CN116908802A - Skywave over-the-horizon radar distance estimation method based on sparse Bayesian algorithm - Google Patents

Abstract

The invention provides a sky wave beyond visual range radar distance estimation method based on a sparse Bayesian algorithm, which comprises the following implementation steps: carrying out wave beam formation on data received by an sky wave beyond visual range radar antenna array; performing pulse compression and moving target detection on echo data of the azimuth of the target to obtain a distance-Doppler matrix; performing two-dimensional constant false alarm detection on the distance-Doppler matrix; and carrying out sparse recovery on echo data of the Doppler channel where the target is positioned by using a sparse Bayesian algorithm to obtain the distance information of the target. The invention can improve the distance resolution capability and the distance estimation precision of the sky wave beyond visual range radar; the calculation amount of the algorithm and the complexity of the system are reduced, and the method is more suitable for engineering application.

Description

Skywave over-the-horizon radar distance estimation method based on sparse Bayesian algorithm

Technical field

The invention belongs to the field of radar technology, and further relates to a distance estimation method applied to skywave over-the-horizon radar OTHR (Skywave Over-the-horizon Radar) in the field of radar signal processing technology. The invention can be used for parameter estimation of echo data received by sky wave radar, and in the case of limited radar bandwidth, it can obtain range super-resolution capability that breaks through the Rayleigh limit and improve the accuracy of target distance estimation.

Background technique

Skywave over-the-horizon radar works in the high frequency (HF) range between 3-30MHz. Skywave radar uses the reflection of high-frequency signals by the ionosphere to detect targets from top to bottom, so it can break through the curvature of the earth. Limitation, it can detect ultra-long-distance (1000~4000km) targets beyond the visual range of conventional system radars. However, spectrum resources in the high-frequency band are limited and there are many types of interference. Therefore, the working bandwidth of OTHR is limited and the distance resolution is not high, generally ranging from a few kilometers to more than ten kilometers. Limited by the bandwidth of the transmitted signal, the distance resolution of OTHR is limited. Therefore, in the case of multiple targets, the ranging accuracy is greatly reduced due to the inability to correctly distinguish the targets.

The University of Electronic Science and Technology of China proposed a method based on OMP and DPL1 algorithms in the patent document "A radar range super-resolution calculation method based on OMP and DPL1 algorithms" (Patent application number: 202210626515.7, application publication number: CN 115564645A) Radar distance super-resolution calculation method. The implementation steps of this method are as follows: first, perform pulse compression on the radar echo signal to determine the radar signal segment of the group target; then frequency deskew the radar signal segment of the group target to obtain a single-frequency signal; and then use the OMP algorithm to obtain the target's Rough position; finally, the dynamic parameter L1 regularization algorithm is used to further super-resolution process the obtained frequency point position to obtain the precise frequency point position of the group target, and convert the frequency point information into distance information to achieve distance super-resolution. The disadvantage of this method is that the digital "deskewing" of linear frequency modulated continuous wave signals requires subsequent processing of all echo signals received by the radar, which results in a large amount of calculation and poor real-time performance. Moreover, this method requires two steps and is highly complex. At the same time, the OMP algorithm is prone to over-matching. If the first step is estimated incorrectly, the previous error will be accumulated in each subsequent step, resulting in poor stability of the algorithm.

The University of Electronic Science and Technology of China proposed a method based on instantaneous autocorrelation matrix sparse decomposition in the patent document "OTHR maneuvering target parameter estimation method based on instantaneous autocorrelation matrix" (Patent application number: CN201711068233.5, application publication number: CN107861115A). OTHR maneuvering target parameter estimation method based on sparse decomposition of autocorrelation matrix. The implementation steps of this method are as follows: first, perform instantaneous autocorrelation transformation on the echo, then perform cross-term suppression on the instantaneous autocorrelation matrix, then perform sparse decomposition of the instantaneous autocorrelation matrix, and finally perform Hough transformation on the decomposed sparse matrix to obtain the signal. instantaneous frequency to estimate the target motion parameters. The shortcoming of this method is that although this method improves the estimation accuracy of Doppler frequency and angle of OTHR in multi-target situations, it does not solve the insufficient accuracy of target range parameter estimation caused by OTHR's low range resolution. The problem.

Contents of the invention

The purpose of the present invention is to propose a high-precision estimation method for sky-wave radar parameters in view of the above-mentioned shortcomings of the prior art, which is used to solve the problem of insufficient parameter estimation accuracy of OTHR due to poor range resolution, as well as the existing frequency deskewing method. The distance super-resolution method has the problem of large computational complexity and poor real-time performance.

The technical solution to achieve the object of the present invention is to construct a basis matrix for the sparse recovery problem by pulse-compressing the data delay of the transmitted linear frequency modulated continuous wave signal. Each column in the basis matrix represents the data delay under different delays. For the pulse pressure waveform data of the linear frequency modulated continuous wave signal, by mapping the time delay to the distance, the pulse pressure waveform data at different distances can be obtained. The distance interval between each column of the base matrix is less than one distance resolution unit, and the observed The vector is matched with each column of the base matrix to obtain a sparse recovery vector, and then the position of each element in the sparse vector is converted into the distance position of the target, which can overcome the problem of poor distance resolution of sky-wave over-the-horizon radar in the existing technology. . This invention uses the signal received by the sky wave over-the-horizon radar antenna array to perform beam forming, accumulates the energy in space, and then performs pulse compression on the signal in the direction of the target, and then performs pulse compression on the echo within a coherent accumulation time of the same distance unit. Perform DFT on the wave data to obtain the range-Doppler matrix of the target, and then perform two-dimensional constant false alarm detection on the range-Doppler matrix; finally, select the pulse-compressed data of the Doppler channel where the detected target is located for sparse recovery. According to the pulse compression results, only part of the pulse pressure signal containing the target information is intercepted as the observation vector, which can effectively reduce the calculation amount of the entire algorithm and overcome the problems of large computational complexity and poor real-time performance of the existing frequency "deskew" distance super-resolution method. . The present invention uses a sparse Bayesian algorithm to sparsely restore the observation vector. This algorithm can process single snapshot data and does not need to set regularization parameters. When the signal-to-noise ratio is low, it still has good resolution and stability. , which overcomes the shortcomings of the existing subspace algorithm that can only handle incoherent sources and the need for multiple snapshot data, and the greedy sparse recovery algorithm that is prone to over-matching, thereby better improving the performance of sky-wave over-the-horizon radar. Distance resolution ability.

The implementation steps of the present invention are as follows:

Step 1: Perform beam forming on the data received by the sky wave over-the-horizon radar antenna array to obtain the echo data of the target's location;

Step 2: Perform pulse compression on the echo data received by the sky wave over-the-horizon radar in the azimuth of the target to obtain the echo data after pulse pressure;

Step 3: Perform moving target detection on the echo signal after the sky wave over-the-horizon radar pulse pressure:

Perform discrete Fourier transform DFT on the pulse pressure post-echo data within a coherent accumulation time of the same range unit to obtain the range-Doppler matrix of the target;

Step 4: Perform two-dimensional constant false alarm detection on the range-Doppler matrix of the target to obtain the range unit and Doppler unit where the target is located;

Step 5: Use the sparse Bayesian algorithm to sparsely restore the signal of the Doppler unit where the target is located:

By compressing the signal delay of the linear frequency modulated continuous wave signal emitted by the sky wave over-the-horizon radar, a measurement matrix is constructed according to the dimensions of the observation vector; the sparse Bayesian algorithm is used to sparsely restore the observation vector to obtain a sparse signal vector. Convert the position of each element in the sparse vector into the distance value of the target.

Compared with the prior art, the present invention has the following advantages:

First, because the present invention uses the sparse Bayesian algorithm to estimate the distance parameters of the sky-wave over-the-horizon radar, it overcomes the problem of poor distance resolution of the sky-wave over-the-horizon radar in the existing technology, making the present invention able to detect sky-wave over-the-horizon radars. The range radar achieves a range resolution that is more than three times higher than conventional range parameter estimation methods.

Second, because the present invention selects the pulse compression data of the Doppler channel where the detected target is located for range super-resolution processing, and intercepts part of the pulse pressure signal according to the pulse compression result as the observation vector, it overcomes the existing frequency "deskewing" The distance super-resolution method has the problems of large computational complexity and poor real-time performance, which makes the present invention greatly reduce the computational complexity of the algorithm and the system complexity, making it more suitable for engineering applications.

Third, since the present invention uses the sparse Bayesian algorithm to sparsely restore the observation vector, the algorithm can process single snapshot data and does not need to set regularization parameters. It still has good resolution when the signal-to-noise ratio is low. power and stability, and overcomes the problems that the existing subspace algorithm can only handle incoherent sources and requires multiple snapshot data, as well as the over-matching problem of the greedy sparse recovery algorithm, making the present invention suitable for sky-wave over-the-horizon radar. The distance parameter estimation is more accurate and more stable, thus improving the distance resolution capability of the sky wave over-the-horizon radar.

Description of the drawings

Figure 1 is a flow chart of the present invention;

Figure 2 is a diagram showing the results of processing sky wave over-the-horizon radar echo data using the method of the present invention under simulation condition 1;

Figure 3 is a diagram showing the results of processing sky wave over-the-horizon radar echo data using the method of the present invention under simulation condition 2;

Figure 4 is a diagram showing the results of processing sky wave over-the-horizon radar echo data using the method of the present invention under simulation condition 3.

Detailed ways

The present invention will be described in further detail below with reference to the accompanying drawings and examples.

Referring to Figure 1, the specific implementation steps of the embodiment of the present invention are described in further detail.

Step 1: Perform beam forming on the data received by the sky wave over-the-horizon radar antenna array, accumulate the energy in space, and obtain the echo data of the target's location.

Step 2: Perform pulse compression on the echo data received by the sky wave over-the-horizon radar in the azimuth of the target to obtain the echo data after pulse pressure.

The steps for pulse compression of the echo data received by sky wave over-the-horizon radar are as follows:

In the first step, based on the transmission signal of the sky wave over-the-horizon radar, the impulse response of the matched filter is constructed as Among them, s represents the discrete sequence obtained after discrete sampling of the chirp continuous wave signal s(t) emitted within one pulse repetition period of the sky-wave over-the-horizon radar, s=[s ₁ , s ₂ ,..., s _G ], G represents the number of sampling points of the discrete sequence within a pulse repetition period, s _g represents the g-th element of s, g=1,2,...G, (·) ^* represents the conjugate operation;

In the second step, the sky wave over-the-horizon radar echo data is convolved with the impulse response of the matched filter to obtain the echo data after pulse pressure.

Step 3: Perform moving target detection on the echo signal after the sky wave over-the-horizon radar pulse pressure:

Perform discrete Fourier transform DFT on the pulse pressure post-echo data within a coherent accumulation time of the same range unit to obtain the range-Doppler matrix of the target.

Step 4: Perform two-dimensional constant false alarm detection on the range-Doppler matrix of the target to obtain the range unit and Doppler unit where the target is located.

The steps of the two-dimensional constant false alarm detection are as follows:

The first step is to select unselected elements in the distance-Doppler matrix as units to be detected;

In the second step, N reference units are selected on both sides of the unit to be detected, N is selected as an integer power of 2, the average value of the 2N reference units is calculated, and the average value is used as the power estimate Z _ca of the background noise;

The third step is to multiply the background noise power estimate Z _ca by the threshold product factor k to obtain the threshold value s of the unit to be detected;

The fourth step is to compare the signal value of the unit to be detected with the threshold value s. If the signal value of the unit to be detected is greater than the threshold value, the signal is determined to be the target signal and assigned a value of 1; otherwise, the signal is determined to be the target signal. For the noise signal, the value is assigned to 0;

The fifth step is to determine whether all elements in the range-Doppler matrix have been selected. If so, map the coordinate values of the elements determined to be the target signals into the range-Doppler matrix to obtain the range unit and Doppler where the target is located. unit; otherwise, perform the first step.

Step 5: Use the sparse Bayesian algorithm to sparsely restore the signal of the Doppler unit where the target is located:

By compressing the signal delay of the linear frequency modulated continuous wave signal emitted by the sky wave over-the-horizon radar, a measurement matrix is constructed according to the dimensions of the observation vector; the sparse Bayesian algorithm is used to sparsely restore the observation vector to obtain a sparse signal vector. Convert the position of each element in the sparse vector into the distance value of the target.

The observation vector refers to selecting the pulse-compressed echo data in the Doppler unit where the target is located, and intercepting the data of the main lobe width and length of the echo data to form an observation vector.

The steps to construct the measurement matrix are as follows:

The first step is to construct a measurement matrix with dimensions P×M whose initial values are all 0; the value of P is equal to the length of the observation vector, M=KP, and the value of K is the sampling frequency of the linear frequency modulation signal and the observation vector The ratio of the sampling frequency, in the embodiment of the present invention, K=5;

The second step is to perform pulse compression on the linear frequency modulation continuous wave signal and the matched filter impulse response to obtain the basis vector, find the peak point position q of the basis vector, and intercept the q-b to q+b elements of the main lobe of the basis vector, b The value of is one-half of the number of sampling points in the main lobe of the basis vector. Put all the elements in the main lobe of the basis vector into the first row to row 2b of the first column of the measurement matrix, and so on. Put the main lobe of the basis vector into All elements in the lobe are placed in the kth row to the k+2b-1th row of the kth column of the measurement matrix, k=1,2,...,M;

The third step is to intercept the b-th to P+b-1 rows of the measurement matrix. The first column of the measurement matrix represents the pulse pressure waveform data of the chirp continuous wave signal at time 0, and the k-th column represents the k×Δt time. The pulse pressure waveform data of linear frequency modulation continuous wave signal, Δt represents the time interval between each column of the measurement matrix;

The fourth step is to perform K-fold extraction on all column vectors of the measurement matrix to make the dimensions of the pulse pressure waveform data of the linear frequency modulated continuous wave signal consistent with the observation vector.

The specific steps of using the sparse Bayesian algorithm to sparsely restore the observation vector to obtain the sparse signal vector are as follows:

The first step is to initialize the hyperparameters α and α ₀ , α = (α ₁ , α ₂ ,…, α _M ) represents the hyperparameter of the sparse signal vector, each element of α is initialized to 1; α ₀ represents the noise variance, initialized to 1;

In the second step, according to the following formula, calculate the variance matrix R and mean vector μ of the posterior probability density function of the sparse signal vector to be found;

R＝(α ₀ Φ ^T Φ+Λ) ^-1

μ＝α ₀ RΦ ^T y

Among them, Φ represents the measurement matrix, (·) ^T represents the transpose operation, Λ represents the diagonal matrix of the hyperparameter α, y represents the observation vector, and (·) ^-1 represents the inversion operation;

The third step is to update the hyperparameters according to the following formula and α ^new ;

in, represents the i-th column vector of α ^new , γ _i represents the i-th element of the quantization factor, γ _i =1-α _i R _ii , ∑ represents the summation operation, i = 1, 2,...,M;

The fourth step is to determine whether the root mean square error of the current iteration satisfies the convergence condition or reaches the maximum number of iterations. If so, perform step five. Otherwise, perform step two; the convergence condition is the difference between the mean vector obtained by the current iteration and the previous iteration. The root mean square error of the mean vector obtained in one iteration is less than e ^-4 , and the maximum number of iterations is 1000;

The fifth step is to use the mean vector obtained in the current iteration as a sparse signal vector.

The conversion of the position of each element in the sparse vector into the distance value of the target refers to: using the formula Convert the position of each element in the sparse vector into the distance value of the target; where c represents the speed of light, L represents the starting coordinate value in the echo data after intercepting the pulse compression in the Doppler unit where the target is located, and f _s represents the observation The sampling frequency of the vector.

The effect of the present invention will be further explained below in combination with simulation experiments:

1. Conditions for simulation experiments:

The platform for the simulation experiment of the present invention is: Windows 10 operating system and Matlab R2020b.

Simulation experiment 1: The time width of the linear frequency modulated continuous wave signal emitted by the radar is T _p =15×10 ^-3 s, the bandwidth B is 1×10 ⁴ Hz, and the conventional pulse pressure distance resolution ΔR=c/2B=15000m. The number of receiving antennas is 32, corresponding to an angular resolution of 3.169. The echo signal contains target information and Gaussian white noise. The sampling frequency of the echo signal is f _s =2×10 ⁴ Hz. The signal-to-noise ratios of the two targets are SNR1=10dB and SNR2=10dB respectively; the targets are located at 1° and 1.5 respectively. ° bearing; the distances are R1=313500m, R2=318000m respectively; the target speeds are v1=39m/s, v2=39m/s respectively, and the corresponding Doppler frequencies are f _d 1=2.6Hz, f _d 2=2.6 Hz.

Simulation experiment 2: The target velocities are v1=39m/s, v2=62m/s, and the corresponding Doppler frequencies are f _d 1 = 2.6 Hz and f _d 2 = 4.13 Hz respectively. The other parameters are the same as simulation experiment 1.

Simulation experiment 3: The distances of simulation experiment 3 are R1=310500m, R2=318000m respectively; the target speeds are v1=39m/s, v2=62m/s respectively, and the corresponding Doppler frequencies are f _d 1=2.6Hz, f _d2 =4.13Hz. The remaining parameters are the same as simulation experiment 1.

2. Simulation content and result analysis:

There are three simulation experiments in this invention.

Simulation experiment 1. Simulation experiment 1 illustrates the situation where the two targets cannot be distinguished when the Doppler velocity of the target is the same and the distance interval between the two targets is less than one distance resolution unit. Perform conventional beamforming on the echo signal received by the antenna, and obtain the orientation of the target, as shown in Figure 2(a). The abscissa in Figure 2(a) represents the angle, and the ordinate represents the normalized amplitude. You can see that the conventional Beamforming cannot separate two targets in the azimuthal dimension. The echo data after beam formation is subjected to pulse compression processing. The pulse pressure results are shown in Figure 2(b). The abscissa in Figure 2(b) represents the distance value, and the ordinate represents the normalized amplitude. The distance information of the target is obtained. Since the distance between the two targets is less than one distance resolution unit, only one distance value is estimated to be 315km, and the two targets cannot be distinguished from the distance dimension. Perform DFT on the echo data within a coherent accumulation time of the same range unit, and perform constant false alarm detection to obtain the range-Doppler diagram of the target, as shown in Figure 2(c), the x-axis of Figure 2(c) represents distance, the y-axis represents Doppler frequency, and the z-axis represents amplitude. It can be seen that the value of Doppler frequency is 2.60417Hz, which is consistent with the actual value. However, because the target is in the same range resolution unit, it is impossible to distinguish the two distance information of two targets, so the two targets cannot be distinguished; the two-dimensional CFAR detection results of the range-Doppler diagram are shown in Figure 2(d). The x-axis in Figure 2(d) represents the distance, and the y-axis represents Doppler frequency, the z-axis represents the discriminant value. The sparse Bayes algorithm is used to estimate the distance parameter for the post-pulse pressure echo data in the Doppler channel with a discriminant value of 1 in the two-dimensional CFAR detection results. The results are shown in Figure 2 As shown in (e), the dotted line is the true value of the distance, and the peak value of the curve represents the target distance value estimated by the present invention. It can be seen that the distance information of the two targets can be distinguished, and the obtained distance values are 313.5km and 318km respectively. consistent with the actual value.

Simulation experiment 2. Simulation experiment 2 illustrates that when the target is resolvable in the Doppler domain, but because the estimable distance value is affected by the sampling rate of the radar echo signal, the distance value at the sampling point can only be estimated, resulting in a Dimensions are still inseparable. Perform conventional beamforming on the echo signal received by the antenna, and obtain the target orientation as shown in Figure 3(a). The horizontal axis of Figure 3(a) represents the angle, and the vertical axis represents the amplitude. It can be seen that conventional beamforming cannot The two targets are separated in azimuth. The echo data after beam formation is subjected to pulse compression processing. The pulse pressure results are shown in Figure 3(b). The abscissa in Figure 3(b) represents the distance value, and the ordinate represents the normalized amplitude. The distance information of the target is obtained as 315km, it can be seen that the two targets cannot be distinguished from the distance dimension. Perform DFT on the echo data within a coherent accumulation time of the same range unit, and perform constant false alarm detection to obtain the range-Doppler diagram of the target, as shown in Figure 3(c), the x-axis of Figure 3(c) represents distance, the y-axis represents Doppler frequency, and the z-axis represents amplitude. It can be seen that the values of Doppler frequency are 2.60417Hz and 4.16667Hz respectively, which are consistent with the actual values. Although the Doppler frequency is separable, since only the corresponding distance value at the sampling point can be estimated, when the distance interval between the two targets is small, the distance value of the two targets is estimated as the value at the same sampling point. It is estimated to be 315km, so it is still impossible to distinguish the specific distance information of the two targets. The results of two-dimensional CFAR detection on the range-Doppler map are shown in Figure 3(d). The x-axis in Figure 3(d) represents the distance, the y-axis represents the Doppler frequency, and the z-axis represents the discriminant value. For the two-dimensional In the CFAR detection results, the pulse pressure post-echo data in the Doppler channel with a discriminant value of 1 uses the sparse Bayesian algorithm to estimate the distance parameter. The results are shown in Figure 3(e). The dotted line is the true value of the distance, and the curve The peak value represents the distance value estimated by the present invention. It can be seen that the distance information of the two targets can be distinguished. The obtained distance values are 313.5km and 318km respectively, which are consistent with the actual values. It can be seen that this method can improve the sky-wave over-the-horizon radar. Distance estimation accuracy.

Simulation experiment 3. Simulation experiment 3 illustrates that when the target is resolvable in the Doppler domain, because the estimable distance value is affected by the sampling rate, the distance value at the grid point can only be estimated, although the distance dimension is resolvable. , but there is an error between the estimated distance value and the actual value. Perform conventional beamforming on the echo signal received by the antenna, and obtain the orientation of the target, as shown in Figure 4(a). The horizontal axis of Figure 4(a) represents the angle, and the vertical axis represents the amplitude. It can be seen that conventional beamforming cannot Separate two targets from the dimension of orientation. The echo data after beam formation is subjected to pulse compression processing. The pulse pressure results are shown in Figure 4(b). The abscissa in Figure 4(b) represents the distance value, and the ordinate represents the normalized amplitude. The distance information of the target is obtained as 315km, it is impossible to distinguish the two targets simply from the distance dimension. Perform DFT on the echo data within a coherent accumulation time of the same range unit, and perform constant false alarm detection to obtain the range-Doppler diagram of the target, as shown in Figure 4(c), the x-axis of Figure 4(c) represents the distance, the y-axis represents the Doppler frequency, and the z-axis represents the amplitude. It can be seen that the values of the Doppler frequency are 2.60417Hz and 4.16667Hz respectively, which are consistent with the actual values. Although the Doppler frequency can be divided, the distance dimension can also be points, but the distance values of the two targets are estimated as the values at the grid points closest to the true values, which are 307.5km and 315km respectively, which is inconsistent with the actual distance values. The results of two-dimensional CFAR detection on the range-Doppler map are shown in Figure 4(d). The x-axis in Figure 4(d) represents the distance, the y-axis represents the Doppler frequency, and the z-axis represents the discriminant value. For the two-dimensional In the CFAR detection results, the pulse pressure post-echo data in the Doppler channel with a discriminant value of 1 uses the sparse Bayes algorithm for distance estimation. The results are shown in Figure 4(e). The dotted line is the true value of the distance, and the peak value of the curve is It means that using the distance values estimated by the present invention, it can be seen that the distance between the two targets can be distinguished, and the obtained distance values are 310.5km and 318km respectively, which are consistent with the actual values. It can be seen that this method can improve the distance estimation of sky wave over-the-horizon radar. Accuracy.

The above simulation experiments show that the present invention proposes a distance estimation method for skywave over-the-horizon radar based on the sparse Bayesian algorithm, which is similar to the sky-wave over-the-horizon radar's limited operating bandwidth, low range resolution, and insufficient distance estimation accuracy. Compared with the conventional distance parameter estimation method of over-the-horizon radar, the distance estimation accuracy can be improved by more than three times.

Claims (7)

1. A distance estimation method for sky-wave over-the-horizon radar based on the sparse Bayesian algorithm, which is characterized by using the linear frequency modulated continuous wave signal emitted by the sky-wave over-the-horizon radar to construct a measurement matrix, and using the sparse Bayesian algorithm to estimate the location of the target. The pulse compression data of the Doppler channel is sparsely restored; the steps of the distance estimation method include the following: Step 1: Perform beam forming on the data received by the sky wave over-the-horizon radar antenna array to obtain the echo data of the target's location; Step 2: Perform pulse compression on the echo data received by the sky wave over-the-horizon radar in the azimuth of the target to obtain the echo data after pulse pressure; Step 3: Perform moving target detection on the echo signal after the sky wave over-the-horizon radar pulse pressure: Perform discrete Fourier transform DFT on the pulse pressure post-echo data within a coherent accumulation time of the same range unit to obtain the range-Doppler matrix of the target; Step 4: Perform two-dimensional constant false alarm detection on the range-Doppler matrix of the target to obtain the range unit and Doppler unit where the target is located; Step 5: Use the sparse Bayesian algorithm to sparsely restore the signal of the Doppler unit where the target is located: By compressing the signal delay of the linear frequency modulated continuous wave signal emitted by the sky wave over-the-horizon radar, a measurement matrix is constructed according to the dimensions of the observation vector; the sparse Bayesian algorithm is used to sparsely restore the observation vector to obtain a sparse signal vector. Convert the position of each element in the sparse vector into the distance value of the target. 2. The sky wave over-the-horizon radar distance estimation method based on the sparse Bayesian algorithm according to claim 1, characterized in that: the step of performing pulse compression on the echo data received by the sky wave over-the-horizon radar in step 2 is as follows : In the first step, based on the transmission signal of the sky wave over-the-horizon radar, the impulse response of the matched filter is constructed as Among them, s represents the discrete sequence obtained after discrete sampling of the chirp continuous wave signal s(t) emitted within one pulse repetition period of the sky-wave over-the-horizon radar, s=[s ₁ , s ₂ ,..., s _G ], G represents the number of sampling points of the discrete sequence within a pulse repetition period, s _g represents the g-th element of s, g=1,2,G, (·) ^* represents the conjugate operation; In the second step, the sky wave over-the-horizon radar echo data is convolved with the impulse response of the matched filter to obtain the echo data after pulse pressure. 3. The sky-wave over-the-horizon radar distance estimation method based on sparse Bayesian algorithm according to claim 1, characterized in that: the steps of two-dimensional constant false alarm detection in step 4 are as follows: The first step is to select unselected elements in the distance-Doppler matrix as units to be detected; In the second step, N reference units are selected on both sides of the unit to be detected, N is selected as an integer power of 2, the average value of the 2N reference units is calculated, and the average value is used as the power estimate Z _ca of the background noise; The third step is to multiply the background noise power estimate Z _ca by the threshold product factor k to obtain the threshold value s of the unit to be detected; The fourth step is to compare the signal value of the unit to be detected with the threshold value s. If the signal value of the unit to be detected is greater than the threshold value, the signal is determined to be the target signal and assigned a value of 1; otherwise, the signal is determined to be the target signal. For the noise signal, the value is assigned to 0; The fifth step is to determine whether all elements in the range-Doppler matrix have been selected. If so, map the coordinate values of the elements determined to be the target signals into the range-Doppler matrix to obtain the range unit and Doppler where the target is located. unit; otherwise, perform the first step. 4. The sky-wave over-the-horizon radar distance estimation method based on sparse Bayesian algorithm according to claim 1, characterized in that: the observation vector described in step 5 refers to selecting the Doppler unit where the target is located. After pulse compression, the echo data is intercepted and the main lobe width and length of the echo data are intercepted to form an observation vector. 5. The sky-wave over-the-horizon radar distance estimation method based on sparse Bayesian algorithm according to claim 1, characterized in that: the steps of constructing the measurement matrix in step 5 are as follows: The first step is to construct a measurement matrix with dimensions P×M whose initial values are all 0; the value of P is equal to the length of the observation vector, M=KP, and the value of K is the sampling frequency of the linear frequency modulation signal and the observation vector The ratio of sampling frequencies; The second step is to perform pulse compression on the linear frequency modulation continuous wave signal and the matched filter impulse response to obtain the basis vector, find the peak point position q of the basis vector, and intercept the q-b to q+b elements of the main lobe of the basis vector, b The value of is one-half of the number of sampling points in the main lobe of the basis vector. Put all the elements in the main lobe of the basis vector into the first row to row 2b of the first column of the measurement matrix, and so on. Put the main lobe of the basis vector into All elements in the lobe are placed in the kth row to the k+2b-1th row of the kth column of the measurement matrix, k=1,2,...,M; The third step is to intercept the b-th to P+b-1 rows of the measurement matrix. The first column of the measurement matrix represents the pulse pressure waveform data of the chirp continuous wave signal at time 0, and the k-th column represents the k×Δt time. The pulse pressure waveform data of linear frequency modulation continuous wave signal, Δt represents the time interval between each column of the measurement matrix; The fourth step is to perform K-fold extraction on all column vectors of the measurement matrix to make the dimensions of the pulse pressure waveform data of the linear frequency modulated continuous wave signal consistent with the observation vector. 6. The sky-wave over-the-horizon radar distance estimation method based on the sparse Bayesian algorithm according to claim 1, characterized in that: using the sparse Bayesian algorithm in step 5 to sparsely restore the observation vector to obtain the sparse signal vector The specific steps are as follows: The first step is to initialize the hyperparameters α and α ₀ , α = (α ₁ , α ₂ ,, α _M ) represents the hyperparameter of the sparse signal vector, each element of α is initialized to 1; α ₀ represents the noise variance, initialization is 1; In the second step, according to the following formula, calculate the variance matrix R and mean vector μ of the posterior probability density function of the sparse signal vector to be found; R＝(α ₀ Φ ^T Φ+Λ) ^-1 μ＝α ₀ RΦ ^T y Among them, Φ represents the measurement matrix, (·) ^T represents the transpose operation, Λ represents the diagonal matrix of the hyperparameter α, y represents the observation vector, and (·) ^-1 represents the inversion operation; The third step is to update the hyperparameters according to the following formula and α ^new ; in, represents the i-th column vector of α ^new , γ _i represents the i-th element of the quantization factor, γ _i =1-α _i R _ii , ∑ represents the summation operation, i = 1, 2,,M; The fourth step is to determine whether the root mean square error of the current iteration satisfies the convergence condition or reaches the maximum number of iterations. If so, perform step five. Otherwise, perform step two; the convergence condition is the difference between the mean vector obtained by the current iteration and the previous iteration. The root mean square error of the mean vector obtained in one iteration is less than e ^-4 , and the maximum number of iterations is 1000; The fifth step is to use the mean vector obtained in the current iteration as a sparse signal vector. 7. The sky-wave over-the-horizon radar distance estimation method based on sparse Bayesian algorithm according to claim 5, characterized in that: in step 5, the position of each element in the sparse vector is converted into the distance value of the target. Yes: Use the formula Convert the position of each element in the sparse vector into the distance value of the target; where c represents the speed of light, L represents the starting coordinate value in the echo data after intercepting the pulse compression in the Doppler unit where the target is located, and f _s represents the observation The sampling frequency of the vector.