[0020]

[0021] When it is time, select the remaining whales to perform a comprehensive spiral update strategy and update their positions

[0022]

[0023] Step 4-5: Recalculate the hunger levels of all whales based on the updated positions and update to generate new crossover solutions

[0024] .

[0025] Step 4-6: Update and enter the next iteration. Repeat the above process until and finally the generated is the estimated value of the target vehicle's position. Figure 2 shows that under the same number of Doppler dimension grids , the NRMSE (Normalized Root Mean Squared Error) 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 movement direction of the target vehicle, we also need to perform positioning perception on the transmitting source vehicle. Using the Chan positioning algorithm based on TDOA, the relational expression

[0027] can be obtained,

[0028] and the preliminary estimate of the position of the transmitting source vehicle is obtained.

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

[0030] ,

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

[0032] Step 6-1: Determine the maximum number of iterations , the number of whales , the dimension , the initial estimate obtained using the Chan positioning algorithm is used to narrow the search space and generate a new search space , randomly generate initial positions in the search space and initialize the best position of the whales in the current iteration , the worse position and the crossover position generated by crossing the worst position and the worse position .

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

[0034] .

[0035] Step 6-3: Randomly select whales to perform the migration search strategy and update the positions

[0036] .

[0037] Step 6-4: When and , select the remaining whales to perform the rich encircling prey strategy and update the positions

[0038]

[0039] When and , select the remaining whales to perform the priority selection strategy and update the positions

[0040]

[0041] When , select the remaining whales to perform the comprehensive spiral update strategy, and a non-linear time-varying adaptive weight is introduced in this spiral update strategy:

[0042] ;

[0043] Among them, is the non-linear time-varying adaptive weight, is the current iteration number, is the maximum iteration number,

[0044] In different iteration numbers, the weight parameters of the spiral update strategy can be adjusted according to the non-linear adaptive weight, and the positions are updated

[0045] ,

[0046] Step 6-5: Recalculate the hunger levels of all whales based on the updated positions , and update to generate new cross solutions

[0047] .

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

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

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

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

[0052] By adopting the above technical solution, the RIS technology is combined with the vehicle networking technology. According to the virtual node construction mechanism and using the easy-to-deploy characteristic of RIS, multiple RISs are flexibly arranged in the vehicle networking environment. In the virtual node scenario composed of a transmitting source vehicle with unknown position and multiple RISs, we build a system model without LOS paths and without strict synchronization of the transceiver clocks, which broadens the system application scenario and makes the model more valuable for practical applications.

[0053] The OTFS system is combined with the vehicle networking technology, and the OTFS modulation technology is used to complete the modulation of signals. Different from traditional spatial spectrum estimation methods, OTFS reduces the influence of the time-varying wireless channel caused by the high-speed movement of vehicles, and can also estimate the DOA in the environment of communication and perception integration. 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 preliminary 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 transmitting source vehicle is improved. Finally, the simulation results verify the superiority and reliability of the proposed method. Description of the Drawings

[0055] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the accompanying drawings required for the description of the embodiments or the prior art. Obviously, the accompanying drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other accompanying drawings can also be obtained based on these drawings.

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

[0057] Figure 2 It is a comparison diagram of the positioning performance of the EWOA algorithm and the GWO algorithm assisted by OTFS channel estimation when perceiving the target vehicle according to an embodiment of the present invention;

[0058] Figure 3 It is a comparison diagram of the positioning performance of the Chan-EWOA algorithm, the Chan algorithm, and the EWOA algorithm when perceiving the transmitting source vehicle according to an embodiment of the present invention;

[0059] Figure 4 It is a schematic diagram of the positioning performance of the Chan-EWOA algorithm under different numbers of RIS according to an embodiment of the present invention. Detailed implementation manners

[0060] It should be noted that, without conflict, the embodiments in the present invention and the features in the embodiments can be combined with each other.

[0061] In the description of the present invention, it should be understood that the orientation or positional relationship indicated by the terms "center", "longitudinal", "transverse", "up", "down", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc. is based on the orientation or positional relationship shown in the accompanying drawings, and is 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 should not be construed as a limitation to the present invention. In addition, the terms "first", "second", etc. are only used for descriptive purposes and cannot be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, the features defined with "first", "second", etc. may explicitly or implicitly include one or more of such features. In the description of the present invention, unless otherwise specified, the meaning of "a plurality" is two or more.

[0062] In the description of the present invention, it should be noted that unless otherwise clearly specified and defined, the terms "installation", "connection", and "coupling" 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 an indirect connection through an intermediate medium, and it can be the communication inside two components. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0063] Embodiment 1

[0064] A vehicle sensing method in a multi-RIS assisted OTFS system in a vehicle networking environment provided in this embodiment aims to construct virtual nodes based on the reflection characteristics of RISs in wireless communication, obtain the parameters required for vehicle sensing through OTFS channel estimation, and construct the objective function required for vehicle sensing according to geometric relationship projection and the optimization criterion of the objective function. Finally, the Chan-EWOA algorithm is proposed to achieve vehicle positioning and sensing without LOS paths and without node-to-node timing synchronization, and improve the positioning performance of the system in the vehicle networking scenario. The specific steps are as follows:

[0065] First, RISs with positions of are arranged on the RUSs to construct virtual nodes. As shown in Figure 1 , a RIS-assisted multipath channel model is established. A transmitting source vehicle S at position transmits a set of information symbols arranged on the time-delay Doppler information grid . After passing through the wireless channel characterized by , the signal received by the target vehicle G at position can be expressed as:

[0066] .

[0067] Where is the sampling time interval, is the sampling frequency interval, and respectively represent the quantization steps of time delay and Doppler. , respectively represent the complex gain coefficient, time delay and Doppler frequency shift related to the th path, and respectively represent the corresponding time-delay tap and Doppler frequency-shift tap. , is Gaussian white noise with a variance of , , and the Doppler tap is and the time delay tap is the indicated value of the path that satisfies . denotes the mod N operation. denotes the Dirac function.

[0068] Next, channel estimation is performed using the received signal and the embedded pilot, and the integer Doppler shift can be estimated. The integer Doppler shift is used to replace the actual Doppler shift in the reflected path to calculate the DOA (Direction of Arrival, DOA). Since the Doppler quantization step size is , there will be an error between the obtained integer Doppler shift and the true Doppler shift .

[0069] When the transmitting source vehicle is driving normally, the Doppler of the reflected path can be expressed as , where is the propagation speed of the signal in the medium, is the DOA of the th path, is the magnitude of the target vehicle's moving speed, is the center frequency of the signal received by the th RIS and can be obtained by the spectrum sensing function of the RIS. The target vehicle G can obtain the position of the RIS and the magnitude of the vehicle's driving speed through information interaction in the vehicle network. Therefore, the Doppler shift produced by this reflected path is , where is the center frequency of the transmitted signal. At this time, the DOA of the signal arriving at the target vehicle G is:

[0070] .

[0071] Then, according to the geometric relationship projection of the vehicle and the RIS position as shown in Figure 1 , the system of equations

[0072] can be obtained.

[0073] Among them, is the moving direction of the target vehicle to be solved.

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

[0075] Among them, , .

[0076] The matrix is expressed as:

[0077] ,

[0078] Wherein:

[0079] ,

[0080] .

[0081] ,

[0082] Wherein, .

[0083] According to the Rouché-Capelli theorem, the system of equations has a unique solution if and only if . Wherein represents the augmented matrix of the coefficient matrix . Each column of the coefficient matrix is linearly independent, so it can be directly shown that . The number of columns of the augmented matrix is . To make , then should be in the subspace of , so it can be obtained that , and the estimated value of the moving direction of the target vehicle can be obtained therefrom.

[0084] After obtaining the moving direction of the target vehicle, the position error vector can be obtained according to the geometric relationship projection system of equations. Wherein,

[0085] ,

[0086] Based on the norm minimum criterion of the position error vector, the objective function is constructed. Using the EWOA algorithm assisted by OTFS channel estimation, the estimated value of the target vehicle position can be found. The specific content of the algorithm is as follows:

[0087] Determine the maximum number of iterations , the number of whales , the dimension , the search space . Randomly generate in the search space initial positions, initialize the best position of the whales in the current iteration, the worse position and the crossover position generated by crossing the worst position and the worse position.

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

[0089] Randomly select whales to perform the migration search strategy and update their positions

[0090] .

[0091] When and , select the remaining whales to perform the rich encircling predation strategy and update their positions

[0092]

[0093] When and , select the remaining whales to perform the priority selection strategy and update their positions

[0094]

[0095] When , select the remaining whales to perform the comprehensive spiral update strategy. To improve the solution efficiency of the algorithm, a non-linear time-varying adaptive weight is added

[0096] ,

[0097] Let the algorithm adjust the control parameters required for the corresponding search strategy according to the non-linear adaptive weight in different iteration times. The comprehensive spiral update strategy is expressed as

[0098] ,

[0099] where , is a random number between 0 and 1.

[0100] Recalculate the hunger levels of all whales based on the updated positions , and update , generate a new crossover solution

[0101] 。

[0102] Update and enter the next iteration. Repeat the above process until and the finally generated is the estimated value of the target vehicle position.

[0103] After obtaining the position and motion direction of the target vehicle, it is also necessary to perform positioning perception on the transmitting source vehicle. Taking 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 to obtain the TDOA between different RISs and the path of the transmitting source vehicle, denoted as

[0104] ,

[0105] where is the estimated distance between the target vehicle and the th RIS, and the distance difference between different RISs and the path of the transmitting source vehicle is 。

[0106] Using the Chan positioning algorithm based on TDOA, the relational expression

[0107] ,

[0108] where can be solved using the quadratic formula, and the parameter can be expressed as

[0109] ,

[0110] respectively represent the x-axis coordinate difference and y-axis coordinate difference between the th RIS and the first RIS, 。The parameter is similar to the parameter , and only the corresponding x-axis coordinates and y-axis coordinates need to be interchanged. Obviously, the position estimate of the transmitting source vehicle obtained using the Chan positioning algorithm has errors, so it can only be regarded as a preliminary estimate of the position of the transmitting source vehicle.

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

[0112] ,

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

[0114] Determine the maximum number of iterations , the number of whales , the dimension , use the preliminary estimate obtained by the Chan positioning algorithm to narrow the search space and generate a new search space , randomly generate initial positions in the search space, and initialize the best position of the whales in the current iteration , the worse position and the crossover position generated by crossing the worst position and the worse position 。

[0115] Initialize , update the parameters

[0116] 。

[0117] Randomly select whales for the migration search strategy and update the positions

[0118] 。

[0119] When and , select the remaining whales for the rich encircling prey strategy and update the positions

[0120]

[0121] When and , select the remaining whales for the preferential selection strategy and update the positions

[0122]

[0123] When , select the remaining whales for the comprehensive spiral update strategy, and in this spiral update strategy, the non-linear time-varying adaptive weight is:

[0124] ;

[0125] Among them, is the non-linear time-varying adaptive weight, is the current number of iterations, is the maximum number of iterations,

[0126] In different numbers of iterations, the weight parameters of the spiral update strategy can be adjusted according to the non-linear adaptive weight; and the position is updated

[0127] ,

[0128] Recalculate the hunger levels of all whales according to the updated position , and update , generate a new crossover solution

[0129] .

[0130] Update , enter the next iteration. Repeat the above process until , and finally the generated is the estimated value of the emitter vehicle position.

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

[0132] Embodiment 2

[0133] This embodiment provides a vehicle networking system, including the vehicle perception method disclosed in Embodiment 1, for realizing the joint estimation of vehicle position and movement direction.

[0134] Embodiment 3

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

[0136] The above has described the embodiments of the present invention in detail with reference to the accompanying drawings, but the present invention is not limited to the described embodiments. For those skilled in the art, without departing from the principles and spirit of the present invention, various changes, modifications, substitutions, and variations to these embodiments including components still fall within the protection scope 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 estimated position of the target vehicle, construct the objective function of minimizing the distance difference error, apply the Chan-EWOA algorithm to optimize, and output the final position estimate of the source vehicle; The implementation of the Chan-EWOA optimization algorithm 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: Among them, γ is the nonlinear time-varying adaptive weight, t is the current iteration number, M max 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.

2. The vehicle perception method according to claim 1, characterized in that: In step 1, the information symbol is defined as: {x[k,l], k=0,...,N-1,l=0,...,M-1}, and the OTFS modulation technology arranges the information symbols in a delay-Doppler domain grid, and the expression is as follows: Where Δf is the sampling frequency interval, 1 / MΔf and 1 / NT represent the quantization step sizes of delay and Doppler, respectively, and N and M 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: Where c is the propagation speed of the signal in the medium, is the DOA of the ith path, v G is the speed of the target vehicle, ν i is the actual Doppler shift, is the center frequency of the signal received by the i-th RIS, f c 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: Among them, ω is the target vehicle movement direction to be solved, (x, y) 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: In the step 5-2, The update formula of the migration search strategy is: When p < 0.5 and A < 0.5, select the remaining j whales to perform a rich encirclement predation strategy and update their positions: When p < 0.5 and A ≥ 0.5, select the remaining j whales for priority selection strategy and update the position: P(x,y)=P(x,y)+A×(C×P rnd1 (x,y)-P rnd2 (x,y)); When p ≥ 0.5, select the remaining j whales to perform a comprehensive spiral update strategy and update their positions: in, YesN R The updated position of the whale, P rnd (x,y) is a random position in the search space, P brnd (x, y) is a random position near the optimal solution, p is the random probability of selecting different strategies, A is the action step parameter, expressed as A = 0.5 + 0.1tan(π(r-0.5)), r is a random number between 0 and 1, P(x, y) is the updated position of the remaining j whales, S B (x,y) is the best position of the whale in the current iteration, C is a random update coefficient between 0 and 2, and P rnd1 (x,y),P rnd2 (x,y),P rnd3 (x, y) are three randomly selected positions in the crossover solution, and l is the spiral update coefficient, expressed as 6. The vehicle perception method according to claim 1, 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. Among them, N RIS is the number of RIS used in the scene, (x S ,y S ) is the position of the source vehicle, r Ri,1S is the distance difference between different RIS and the source vehicle path.

7. 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.

8. 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.

9. A vehicle networking system, characterized in that: The vehicle perception method comprising any one of claims 1-8 is used to realize joint estimation of vehicle position and movement direction.

10. 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 8 is implemented.

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