OFDM super-resolution target parameter estimation method based on weighted sequence and particle swarm optimization

Abstract

The application discloses an OFDM omnidirectional sensing integrated super-resolution target parameter estimation method based on a weighted sequence and a particle swarm optimization, adopts a two-stage estimation framework, utilizes three-dimensional DFT and a unit average constant false alarm rate algorithm to complete target detection and parameter coarse estimation in a coarse estimation stage, designs a three-dimensional weighted sequence crossing a time-frequency-space domain, constructs an optimization model with maximum correlation with an echo signal, and combines a local search strategy and a particle swarm optimization algorithm to realize significant improvement of parameter estimation resolution. The method overcomes the resolution limitation of a traditional 3D-DFT algorithm and the high computational complexity of an existing super-resolution algorithm, and realizes joint high-precision estimation of multiple target distances, speeds and angles with low complexity.

Description

Technical Field

[0001] This invention belongs to the field of radar signal processing technology, and specifically relates to an OFDM integrated super-resolution target parameter estimation method based on weighted sequence and particle swarm optimization. Background Technology

[0002] As the vision of 6G wireless communication becomes clearer, its mission is no longer limited to increasing communication speed and connection scale, but rather to providing key support for emerging applications such as autonomous driving, smart manufacturing, and immersive interaction. Therefore, 6G systems not only need to meet higher communication performance requirements, but also must possess real-time and accurate environmental perception capabilities to achieve deep integration of communication and sensing functions. Sensing-communication integration has thus become an important technological direction for promoting the intelligence and efficiency of 6G networks. Among numerous candidate waveforms, OFDM, with its advantages of high spectral efficiency, simple implementation, and high compatibility with existing communication standards, is widely considered the ideal choice for building a sensing-communication integrated system.

[0003] However, achieving high-precision joint estimation of target range, velocity, and angle remains a challenging issue for OFDM sensing systems employing multiple receiving antennas. While existing two-dimensional and one-dimensional subspace algorithms can estimate range, velocity, and angle separately or step-by-step, they often struggle to guarantee effective and reliable pairing of three-dimensional parameters in multi-target scenarios. Traditional 3D-DFT algorithms, although capable of joint estimation of three-dimensional target parameters, suffer from resolution limitations imposed by the number of receiving antennas, subcarriers, and symbols, making it difficult to achieve satisfactory parameter estimation performance.

[0004] Therefore, there is an urgent need to further study a joint estimation algorithm for target parameters that combines low complexity and super-resolution capability for multi-receiver antenna OFDM inductive systems. Summary of the Invention

[0005] The purpose of this invention is to provide a method for estimating target parameters of OFDM integrated super-resolution based on weighted sequence and particle swarm optimization.

[0006] The technical solution to achieve the purpose of this invention is: a method for estimating target parameters of OFDM integrated super-resolution based on weighted sequence and particle swarm optimization, comprising the following steps:

[0007] Step 1: Perform time-frequency-spatial smoothing on the received signal matrix to obtain several received signal sub-matrices; perform 3D-DFT on each received signal sub-matrix and use the three-dimensional element CA-CFAR algorithm for target detection and coarse parameter estimation to obtain... One target peak index;

[0008] Step 2, using the first The search range for the three-dimensional weighted sequence is calculated using the target peak index. A three-dimensional weighted sequence and echo signal maximum correlation optimization model is constructed, and a three-dimensional frequency objective function is defined. The PSO algorithm is used to perform continuous optimization search to obtain the optimal three-dimensional frequency, and the i-th frequency is calculated. Detailed estimation results for each target;

[0009] Step 3: Eliminate the echo signal of the estimated target using three-dimensional weighted sequence projection;

[0010] Step 4: Repeat steps 2 to 3 until all received signal sub-matrices and all their target parameters are estimated; average the target parameter estimates obtained from all received signal sub-matrices to obtain the final estimation result.

[0011] An electronic device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the steps of the method described above.

[0012] A computer-readable storage medium having a computer program stored thereon that, when executed by a processor, implements the steps of the above-described method.

[0013] A computer program product includes a computer program that, when executed by a processor, implements the steps of the above-described method.

[0014] Compared with the prior art, the present invention has the following significant advantages: (1) The accuracy of the target parameters estimated by the present invention is much higher than that of the traditional method; (2) The present invention solves the problem of high computational complexity of the traditional super-resolution algorithm; (3) The parameter estimation results of the present invention are less different from the preset results and are more accurate.

[0015] The present invention will now be described in further detail with reference to the accompanying drawings. Attached Figure Description

[0016] Figure 1 This is a flowchart of the method of the present invention.

[0017] Figure 2 It is the time-frequency-spatial smoothing graph in step 1 of the algorithm of this invention.

[0018] Figure 3 This is a performance comparison chart of the present invention embodiment with traditional parameter estimation algorithms in a single-target scenario.

[0019] Figure 4 This is a performance comparison chart of the present invention embodiment with traditional parameter estimation algorithms in a multi-object scenario.

[0020] Figure 5This refers to different shape parameters when using Kaiser-Bessel sequences in embodiments of the present invention. Comparison of parameter estimation performance under different conditions.

[0021] Figure 6 This is a comparison chart of parameter estimation performance under different linear sidelobe ratios R when using the Dolph-Chebyshev sequence in an embodiment of the present invention.

[0022] Figure 7 This is a comparison chart of parameter estimation performance under different sidelobe levels (SLL) when using Taylor sequences in an embodiment of the present invention. Detailed Implementation

[0023] This invention proposes a super-resolution target parameter estimation method based on weighted sequences and particle swarm optimization (PSO). The method employs a two-stage estimation framework. In the coarse estimation stage, target detection and coarse parameter estimation are completed using Three Dimensional DFT (3D-DFT) and Cell-Averaging Constant False Alarm Rate (CA-CFAR) algorithms. In the fine estimation stage, a three-dimensional weighted sequence spanning the time-frequency-spatial domain is designed, and an optimization model maximizing its correlation with the echo signal is constructed. This is combined with a local search strategy and Particle Swarm Optimization (PSO) algorithm to significantly improve the parameter estimation resolution. The proposed method overcomes the limitations of traditional 3D-DFT algorithms in resolution and the high computational complexity of existing super-resolution algorithms, achieving low-complexity, joint high-precision estimation of distance, velocity, and angle for multiple targets. The detailed algorithm flow is as follows: Figure 1 As shown.

[0024] The specific steps of the super-resolution target parameter estimation method based on weighted sequence and particle swarm optimization of the present invention are described in detail below. The method includes:

[0025] Step 1: Receive the signal matrix of the OFDM integrated sensing system. Re-divided into several received signal sub-matrices, , and These represent the subcarrier dimension, symbol dimension, and receiver antenna dimension of the received signal matrix, respectively. For example... Figure 2 As shown, the received signal matrix Re-divided into several sizes The received signal submatrix, , and These represent the subcarrier dimension, symbol dimension, and receive antenna dimension of the subsignal matrix, respectively. One received signal submatrix It can be represented as

[0026]

[0027] in, , , Denotes the sub-signal matrix index, where

[0028]

[0029]

[0030]

[0031] Therefore, the number of sub-signal matrices can be expressed as: The corresponding step size , , After obtaining the signal submatrix, it needs to be processed to obtain initial target detection and parameter estimation results. Specifically, a 3D-DFT is first performed on the sub-signal matrix to obtain... Then, the CA-CFAR algorithm for three-dimensional elements is used for target detection, and the indices of the detected targets are recorded. Assuming there are a total of... The first goal, the... In the nth received sub-signal matrix The peak indices of the targets are respectively represented as: , , , .

[0032] Step 2, using the first The search range for the three-dimensional weighted sequence is calculated using the target peak index. A three-dimensional weighted sequence and echo signal maximum correlation optimization model is constructed, and a three-dimensional frequency objective function is defined. The PSO algorithm is used to perform continuous optimization search to obtain the optimal three-dimensional frequency, and the i-th frequency is further calculated. Detailed estimation results for each target.

[0033] Step 2-1, using the first The search range for the three-dimensional weighted sequence is calculated using the target peak index, i.e.:

[0034]

[0035] The three-dimensional frequency search interval is denoted as

[0036]

[0037]

[0038]

[0039] Its three-dimensional frequency upper and lower boundaries are respectively

[0040]

[0041]

[0042]

[0043] When the upper bound of the above interval exceeds 1, it is truncated to 1.

[0044] Step 2-2: Construct a three-dimensional weighted sequence and echo signal maximum correlation optimization model, i.e.

[0045]

[0046] in, yes No. The optimal three-dimensional frequency of a target The residual signal obtained after performing three-dimensional weighted sequential projection elimination processing on the previous target. It is a three-dimensional weighted sequence. For the conjugate transpose, for any candidate three-dimensional frequency It is possible to construct, for example, a three-dimensional weighted sequence.

[0047]

[0048] These correspond to the subcarrier dimension, symbol dimension, and antenna dimension frequencies, respectively. The center frequency is Bandwidth is The weighted sequence, The center frequency is Bandwidth is The weighted sequence, The center frequency is Bandwidth is a weighted sequence;

[0049] Furthermore, it is possible to define a three-dimensional frequency objective function.

[0050]

[0051] Steps 2-3: Use the PSO algorithm to perform continuous optimization search to obtain the optimal three-dimensional frequency. This is to search within the three-dimensional frequency range. To find the optimal three-dimensional frequency combination, the PSO algorithm is introduced for continuous optimization search. Let the... The number of particles corresponding to each target is , No. The first iteration The three-dimensional frequency vector and velocity of each particle are respectively expressed as:

[0052]

[0053] make This indicates that the particle has reached the [number]th [number]. The optimal position of the individual in the next iteration. If the position is the globally optimal position among all particles, then we have

[0054]

[0055]

[0056] Particle swarm in the The velocity and position are updated in the next iteration.

[0057]

[0058]

[0059]

[0060] in, Inertial weights; and These are the weighting coefficients for individual cognitive items and group social items; and The three components independently obey Uniform distribution; operator This represents projecting the particle position into a three-dimensional frequency search space. Above, that is, boundary constraints are applied to each dimension, as shown below.

[0061]

[0062]

[0063]

[0064] In each iteration, the velocities and positions of all particles are updated, and new objective function values ​​are calculated. Update the individual's optimal position accordingly. and the global optimal position When the number of iterations reaches the preset limit... Alternatively, stop the search when the objective converges. Ultimately, the [number]th [objective] can be obtained. The detailed estimation results of the three-dimensional frequency of each target are as follows:

[0065]

[0066] Steps 2-4: Calculate the fine estimation results of the target parameters using the optimal three-dimensional frequency, i.e.

[0067]

[0068]

[0069]

[0070] in, For the first Distance to each target For the first The speed of the target For the first From the perspective of a single goal; At the speed of light, For subcarrier spacing, For wavelength, For symbol period, The distance between array elements.

[0071] Step 3: Eliminate the echo signal of the estimated target using three-dimensional weighted sequence projection, i.e.

[0072]

[0073] in, It is a three-dimensional weighted sequence at the estimated optimal three-dimensional frequencies of the target.

[0074] Step 4: Repeat steps 2 to 3 until all received signal sub-matrices and all target parameters are estimated. Average the target parameter estimates obtained from all received signal sub-matrices to obtain the final estimation result.

[0075] The algorithm is summarized as follows:

[0076]

[0077] The following is a detailed description with reference to the embodiments:

[0078] Example

[0079] The system simulation parameters are set as shown in Table 1. The algorithm is simulated under single-target and multi-target conditions. For the single-target case, the target angle, distance, and velocity parameters are set as follows: For multiple objectives, set the number of objectives to 3 and the parameter to [value]. , , .

[0080] Table 1

[0081]

[0082] Figure 3 The figure illustrates the parameter estimation performance of the proposed algorithm, 3D-DFT algorithm, and subspace-based super-resolution algorithm as a function of signal-to-noise ratio in a single-target scene, where RCRB is the square root of the Cramer-Rao lower bound. As shown in the figure, the proposed algorithm exhibits better target parameter estimation performance compared to other algorithms, and its performance is very close to that of RCRB.

[0083] Figure 4 The figure illustrates the parameter estimation performance of the proposed algorithm, 3D-DFT algorithm, and subspace-based super-resolution algorithm in a multi-object scenario, as a function of signal-to-noise ratio. As shown in the figure, the proposed algorithm exhibits better target parameter estimation performance compared to other algorithms, and is very close to that of RCRB.

[0084] Figure 5 This demonstrates the algorithm of the present invention with different shape parameters when using Kaiser-Bessel sequences. The comparison chart of parameter estimation performance under the given conditions shows that adjusting the shape parameters of the weighted sequence can significantly affect the parameter estimation performance of the algorithm. As the width of the main lobe of the weighted sequence decreases, the parameter estimation performance gradually improves. However, when the width of the main lobe decreases to a certain extent, the increase in the side lobes will lead to a deterioration in the estimation performance. Therefore, the design of the weighted sequence should balance the relationship between the main lobe and the side lobes.

[0085] Figure 6 The diagram shows a comparison of parameter estimation performance under different linear sidelobe ratios R when using the Dolph-Chebyshev sequence in this invention. It can be seen that adjusting the linear sidelobe ratio of the weighted sequence significantly affects the parameter estimation performance of the algorithm. As the main lobe width of the weighted sequence decreases, the parameter estimation performance gradually improves. However, when the main lobe width decreases to a certain extent, the increase in sidelobes leads to a deterioration in estimation performance. Therefore, the design of the weighted sequence should balance the relationship between the main lobe and the sidelobes.

[0086] Figure 7 The diagram shows a comparison of parameter estimation performance under different sidelobe levels (SLL) when using Taylor sequences in this invention. It can be seen that adjusting the sidelobe levels of the weighted sequence significantly affects the parameter estimation performance of the algorithm. As the main lobe width of the weighted sequence decreases, the parameter estimation performance gradually improves. However, when the main lobe width decreases to a certain extent, the increase in sidelobe levels leads to a deterioration in estimation performance. Therefore, the design of the weighted sequence should balance the relationship between the main lobe and the sidelobe.

Claims

1. A method for estimating target parameters in OFDM synesthetic super-resolution based on weighted sequence and particle swarm optimization, characterized in that, Includes the following steps: Step 1: Perform time-frequency-spatial smoothing on the received signal matrix to obtain several received signal sub-matrices; perform 3D-DFT on each received signal sub-matrix and use the three-dimensional element CA-CFAR algorithm for target detection and coarse parameter estimation to obtain... One target peak index; Step 2, using the first The search range for the three-dimensional weighted sequence is calculated using the target peak index. A three-dimensional weighted sequence and echo signal maximum correlation optimization model is constructed, and a three-dimensional frequency objective function is defined. The PSO algorithm is used to perform continuous optimization search to obtain the optimal three-dimensional frequency, and the i-th frequency is calculated. Detailed estimation results for each target; Step 3: Eliminate the echo signal of the estimated target using three-dimensional weighted sequence projection; Step 4: Repeat steps 2 to 3 until all received signal sub-matrices and all their target parameters are estimated; average the target parameter estimates obtained from all received signal sub-matrices to obtain the final estimation result.

2. The OFDM synesthetic super-resolution target parameter estimation method based on weighted sequence and particle swarm optimization according to claim 1, characterized in that, The specific process of step 1 is as follows: OFDM inductive integrated system receive signal matrix Re-divided into several sizes The received signal submatrix, , and These represent the subcarrier dimension, symbol dimension, and receiver antenna dimension of the received signal matrix, respectively. , and These represent the subcarrier dimension, symbol dimension, and receive antenna dimension of the subsignal matrix, respectively; the... One received signal submatrix Represented as ； in, , , Denotes the sub-signal matrix index, where ， ， ； Therefore, the number of sub-signal matrices is expressed as ; where the corresponding step size , , After obtaining the signal sub-matrix, it needs to be processed to obtain initial target detection and parameter estimation results; first, perform 3D-DFT on the sub-signal matrix to obtain... Then, the CA-CFAR algorithm for three-dimensional elements is used for target detection, and the indices of the detected targets are recorded. Assuming there are a total of... The first goal, the... In the nth received sub-signal matrix The peak indices of the targets are respectively represented as: , , , .

3. The OFDM synesthetic super-resolution target parameter estimation method based on weighted sequence and particle swarm optimization according to claim 2, characterized in that, The specific process of step 2 is as follows: Step 2-1, using the first The search range of the three-dimensional weighted sequence is calculated using the target peak index, i.e. ； in ， ， ， in ， ， ； Step 2-2: Construct a three-dimensional weighted sequence and echo signal maximum correlation optimization model, i.e. ； in, The residual signal obtained after performing three-dimensional weighted sequential projection elimination processing on the previous target. It is a three-dimensional weighted sequence. For the conjugate transpose; define the three-dimensional frequency objective function. ； Steps 2-3: Use the PSO algorithm to perform continuous optimization search to obtain the optimal three-dimensional frequency; in order to search within the three-dimensional frequency range Find the optimal three-dimensional frequency combination within the range, and introduce the PSO algorithm to continuously optimize and search for it; let the first... The number of particles corresponding to each target is , No. The first iteration The three-dimensional frequency vector and velocity of each particle are respectively expressed as: ； make This indicates that the particle has reached the [number]th [number]. The optimal position of the individual in the next iteration. If the position is the globally optimal position among all particles, then we have ； ； Particle swarm in the The velocity and position are updated in the next iteration. ； ； ； in, Inertial weights; and These are the weighting coefficients for individual cognitive items and group social items; and The three components independently obey Uniform distribution; operator This represents projecting the particle position into a three-dimensional frequency search space. Above, that is, boundary constraints are applied to each dimension, as shown below. ； ； ； In each iteration, the velocities and positions of all particles are updated, and new objective function values ​​are calculated. Update the individual's optimal position accordingly. and the global optimal position When the number of iterations reaches the preset limit. Alternatively, stop the search when the objective converges; finally, the [number]th [item] is obtained. The detailed estimation results of the three-dimensional frequency of each target are as follows: ； Steps 2-4: Calculate the fine estimation results of the target parameters using the optimal three-dimensional frequency.

4. The OFDM synesthetic super-resolution target parameter estimation method based on weighted sequence and particle swarm optimization according to claim 3, characterized in that, The specific process of step 3 is as follows: The echo signal of the estimated target is eliminated by using three-dimensional weighted sequence projection, i.e. ； in, It is a three-dimensional weighted sequence at the estimated optimal three-dimensional frequencies of the target.

5. An electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the method as described in any one of claims 1 to 4.

6. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the method as described in any one of claims 1 to 4.

7. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the method described in any one of claims 1 to 4.

Images