DOA estimation method based on multi-moment joint perception signal enhancement

Abstract

The invention discloses a DOA estimation method based on multi-moment joint sensing signal enhancement, and the method comprises the following steps: building a sensing channel model for a moving target based on a water surface scene; in order to improve the DOA estimation precision of a water surface object, a time-varying channel estimation result is optimized through multi-moment joint denoising; for time-varying channel estimation, a deep learning algorithm based on Double U-Net is adopted to carry out de-noising in two dimensions of a space domain and an angle domain; and carrying out DOA (Direction of Arrival) estimation by using the de-noised channel matrix and a MUSIC (Multiple USIC) algorithm. According to the method, complex features of the channel are extracted from multi-moment joint perception observation data by adopting the Double U-Net algorithm based on the deep convolutional neural network, channel denoising and enhancement are realized, DOA estimation precision is further improved, and accurate recognition and tracking of water surface objects are effectively realized. Compared with a traditional method, the DOA estimation method provided by the invention has higher estimation precision.

Description

Technical Field

[0001] The present invention belongs to the technical field of DOA estimation in 5G-A / 6G synaesthesia and intelligent computing integration scenarios, and specifically relates to a DOA estimation method based on multi-time joint perception signal enhancement. Background Art

[0002] The fifth generation of mobile communications (5G-A) and the sixth generation of mobile communications (6G) are envisioned as multi-purpose systems that can provide users with integrated communication, perception, and intelligent computing services. One of the key technologies for providing this service is Direction of Arrival (DOA) estimation. DOA estimation can help the system determine the direction of the signal source, thereby optimizing the reception and processing of the signal, and improving the communication quality and sensing performance. In this context, the importance of direction of arrival estimation as one of the key technologies is becoming increasingly prominent. Because DOA estimation can effectively extract the location information of the target and realize the accurate identification and tracking of surface objects. However, the complexity of the water surface environment poses a major challenge to signal processing. The unique electromagnetic characteristics of the water surface, the influence of fluctuations, and the reflection, scattering, and multipath effects encountered during signal propagation will significantly affect the accuracy of DOA estimation.

[0003] Existing DOA estimation methods include the conventional beamforming (CBF) method, which is essentially a simple extension of the time domain Fourier spectrum estimation method to the spatial domain. The principle is simple, but the resolution is limited and will be limited by the array Rayleigh limit. The multiple signal classification (MUSIC) method and the estimation of signal parameters via rotational invariance technique (ESPRIT) method both use the properties of the signal subspace to achieve super-resolution direction finding. However, these two methods are based on covariance and require a sufficient number of data samples (snapshots) to accurately estimate the true covariance matrix. In recent years, with the development of artificial intelligence (AI), many DOA estimation methods based on deep learning have also emerged, such as using the denoising autoencoder (DAE) to learn the mapping from sample covariance to the true covariance matrix, and then perform DOA estimation.

[0004] In order to improve the accuracy of DOA estimation, and for the recognition and positioning of surface objects, especially DOA estimation under the framework of synaesthesia integration technology. It is necessary to build a channel model suitable for the surface environment, fully considering the special electromagnetic characteristics of the water surface, including the impact of fluctuations on signals and the impact of multipath effects on signal quality, and optimize the performance of DOA estimation on this basis. Accurate channel modeling can provide reliable channel state information for DOA estimation, thereby improving the accuracy of target recognition and signal-to-noise ratio, and further optimizing the collaborative work of communication and perception. Summary of the invention

[0005] From the perspective of improving the accuracy of DOA estimation, the present invention proposes a channel estimation method based on multi-time joint sensing signal enhancement for the mobile characteristics of surface sensing detection targets. The DOA estimation under the Integrated Sensing and Communications (ISAC) system is decomposed into two processes: multi-time joint channel denoising and MUSIC spectrum estimation. Its performance is better than that of traditional algorithms.

[0006] In order to achieve the above object, the present invention adopts the following technical solution: a DOA estimation method based on multi-time joint perception signal enhancement, characterized in that it includes:

[0007] Step 1: Determine the path loss formula for different scenarios on the water surface and in the suburbs, and build a time-varying perception channel model for the mobile target;

[0008] Step 2: Based on the time-varying perceptual channel model in step 1, obtain the noise-free channel H gt (t), and preprocess it using least squares to obtain the LS estimation result And sample the values ​​at T moments;

[0009] Step 3: Based on the noiseless channel in step 2 And LS estimation results Offline training of the Double U-Net network;

[0010] Step 4: Obtain the new real channel to be estimated online, perform least squares processing on it and sample the results at T moments and input them into the Double U-Net network trained offline for joint denoising to obtain the denoised channel matrix

[0011] Step 5: Denoised channel matrix The joint MUSIC algorithm is used for DOA estimation.

[0012] Furthermore, the specific steps of step 1 include:

[0013] Step 11: Path loss is divided into line-of-sight and non-line-of-sight. For water surface scenes, the roughness factor C is introduced, and the calculation formula for the path loss on the water surface is:

[0014]

[0015] Among them, n OS represents the path loss factor on the water surface, I0 represents the indicator function of the reflection path, Γ0 is the reflection coefficient, is the phase difference between the direct path and the reflected path, I1 represents the indicator function of the scattered path, Γ1 is the scattering coefficient, is the phase difference between the direct path and the scattered path, λ represents the wavelength, and j is the imaginary unit;

[0016] Step 12: Based on the sparsity of the perceptual channel, the classic SV channel model is used, and combined with the mobility characteristics of the mobile target, a time-varying perceptual channel model is constructed as

[0017]

[0018] Where t is the time variable, γ is the scalar coefficient, They represent the arrival angle and departure angle of the lth path at time t, α l (t) is the attenuation of the lth multipath, The steering vector representing the arrival angle of the lth path at time t, The steering vector representing the departure angle of the lth path at time t.

[0019] Furthermore, the expression of the roughness factor C in step 11 is:

[0020]

[0021] Among them, σ r represents roughness, ψ is the ground contact angle, which is the complementary angle of the arrival angle θ, and λ represents the wavelength.

[0022] Furthermore, step 2 specifically includes:

[0023] Step 21: Generate the original uncontaminated data set using the time-varying perceptual channel model, denoted as the true channel matrix H gt (t);

[0024] Step 22: For the real channel matrix H gt (t) Use LS method for preprocessing to obtain an initial rough estimate

[0025] Step 23: For the real channel matrix H gt (t) and an initial rough estimate Sample T moments respectively to obtain a noise-free channel And LS estimation results

[0026] Furthermore, the specific steps of step 3 include:

[0027] Step 31: Initialize the training parameters, where the total number of training rounds is E, the learning rate is η, the side length of the convolution kernel is k, the training batch size is B, the weight coefficient is α, the number of training layers is L, and the random initial vector at T moments is the parameter Θ in the Double U-Net network;

[0028] Step 32: Noise-free channel As training labels, LS estimation results As the data set to be trained;

[0029] Step 33: LS estimation results Perform DFT transformation to obtain the angle domain result, input it into the Double U-Net network, and obtain the angle domain noise estimation value and the spatial domain noise estimation value;

[0030] Step 34: Combine the LS estimation results, the angle domain noise estimation value, and the spatial domain noise estimation value After multiple iterations until the loss function value is minimized, the trained Double U-Net network is obtained.

[0031] Furthermore, the channel matrix after Double U-Net denoising is The expression is

[0032]

[0033] in, represents the spatial noise estimate, Θ = {W, b} is used as a parameter, including the weight W and the offset b.

[0034] Furthermore, the loss function is

[0035]

[0036] Among them, ||D t || is the number of samples included.

[0037] Furthermore, the step 4 specifically includes:

[0038] Step 41: obtain a new real noise-free channel with estimation online, perform LS estimation on it and sample T time instants;

[0039] Step 42: Input the above results and the results after DFT transformation into the trained Double U-Net network to obtain the denoised channel matrix

[0040] Furthermore, the step 5 specifically includes:

[0041] Step 51: Obtain the denoised channel matrix at T time points According to the time, it is divided into T groups, denoted as H t (t=1,L,T), calculate the covariance matrix respectively:

[0042] R t =H t (H t ) H ;

[0043] Step 52: Perform eigenvalue decomposition on the covariance matrix to decompose it into a signal subspace and a noise subspace:

[0044]

[0045] in, is the signal subspace, is the noise subspace, and are their corresponding eigenvalue matrices respectively;

[0046] Step 53: Noise Subspace Based For the receiving direction θ r (t), the MUSIC spectrum function is defined as

[0047]

[0048] And by searching the spectral function P MUSIC (θ r (t)) and estimate the arrival angle θ r (t);

[0049] Step 54: Use the number of missed detections as a performance indicator to detect and estimate the arrival angle θ r (t).

[0050] Furthermore, the specific steps of step 54 are:

[0051] If there are L detection targets in the group at the current t-th moment, estimate the angle of each target and get the estimated angle With real angle By comparison, the average angle error is

[0052]

[0053] like It is considered that there is a missed detection in this group of data.

[0054] Beneficial effects:

[0055] 1. Compared with the DOA estimation scenario, the present invention uses the step 1 and considers the perception channel wireless model based on the surface mobile detection target which is different from the traditional communication perception scenario;

[0056] 2. Compared with the traditional DOA estimation method, the present invention uses steps 2 to 5, considers the impact of low signal-to-noise ratio (SNR) on DOA estimation in water scenes, and faces the DOA estimation problem under time-varying perception channels, decomposing it into two stages: multi-time joint channel denoising based on Double U-Net algorithm and multi-time joint DOA estimation;

[0057] 3. Compared with the traditional method, the present invention uses steps 3 to 5. The channel denoising ratio based on the Double U-Net algorithm has higher accuracy than the traditional LS, denoising convolutional neural network (DnCNN) algorithm and single-time Double U-Net. At the same time, the DOA estimation accuracy based on this is also improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] Figure 1 is a flow chart of a DOA estimation method based on multi-time joint perception signal enhancement provided in an embodiment of the present invention;

[0059] Figure 2 is a schematic diagram of a water surface sensing system provided in an embodiment of the present invention;

[0060] Figure 3 Schematic diagram of the Double U-Net network structure provided in an embodiment of the present invention;

[0061] Figure 4 1 is a schematic diagram of NMSE results of channel estimation with SNR changes using different methods provided in an embodiment of the present invention;

[0062] Figure 5 It is a schematic diagram of the number of missed detections when the number of paths provided in the embodiment of the present invention is 5 and different methods are used for DOA estimation as the SNR changes. DETAILED DESCRIPTION

[0063] The present invention will be further explained below in conjunction with the accompanying drawings.

[0064] like Figure 1 As shown, the present invention provides a DOA estimation method based on multi-time joint perception signal enhancement, comprising:

[0065] Step 1: Determine the path loss formula for the water surface scenario and construct a time-varying perception channel model for the mobile target. Step 2: Based on the time-varying perception channel model in step 1, obtain the noise-free channel H gt (t), and preprocess it using least squares to obtain the LS estimation result And sample the values ​​at T moments.

[0066] Step 3: Based on the noiseless channel in step 2 And LS estimation results The Double U-Net network is trained offline.

[0067] Step 4: Obtain the new real channel to be estimated online, perform least squares processing on it and sample the results at T moments and input them into the Double U-Net network trained offline for joint denoising to obtain the denoised channel matrix

[0068] Step 5: Denoised channel matrix The joint MUSIC algorithm is used for DOA estimation.

[0069] Step 1 specifically includes the following steps:

[0070] Step 11: Path loss is divided into line-of-sight and non-line-of-sight. For water surface scenes, the roughness factor C is introduced to obtain the water surface path loss calculation formula:

[0071]

[0072] Among them, n OS represents the path loss factor on the water surface, I0 represents the indicator function of the reflection path, Γ0 is the reflection coefficient, is the phase difference between the direct path and the reflected path, I1 represents the indicator function of the scattered path, Γ1 is the scattering coefficient, is the phase difference between the direct path and the scattered path, λ represents the wavelength, and j is the imaginary unit.

[0073] Step 12: Based on the sparsity of the perceptual channel, the classic SV channel model is used, and combined with the mobility characteristics of the mobile target, a time-varying perceptual channel model is constructed as

[0074]

[0075] Where t is the time variable, γ is the scalar coefficient, They represent the arrival angle and departure angle of the lth path at time t, αl (t) is the attenuation of the lth multipath, The steering vector representing the arrival angle of the lth path at time t, The steering vector representing the departure angle of the lth path at time t.

[0076] The expression of the roughness factor C in step 11 is

[0077]

[0078] Among them, σ r represents roughness, ψ is the ground contact angle, which is the complementary angle of the arrival angle θ, and λ represents the wavelength.

[0079] In step 12, based on the ULA model, the steering vector a Ω (θ(t)) can be expressed as

[0080]

[0081] Among them, N Ω is the number of antenna arrays, d and λ represent the spacing between adjacent antennas and the signal wavelength, respectively. Therefore, by steering vector a Ω The formula (θ(t)) can be calculated and

[0082] like Figure 2 As shown, this embodiment is located in a water surface scene. There are 5 detection targets in the scene. The base station is set at the position (0,0,20). There are 5 detection target data points in the rectangular area formed by the four points (-200,50,0), (-200,250,0), (200,50,0), and (200,250,0). For multi-target perception of objects on the water surface, the interaceptive integrated perception method is divided into active perception and passive perception, depending on whether the party receiving the signal knows the signal sent by the sender in advance. When active perception is used, and the receiver and sender use the same antenna, the parameters are set to the base station height h=20m and the number of antennas N. t 、N r =64, the carrier frequency is f=2.7GHz, the distance between the water surface and the base station is R=10m, the length and width of the water surface are both W=500m, and the departure angle and arrival angle of the antenna are θ. The object is sensed by the echo signal reflected by the object. For a moving target, the target has an initial velocity v(m / s), and accordingly, the channel transfer matrix H will also change in real time, that is, it becomes a function of time H(t).

[0083] Step 2 The specific steps include:

[0084] Step 21: Considering the scene of the target moving on the water surface, the time-varying perception channel model is used to generate the original uncontaminated data set, which is recorded as the real channel matrix H gt (t).

[0085] Step 22: Considering the low complexity of LS estimation, the real channel matrix H gt (t) Perform LS estimation to obtain an initial rough estimate

[0086] Step 23: Due to the time-varying nature of the perceived channel, joint denoising is performed using temporal correlation. Specifically, for the real channel matrix H gt (t) and an initial rough estimate Sample T moments respectively to obtain a noise-free channel And LS estimation results

[0087] Step 3 The specific steps include:

[0088] Step 31: Initialize the training parameters, where the total number of training rounds is E, the learning rate is η, the side length of the convolution kernel is k, the training batch size is B, the weight coefficient α, the number of training layers is L, and the random initial vector at T moments is the parameter Θ in the Double U-Net network.

[0089] Step 32: Noise-free channel As training labels, LS estimation results As the data set to be trained; Step 33: Define the loss function as:

[0090]

[0091] Among them, ||D t || is the number of samples included.

[0092] Step 34: LS estimation results Perform DFT transformation to obtain the angle domain result, input it into the Double U-Net network, and obtain the angle domain noise estimation value and the spatial domain noise estimation value.

[0093] Step 35: Combine the LS estimation results, the angle domain noise estimation value, and the spatial domain noise estimation value After multiple iterations until the loss function value is minimized, the trained Double U-Net network is obtained.

[0094] In this embodiment, the total number of training rounds is E = 100, and the learning rate is η = 10 -2, the convolution kernel side length is k = 3, the training batch size is B = 128, the weight coefficient α = 0.5, the number of training layers is L = 9, and the goal of the Double U-Net algorithm is to minimize the estimated denoised channel matrix With noiseless channel The NMSE value between , so the above loss function is defined.

[0095] like Figure 3 As shown, the Double U-Net model of this embodiment adopts a dual-path encoder-decoder architecture. The LS channel estimation values ​​of T time sampling points are simultaneously transmitted as multi-channels, and channel denoising is achieved through joint processing. One path processes the angle domain data after DFT, and the other path directly processes the original spatial domain data. The two paths are processed in parallel to extract the features of the angle domain and the spatial domain respectively, and finally the comprehensive suppression of noise is achieved by combining the results. The specific output of the Double U-Net algorithm can be expressed as follows:

[0096]

[0097] in, and Respectively represent the LS estimates in the spatial domain and the angular domain The noise estimate obtained by the U-Net network is represents the spatial noise estimate, Θ = {W, b} is used as a parameter, including the weight W and the offset b.

[0098] Step 4 The specific steps include:

[0099] Step 41: obtain a new real noise-free channel with estimation online, perform LS estimation on it and sample T time instants;

[0100] Step 42: Input the above results and the results after DFT transformation into the trained Double U-Net network to obtain the denoised channel matrix

[0101] Select SNR (dB) values ​​of -10, -5, 0, 5, and 10, and calculate the NMSE values ​​for the LS estimation results, DnCNN, Double U-Net, and multi-time joint estimation results. The results are as follows Figure 4 shown.

[0102] Step 5 The specific steps include:

[0103] Step 51: Obtain the denoised channel matrix at T time points According to the time, it is divided into T groups, denoted as H t(t=1,L,T), calculate the covariance matrix respectively:

[0104] R t =H t (H t ) H .

[0105] Step 52: Perform eigenvalue decomposition on the covariance matrix to decompose it into a signal subspace and a noise subspace:

[0106]

[0107] in, is the signal subspace, is the noise subspace, and are their corresponding eigenvalue matrices respectively.

[0108] Step 53: Noise Subspace Based For the receiving direction θ r (t), the MUSIC spectrum function is defined as

[0109]

[0110] And by searching the spectral function P MUSIC (θ r (t)) and estimate the arrival angle θ r (t).

[0111] Step 54: Use the number of missed detections as a performance indicator to detect and estimate the arrival angle θ r (t).

[0112] In step 54, the specific steps of using the number of missed detections as a performance indicator include:

[0113] If there are L detection targets in the group at the current t-th moment, estimate the angle of each target and get the estimated angle With real angle By comparison, the average angle error is

[0114]

[0115] like It is considered that there is a missed detection in this group of data. Therefore, the DOA estimation result can be tested.

[0116] Select SNR (dB) values ​​of -10, -5, 0, 5, and 10. Perform DOA estimation on the LS estimation results, DnCNN and DoubleU-Net results, and the LS estimation results and DoubleU-Net estimation results of multiple moments. A total of 2500 tests were performed, and the missed detection results are as follows: Figure 5 shown.

[0117] The present invention compares the estimation performance of multi-time joint estimation based on DoubleU-Net algorithm with other traditional algorithms DnCNN, LS algorithm, and methods without multi-time joint estimation under different SNRs. The horizontal axis represents SNR, and the vertical axis represents the mean square error between the obtained channel estimation value and the standard value, as well as the number of missed detections. As the SNR increases, the mean square error of channel estimation decreases, and the number of missed detections of DOA estimation also decreases; comparing the Double U-Net algorithm we proposed with other algorithms, our algorithm has better performance under the condition of the same SNR, which shows that the DOA estimation method proposed in this embodiment effectively solves the perception positioning and tracking problems in the scene of surface mobile targets, and improves the accuracy of DOA estimation.

[0118] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A DOA estimation method based on multi-time joint perception signal enhancement, characterized in that: include: Step 1: Determine the path loss formula for different scenarios on the water surface and in the suburbs, and build a time-varying perception channel model for the mobile target; Step 2: Based on the time-varying perceptual channel model in step 1, obtain the noise-free channel H gt (t), and preprocess it using least squares to obtain the LS estimation result And sample the values ​​at T moments; Step 3: Based on the noiseless channel in step 2 And the LS estimation results Offline training of the Double U-Net network; Step 4: Obtain the new real channel to be estimated online, perform least squares processing on it and sample the results at T moments and input them into the Double U-Net network trained offline for joint denoising to obtain the denoised channel matrix Step 5: Denoised channel matrix The joint MUSIC algorithm is used for DOA estimation.

2. The DOA estimation method based on multi-time joint perception signal enhancement according to claim 1, characterized in that: The specific steps of step 1 include: Step 11: Path loss is divided into line-of-sight and non-line-of-sight. For water surface scenes, the roughness factor C is introduced, and the calculation formula for the path loss on the water surface is: Among them, n OS represents the path loss factor on the water surface, I0 represents the indicator function of the reflection path, Γ0 is the reflection coefficient, is the phase difference between the direct path and the reflected path, I1 represents the indicator function of the scattered path, Γ1 is the scattering coefficient, is the phase difference between the direct path and the scattered path, λ represents the wavelength, and j is the imaginary unit; Step 12: Based on the sparsity of the perceptual channel, the classic SV channel model is used, and combined with the mobility characteristics of the mobile target, a time-varying perceptual channel model is constructed as Where t is the time variable, γ is the scalar coefficient, They represent the arrival angle and departure angle of the lth path at time t, α l (t) is the attenuation of the lth multipath, The steering vector representing the arrival angle of the lth path at time t, The steering vector representing the departure angle of the lth path at time t.

3. The DOA estimation method based on multi-time joint perception signal enhancement according to claim 2 is characterized in that: The expression of the roughness factor C in step 11 is: Among them, σ r represents roughness, ψ is the ground contact angle, which is the complementary angle of the arrival angle θ, and λ represents the wavelength.

4. The DOA estimation method based on multi-time joint perception signal enhancement according to claim 1, characterized in that: Step 2 The specific steps include: Step 21: Generate the original uncontaminated data set using the time-varying perceptual channel model, denoted as the true channel matrix H gt (t); Step 22: For the real channel matrix H gt (t) Use LS method for preprocessing to obtain an initial rough estimate Step 23: For the real channel matrix H gt (t) and an initial rough estimate Sample T moments respectively to obtain a noise-free channel And the LS estimation results 5. The DOA estimation method based on multi-time joint perception signal enhancement according to claim 1, characterized in that: The specific steps of step 3 include: Step 31: Initialize the training parameters, where the total number of training rounds is E, the learning rate is η, the side length of the convolution kernel is k, the training batch size is B, the weight coefficient α, the number of training layers is L, and the random initial vector at T moments is the parameter Θ in the Double U-Net network; Step 32: Noise-free channel As training labels, LS estimation results As the data set to be trained; Step 33: LS estimation results Perform DFT transformation to obtain the angle domain result, input it into the Double U-Net network, and obtain the angle domain noise estimation value and the spatial domain noise estimation value; Step 34: Combine the LS estimation results, the angle domain noise estimation value, and the spatial domain noise estimation value After multiple iterations until the loss function value is minimized, the trained Double U-Net network is obtained.

6. The DOA estimation method based on multi-time joint perception signal enhancement according to claim 5, characterized in that: The channel matrix after Double U-Net denoising The expression is in, represents the spatial noise estimate, Θ = {W, b} is used as a parameter, including the weight W and the offset b.

7. The DOA estimation method based on multi-time joint perception signal enhancement according to claim 5, characterized in that: The loss function is Among them, ||D t || is the number of samples included.

8. The DOA estimation method based on multi-time joint perception signal enhancement according to claim 1, characterized in that: The step 4 specifically includes: Step 41: obtain a new real noise-free channel with estimation online, perform LS estimation on it and sample T time instants; Step 42: Input the above results and the results after DFT transformation into the trained Double U-Net network to obtain the denoised channel matrix 9. The DOA estimation method based on multi-time joint perception signal enhancement according to claim 1, characterized in that: The step 5 specifically includes: Step 51: Obtain the denoised channel matrix at T time points According to the time, it is divided into T groups, denoted as H t (t=1,L,T), calculate the covariance matrix respectively: R t =H t (H t ) H ; Step 52: Perform eigenvalue decomposition on the covariance matrix to decompose it into a signal subspace and a noise subspace: in, is the signal subspace, is the noise subspace, and are their corresponding eigenvalue matrices respectively; Step 53: Noise Subspace Based For the receiving direction θ r (t), the MUSIC spectrum function is defined as And by searching the spectral function P MUSIC (θ r (t)) and estimate the arrival angle θ r (t); Step 54: Use the number of missed detections as a performance indicator to detect and estimate the arrival angle θ r (t).

10. The DOA estimation method based on multi-time joint perception signal enhancement according to claim 9, characterized in that: The specific steps of step 54 are: If there are L detection targets in the group at the current t-th moment, estimate the angle of each target and get the estimated angle With real angle By comparison, the average angle error is like It is considered that there is a missed detection in this group of data.

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