Angle information enhanced orthogonal time-frequency-space signal delay Doppler estimation method and application

Abstract

The invention discloses an angle information enhanced orthogonal time-frequency-space signal time delay Doppler estimation method and application, and the method comprises the following steps: building a multi-input multi-output orthogonal time-frequency-space self-transmitting and self-receiving mode perception test simulation platform, the platform comprises an OTFS modulation module, a pulse shaping module, a precoding module, a wireless channel module, a receiving module, an OTFS demodulation module and a sensing estimation module. According to the method, a low-complexity time delay-Doppler estimation framework is provided, firstly, the complexity is reduced on the premise of not losing performance loss by adopting a time delay-Doppler decoupling and angle information enhancement method, and then off-network fractional time delay-Doppler index values are searched; in addition, according to the two-dimensional grid segmentation method provided by the invention, an optimized segmentation coefficient is adopted according to a target time delay-Doppler distribution rule, the number of iterations is reduced, the accuracy is higher, and a fractional time delay-Doppler index value can be positioned more accurately.

Description

Technical Field

[0001] The present invention relates to the field of wireless communication technology, and in particular to an angle information enhanced orthogonal time-frequency-space signal delay Doppler estimation method and application. Background Art

[0002] Orthogonal Time Frequency Space (OTFS) is a new modulation technology for wireless communications. Specifically, OTFS can effectively resist multipath interference and frequency offset by orthogonalizing the signal in time and frequency. Its basic principle is to map information symbols to a two-dimensional time-frequency grid and achieve better performance by modulating in time and frequency at the same time. OTFS performs particularly well in communications in high-speed mobile environments (such as high-speed trains, cars, etc.), and can improve signal reliability and transmission efficiency. This technology has broad application prospects in 5G and future communication systems.

[0003] However, the existing technology is usually based on MLE OTFS delay-Doppler estimation, but the complexity is high, especially in multi-dimensional joint estimation, it is difficult to meet the real-time requirements. At the same time, the linear minimum mean square error (LMMSE) method is used to decouple angle estimation and delay-Doppler estimation, but it is still limited by the grid resolution problem, and fractional delay-Doppler estimation is performed based on the generalized likelihood ratio detection method, but the computational complexity is high. Therefore, the existing delay-Doppler estimation method has low accuracy when processing fractional delay-Doppler, and the computational complexity is high, which makes it difficult to meet the requirements of high real-time scenarios. To solve the above problems, we propose an orthogonal time-frequency-space signal delay-Doppler estimation method and application with angle information enhancement. Summary of the invention

[0004] In view of the deficiencies in the prior art, the present invention provides an angle information enhanced orthogonal time-frequency-space signal delay-Doppler estimation method and application to solve the problems raised in the background technology.

[0005] The above technical objectives of the present invention are achieved through the following technical solutions:

[0006] A method for estimating the delay Doppler of orthogonal time-frequency-space signals with enhanced angle information comprises the following steps:

[0007] S1. Build a multiple-input multiple-output orthogonal time-frequency-space (MIMO-OTFS) self-transmitting and self-receiving mode perception test simulation platform, which includes an OTFS modulation module, a pulse shaping module, a precoding module, a wireless channel module, a receiving module, an OTFS demodulation module and a perception estimation module;

[0008] S2. Generate an OTFS transmit signal based on the MIMO-OTFS simulation platform, and perform reception processing to obtain a signal to be estimated;

[0009] S3. Decoupling the received signal to be estimated by delay-Doppler information, respectively extracting the angle-delay perception information component and the angle-Doppler perception information component;

[0010] S4. Enhance the estimation of delay and Doppler parameters by angle information to obtain integer delay and integer Doppler;

[0011] S5. Use a two-dimensional grid partitioning search method to estimate the fractional delay and fractional Doppler of the obtained integer delay and Doppler to obtain a complete delay-Doppler estimation value.

[0012] Preferably, the OTFS modulation module is used for:

[0013] A1. Modulate the input bit stream and map it to multiple resource blocks in the delay-Doppler domain to obtain modulated data on the delay and Doppler grid;

[0014] A2. Convert the delay-Doppler domain signal into a time-frequency domain signal by inverse symplectic finite Fourier transform (ISFFT);

[0015] A3. The time-frequency domain signal is converted into a baseband time-domain signal using Heisenberg transform, and the time-domain signal is mapped to a multi-antenna system through precoding to form a MIMO-OTFS signal.

[0016] Preferably, the wireless channel module establishes a sensing channel based on a tapped delay line model, wherein the sensing channel includes a line-of-sight (LoS) path, and the line-of-sight path is associated with parameters such as target distance and speed to generate time delay and Doppler information of the target.

[0017] 4. The method for estimating the delay-Doppler of orthogonal time-frequency-space signals with enhanced angle information according to claim 1, wherein the receiving module processes the received OTFS signal at the receiving end by the following steps:

[0018] B1. Convert the received signal from the time domain into a time-frequency domain signal by Wigner transform;

[0019] B2. Convert the received signal from the time-frequency domain to a DD domain signal by using a symplectic finite Fourier transform (SFFT);

[0020] B3. Extract and retain the DD domain channel state information, remove the data symbol part, and obtain the angle-delay and angle-Doppler perception information.

[0021] Preferably, the delay-Doppler information decoupling process comprises:

[0022] C1. Extract the angle-delay perception information component in the DD domain signal;

[0023] C2. Extract the angle-Doppler sensing information component in the DD domain signal to decouple the angle information from the delay and Doppler information;

[0024] C3. Enhance the estimation of delay and Doppler parameters through known angle information, reduce the search dimension and improve the estimation accuracy.

[0025] Preferably, the estimation process of the time delay and Doppler parameters is divided into two stages:

[0026] In the first stage, integer delay and integer Doppler estimation are assisted by angle information to simplify the search dimension, simplifying the delay and Doppler estimation into a one-dimensional search problem and reducing the computational complexity.

[0027] In the second stage, a two-dimensional grid partitioning search method is used to accurately estimate the fractional delay and fractional Doppler of the integer delay and integer Doppler obtained in the first stage, and a complete delay-Doppler estimation value is obtained.

[0028] Preferably, the two-dimensional grid segmentation search method is implemented by the following steps:

[0029] D1. Set the segmentation step size of the two-dimensional grid and search for fractional delay and fractional Doppler based on the segmented grid;

[0030] D2. Further search for fractional values ​​based on the position of the integer index on the delay-Doppler plane by optimizing the grid segmentation step size;

[0031] D3. Use the limit center theorem to divide the search area and select the area with more concentrated distribution to search for fractional delay and Doppler to improve the estimation accuracy.

[0032] Preferably, the angle information is used as a priori condition to assist in the estimation of delay and Doppler parameters by introducing the angle degree of freedom of the target, thereby further enhancing the accuracy of delay-Doppler estimation and reducing computational complexity.

[0033] The present invention also provides an application of the angle information enhanced orthogonal time-frequency-space signal delay Doppler estimation method, which is suitable for wireless communication systems in high-speed mobile scenarios, including autonomous driving, vehicle networking, radar perception, and target perception and parameter estimation in 5G / 6G communication systems.

[0034] In summary, the present invention mainly has the following beneficial effects:

[0035] 1. The solution of the present invention proposes a low-complexity delay-Doppler estimation framework. First, the delay-Doppler decoupling and angle information enhancement methods are used to reduce the complexity without losing performance, and then the search for the off-grid fractional delay-Doppler index value is considered.

[0036] 2. The scheme of the present invention proposes an angle-enhanced delay-Doppler estimation method, which introduces the known angle as a new degree of freedom information, improves the integer delay-Doppler estimation performance, provides a priori boundary conditions for one-dimensional delay or Doppler estimation, improves the integer estimation accuracy, and reduces the time overhead of estimation.

[0037] 3. The present invention proposes a two-dimensional grid segmentation method and designs an optimized segmentation coefficient according to the target delay-Doppler distribution law. It effectively searches for fractional delay-Doppler, has fewer iterations, higher accuracy, and can more accurately locate the fractional delay-Doppler index value. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 It is the MIMO-OTFS sensing signal processing flow chart;

[0039] Figure 2 This is a schematic diagram of one-dimensional estimation assisted by angle information;

[0040] Figure 3 It is a partition diagram of the fractional index distribution area;

[0041] Figure 4 It is a two-dimensional grid segmentation search algorithm diagram;

[0042] Figure 5 is a diagram of the delay-Doppler estimation algorithm with enhanced angle information;

[0043] Figure 6 This is a comparison chart of RMSE of distance (delay) estimation under different signal-to-noise ratios;

[0044] Figure 7 This is a comparison chart of RMSE of velocity (Doppler) estimation under different signal-to-noise ratios. DETAILED DESCRIPTION

[0045] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution of the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings of the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all the embodiments. Based on the described embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work belong to the scope of protection of the present invention.

[0046] The following examples are used to illustrate the present invention, but they cannot be used to limit the scope of protection of the present invention. The conditions in the examples can be further adjusted according to specific conditions. Simple improvements to the method of the present invention under the premise of the concept of the present invention belong to the scope of protection claimed in the present invention.

[0047] refer to Figure 1-Figure 7 , a method for estimating the delay-Doppler of orthogonal time-frequency-space signals with enhanced angle information, comprising the following steps:

[0048] S1. Build a multiple-input multiple-output orthogonal time-frequency-space (MIMO-OTFS) self-transmitting and self-receiving mode perception test simulation platform, which includes an OTFS modulation module, a pulse shaping module, a precoding module, a wireless channel module, a receiving module, an OTFS demodulation module and a perception estimation module;

[0049] S2. Generate an OTFS transmit signal based on the MIMO-OTFS simulation platform, and perform reception processing to obtain a signal to be estimated;

[0050] S3. Decoupling the received signal to be estimated by delay-Doppler information, respectively extracting the angle-delay perception information component and the angle-Doppler perception information component;

[0051] S4. Enhance the estimation of delay and Doppler parameters by angle information to obtain integer delay and integer Doppler;

[0052] S5. Use a two-dimensional grid partitioning search method to estimate the fractional delay and fractional Doppler of the obtained integer delay and Doppler to obtain a complete delay-Doppler estimation value.

[0053] The OTFS modulation module is used to:

[0054] A1. Modulate the input bit stream and map it to multiple resource blocks in the delay-Doppler domain to obtain modulated data on the delay and Doppler grid;

[0055] A2. Convert the delay-Doppler domain signal into a time-frequency domain signal by inverse symplectic finite Fourier transform (ISFFT);

[0056] A3. The time-frequency domain signal is converted into a baseband time-domain signal using Heisenberg transform, and the time-domain signal is mapped to a multi-antenna system through precoding to form a MIMO-OTFS signal.

[0057] The wireless channel module establishes a sensing channel based on a tapped delay line model, wherein the sensing channel includes a line-of-sight (LoS) path, and the line-of-sight path is associated with parameters such as target distance and speed to generate time delay and Doppler information of the target.

[0058] The receiving module processes the received OTFS signal at the receiving end through the following steps:

[0059] B1. Convert the received signal from the time domain into a time-frequency domain signal by Wigner transform;

[0060] B2. Convert the received signal from the time-frequency domain to a DD domain signal by using a symplectic finite Fourier transform (SFFT);

[0061] B3. Extract and retain the DD domain channel state information, remove the data symbol part, and obtain the angle-delay and angle-Doppler perception information.

[0062] The delay-Doppler information decoupling process comprises:

[0063] C1. Extract the angle-delay perception information component in the DD domain signal;

[0064] C2. Extract the angle-Doppler sensing information component in the DD domain signal to decouple the angle information from the delay and Doppler information;

[0065] C3. Enhance the estimation of delay and Doppler parameters through known angle information, reduce the search dimension and improve the estimation accuracy.

[0066] The estimation process of the time delay and Doppler parameters is divided into two stages:

[0067] In the first stage, integer delay and integer Doppler estimation are assisted by angle information to simplify the search dimension, simplifying the delay and Doppler estimation into a one-dimensional search problem and reducing the computational complexity.

[0068] In the second stage, a two-dimensional grid partitioning search method is used to accurately estimate the fractional delay and fractional Doppler of the integer delay and integer Doppler obtained in the first stage, and a complete delay-Doppler estimation value is obtained.

[0069] The two-dimensional grid segmentation search method is implemented by the following steps:

[0070] D1. Set the segmentation step size of the two-dimensional grid and search for fractional delay and fractional Doppler based on the segmented grid;

[0071] D2. Further search for fractional values ​​based on the position of the integer index on the delay-Doppler plane by optimizing the grid segmentation step size;

[0072] D3. Use the limit center theorem to divide the search area and select the area with more concentrated distribution to search for fractional delay and Doppler to improve the estimation accuracy.

[0073] The angle information is used as a priori condition to assist in the estimation of delay and Doppler parameters by introducing the angle degree of freedom of the target, thereby further enhancing the accuracy of delay-Doppler estimation and reducing computational complexity.

[0074] The present invention also provides an application of the angle information enhanced orthogonal time-frequency-space signal delay Doppler estimation method, which is suitable for wireless communication systems in high-speed mobile scenarios, including autonomous driving, vehicle networking, radar perception, and target perception and parameter estimation in 5G / 6G communication systems.

[0075] Example 1

[0076] refer to Figure 1 Schematic diagram of the signal receiving and transmitting process, which includes three parts: OTFS signal generation design at the transmitting end, wireless sensing channel, and signal demodulation and sensing processing at the receiving end. The OTFS signal generation design part at the transmitting end is used to generate the OTFS sensing signal. The wireless sensing channel part generates a self-transmitting and self-receiving mode wireless channel based on the randomly generated target azimuth information and the statistical channel model, and applies the channel fading to the OTFS signal. The signal demodulation and sensing processing part at the receiving end first demodulates the received OTFS signal to obtain the signal to be estimated, and then uses the delay-Doppler estimation method enhanced by angle information to obtain the target's delay and Doppler information.

[0077] Among them, the transmitting end OTFS signal generation design mainly performs OTFS modulation processing on the data symbols, maps the data to the DD domain resources, and then obtains the time domain transmission signal through two transformations. Finally, the time domain signal is mapped to multiple antennas through precoding, RF and other modules to obtain MIMO-OTFS perception signals. The key steps of the transmitting end OTFS signal generation design include the DD domain resource mapping process, the DD domain signal transformation into the time domain signal process and the precoding process.

[0078] consider Figure 1 The transmitting end maps the transmitted bit stream into the DD domain data to be transmitted. Each data frame contains M′N information symbols mapped by orthogonal amplitude modulation (QAM). M and N represent the delay and Doppler grid number in the DD domain, respectively. Furthermore, M and N also represent the number of subcarriers and symbols in the time-frequency TF domain. The data symbol X DD Randomly generated, with mean zero and independent and identically distributed, and satisfying Where X DD [m,n] is X DD The [m,n]th element of , m=0,1,...,M-1,n=0,1,...,N-1.

[0079] In order to facilitate the transmitter to send the data to be transmitted, the latter needs to be converted into time domain form. This process mainly consists of ISFFT processing and Heisenberg transform processing. ISFFT is essentially a two-dimensional transform composed of Fourier transform (Finite Fourier Transform, FFT) and inverse Fourier transform (Inverse Finite Fourier Transform, IFFT) IFFT. It transforms the delay domain dimension in the DD domain data into the frequency domain and the Doppler domain dimension into the time domain, thereby obtaining a time-frequency domain signal similar to that in traditional OFDM modulation. Then, we use Heisenberg transform to transform the TF domain signal into a baseband time domain transmission signal.

[0080] For the MIMO system, the MIMO-OTFS signal is obtained through precoding processing, and the precoding location is: where f BF represents the precoding vector, N T is the number of transmitting antennas.

[0081] Among them, in order to achieve the perception of the target, delay and Doppler estimation are important links. The delay information is often related to the distance of the target from the base station, and the Doppler information is often related to the target speed. Assuming that this application only considers the perception of Line of Sight (LoS) targets, the modeling of the perception channel first needs to convert the distance and speed of the LoS target to obtain the delay and Doppler information, and then substitute it into the statistical channel model to generate the wireless channel for each target. Specifically, this application models the DD domain perception channel based on the tapped delay line model in the statistical channel model.

[0082] In order to realize the demodulation and perception processing of the receiving end signal, the receiving signal model is first derived, and then the receiving signal is processed by OTFS demodulation to obtain the DD domain receiving data. Secondly, the separability of the DD domain data is used to decouple the delay dimension and Doppler dimension information. Finally, the decoupled signal is input into the perception processing module to perform a two-stage delay-Doppler estimation algorithm of first integer and then fraction to obtain the target delay and Doppler estimation results.

[0083] In the time domain, we calculate r = Hs + w to give the received signal in the time domain, where w represents additive white Gaussian noise. For the sampling time t = aT / M, a = 0, 1, ..., MN-1, the received signal expression is:

[0084]

[0085] Further, Indicates the time domain signal s(tt p) The sampling result at time t = kT + lT / M, where l = 0, 1, ..., M-1, k = 0, 1, ..., N-1;

[0086] Due to the existence of fractional delay-Doppler, it needs to be rewritten as follows:

[0087]

[0088] Represented as different sampling results, its matrix form is expressed as:

[0089]

[0090] Therefore, the received signal matrix form is expressed as:

[0091]

[0092] The received time domain signal is converted into a DD domain signal through OTFS demodulation (Wigner transform and SFFT). The process of converting the time domain signal into the DD domain signal is described below:

[0093] The received signal is converted into the TF domain by Wigner transform, that is, Y TF =F M R, where R = nonvec(r TDS ) is the inverse vectorized received signal, and then the TF domain signal is transformed into the DD domain signal through SFFT, that is,

[0094] Since we believe that the angle information has been estimated, we can define a discrete angle grid W to represent the direction of the signal, where |Ω| = p / a, a is the resolution of the angle grid. The known angle makes this resolution arbitrarily high. The target direction angle q p can be converted to an index w in the grid p , so we can construct the angle-delay grid Γ1 = {(w, l / MΔf)}, where w = -p / 2a, ..., p / 2a, l = 0, ..., M-1, and the angle-Doppler grid Γ2 = {(w, k / NT)}, w = -p / 2a, ..., p / 2a, k = 0, ..., N-1. At this time, Defined as the reshaped form of y, and then by averaging Y in the Doppler or delay dimension, we can get the angle-delay received signal matrix and represents the angle-Doppler received signal matrix This is because Y is separable in the delay, Doppler and angle dimensions.

[0095] First, a generalized problem description of delay-Doppler estimation is given. For the parameters to be estimated, the complex gain coefficient (in order to complete SIC, the complex gain coefficient also needs to be estimated), the delay, and the Doppler set T = C creates RR, we give the following ML estimation problem model:

[0096]

[0097] Directly searching for these parameters requires high complexity, so we transform the problem into a one-dimensional ML estimation of delay and Doppler. For the pth target, let and Respectively represent the useful signal component and the interference signal component in the signal to be estimated. After the pth target is estimated, we use SIC to eliminate its influence in the signal to be estimated, so that the next target estimation can be performed. The principle of one-dimensional estimation assisted by angle information is as follows: Figure 2 As shown, by substituting known angle information, the search area of ​​delay and Doppler is refined to improve the estimation efficiency and accuracy.

[0098] For delay The estimation is based on y1=vec(Y1), and the one-dimensional delay estimation ML problem is modeled as:

[0099]

[0100] It boils down to the following maximization problem:

[0101]

[0102] Since the known angles are represented as specific indices on the angle grid, we only need to estimate along the delay dimension This reduces complexity.

[0103] For Doppler delay The estimation is based on y2=vec(Y2), and the one-dimensional Doppler estimation ML problem is modeled as:

[0104]

[0105] It can be reduced to the following maximization problem:

[0106]

[0107] The delay and Doppler obtained by solving are only integer values. In order to further obtain the fractional delay-Doppler, a two-dimensional grid segmentation search method is proposed. This method finds the index value of the fractional part of the delay-Doppler by segmenting the search step (this part of the index is off-grid). We found that any fractional index originates from the random offset based on the integer index on the two-dimensional delay-Doppler plane, which means that the fractional index is randomly distributed around the integer index. According to the limit center theorem, the area is divided and the area with more concentrated distribution is selected, such as Figure 3 As shown, we therefore approximately give an optimized grid partitioning coefficient h = p / 4 so as to search in areas where the distribution is more concentrated.

[0108] Example 2

[0109] refer to Figure 4 The two-dimensional grid segmentation search algorithm diagram first estimates the integer values ​​of delay and Doppler, then uses the algorithm to estimate the fractional delay and Doppler, and finally calculates the channel coefficient as follows:

[0110]

[0111] Search Index p The corresponding target delay and Doppler, after the pth target search is completed, perform SIC and remove the w p , repeat the above search steps until all targets are searched. The complete estimation algorithm can be found in Figure 5 As shown in Figure 3, after the time delay and Doppler estimation are completed, we obtain the target's distance and relative speed information through physical conversion.

[0112] The present invention is aimed at high-mobility scenarios, and realizes an angle-information-enhanced orthogonal time-frequency-space signal delay-Doppler estimation method and application, reduces complexity by decoupling parameters, and utilizes angle information to enhance delay-Doppler estimation. In addition, two-dimensional grid segmentation search realizes fractional delay-Doppler estimation. We provide an optimal segmentation coefficient to ensure that the algorithm can obtain a suitable step size in each search iteration, thereby achieving accurate fractional value estimation. The present invention simulates and compares four methods, namely, traditional ML non-off-grid estimation (ML-OnGrid), ML integer estimation and golden section fractional estimation (ML-GoldenSection), two-dimensional correlation integer estimation and difference method fractional estimation (Correlation-Difference), and the proposed angle-information-enhanced integer estimation and two-dimensional grid segmentation fractional estimation (Angle aided Dalay Doppler Estimation_2DGridSegmentation, AADDE-2DGS). The root mean square error (RMSE) of speed and distance measurement of each method is used as the estimation performance. The simulation comparison diagram is shown in FIG. Figure 6-Figure 7 As shown, the Cramer-Rao lower bound (CRLB) is used as the estimated performance limit.

[0113] In summary, the present invention proposes a low-complexity delay-Doppler estimation framework. First, the delay-Doppler decoupling and angle information enhancement methods are used to reduce the complexity without losing performance. Then, the off-grid fractional delay-Doppler index value is searched, which solves the problem of lack of fractional delay-Doppler estimation in the two comparison schemes.

[0114] At the same time, not only does the decoupling process reduce the estimation complexity in the integer estimation stage, but also the angle information is introduced to assist in enhancing the integer estimation stage. A more efficient two-dimensional grid segmentation search is designed to optimize the fractional delay-Doppler estimation process.

[0115] In addition, the two-dimensional grid segmentation method proposed in this proposal adopts an optimized segmentation coefficient according to the target delay-Doppler distribution law, which reduces the number of iterations, has higher accuracy, and can more accurately locate the fractional delay-Doppler index value.

[0116] Although the embodiments of the present invention have been shown and described, it is understandable to those skilled in the art that, unless otherwise defined, the technical terms or scientific terms used in the present invention shall have the usual meanings understood by persons with ordinary skills in the field to which the present invention belongs. The words "including" or "comprising" and the like used in the present invention mean that the elements or objects appearing before the word include the elements or objects listed after the word and their equivalents, without excluding other elements or objects. The words "connect" or "connected" and the like are not limited to physical or mechanical connections, but may also include electrical connections, whether direct or indirect. "Up", "down", "left", "right", etc. are only used to indicate relative position relationships. When the absolute position of the described object changes, the relative position relationship may also change accordingly.

[0117] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for estimating the delay-Doppler of orthogonal time-frequency-space signals with enhanced angle information, characterized in that: The following steps are involved: S1. Build a multiple-input multiple-output orthogonal time-frequency-space (MIMO-OTFS) self-transmitting and self-receiving mode perception test simulation platform, which includes an OTFS modulation module, a pulse shaping module, a precoding module, a wireless channel module, a receiving module, an OTFS demodulation module and a perception estimation module; S2. Generate an OTFS transmit signal based on the MIMO-OTFS simulation platform, and perform reception processing to obtain a signal to be estimated; S3. Decoupling the received signal to be estimated by delay-Doppler information, respectively extracting the angle-delay perception information component and the angle-Doppler perception information component; S4. Estimation of delay and Doppler parameters is enhanced by angle information to obtain integer delay and integer Doppler; S5. Use a two-dimensional grid partitioning search method to estimate the fractional delay and fractional Doppler of the integer delay and Doppler obtained to obtain a complete delay-Doppler estimation value.

2. The method for estimating the delay-Doppler of orthogonal time-frequency-space signals with enhanced angle information according to claim 1, characterized in that: The OTFS modulation module is used to: A1. Modulate the input bit stream and map it to multiple resource blocks in the delay-Doppler domain to obtain modulated data on the delay and Doppler grid; A2. Convert the delay-Doppler domain signal into a time-frequency domain signal by inverse symplectic finite Fourier transform (ISFFT); A3. The time-frequency domain signal is converted into a baseband time-domain signal using Heisenberg transform, and the time-domain signal is mapped to a multi-antenna system through precoding to form a MIMO-OTFS signal.

3. The method for estimating the delay-Doppler of orthogonal time-frequency-space signals with enhanced angle information according to claim 1, characterized in that: The wireless channel module establishes a sensing channel based on a tapped delay line model, wherein the sensing channel includes a line-of-sight (LoS) path, and the line-of-sight path is associated with parameters such as target distance and speed to generate time delay and Doppler information of the target.

4. The method for estimating the delay-Doppler of orthogonal time-frequency-space signals with enhanced angle information according to claim 1, characterized in that: The receiving module processes the received OTFS signal at the receiving end through the following steps: B1. Convert the received signal from the time domain into a time-frequency domain signal by Wigner transform; B2. Convert the received signal from the time-frequency domain to a DD domain signal by using a symplectic finite Fourier transform (SFFT); B3. Extract and retain the DD domain channel state information, remove the data symbol part, and obtain the angle-delay and angle-Doppler perception information.

5. The method for estimating the delay-Doppler of orthogonal time-frequency-space signals with enhanced angle information according to claim 1, characterized in that: The delay-Doppler information decoupling process comprises: C1. Extract the angle-delay perception information component in the DD domain signal; C2. Extract the angle-Doppler sensing information component in the DD domain signal to decouple the angle information from the delay and Doppler information; C3. Enhance the estimation of delay and Doppler parameters through known angle information, reduce the search dimension and improve the estimation accuracy.

6. The method for estimating the delay-Doppler of orthogonal time-frequency-space signals with enhanced angle information according to claim 1, characterized in that: The estimation process of the time delay and Doppler parameters is divided into two stages: In the first stage, integer delay and integer Doppler estimation are assisted by angle information to simplify the search dimension, simplifying the delay and Doppler estimation into a one-dimensional search problem and reducing the computational complexity. In the second stage, a two-dimensional grid partitioning search method is used to accurately estimate the fractional delay and fractional Doppler of the integer delay and integer Doppler obtained in the first stage, and a complete delay-Doppler estimation value is obtained.

7. The method for estimating the delay-Doppler of orthogonal time-frequency-space signals with enhanced angle information according to claim 1, characterized in that: The two-dimensional grid segmentation search method is implemented by the following steps: D1. Set the segmentation step size of the two-dimensional grid and search for fractional delay and fractional Doppler based on the segmented grid; D2. Further search for fractional values ​​based on the position of the integer index on the delay-Doppler plane by optimizing the grid segmentation step size; D3. Use the limit center theorem to divide the search area and select the area with more concentrated distribution to search for fractional delay and Doppler to improve the estimation accuracy.

8. The method for estimating the delay-Doppler of orthogonal time-frequency-space signals with enhanced angle information according to claim 1, characterized in that: The angle information is used as a priori condition to assist in the estimation of delay and Doppler parameters by introducing the angle degree of freedom of the target, thereby further enhancing the accuracy of delay-Doppler estimation and reducing computational complexity.

9. An application of the orthogonal time-frequency-space signal delay-Doppler estimation method based on the angle information enhancement described in any one of claims 1 to 8, characterized in that: The method is applicable to wireless communication systems in high-speed mobile scenarios, including autonomous driving, vehicle networking, radar perception, and target perception and parameter estimation in 5G / 6G communication systems.

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