A public strong point target-based long-distance waveform radar polarization calibration method

Abstract

This invention provides a long-range waveform radar polarization calibration method based on a common strong point target, which can be used to calibrate the long-range waveforms of fully polarimetric radars. The invention first designs an experimental scheme considering the characteristics of multi-waveform radars and proposes a passive polarization calibration method based on a common strong point target in a common measurement area for different waveforms. Compared to existing polarization calibration algorithms, this method addresses the difficulty of conducting long-range waveform field experiments by using easily calibrated short-range waveforms to calibrate long-range waveforms.

Description

Technical Field

[0001] This invention belongs to the field of fully polarimetric radar technology, specifically relating to a long-range waveform radar polarization calibration method based on common strong point targets. Background Technology

[0002] Numerous studies have demonstrated the significant value of polarization information in radar detection, tracking, and parameter inversion. Therefore, many radar systems have incorporated fully polarimetric measurement techniques, such as polarimetric synthetic aperture radar, fully polarimetric ground-penetrating radar, and insect radar. The calibration process is crucial for obtaining accurate polarimetric information.

[0003] Traditional fully polarimetric radar polarization calibration methods are relatively mature in laboratory applications, but their performance in actual field environments is not outstanding, especially for long-range radar waveforms, where far-field experimental conditions are difficult to achieve, making calibration experiments difficult to conduct. Summary of the Invention

[0004] In view of this, the present invention provides a long-range waveform radar polarization calibration method based on common strong point targets. Taking advantage of the characteristic of multi-waveform radar where short-range and long-range waveforms share the same antenna, this method utilizes the easy-to-calibrate short-range waveform to calibrate the difficult-to-calibrate long-range waveform. This helps multi-waveform fully polarimetric radar obtain more accurate polarization information when detecting long-range targets.

[0005] This invention is operated according to the following steps:

[0006] Step 1: Perform polarization calibration on short-range waveforms that are easy to calibrate.

[0007] Step 2: Locate the common target in the observation area of ​​both near-range and far-range waveforms;

[0008] Step 3: Measure the common target using both near-range and far-range waveforms;

[0009] Step 4: Establish a long-distance waveform system model and solve for the long-distance waveform system error;

[0010] Step 5: Use the system error obtained from the solution to compensate for other objectives.

[0011] The present invention has the following beneficial effects:

[0012] This invention presents a long-range waveform radar polarization calibration method based on common strong point targets, applicable to the polarization calibration of long-range waveforms in multi-waveform fully polarimetric radars. The invention first designs an experimental scheme considering the characteristics of multi-waveform radars and proposes a passive polarization calibration method for long-range waveforms based on common strong point targets within overlapping detection areas of different waveforms. Compared to existing polarization calibration algorithms, this method addresses the difficulty of conducting long-range radar waveform field experiments by using close-range waveforms to calibrate long-range waveforms. Attached Figure Description

[0013] Figure 1 This is a curve showing the difference between the algorithm's solution error and the preset real error in the simulation experiment as a function of the number of common targets.

[0014] Figure 2 This is a curve showing the phase difference between the algorithm's solution error and the preset real error in the simulation experiment as a function of the number of common targets. Detailed Implementation

[0015] This invention is a long-range waveform radar polarization calibration method based on common strong point targets. The basic idea is as follows: First, we switch between different waveforms to measure common targets within the measurement range. Next, we establish a numerical optimization model based on the measurement results of different waveforms. Finally, we use the least squares method to estimate error parameters and complete the polarization calibration.

[0016] The present invention will now be described in detail with reference to the accompanying drawings and embodiments.

[0017] Step 1: Perform polarization calibration on short-range waveforms that are easy to calibrate.

[0018] Conventional polarization calibration methods, such as measuring metal spheres and dihedral angles, are used to calibrate the short-range waveform of the multi-waveform radar to ensure the accuracy of the short-range waveform measurement results. In this way, we can consider the short-range measurement results as the true value of the target.

[0019] Step 2: Identify the common target in the observation area of ​​both near-range and far-range waveforms.

[0020] Near-range and far-range waveforms have different pulse widths, and their detection ranges overlap. Within the overlapping detection range of near and medium ranges, a rotating radar antenna is used to locate strong targets. These targets can be naturally occurring ground features. To ensure accuracy, only strong targets with high signal-to-noise ratios are selected. The positions of these strong targets, along with the azimuth and elevation angles of the antenna, are recorded to create a target position table.

[0021] Step 3: Measure the common target using both near-range and far-range waveforms.

[0022] Rotate the radar antenna to the designated position according to the antenna azimuth and elevation angle of the first target in the target location table. Set the radar to close-range mode and measure the close-range measurement result of this target. Keeping the antenna angle unchanged, switch the radar to long-range mode and measure the long-range measurement result of this target. Then, rotate the radar antenna to the designated position according to the antenna azimuth and elevation angle of the second target in the target location table. Repeat the above steps to measure all targets and obtain the close-range and long-range results of all targets.

[0023] Step 4: Establish a long-distance waveform system model and solve for the long-distance waveform system error using the following algorithm.

[0024] Let the target scattering matrix (PSM) be:

[0025]

[0026] Where S hh S vv S hv and S vh These are the channel elements of the scattering matrix.

[0027] The scattering matrix of the target measured by radar is:

[0028]

[0029] Where M hh M vv M hv and M vh These are the channel elements of the scattering matrix.

[0030] The fully polarized system model can be represented as:

[0031]

[0032] The measured scattering matrix M is related to the actual target scattering matrix S. λ is a constant representing the gain and loss (amplitude and phase) of the radar system, r represents the distance between the radar and the target, and k0 = 2π / λ represents the wavenumber of the waveform. ij (i,j=h,v) represents the data processing gain of the four channels. The symbol ⊙ represents the Hadamard product, R h R v T h and T v This indicates that the amplitude and phase of the receiving and transmitting channels are inconsistent. C1 and C2 represent crosstalk signals between different antenna channels. S 13 and S 24 This indicates that the amplitude and phase of the antenna's H and V channels are inconsistent.

[0033] For systems with high isolation, crosstalk factors C1 and C2 can be ignored, and the new system model can be expressed as follows:

[0034]

[0035] Right now:

[0036]

[0037] Where: E is the system error matrix, E ij (i,j=h,v) represents the amplitude-phase imbalance of the four polarization channels. g is a common parameter factor of each element of the polarization scattering matrix, which can be obtained by measuring the metal sphere, so its influence is not considered.

[0038] The same measurement error model is used for both near-range and far-range waveforms. Although the causes of error are the same for both near-range and far-range waveforms, the error parameters are not the same due to differences in the transmitted waveforms and power. Within the common observation range of the near-range and far-range waveforms, the k-th common target is selected:

[0039]

[0040] In this context, the subscript S represents a near-range waveform, and the subscript L represents a far-range waveform. S and M L E represents the measurement scattering matrices of the near-range and far-range waveforms, respectively. S and E L Representing the systematic errors of the near-range and far-range waveforms respectively, solve for E. L The problem can be transformed into an optimization problem of minimizing the differences in measurement results from different modes.

[0041] The scenario assumes that the near-field waveform has already been calibrated, i.e., E S Given, construct the cost function:

[0042]

[0043] Among them, S S (k) and S L (k) represents the target scattering matrix after compensation of the measurement results for the near-range and far-range waveforms, respectively.

[0044]

[0045] At this point, the cost function is the difference between two matrices. For ease of calculation, we convert it to a numerical difference. Therefore, we first sum the results for the four polarization channels and then subtract the results from the near-range waveform. Since this difference is a complex number, we first take the modulus of the difference and then calculate the sum of squares of the differences for all common targets, i.e.:

[0046]

[0047] In this context, |·| represents the modulo symbol.

[0048]

[0049] Transform (10) into vector form as follows:

[0050]

[0051] Right now:

[0052] P L =M L W (12)

[0053] Among them, P L W is an N×1 matrix, and M is a 4×1 matrix. L For an N×4 matrix, P S It can be obtained using the same method. Next, the target loss function is simplified:

[0054]

[0055] in,(·) T Represents the matrix transpose symbol. The matrix represents the conjugate of the matrix.

[0056] To obtain the parameter values ​​when the cost function is minimized, we need to differentiate the cost function until its derivative equals zero. At this point, the optimal parameter estimates can be obtained.

[0057]

[0058] in,(·) -1 This represents the inversion of the matrix. Then, we can obtain...

[0059]

[0060] in, This represents the system error of the long-distance waveform obtained by using this algorithm. This represents the error of the four polarization channels.

[0061] Step 5: Use the system error obtained from the solution to compensate for other objectives.

[0062] At this point, the error matrix of the long-range waveform has been obtained, and polarization calibration has been completed. The actual scattering matrix of the long-range waveform for measuring other targets can be obtained by the following formula:

[0063]

[0064] Therefore, this invention provides a long-range waveform radar polarization calibration method based on common strong point targets, employing the long-range waveform radar polarization calibration method based on common strong point targets described in this invention. The method's effects are as follows: Figure 1 and Figure 2 As shown.

[0065] This method is applicable to multi-waveform fully polarimetric radars with overlapping regions between waveforms, and can solve and compensate for certain waveform system error parameters.

[0066] In summary, the above are merely embodiments of the present invention based on single-data examples and are not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A long-range waveform radar polarization calibration method based on common strong point targets, characterized in that, include: Step 1: Perform polarization calibration on short-range waveforms that are easy to calibrate. Step 2: Locate the common target in the observation area of ​​both near-range and far-range waveforms; Step 3: Measure the common target using both near-range and far-range waveforms; Step 4: Establish a long-distance waveform system model and solve for the long-distance waveform system error; Step 5: Use the system error obtained from the solution to compensate for other objectives; In step four, for systems with high isolation, the crosstalk factor is ignored. and The new system model is represented as ； in, It is the system error matrix. The amplitude and phase imbalance represents the four polarization channels; It is a common parameter factor of all elements of the polarization scattering matrix; Indicates the wave number of the waveform; Indicates the distance between the radar and the target; symbol It represents the Hadamardi (or Hadama) product; Within the common observation range of near-range and far-range waveforms, the k-th common target is selected: ； Wherein, the subscript S represents a near-distance waveform, and the subscript L represents a far-distance waveform; and The measurement scattering matrices represent the near-range and far-range waveforms, respectively. and Representing the systematic errors of near-range and far-range waveforms respectively, solve... The problem can be transformed into an optimization problem of minimizing the differences in measurement results from different modes; The scenario assumes that the near-field waveform has already been calibrated, i.e. Given, construct the cost function: ； in, and These represent the target scattering matrices after compensation of the measurement results for near-range and far-range waveforms, respectively. ； First, take the modulo of the differences, then calculate the sum of squared differences for all common objectives, i.e.: ； in, Represents the modulo symbol; Will: ； Converted to vector form, it looks like this: ； Right now: ； in, for matrix, for matrix, for matrix, It can be obtained by the same method; The objective loss function simplifies to: ； in, Represents the matrix transpose symbol. The matrix represents the conjugate of the matrix.

2. The long-range waveform radar polarization calibration method based on common strong point targets as described in claim 1, characterized in that, The fully polarized system model in step four is represented as follows: ； Among them, the measured scattering matrix Scattering matrix of the actual target Related, The constant represents the gain and loss of the radar system. Indicates the distance between the radar and the target. Indicates the wave number of the waveform; Indicates the data processing gain of the four channels; symbol It represents the Hadamah accumulation. , , and This indicates that the amplitude and phase of the receiving and transmitting channels are inconsistent. and This indicates the crosstalk signal between different channels of the antenna. and This indicates that the amplitude and phase of the antenna's H and V channels are inconsistent.

3. The long-range waveform radar polarization calibration method based on common strong point targets as described in claim 1, characterized in that, The optimal parameter estimate in step four is: ； in, This represents taking the inverse of the matrix; then, we obtain... : ； in, This represents the system error of the long-distance waveform obtained by using this algorithm. This represents the error of the four polarization channels.

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