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CN-122017881-A - Motion compensation imaging method based on Radon transformation and SWO algorithm

CN122017881ACN 122017881 ACN122017881 ACN 122017881ACN-122017881-A

Abstract

The invention discloses a motion compensation imaging method based on Radon transformation and SWO algorithm, and belongs to the technical field of laser radar signal processing. The method comprises the steps of firstly carrying out declivity receiving and distance pulse compression on ISAL echo signals, detecting energy peaks through Radon transformation, roughly estimating target radial speed, then constructing a local constraint search space based on roughly estimated speed, carrying out fine search on speed and acceleration parameters in the constraint space by taking image contrast as an fitness function through an SWO algorithm, and finally constructing a full-aperture phase compensation function through optimal parameters for imaging. The method solves the problems of low precision, slow iteration and easy sinking into local extremum of the traditional cross-correlation method, and remarkably improves imaging quality and algorithm efficiency.

Inventors

  • Wu Chongpei
  • CAO CHANGQING
  • YANG BOYA
  • TANG JIAN
  • FENG ZHEJUN

Assignees

  • 西安电子科技大学

Dates

Publication Date
20260512
Application Date
20260205

Claims (10)

  1. 1. A motion compensated imaging method based on Radon transform and SWO algorithm, comprising the steps of: Step S1, performing declivity receiving processing and distance pulse compression on an echo signal ISAL to obtain a one-dimensional distance image signal containing a motion error; S2, carrying out Radon integral transformation on the signal amplitude diagram output in the step S1, and searching the maximum energy peak value in a Radon domain to obtain the rough radial speed of the target; s3, constructing a local constraint search space according to the rough radial velocity obtained in the step S2, and setting a velocity search range and an acceleration search range; S4, initializing SWO algorithm population in the local constraint search space defined in the step S3, and iteratively searching optimal speed estimation value and acceleration estimation value; And S5, constructing a phase compensation function by utilizing the optimal speed estimation value and the optimal acceleration estimation value, compensating the original data, and carrying out azimuth FFT processing to obtain ISAL images.
  2. 2. The method according to claim 1, wherein in step S1, a one-dimensional range profile signal comprising motion errors is obtained Expressed as: In the formula, Is the signal amplitude after the pulse pressure, In order to be able to take a short time, As an absolute time of day, In order to be a slow time period, For the bandwidth to be available, In order to achieve the light velocity, the light beam is, For the range history of the target, In imaginary units.
  3. 3. The method according to claim 1, wherein in step S2, the calculation formula of the Radon integral transformation is: Wherein, the Is an amplitude graph of the one-dimensional range profile signal, As a function of the impulse, Is a diameter of the electrode, the diameter of the electrode is the diameter of the electrode, In the form of a polar angle, the angle of the polar, Is Radon domain coordinates.
  4. 4. The method of claim 3, wherein step S2 further comprises binarizing the distance-compressed image to suppress background noise, highlighting the target trajectory skeleton, and then performing a Radon transform, in the Radon domain The maximum energy peak value coordinates are searched through the traversal, and the rough radial velocity is calculated according to the following formula : Wherein, the Is the pulse repetition frequency.
  5. 5. The method of claim 1, wherein in step S3, the local constrained search space is constructed with a speed search range set to Wherein For the rough radial velocity obtained in step S2, A preset speed floating threshold value, and an acceleration searching range is set as 。
  6. 6. The method of claim 5, wherein the speed float threshold Set to 5m/s, the acceleration search range is set to 。
  7. 7. The method according to claim 1, wherein in step S4, the SWO algorithm uses image contrast as a fitness function The calculation formula is as follows: Wherein, the The gray value of the image is represented, And The size of the images, respectively.
  8. 8. The method of claim 7, wherein the specific process of step S4 includes: Random generation within a locally constrained search space Individual individuals containing speed and acceleration information are used as an initial population; Calculating the fitness value of each individual; updating the individual position according to the fitness value until a preset termination condition is met; Outputting the corresponding speed estimated value when the fitness value is maximum And acceleration estimation value 。
  9. 9. The method according to claim 1, wherein in step S5, the phase compensation function The expression of (2) is: In the formula, For the frequency modulation slope, In the light wave length of the light wave, In imaginary units.
  10. 10. A computer readable storage medium, on which a computer program is stored, characterized in that the program, when being executed by a processor, implements the method according to any one of claims 1 to 9.

Description

Motion compensation imaging method based on Radon transformation and SWO algorithm Technical Field The invention relates to the field of laser radar signal processing, in particular to a ISAL maneuvering target joint compensation algorithm based on Radon transformation and SWO (SPIDER WASP Optimization) algorithm. Background ISAL (inverse synthetic aperture lidar) has extremely short working wavelength (usually in the micron order), can provide finer target characteristics (such as texture and shape details) compared with the traditional microwave radar, and has great strategic application value in the fields of national defense and aerospace. However, the extremely short wavelength makes ISAL imaging extremely sensitive to relative motion between the target and the radar. Small non-uniform motion can introduce significant phase errors in the echo signal, resulting in imaging defocus or complete failure. Therefore, how to quickly and accurately estimate and compensate for motion errors from echo data is a core bottleneck of ISAL technology. Currently, the existing ISAL motion error compensation methods are mainly divided into two categories: 1) The cross-correlation method is combined with the PGA algorithm, wherein the cross-correlation method is used for simple envelope alignment, and the PGA (phase gradient self-focusing) algorithm is used for phase compensation. In ISAL, the accuracy of the method is not high, the imaging quality is affected by errors generated by separate compensation, and the PGA algorithm is easily affected by the distribution of strong scattering points. 2) Direct optimization parameter estimation method-target parameters are directly estimated using a swarm intelligence algorithm (e.g., PSO). Although a high-quality image can be obtained, the iteration time is too long and local extremum is easily trapped due to the large search space. Therefore, there is a need for a ISAL motion compensation method that can ensure imaging accuracy, improve convergence speed, and avoid local extremum. Disclosure of Invention The invention aims to solve the technical problems that the existing cross-correlation method and PGA method are low in compensation precision, and the direct optimization method is overlong in iteration time and easy to fall into local extremum, and provides an inverse synthetic aperture laser radar motion error compensation method using Radon transformation combined SWO algorithm. In order to achieve the above purpose, the present invention adopts the following technical scheme: in a first aspect, the present invention provides a motion compensated imaging method based on Radon transform and SWO algorithm, comprising the steps of: Step S1, performing declivity receiving processing and distance pulse compression on an echo signal ISAL (inverse synthetic aperture laser radar) to obtain a one-dimensional range profile signal containing motion errors; S2, carrying out Radon integral transformation on the signal amplitude diagram output in the step S1, and searching the maximum energy peak value in a Radon domain to obtain the rough radial speed of the target; s3, constructing a local constraint search space according to the rough radial velocity obtained in the step S2, and setting a velocity search range and an acceleration search range; S4, initializing SWO algorithm population in the local constraint search space defined in the step S3, and iteratively searching optimal speed estimation value and acceleration estimation value; And S5, constructing a phase compensation function by utilizing the optimal speed estimation value and the optimal acceleration estimation value, compensating the original data, and carrying out azimuth FFT processing to obtain ISAL images. Preferably, in step S1, a one-dimensional range profile signal containing motion errorsExpressed as: In the formula, Is the signal amplitude after the pulse pressure,In order to be able to take a short time,As an absolute time of day,In order to be a slow time period,For the bandwidth to be available,In order to achieve the light velocity, the light beam is,For the range history of the target,In imaginary units. Preferably, in step S2, the calculation formula of the Radon integral transformation is: Wherein, the Is an amplitude graph of the one-dimensional range profile signal,Is Radon domain coordinates. Preferably, the step S2 further comprises binarizing the distance-compressed image to suppress background noise, highlighting the target track skeleton, and then performing Radon transformationThe maximum energy peak value coordinates are searched through the traversal, and the rough radial velocity is calculated according to the following formula: Wherein, the Is the pulse repetition frequency. Preferably, in step S3, the construction strategy of the local constraint search space is that the speed search range is set as followsWhereinFor the rough radial velocity obtained in step S2,A preset speed floating threshol