Vehicle Positioning and Speed Measurement Method for 6G Millimeter-Wave Communication and Sensing Integrated System

Abstract

The present invention proposes a vehicle positioning and speed measurement method for a 6G millimeter-wave communication and sensing integrated system, which relates to the technical field of vehicle positioning and speed measurement. Aiming at the technical challenges faced by vehicle state perception, such as random channel fading and Doppler effect, firstly, the echo signals received by each roadside unit are processed to fully exploit the structural characteristics of channel models such as random channel fading and Doppler effect. The multi-carrier signal echo sensing technology is adopted to package Doppler and time delay into one parameter, and the influence of Doppler frequency shift on time delay estimation is eliminated by using multi-carriers, so as to calculate the vehicle distance, azimuth angle and channel state, and jointly estimate the distance, azimuth angle between the vehicle and each roadside unit and the radial speed of the vehicle relative to each roadside unit. Secondly, by fusing the information calculated by multiple roadside units, the actual running speed of the vehicle is obtained. The present invention has good positioning and speed measurement performance.

Description

Technical Field

[0001] The present invention relates to the technical field of vehicle positioning and speed measurement, and more specifically, to a vehicle positioning and speed measurement method for a 6G millimeter-wave communication and sensing integrated system. Background Art

[0002] Communication and sensing integration refers to a new information processing technology that simultaneously realizes the coordination of sensing and communication functions, and will be widely applied to future vehicle-to-everything (V2X) networks. The V2X network not only needs to achieve low-latency data transmission in a high-mobility environment, but also requires high-precision position and speed estimation.

[0003] 5G communication technology can utilize a large-scale multiple-input multiple-output (MIMO) antenna array and millimeter-wave frequency band to meet the positioning and communication functions of the V2X network. Among them, the MIMO antenna array can significantly improve the communication network capacity by deeply exploring spatial dimension resources, and the millimeter-wave frequency band has a rich available bandwidth. Moreover, due to its sparse channel, it only contains a few multipath components, which can not only achieve higher data transmission efficiency, but also significantly improve the range resolution, which is very beneficial to vehicle positioning. The road side unit (RSU) in the vehicle network analyzes the road conditions by receiving vehicle information, directs traffic, and sends warning information to relevant vehicles if an accident risk is detected, which helps to improve the driving safety of vehicles. The roadside unit forms a narrow beam by equipping a large-scale antenna array, can sense the environment and target state, and at the same time can also use the target position and speed information to help the roadside unit perform beamforming, establish a communication link, and complete real-time beam tracking in a mobile scenario, enhancing the reliability of data transmission. Therefore, it is necessary to simultaneously realize communication and sensing functions in the roadside unit.

[0004] In the prior art, a method for estimating the angle and distance formed by the roadside unit and the vehicle using the echo signal reflected by the vehicle is disclosed to achieve vehicle positioning, but the influence caused by vehicle movement is not considered. Moreover, vehicle movement will generate Doppler frequency shift, which will change the frequency of the received echo signal. The Doppler frequency shift will affect vehicle positioning and the positioning accuracy; in addition, some scholars estimate the radial velocity of the vehicle through a matched filter, but do not consider the influence of the complex channel and time-delay Doppler coupling on the matched filter, resulting in inaccurate matching results, and only the radial velocity of the vehicle running can be obtained, and the actual running speed cannot be known; some scholars also perform positioning through the signal of the roadside unit received by the vehicle, and then let the vehicle send its own position and speed information to the roadside unit. Although this method can achieve positioning and speed measurement, the cost is high and the real-time performance is not strong. Summary of the Invention

[0005] To solve the problem of how to achieve high-precision vehicle positioning and speed measurement in the context of communication-perception integrated vehicle networking applications, the present invention proposes a vehicle positioning and speed measurement method for a 6G millimeter-wave communication-perception integrated system, which simultaneously estimates the vehicle distance, azimuth angle, radial velocity, and channel gain, eliminates the influence of Doppler frequency shift during time delay estimation, and fuses the information of multiple roadside units to obtain the actual running speed of the vehicle, having better vehicle positioning and speed measurement performance.

[0006] To achieve the above technical effects, the technical solution of the present invention is as follows:

[0007] A vehicle positioning and speed measurement method for a 6G millimeter-wave communication-perception integrated system, the method being for a vehicle networking system integrating 6G millimeter-wave communication and perception, and comprising the following steps:

[0008] S1. Construct roadside units and set the number of transmitting antennas and receiving antennas for each roadside unit;

[0009] S2. Assume there are multiple roadside units. Each roadside unit sends an orthogonal frequency-division multiplexing communication signal to the moving vehicle through the transmitting antenna, receives the vehicle echo signal through the receiving antenna, and forms a channel model from each roadside unit to the vehicle and a vehicle echo signal model received by the roadside unit;

[0010] S3. Introduce the joint parameters of Doppler frequency and time delay. Based on the downlink channel model from each roadside unit to the vehicle and the vehicle echo signal model received by the roadside unit, obtain and solve the optimization model for each roadside unit, and simultaneously estimate and obtain the distance, azimuth angle between the vehicle and each roadside unit, and the radial velocity of the vehicle relative to each roadside unit;

[0011] S4. Use the radial velocity and azimuth angle obtained by multiple roadside units to obtain the actual moving speed of the vehicle.

[0012] This technical solution is for the integrated communication and sensing vehicle networking application. Aiming at the technical challenges such as random channel fading and Doppler effect faced by vehicle state sensing, first, channel parameter estimation is performed on the echo signals received by each roadside unit. Doppler and time delay are packed into one parameter. The influence of Doppler frequency shift during time delay estimation is eliminated by using multi-carrier. At the same time, the distance, azimuth angle between the vehicle and each roadside unit, and the radial velocity of the vehicle relative to each roadside unit are estimated and obtained. The structural characteristics of channel models such as random channel fading and Doppler effect are fully exploited. The multi-carrier signal echo sensing technology is used to calculate the vehicle distance, azimuth angle, and channel state. Second, the vehicle speed is jointly estimated. By fusing the information of multiple roadside units, the actual running speed of the vehicle is obtained. It has better positioning and speed measurement performance. Only the echo signals of the vehicle are used, and the information of multiple roadside units is fully utilized. There is no need for the vehicle to send additional signals to obtain the position and speed, with less expenditure and higher real-time performance.

[0013] Preferably, in step S1, each roadside unit constructed has N t transmitting antennas and N r receiving antennas. Each roadside unit and the vehicle are configured with uniform linear arrays, that is, the spacing distances between adjacent antennas in the transmitting antennas and receiving antennas are equal and arranged in a row. There are J roadside units in total, and the positions of the roadside units are known.

[0014] Here, when constructing the roadside unit, the antenna placement method is considered as a uniform linear array, which ensures the signal transmission and reception performance in the follow-up.

[0015] Preferably, when the vehicle is driving on the road, communication is carried out between the roadside unit and the vehicle. Each roadside unit sends an orthogonal frequency division multiplexing (OFDM) communication signal to the driving vehicle through the transmitting antenna and receives the vehicle echo signal OFDM through the receiving antenna. After each OFDM communication signal is transmitted to the vehicle, it is reflected by the vehicle body, and the roadside unit receives the vehicle echo signal. Considering the millimeter-wave multiple-input multiple-output (MIMO) channel with only the direct path, this process is a two-way "roadside unit - vehicle - roadside unit".

[0016] When each roadside unit sends an OFDM communication signal to the driving vehicle through the transmitting antenna, receives the vehicle echo signal OFDM through the receiving antenna, and during the process that each OFDM communication signal is reflected by the vehicle body after being transmitted to the vehicle, the signaling and cyclic prefix length of each OFDM communication signal exceed the maximum delay, and the two-way time delay (TOA), angle of departure (AOD), angle of arrival (AOA), and channel fading coefficient remain unchanged within an interval in the channel coherence time.

[0017] Preferably, for each roadside unit, let θ denote the azimuth angle formed by the vehicle and the roadside unit, and let τ and h denote the round-trip time delay of the received signal "roadside unit - vehicle - roadside unit" and the complex channel fading coefficient respectively. Let f d denote the Doppler frequency shift caused by the vehicle movement, and its relationship with the radial velocity of the vehicle is:

[0018]

[0019] where, f represents the carrier frequency, represents the radial velocity of the vehicle, α represents the angle between the vehicle movement direction and the roadside unit, and c represents the speed of light;

[0020] Assume that each roadside unit has N subcarriers. After fd, τ, θ, and h are given, the channel model of the nth subcarrier in the downlink, that is, the channel from each roadside unit to the vehicle, is:

[0021]

[0022] where, represents the antenna array gain, and a(θ) and b(θ) represent the receiving and transmitting steering vectors of the roadside unit array, and their expressions are:

[0023]

[0024]

[0025] where, λ n represents the wavelength of the nth subcarrier, and d A represents the spacing between array elements, which is set to be half a wavelength.

[0026] Preferably, for each roadside unit, the expression of the vehicle echo signal model received by the roadside unit is:

[0027]

[0028] where, p represents the transmit power, s[n] represents the OFDM signal transmitted on the nth subcarrier, and z[n] represents Gaussian white noise.

[0029] Preferably, let the joint parameter of the Doppler frequency and the time delay be q, that is, the Doppler frequency and the time delay are packed into a joint parameter q, and q = (f n - f d )τ. The optimization model for each roadside unit is expressed as:

[0030]

[0031]

[0032]

[0033] Among them, represents the estimation of each parameter, represents the radial velocity of the vehicle;

[0034] When solving the model, the complex channel coefficient h, the angle of arrival θ, and the delay-Doppler parameter q are solved by least-squares Newton iteration. The influence of Doppler frequency shift on time delay estimation is eliminated by using multi-carrier signals, and then the Doppler frequency is estimated, that is, for each carrier, Then use to solve the time delay estimation. For n represents the carrier number, The obtained result eliminates the Doppler frequency shift parameter, realizing the influence of multi-carrier signals eliminating Doppler frequency shift on time delay estimation. Finally, the distance, azimuth angle between the vehicle and each roadside unit, and the radial velocity of the vehicle relative to each roadside unit are obtained through continuous iteration.

[0035] Preferably, in step S4, the process of obtaining the actual motion speed of the vehicle from the radial velocity and azimuth angle obtained by using multiple roadside units is carried out in the cloud computing center.

[0036] Preferably, in the cloud computing center, the radial velocity and azimuth angle obtained by using two roadside units are used to obtain the actual motion speed of the vehicle. The process is as follows:

[0037] Let the two roadside units be the first roadside unit and the second roadside unit respectively, represents the radial velocity of the vehicle relative to the first roadside unit, represents the radial velocity of the vehicle relative to the second roadside unit, then:

[0038]

[0039]

[0040] α 2 -α 1 =θ 2 -θ 1

[0041] Finally, the expression for obtaining the actual motion speed of the vehicle is:

[0042]

[0043] Among them, α 1 represents the angle between the vehicle motion direction and the first roadside unit; α 2 represents the angle between the vehicle motion direction and the second roadside unit; θ1 Denote the azimuth angle formed by the vehicle and the first roadside unit; θ 2 Denote the azimuth angle formed by the vehicle and the second roadside unit, and v denote the actual moving speed of the vehicle.

[0044] A vehicle positioning and speed measurement system for a 6G millimeter wave communication and sensing integrated system, the system comprising:

[0045] A plurality of roadside units, each roadside unit is configured with a transmitting antenna and a receiving antenna, and each roadside unit sends an orthogonal frequency division multiplexing communication signal to the traveling vehicle through the transmitting antenna, and receives the vehicle echo signal through the receiving antenna;

[0046] A model construction module, configured to form a channel model from each roadside unit to the vehicle and a vehicle echo signal model received by the roadside unit;

[0047] A vehicle positioning module, configured to introduce the joint parameters of Doppler frequency and time delay, and based on the downlink channel model from each roadside unit to the vehicle and the vehicle echo signal model received by the roadside unit, obtain and solve the optimized model relative to each roadside unit, and simultaneously estimate and obtain the distance, azimuth angle between the vehicle and each roadside unit, and the radial speed of the vehicle relative to each roadside unit;

[0048] A vehicle speed measurement module, configured to obtain the actual moving speed of the vehicle by using the radial speeds and azimuth angles obtained by multiple roadside units.

[0049] Preferably, the vehicle speed measurement module is located in the cloud computing center, and in the cloud computing center, the vehicle speed measurement module obtains the actual moving speed of the vehicle by using the radial speeds and azimuth angles obtained by multiple roadside units.

[0050] Compared with the prior art, the beneficial effects of the technical solution of the present invention are:

[0051] The present invention proposes a vehicle positioning and speed measurement method for a 6G millimeter wave interawareness integrated system. In view of the technical challenges such as random channel fading and Doppler effect faced by vehicle state perception, the present invention firstly estimates the channel parameters of the echo signal received by each roadside unit, packages the Doppler and delay into one parameter, and uses multi-carrier to eliminate the influence of Doppler frequency shift during delay estimation. At the same time, the distance and azimuth between the vehicle and each roadside unit and the radial speed of the vehicle relative to each roadside unit are estimated and obtained. The structural characteristics of channel models such as random channel fading and Doppler effect are fully explored, and the vehicle distance, azimuth and channel state are calculated by using multi-carrier signal echo sensing technology. Secondly, the vehicle speed is jointly estimated, and the actual running speed of the vehicle is obtained by fusing the information of multiple roadside units. The method has better positioning and speed measurement performance, only uses the echo signal of the vehicle, and makes full use of the information of multiple roadside units. The vehicle does not need to send additional signals to obtain the position speed, so the cost is small and the real-time performance is high. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 A schematic diagram showing a flow chart of a vehicle positioning and speed measurement method for a 6G millimeter wave interawareness integrated system proposed in Embodiment 1 of the present invention;

[0053] Figure 2 A diagram showing an application scenario of the vehicle positioning and speed measurement method for a 6G millimeter wave interawareness integrated system proposed in Embodiment 1 of the present invention;

[0054] Figure 3 A schematic diagram showing a flow chart of solving the optimization model for each roadside unit proposed in Embodiment 1 of the present invention;

[0055] Figure 4 A graph showing the variation of the vehicle position estimation error with the signal-to-noise ratio when the vehicle is around two roadside units in the actual experimental simulation proposed in Example 2 of the present invention;

[0056] Figure 5 A graph showing the variation of the vehicle angle estimation error with the signal-to-noise ratio when the vehicle is around two roadside units in the actual experimental simulation proposed in Example 2 of the present invention;

[0057] Figure 6 A diagram showing the variation of the vehicle radial velocity estimation with the signal-to-noise ratio when the vehicle is around two roadside units in the actual experimental simulation proposed in Example 2 of the present invention;

[0058] Figure 7 A diagram showing the variation of the estimated actual running speed of a vehicle with the signal-to-noise ratio in the actual experimental simulation proposed in Example 2 of the present invention;

[0059] Figure 8It shows the structural diagram of the vehicle positioning and speed measurement system for the 6G millimeter-wave communication and sensing integrated system proposed in Embodiment 3 of the present invention. Detailed implementation manners

[0060] The drawings are only for illustrative purposes and should not be construed as limitations on this patent.

[0061] For better illustration of this embodiment, some parts of the drawings are omitted, enlarged or reduced, and do not represent the actual size.

[0062] For those skilled in the art, it is understandable that some well-known content descriptions in the drawings may be omitted.

[0063] The technical solutions of the present invention will be further described below with reference to the drawings and embodiments.

[0064] The description of the positional relationship in the drawings is only for illustrative purposes and should not be construed as limitations on this patent.

[0065] Embodiment 1

[0066] This embodiment proposes a vehicle positioning and speed measurement method for the 6G millimeter-wave communication and sensing integrated system. The flow schematic diagram of this method is shown in Figure 1 This method is applied to the vehicle networking system of 6G millimeter-wave communication and sensing integration, and includes the following steps:

[0067] S1. Construct roadside units and set the number of transmitting antennas and receiving antennas of each roadside unit.

[0068] In this embodiment, each constructed roadside unit has N t transmitting antennas and N r receiving antennas. Each roadside unit and the vehicle are configured with uniform linear arrays, that is, the spacing between adjacent antennas in the transmitting antennas and receiving antennas is equal and arranged in a row, ensuring the subsequent signal transmission and reception performance. There are J roadside units in total, and the positions of the roadside units are known.

[0069] As shown in Figure 2 , Figure 2 the label 1 in it represents the roadside unit. For convenience of illustration, Figure 2Two roadside units are shown. When a vehicle is traveling on the road, communication occurs between the roadside unit and the vehicle. So, the roadside unit sends a communication signal to the vehicle to convey information, and the emitted communication signal will also bounce back from the vehicle body. Therefore, the roadside unit can receive the echo signal of the vehicle. Based on this echo signal, the roadside unit needs to locate and measure the speed of the vehicle. At this time, only the radial speed can be located and measured. Subsequently, if the information obtained by multiple roadside units is transmitted to the cloud computing center, the actual running speed of the vehicle can be measured. This process is similar to radar. Radar emits a signal and then locates based on the signal bounced back from the object. However, the signal sent by radar is not a communication signal and does not have the ability to convey information. The signal in this application is a communication signal.

[0070] Regarding the above analysis, specifically as follows: Each roadside unit sends an orthogonal frequency division multiplexing (OFDM) communication signal to the moving vehicle through a transmitting antenna and receives the vehicle echo signal OFDM through a receiving antenna. After each OFDM communication signal is transmitted to the vehicle, it bounces back from the vehicle body, and the roadside unit receives the vehicle echo signal. In this embodiment, a millimeter-wave multiple-input multiple-output (MIMO) channel with only a direct path is considered. This channel is used to transmit the signals mentioned in this embodiment. Only having a direct path means the signal path is "roadside unit - vehicle - roadside unit". Millimeter-wave represents the frequency band, and MIMO represents multiple input and multiple output, indicating that the number of antennas of the roadside unit is not 1.

[0071] When each roadside unit sends an OFDM communication signal to the moving vehicle through a transmitting antenna, receives the vehicle echo signal OFDM through a receiving antenna, and during the process that each OFDM communication signal bounces back from the vehicle body after being transmitted to the vehicle, each OFDM communication signal is divided into several parts, including signaling, cyclic prefix, data, etc. The lengths of the signaling and cyclic prefix of each OFDM communication signal exceed the maximum delay. The two-way time of arrival (TOA), angle of departure (AOD), angle of arrival (AOA), and channel fading coefficient remain unchanged within an interval in the channel coherence time. That is to say, when we study it, its parameters do not change. Otherwise, if the parameters themselves are changing during our estimation, it is impossible to estimate. Due to the small time interval, the speed of the vehicle does not change.

[0072] S2. Assume there are multiple roadside units. Each roadside unit sends an OFDM communication signal to the moving vehicle through a transmitting antenna, receives the vehicle echo signal through a receiving antenna, and forms a channel model from each roadside unit to the vehicle and a vehicle echo signal model received by the roadside unit.

[0073] For each roadside unit, let θ represent the azimuth angle formed by the vehicle and the roadside unit (such as Figure 2As shown, if there are two roadside units, the azimuth angles formed by the vehicle and the roadside units are θ 1 and θ 2 ), τ and h respectively represent the round-trip delay of the received signal "roadside unit - vehicle - roadside unit" and the complex channel fading coefficient, and f d represents the Doppler frequency shift caused by vehicle movement, and its relationship with the radial velocity of the vehicle is:

[0074]

[0075] Among them, f represents the carrier frequency, represents the radial velocity of the vehicle, α represents the angle between the vehicle movement direction and the roadside unit, and c represents the speed of light;

[0076] Assume that each roadside unit has N subcarriers. After given fd, τ, θ, and h, the channel model of the nth subcarrier in the downlink, that is, the channel from each roadside unit to the vehicle, is:

[0077]

[0078] Among them, represents the antenna array gain, and a(θ), b(θ) represent the receiving and transmitting steering vectors of the roadside unit array, and the expressions are respectively:

[0079]

[0080]

[0081] Among them, λ n represents the wavelength of the nth subcarrier, and d A represents the interval between array elements, which is set to be half a wavelength.

[0082] For each roadside unit, the expression of the vehicle echo signal model received by the roadside unit is:

[0083]

[0084] Among them, p represents the transmission power, s[n] represents the OFDM signal transmitted on the nth subcarrier, and z[n] represents Gaussian white noise.

[0085] Here, the downlink refers to the channel from the roadside unit to the vehicle. The roadside unit is similar to a base station and sends information to the vehicle. However, this application is not satisfied with the roadside unit sending signals and the vehicle receiving signals. It also uses the echo of the signal sent by the roadside unit (that is, the signal is sent to the vehicle and then bounced back to the roadside unit by the vehicle body) to perform positioning and speed measurement.

[0086] S3. Introduce the joint parameter of Doppler frequency and time delay. Based on the downlink channel model from each roadside unit to the vehicle and the vehicle echo signal model received by the roadside unit, obtain the optimization model for each roadside unit and solve it. Meanwhile, estimate and obtain the distance, azimuth angle between the vehicle and each roadside unit, and the radial velocity of the vehicle relative to each roadside unit.

[0087] Let the joint parameter of Doppler frequency and time delay be q, that is, pack the Doppler frequency and time delay into a joint parameter q, q = (f n -f d )τ. The optimization model for each roadside unit is expressed as:

[0088]

[0089]

[0090]

[0091] Among them, represents the estimation of each parameter, represents the radial velocity of the vehicle; use the multi-carrier signal to eliminate the influence of Doppler frequency shift on time delay estimation, and then estimate the Doppler frequency, that is, calculate for each carrier, and then solve the time delay estimation. For n represents the carrier number, The obtained result eliminates the Doppler frequency shift parameter, realizing that the multi-carrier signal eliminates the influence of Doppler frequency shift on time delay estimation. Finally, continuously iterate to obtain the distance, azimuth angle between the vehicle and each roadside unit, and the radial velocity of the vehicle relative to each roadside unit. When solving the model, solve the complex channel coefficient h, the angle of arrival θ, and the time delay-Doppler parameter q through least squares Newton iteration. Starting from an initial point, use the proposed algorithm to iterate until convergence. The solution flow chart is shown in Figure 3 , where RSU represents the roadside unit, which can be specifically divided into the following three parts:

[0092] (1) Estimate θ, h, q simultaneously:

[0093]

[0094]

[0095]

[0096] Among them:

[0097]

[0098]

[0099] n r = [0, 1, 2,......, N r - 1] T

[0100] n t = [0, 1, 2,......, N t - 1] T

[0101]

[0102]

[0103] Iteratively solve until convergence to obtain θ [k] , h [k] , and then calculate for each sub - carrier

[0104]

[0105] (2) Estimate τ:

[0106]

[0107] (3) Estimate f d :

[0108]

[0109] Continuously perform iterative solution to obtain the distance, azimuth angle, and radial velocity between the vehicle and the roadside unit. This embodiment is divided into two parts. One is that the roadside unit processes the echo signal, and the goal is to estimate the channel parameters for the echo signal received by each roadside unit to obtain the distance, azimuth angle, and radial velocity between the vehicle and the roadside unit; the other is that the cloud computing center processes the information fed back by the roadside unit, and the goal is to obtain the actual motion speed of the vehicle. For the second part, specifically, step S4 is adopted:

[0110] S4. Use the radial velocity and azimuth angle obtained by multiple roadside units to obtain the actual motion speed of the vehicle.

[0111] The process of using the radial velocity and azimuth angle obtained by multiple roadside units to obtain the actual motion speed of the vehicle is carried out in the cloud computing center. Suppose in the cloud computing center, the radial velocity and azimuth angle obtained by two roadside units are used to obtain the actual motion speed of the vehicle, and the process is as follows:

[0112] Suppose the two roadside units are the first roadside unit and the second roadside unit, that is, as Figure 2 shown, represents the radial velocity of the vehicle relative to the first roadside unit, represents the radial velocity of the vehicle relative to the second roadside unit, and the label 2 represents the cloud computing center, then:

[0113]

[0114]

[0115] α 2 -α 1 = θ 2 -θ 1

[0116] Finally, the expression for obtaining the actual motion speed of the vehicle is:

[0117]

[0118] where α 1 represents the angle between the vehicle's motion direction and the first roadside unit; α 2 represents the angle between the vehicle's motion direction and the second roadside unit; θ 1 represents the azimuth angle formed by the vehicle and the first roadside unit; θ 2 represents the azimuth angle formed by the vehicle and the second roadside unit, and v represents the actual motion speed of the vehicle.

[0119] Overall, it is aimed at technical challenges such as random channel fading and Doppler effect in vehicle state perception. First, channel parameter estimation is performed on the echo signals received by each roadside unit. Doppler and time delay are packed into one parameter. The influence of Doppler frequency shift during time delay estimation is eliminated using multi-carrier, and the problem of poor performance of the matched filter affected by channel, time delay-Doppler coupling is solved. At the same time, the distance, azimuth angle between the vehicle and each roadside unit, and the radial velocity of the vehicle relative to each roadside unit are estimated and obtained, fully exploiting the structural characteristics of channel models such as random channel fading and Doppler effect. The multi-carrier signal echo sensing technology is used to calculate the vehicle distance, azimuth angle, and channel state. Second, the vehicle speed is jointly estimated. By fusing the information of multiple roadside units, the actual operating speed of the vehicle is obtained, which has better positioning and speed measurement performance. Only the echo signals of the vehicle are used, fully utilizing the information of multiple roadside units, without the need for the vehicle to send additional signals to obtain the position and speed, with less expenditure and higher real-time performance.

[0120] Embodiment 2

[0121] In this embodiment, a vehicle positioning and speed measurement method for a 6G millimeter-wave communication and sensing integrated system proposed in Embodiment 1 is used. To verify the robustness of this method, the following experimental simulations are carried out: Suppose there are 2 roadside units, each roadside unit has 32 transmitting antennas, 32 receiving antennas, the carrier frequency is 60 GHz, and it is divided into 10 subcarriers, that is

[0122] Suppose the vehicle is located at The actual moving speed v of the vehicle is 20 m / s, the first roadside unit is located at the origin, and the second roadside unit is located at d 1 = 100 m, θ 1 = pi / 4, θ 2 = pi / 2, h 1 = 0.8 + 0.9j, h 2 = 0.6 + 0.8j. In practice, any positioning and speed measurement method will be affected by noise. The signal-to-noise ratio represents the ratio of the signal to the noise. The smaller the signal-to-noise ratio, the greater the influence of the noise. Figure 4 represents the change diagram of the vehicle position estimation error with the signal-to-noise ratio when surrounding the two roadside units, Figure 5 represents the change diagram of the vehicle angle estimation error with the signal-to-noise ratio when surrounding the two roadside units; Figure 6 represents the change diagram of the vehicle radial velocity estimation with the signal-to-noise ratio when surrounding the two roadside units, Figures 4 to 6 Among them, RSU1 represents the first roadside unit, and RSU2 represents the second roadside unit, Figure 7 represents the change diagram of the estimated actual running speed of the vehicle obtained in the cloud computing center with the signal-to-noise ratio, Figures 4 to 7 The abscissa SNR of Figures 4 to 7 is the signal-to-noise ratio. The result diagram of Figures 4 to 7 gives the distance, angle, radial velocity, and actual speed estimation performance of the method proposed in the embodiment of the present invention under different signal-to-noise ratio conditions. It can be seen that as the signal-to-noise ratio increases, the estimation errors of the distance, angle, radial velocity, and actual speed estimation gradually decrease, that is, the performance improves accordingly. For every 20 dB increase in the signal-to-noise ratio, the error decreases by about one order of magnitude, indicating the effectiveness of the method proposed in this patent. In the case of a 0 dB signal-to-noise ratio, this method can achieve a positioning error at the centimeter level, showing good performance, and also indicating that the problem of Doppler fading has been solved under different signal-to-noises.

[0123] Embodiment 3

[0124] As Figure 8 shown, this embodiment proposes a vehicle positioning and speed measurement system for a 6G millimeter-wave communication and sensing integrated system. Refer to Figure 8 The system includes:

[0125] A number of roadside units, each roadside unit is configured with a transmitting antenna and a receiving antenna. Each roadside unit sends an orthogonal frequency division multiplexing communication signal to a moving vehicle through the transmitting antenna, and receives the vehicle echo signal through the receiving antenna; in this embodiment, Figure 8 the two roadside units shown, that is, the first roadside unit and the first roadside unit are taken as representatives.

[0126] A model construction module, used to form a channel model from each roadside unit to the vehicle and a vehicle echo signal model received by the roadside unit;

[0127] A vehicle positioning module, used to introduce the joint parameters of Doppler frequency and time delay, based on the downlink channel model from each roadside unit to the vehicle and the vehicle echo signal model received by the roadside unit, obtain the optimized model relative to each roadside unit and solve it, and at the same time estimate and obtain the distance, azimuth angle between the vehicle and each roadside unit, and the radial velocity of the vehicle relative to each roadside unit;

[0128] A vehicle speed measurement module, used to obtain the actual movement speed of the vehicle by using the radial speeds and azimuth angles obtained by multiple roadside units.

[0129] In this embodiment, the vehicle speed measurement module is located in the cloud computing center. In the cloud computing center, the vehicle speed measurement module obtains the actual movement speed of the vehicle by using the radial speeds and azimuth angles obtained by multiple roadside units.

[0130] Obviously, the above embodiments of the present invention are only examples for clearly explaining the present invention, and are not limitations on the implementation manners of the present invention. For those of ordinary skill in the art, other different forms of changes or modifications can be made based on the above description. It is not necessary and impossible to enumerate all the implementation manners here. Any modifications, equivalent replacements, and improvements made within the spirit and principle of the present invention shall be included in the protection scope of the claims of the present invention.

Claims

1. A vehicle positioning and speed measurement method for a 6G millimeter-wave communication and sensing integrated system, characterized in that, the method is for an Internet of Vehicles system integrated with 6G millimeter-wave communication and sensing, and includes the following steps: S1. Construct roadside units and set the number of transmitting antennas and receiving antennas for each roadside unit; S2. Suppose there are multiple roadside units. Each roadside unit sends an orthogonal frequency division multiplexing (OFDM) communication signal to the moving vehicle through a transmitting antenna, receives the vehicle echo signal through a receiving antenna, and forms a channel model from each roadside unit to the vehicle and a vehicle echo signal model received by the roadside unit; When the vehicle is driving on the road, communication occurs between the roadside unit and the vehicle. Each roadside unit sends an OFDM communication signal to the moving vehicle through a transmitting antenna, and receives the vehicle echo signal OFDM through a receiving antenna. After each OFDM communication signal is transmitted to the vehicle, it bounces off the vehicle body, and the roadside unit receives the vehicle echo signal. Considering a millimeter-wave multiple-input multiple-output (MIMO) channel with only a direct path, this process is a "roadside unit - vehicle - roadside unit" two-way process; In the process of each roadside unit sending an OFDM communication signal to the moving vehicle through a transmitting antenna, receiving the vehicle echo signal OFDM through a receiving antenna, and after each OFDM communication signal is transmitted to the vehicle and bounces off the vehicle body, the signaling and cyclic prefix length of each OFDM communication signal exceed the maximum delay, and the two-way time delay (TOA), angle of departure (AOD), angle of arrival (AOA), and channel fading coefficient remain unchanged within an interval in the channel coherence time; For each roadside unit, let denote the azimuth angle formed by the vehicle and the roadside unit, , respectively denote the round-trip delay of the received signal "roadside unit - vehicle - roadside unit" and the complex channel fading coefficient, denote the Doppler frequency shift caused by vehicle movement, and its relationship with the radial velocity of the vehicle is: Among them, , represents the carrier frequency, represents the radial velocity of the vehicle, represents the angle between the vehicle's moving direction and the roadside unit, represents the speed of light; Assume that each roadside unit has N subcarriers. Given f d , After , the channel model of the n-th subcarrier in the downlink, i.e., from each roadside unit to the vehicle, is: Among them, represents the antenna array gain, represents the receiving and transmitting steering vectors of the roadside unit array, and the expressions are respectively: Among them, represents the wavelength of the th sub-carrier, and represents the number of transmitting antennas, and represents the number of receiving antennas. S3. Introduce the joint parameters of Doppler frequency and time delay. Based on the downlink channel model from each roadside unit to the vehicle and the vehicle echo signal model received by the roadside unit, obtain and solve the optimization model for each roadside unit, and simultaneously estimate and obtain the distance, azimuth angle between the vehicle and each roadside unit, and the radial velocity of the vehicle relative to each roadside unit; S4. Use the radial velocities and azimuth angles obtained by multiple roadside units to obtain the actual moving speed of the vehicle.

2. The vehicle positioning and speed measurement method for a 6G millimeter-wave communication and sensing integrated system according to claim 1, characterized in that, In step S1, each roadside unit constructed has transmitting antennas and receiving antennas. Each roadside unit and vehicle are configured with a uniform linear array, that is, the spacing between adjacent antennas in the transmitting antennas and receiving antennas is equal and arranged in a row. There are J roadside units in total, and the positions of the roadside units are known.

3. The vehicle positioning and speed measurement method for a 6G millimeter-wave communication and sensing integrated system according to claim 1, characterized in that, For each roadside unit, the expression of the vehicle echo signal model received by the roadside unit is: in, Indicates the transmit power, Indicates OFDM signal transmitted by subcarriers, represents Gaussian white noise.

4. The vehicle positioning and speed measurement method for a 6G millimeter-wave communication and sensing integrated system according to claim 3, characterized in that, Let the joint parameter introducing the Doppler frequency and time delay be , that is, the Doppler frequency and time delay are packed into a joint parameter q . , and the optimization model for each roadside unit is expressed as: Among them, represents the estimation of each parameter, represents the radial velocity of the vehicle; When solving the model, the complex channel coefficients are solved by least-squares Newton iteration , angle of arrival , time-delay Doppler parameters , the influence of Doppler frequency shift on time-delay estimation is eliminated by using multi-carrier signals, and then the Doppler frequency is estimated, that is, for each carrier, is obtained, and then is used to solve the time-delay estimation. For , n represents the carrier number, , the Doppler frequency shift parameter is eliminated in the obtained result, and the influence of Doppler frequency shift of multi-carrier signals on time-delay estimation is eliminated. Finally, the distance, azimuth angle between the vehicle and each roadside unit, and the radial velocity of the vehicle relative to each roadside unit are obtained by continuous iteration.

5. The vehicle positioning and speed measurement method for a 6G millimeter-wave communication and sensing integrated system according to claim 4, characterized in that, In step S4, the process of using the radial velocities and azimuth angles obtained by multiple roadside units to obtain the actual moving speed of the vehicle is carried out in the cloud computing center.

6. The vehicle positioning and speed measurement method for a 6G millimeter-wave communication and sensing integrated system according to claim 5, characterized in that, It is assumed that in a cloud computing center, the actual moving speed of a vehicle is obtained by using the radial speed and azimuth angle obtained by two roadside units. The process is as follows: Let the two roadside units be the first roadside unit and the second roadside unit respectively, representing the radial velocity of the vehicle relative to the first roadside unit, representing the radial velocity of the vehicle relative to the second roadside unit, then: Finally, the expression for obtaining the actual moving speed of the vehicle is: Among them, represents the angle between the vehicle movement direction and the first roadside unit; represents the angle between the vehicle movement direction and the second roadside unit; represents the azimuth angle formed by the vehicle and the first roadside unit; represents the azimuth angle formed by the vehicle and the second roadside unit, represents the actual movement speed of the vehicle.

7. A vehicle positioning and speed measurement system for a 6G millimeter-wave communication and sensing integrated system, characterized in that the system is used to implement the vehicle positioning and speed measurement method for the 6G millimeter-wave communication and sensing integrated system described in claim 1, and the system includes: a plurality of roadside units, each roadside unit is configured with a transmitting antenna and a receiving antenna, and each roadside unit sends an orthogonal frequency division multiplexing communication signal to the moving vehicle through the transmitting antenna and receives the vehicle echo signal through the receiving antenna; a model construction module, which is used to form a channel model from each roadside unit to the vehicle and a vehicle echo signal model received by the roadside unit; a vehicle positioning module, which is used to introduce the joint parameters of Doppler frequency and time delay, and based on the downlink channel model from each roadside unit to the vehicle and the vehicle echo signal model received by the roadside unit, obtain and solve the optimized model relative to each roadside unit, and at the same time estimate and obtain the distance, azimuth angle between the vehicle and each roadside unit, and the radial speed of the vehicle relative to each roadside unit; a vehicle speed measurement module, which is used to obtain the actual moving speed of the vehicle by using the radial speed and azimuth angle obtained by multiple roadside units.

8. The vehicle positioning and speed measurement system for the 6G millimeter-wave communication and sensing integrated system according to claim 7, characterized in that the vehicle speed measurement module is located in the cloud computing center, and in the cloud computing center, the vehicle speed measurement module uses the radial speed and azimuth angle obtained by multiple roadside units to obtain the actual moving speed of the vehicle.

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