Synthetic aperture radar imaging method based on ultra wide band

Abstract

The invention relates to a synthetic aperture radar imaging method based on an ultra wide band, and the method comprises the steps: S1, fixing an ultra wide band radar on a mobile device, collecting the baseband data of the ultra wide band radar, and obtaining the background noise generated by the energy leakage of an antenna; s2, collecting ultra-wideband radar baseband data in the same motion direction, and then performing preprocessing; s3, carrying out two-dimensional fast Fourier transform processing, and converting the ultra-wideband radar data from a time domain to a frequency domain; s4, setting a reference distance, and calculating a reference function; calculating phase compensation; focusing the target at the reference distance; s5, realizing stolt interpolation through mapping or bending of a distance frequency axis, and obtaining a basic imaging result after two-dimensional inverse fast Fourier transform; and S6, setting a scale factor function on a depth axis, combining the scale factor function and a basic imaging result to realize depth-based energy compensation on the plane image, and finally constructing an environment model contour through extreme value selection. And the construction precision of the environment model is improved.

Description

Technical Field

[0001] The invention belongs to the technical field of indoor mapping, and relates to a synthetic aperture radar imaging algorithm, and in particular to an ultra-wideband-based synthetic aperture radar imaging method. Background Art

[0002] High-rise fires have become one of the major disasters that threaten urban public safety and social development. High-rise fires are characterized by rapid fire spread, difficult evacuation, and difficulty in firefighting. Due to the complex structure and dense population of high-rise buildings, it is difficult to control and escape once a fire breaks out. At this time, understanding the floor plan of the incident site can help rescuers choose the best path and effectively implement the rescue plan. Therefore, the preparation of the building floor plan is of great significance for search and rescue.

[0003] At present, robots equipped with surveying and mapping equipment are usually used to measure indoor floor plans. Commonly used surveying and mapping equipment include lidar, depth cameras, and RGB cameras. Cameras and lasers are both in the visible light band and are easily affected by light and weather. They are not suitable for all-weather working environments. At the same time, lasers can damage optical devices and cameras can cause privacy issues. Although millimeter-wave radar is currently widely used in autonomous driving, it is not suitable for the construction of complex indoor environment models. It has problems such as poor accuracy and stability, weak penetration and mapping capabilities, large minimum detection distance, and high cost that is not conducive to promotion. However, ultra-wideband technology is not affected by light and weather, has high resolution, strong anti-interference capabilities, and is developing rapidly. Ultra-wideband radar has gradually become a hot spot for research and application.

[0004] However, unlike lidar, ultra-wideband radar has a wider beam and weaker directivity, and it is difficult to identify small targets at a long distance. For this reason, it is proposed to refer to synthetic aperture radar imaging technology, allowing the radar to move at a constant speed within a certain distance, and by forming a virtual antenna array, improve the angular resolution and provide spatial information of different targets.

[0005] Chinese patent document CN 114720985 A discloses a fast imaging method based on NCS, which increases the width of the surveying band and achieves good focusing at the edge of the surveying band by replacing the original phase approximation based on Taylor formula expansion with a phase approximation method based on Legendre expansion. However, this method is an approximate imaging algorithm. Although it has the advantages of not requiring interpolation operations, high imaging efficiency, and easy integration with motion compensation processes, the algorithm is often used in large squint imaging models and is not suitable for the construction of environmental models.

[0006] Chinese patent document CN 104407349 A discloses a frequency domain imaging method for a fixed dual-station low-frequency ultra-wideband SAR. First, based on the echo signal of a fixed dual-station low-frequency ultra-wideband SAR, high-order phase errors are compensated in the two-dimensional frequency domain to obtain a pre-processed echo signal. Then, the pre-processed echo signal is processed in the range direction and the azimuth direction to obtain a SAR image. Although the invention adopts the method of compensating high-order phase errors in the two-dimensional frequency domain to correct the errors caused by the second-order Taylor expansion of the range-direction coupling term, thereby reducing the influence of high-order errors on the imaging processing under the low-frequency ultra-wideband signal, and thus realizing high-precision imaging processing of a fixed dual-station low-frequency ultra-wideband SAR, a well-focused image is obtained, but the advantage of the dual-station SAR in this mode is that it not only has the advantages of traditional satellite-borne and airborne dual-station SAR, but also has lower cost, is similar to a single station in terms of imaging processing complexity, and can realize interferometric measurement by configuring multiple receivers; in general, this method is also aimed at large squint imaging measurements of airborne or satellite-borne, and cannot be directly used for the construction of building environment models.

[0007] Chinese patent document CN 110850409 A discloses a dual-station synthetic aperture radar imaging method based on time reversal, which uses an ultra-wideband wireless transceiver model and a stepper motor module to build a dual-station synthetic aperture radar system; constructs frequency domain transmission matrices SF-MDM and FF-MDM according to the target scattering signal; performs singular value decomposition on the two matrices respectively, and divides the linear space based on the left singular vector into a signal subspace and a noise subspace; uses the two subspaces to construct SF and FF imaging functions, and uses the imaging function to achieve high-resolution imaging in all directions. Although the invention has advantages such as greater mobility, greater concealment, and flexible measurement; it can use ultra-wideband signals for target detection and has strong robustness; however, it is a method proposed for the large platform error of the dual-station system and the difficulty and complexity of the imaging processing algorithm, and is not suitable for the application scenario of a single-station ultra-wideband radar environment model.

[0008] Therefore, it is necessary to propose a synthetic aperture radar imaging method based on single-station ultra-wideband to improve the accuracy and richness of environmental model construction and lay the foundation for subsequent path planning and navigation. Summary of the invention

[0009] The present invention provides an ultra-wideband synthetic aperture radar imaging method, which forms a virtual antenna array by moving the radar at a fixed speed, thereby increasing the angular resolution of the ultra-wideband radar and improving the accuracy of environmental model construction.

[0010] In order to solve the above technical problems, the technical solution adopted by the present invention is that the synthetic aperture radar imaging method based on ultra-wideband has the following specific steps:

[0011] S1: Fix the UWB radar on a mobile device and place it in an open environment to collect UWB radar baseband data and obtain background noise generated by antenna energy leakage;

[0012] S2: moving the mobile device at a fixed speed in an open environment, collecting the ultra-wideband radar baseband data in the same moving direction, and then preprocessing the ultra-wideband radar baseband data to obtain ultra-wideband radar data for imaging;

[0013] S3: performing a two-dimensional fast Fourier transform (2D FFT) process on the ultra-wideband radar data for imaging obtained in step S2, converting the ultra-wideband radar data from the time domain to the frequency domain, and obtaining frequency domain ultra-wideband radar data;

[0014] S4: setting a reference distance and calculating a reference function; then calculating phase compensation according to the set reference distance and the reference function, that is, multiplying the frequency domain ultra-wideband radar data obtained in step S3 by the reference function, so that the target at the reference distance is focused; and the target at the non-reference distance is partially focused;

[0015] S5: Stolt interpolation is achieved by mapping or bending the range-frequency axis, changing the data phase in the two-dimensional frequency domain, adjusting the azimuth and range phases, and eliminating residual phase modulation above the second order. Finally, the basic imaging result is obtained after two-dimensional inverse fast Fourier transform (2D IFFT).

[0016] S6: Considering that the target reflection energy will decay with increasing distance, a scaling factor function is set on the depth axis. The scaling factor function is combined with the basic imaging result obtained in step S5 to achieve depth-based energy compensation for the plane image, and finally the environmental model contour is constructed by extreme value selection.

[0017] Preferably, in step S1, the background noise of the ultra-wideband radar is obtained by averaging the ultra-wideband radar baseband data, and the formula is:

[0018] a n =mean(a) (1);

[0019] Among them, a is the data collected by the ultra-wideband radar in an open environment, mean(·) is the averaging function, and a n is the background noise of the ultra-wideband radar.

[0020] Preferably, the preprocessing in step S2 is to sequentially perform background noise removal, normalization, absolute value taking, statistical filtering and Savitzky-Golay (SG) filtering.

[0021] Preferably, the specific steps of step S2 are:

[0022] S21: First, remove the background noise in the ultra-wideband radar baseband data. The formula is:

[0023] c=ba n (2);

[0024] Among them, b is the ultra-wideband radar baseband data, a n is the background noise of the ultra-wideband radar, and c is the baseband data of the ultra-wideband radar after removing the background noise;

[0025] S22: normalizing the ultra-wideband radar baseband data after removing the background noise in step S21, the formula is:

[0026]

[0027] Wherein, c represents the ultra-wideband radar baseband data after removing the background noise, μ c represents the mean value of the ultra-wideband radar baseband data after removing the background noise, σ c represents the standard deviation of the ultra-wideband radar baseband data after background noise removal, and d represents the ultra-wideband radar baseband data after background noise removal and normalization;

[0028] S23: taking the absolute value and statistical filtering of the ultra-wideband radar baseband data processed in step S22, the formula is:

[0029]

[0030] Among them, μ d represents the average value of the background noise removed and normalized ultra-wideband radar baseband data, d j | represents the absolute value of the jth value in the normalized ultra-wideband radar baseband data after background noise removal, e j The ultra-wideband radar base station data is the result after background noise removal, normalization, absolute value taking and statistical filtering;

[0031] S24: using an SG filter to perform secondary smoothing on the ultra-wideband radar data after background noise is removed, normalized, absolute valued, and statistically filtered in step S23, to obtain ultra-wideband radar data for imaging.

[0032] Preferably, in step S3, two-dimensional fast Fourier transform (2D FFT) processing is performed to convert the ultra-wideband radar data from the time domain to the frequency domain. The specific steps are:

[0033] S31: Selecting an echo signal model of ultra-wideband radar data for imaging;

[0034] S32: performing fast Fourier transform on the ultra-wideband radar data used for imaging in the range direction (along the direction in which the radar wave is emitted);

[0035] S33: The stationary phase principle is used in the azimuth direction to obtain an approximation of the azimuth fast Fourier transform result, thereby obtaining frequency domain ultra-wideband radar data.

[0036] Preferably, the echo signal model in step S31 is expressed as:

[0037]

[0038] Among them, A p is the signal gain, which is related to the target material. is a Gaussian model with a mean of The standard deviation is the pulse width σ, r p (t) is the slant distance between the target and the radar, c is the speed of light, and f c is the radar center frequency, τ is the time corresponding to the range direction, also known as the fast time, and t is the azimuth time, also known as the slow time; the formula for the fast Fourier transform in step S32 is:

[0039]

[0040] Among them, A p represents the signal gain, which is related to the target material, t represents the azimuth time, also known as slow time, and f τ represents the frequency corresponding to the distance, σ represents the pulse width, The standard deviation of the Gaussian model, f c represents the radar center frequency, r p (t) represents the slant range between the target and the radar, and c represents the speed of light;

[0041] The formula using the static phase principle in step S33 is:

[0042]

[0043] Among them, A p represents the signal gain, which is related to the target material, f τ represents the frequency corresponding to the distance, σ represents the pulse width, The standard deviation of the Gaussian model, f t represents the frequency corresponding to the azimuth, v represents the speed of the mobile car, x represents the azimuth distance, r represents the depth in the distance, and f c represents the radar center frequency, and c represents the speed of light.

[0044] Preferably, the specific steps of calculating the phase compensation according to the set reference distance and the reference function in step S4 are:

[0045] S41: First, the distance and radar equivalent speed are set at the reference distance, and the best phase compensation of the ultra-wideband radar data in the two-dimensional frequency domain is calculated;

[0046] S42: Calculate the residual phase in the two-dimensional frequency domain based on the phase of the ultra-wideband data for imaging in the two-dimensional frequency domain and the phase compensation at the reference distance to achieve focusing of the target at the reference distance.

[0047] Preferably, the formula for calculating the phase compensation in step S41 is:

[0048]

[0049] Among them, θ ref represents the phase compensation at the reference distance, f t Indicates the frequency corresponding to the azimuth, f τ represents the frequency corresponding to the distance, v represents the speed of the moving car, x ref is the reference distance set in the azimuth direction, r ref is the reference depth set in the distance direction, f c represents the radar center frequency, c represents the speed of light;

[0050] The formula for calculating the residual phase in the two-dimensional frequency domain in step S42 is:

[0051]

[0052] Among them, θ RFM represents the residual phase in the two-dimensional frequency domain, f t Indicates the frequency corresponding to the azimuth, f τ represents the frequency corresponding to the distance, v represents the speed of the moving car, x ref is the reference distance set in the azimuth direction, r ref is the reference depth set in the distance direction, f c represents the radar center frequency, c represents the speed of light, x represents the distance in azimuth, and r represents the depth in distance.

[0053] Preferably, the specific steps of performing depth-based energy compensation on the basic imaging result and obtaining the contour of the environment model by extreme value extraction in step S6 are:

[0054] S61: Depth-based energy compensation reduces the noise impact of targets within 4 meters and enhances the energy of targets at a depth of 4-8 meters by setting a proportional factor;

[0055] S62: When constructing the environmental model profile, a fixed percentage of energy value is selected as the adaptive threshold to reduce the impact of low-power multipath noise and Gaussian white noise and improve the adaptability of the system.

[0056] Preferably, the proportional factor calculation formula in step S61 is:

[0057]

[0058] Among them, r is the depth of the target range, max is the maximum value of the target range depth, and thre is the set threshold.

[0059] Compared with the prior art, the present invention has the following beneficial effects: the synthetic aperture radar imaging method based on ultra-wideband is not affected by light and weather, has strong anti-interference ability, and compared with millimeter-wave radar, the miniaturization of ultra-wideband radar greatly reduces its weight, cost and energy consumption, and has cost advantages in a wider range of applications. At the same time, combined with synthetic aperture radar imaging technology, by allowing the radar to move at a constant speed within a certain distance to form a virtual antenna array, the angular resolution is improved without increasing the hardware cost and complex antenna array, and spatial information of different targets is provided to the system. The synthetic aperture radar imaging method based on ultra-wideband can be widely used in the field of indoor environment model construction technology. The method is suitable for a mobile car or robot containing an ultra-wideband radar chip, which uses ultra-wideband radar baseband data as an observation quantity and is an intelligent platform for building an environmental model by moving in an unfamiliar environment. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] Figure 1 is a flow chart of the ultra-wideband synthetic aperture radar imaging method of the present invention;

[0061] Figure 2 It is a scene schematic diagram of the ultra-wideband synthetic aperture radar imaging method of the present invention;

[0062] Figure 3 It is the original echo signal of the ultra-wideband synthetic aperture radar imaging method of the present invention;

[0063] Figure 4 It is an imaging diagram of the ultra-wideband synthetic aperture radar imaging method of the present invention;

[0064] Figure 5 It is an environmental model outline diagram of the ultra-wideband-based synthetic aperture radar imaging method of the present invention. DETAILED DESCRIPTION

[0065] The embodiments of the present invention are described in detail below in conjunction with the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solutions of the present invention and are not intended to limit the protection scope of the present invention.

[0066] Example: Figure 1 As shown, the ultra-wideband synthetic aperture radar imaging method comprises the following specific steps:

[0067] S1: Fix the ultra-wideband radar on a mobile device and place it in an open environment for about 10 minutes to collect the ultra-wideband radar baseband data and obtain the background noise generated by the antenna energy leakage; the mobile device in this embodiment is a mobile car; Figure 2 As shown;

[0068] In step S1, the background noise of the ultra-wideband radar is obtained by averaging the ultra-wideband radar baseband data, and the formula is:

[0069] a n =mean(a) (1);

[0070] Among them, a is the data collected by the ultra-wideband radar in an open environment, mean(·) is the averaging function, and a n is the background noise of the ultra-wideband radar;

[0071] S2: The mobile device is made to move in an open environment at a fixed speed of 0.1 m / s-0.5 m / s, and the ultra-wideband radar baseband data is collected in the same moving direction, and then the ultra-wideband radar baseband data is pre-processed to obtain ultra-wideband radar data for imaging;

[0072] The preprocessing in step S2 is to perform background noise removal, normalization, absolute value taking, statistical filtering and Savitzky-Golay (SG) filtering in sequence;

[0073] The specific steps of step S2 are:

[0074] S21: First, remove the background noise in the ultra-wideband radar baseband data. The formula is:

[0075] c=ba n (2);

[0076] Among them, b is the ultra-wideband radar baseband data, a n is the background noise of the ultra-wideband radar, and c is the baseband data of the ultra-wideband radar after removing the background noise;

[0077] S22: normalizing the ultra-wideband radar baseband data after removing the background noise in step S21, the formula is:

[0078]

[0079] Wherein, c represents the ultra-wideband radar baseband data after removing the background noise, μ c represents the mean value of the ultra-wideband radar baseband data after removing the background noise, σ crepresents the standard deviation of the ultra-wideband radar baseband data after background noise removal, and d represents the ultra-wideband radar baseband data after background noise removal and normalization;

[0080] S23: taking the absolute value and statistical filtering of the ultra-wideband radar baseband data processed in step S22, the formula is:

[0081]

[0082] Among them, μ d represents the average value of the background noise removed and normalized ultra-wideband radar baseband data, d j | represents the absolute value of the jth value in the normalized ultra-wideband radar baseband data after background noise removal, e j The ultra-wideband radar base station data is the result after background noise removal, normalization, absolute value taking and statistical filtering;

[0083] S24: using an SG filter to perform secondary smoothing on the ultra-wideband radar data after background noise is removed, normalized, absolute valued, and statistically filtered in step S23, to obtain ultra-wideband radar data for imaging;

[0084] S3: performing a two-dimensional fast Fourier transform (2D FFT) process on the ultra-wideband radar data for imaging obtained in step S2, converting the ultra-wideband radar data from the time domain to the frequency domain, and obtaining frequency domain ultra-wideband radar data;

[0085] The specific steps of performing two-dimensional fast Fourier transform (2D FFT) processing in step S3 to convert ultra-wideband radar data from the time domain to the frequency domain are as follows:

[0086] S31: Selecting an echo signal model of ultra-wideband radar data for imaging;

[0087] The echo signal model in step S31 is expressed as:

[0088]

[0089] Among them, A p is the signal gain, which is related to the target material. is a Gaussian model with a mean of The standard deviation is the pulse width σ, r p (t) is the slant distance between the target and the radar, c is the speed of light, and f c is the radar center frequency, τ is the time corresponding to the range direction, also called the fast time, and t is the time corresponding to the azimuth direction, also called the slow time;

[0090] S32: performing fast Fourier transform on the ultra-wideband radar data used for imaging in the range direction (along the direction in which the radar wave is emitted);

[0091] The formula of the fast Fourier transform in step S32 is:

[0092]

[0093] Among them, A p represents the signal gain, which is related to the target material, t represents the azimuth time, also known as slow time, and f τ represents the frequency corresponding to the distance, σ represents the pulse width, The standard deviation of the Gaussian model, f c represents the radar center frequency, r p (t) represents the slant range between the target and the radar, and c represents the speed of light;

[0094] S33: The stationary phase principle is used in the azimuth direction to obtain an approximation of the fast Fourier transform result in the azimuth direction, thereby obtaining frequency domain ultra-wideband radar data;

[0095] The formula using the static phase principle in step S33 is:

[0096]

[0097] Among them, A p represents the signal gain, which is related to the target material, f τ represents the frequency corresponding to the distance, σ represents the pulse width, The standard deviation of the Gaussian model, f t represents the frequency corresponding to the azimuth, v represents the speed of the mobile car, x represents the azimuth distance, r represents the depth in the distance, and f c represents the radar center frequency, c represents the speed of light;

[0098] S4: setting a reference distance and calculating a reference function; then calculating phase compensation according to the set reference distance and the reference function; that is, multiplying the frequency domain ultra-wideband radar data obtained in step S3 by the reference function so that the target at the reference distance is focused and the target at the non-reference distance is partially focused;

[0099] The specific steps of calculating the phase compensation according to the set reference distance and the reference function in step S4 are:

[0100] S41: First, the distance and radar equivalent speed are set at the reference distance, and the best phase compensation of the ultra-wideband radar data in the two-dimensional frequency domain is calculated;

[0101] S42: calculating the residual phase in the two-dimensional frequency domain according to the phase of the ultra-wideband data for imaging in the two-dimensional frequency domain and the phase compensation at the reference distance, so as to achieve focusing of the target at the reference distance;

[0102] The formula for calculating the phase compensation in step S41 is:

[0103]

[0104] Among them, θ ref represents the phase compensation at the reference distance, f t Indicates the frequency corresponding to the azimuth, f τ represents the frequency corresponding to the distance, v represents the speed of the moving car, x ref is the reference distance set in the azimuth direction, r ref is the reference depth set in the distance direction, f c represents the radar center frequency, c represents the speed of light;

[0105] The formula for calculating the residual phase in the two-dimensional frequency domain in step S42 is:

[0106]

[0107] Among them, θ RFM represents the residual phase in the two-dimensional frequency domain, f t Indicates the frequency corresponding to the azimuth, f τ represents the frequency corresponding to the distance, v represents the speed of the moving car, x ref is the reference distance set in the azimuth direction, r ref is the reference depth set in the distance direction, f c represents the radar center frequency, c represents the speed of light, x represents the distance in azimuth, and r represents the depth in range;

[0108] S5: Stolt interpolation is achieved by mapping or bending the range-frequency axis, changing the data phase in the two-dimensional frequency domain, adjusting the azimuth and range phases, and eliminating residual phase modulation above the second order. Finally, the basic imaging result is obtained after two-dimensional inverse fast Fourier transform (2D IFFT).

[0109] S6: Considering that the target reflected energy will decay with increasing distance, a scaling factor function is set in the depth axis, and the scaling factor function is combined with the basic imaging result obtained in step S5 to achieve depth-based energy compensation for the plane image, and finally the environment model contour is constructed by extreme value selection;

[0110] The specific steps of performing depth-based energy compensation on the basic imaging result and obtaining the contour of the environment model by extreme value extraction in step S6 are:

[0111] S61: Depth-based energy compensation reduces the noise impact of targets within 4 meters and enhances the energy of targets at a depth of 4-8 meters by setting a proportional factor;

[0112] The proportional factor calculation formula in step S61 is:

[0113]

[0114] Among them, r is the depth of the target distance, max is the maximum value of the target distance depth, and thre is the set threshold;

[0115] S62: When constructing the environmental model profile, a fixed percentage of 70%-90% of the energy value is selected as the adaptive threshold to reduce the impact of low-power multipath noise and Gaussian white noise and improve the adaptability of the system.

[0116] The ultra-wideband synthetic aperture radar imaging method of the present invention has a simple structure, improves the angular resolution without increasing the hardware cost and complex antenna array, and provides the system with spatial information of different targets. The ultra-wideband synthetic aperture radar imaging method can be widely used in intelligent platforms that move and build environmental models in unfamiliar environments. Figure 3 The original echo signal is shown. The specific effect results are as follows Figure 4 and Figure 5 As shown, Figure 4 For imaging; Figure 5 This is the outline of the environment model.

[0117] The specific embodiments described above further illustrate the purpose, technical solutions and beneficial effects of the present invention. It should be understood that the above are only specific embodiments of the present invention and are not intended to limit the present invention. As long as various non-substantial improvements are made using the method concept and technical solution of the present invention, or the concept and technical solution of the present invention are directly applied to other occasions without improvement, they are all within the protection scope of the present invention.

Claims

1. A synthetic aperture radar imaging method based on ultra-wideband, characterized in that: The specific steps are: S1: Fix the UWB radar on a mobile device and place it in an open environment to collect UWB radar baseband data and obtain background noise generated by antenna energy leakage; S2: moving the mobile device at a fixed speed in an open environment, collecting the ultra-wideband radar baseband data in the same moving direction, and then preprocessing the ultra-wideband radar baseband data to obtain ultra-wideband radar data for imaging; S3: performing a two-dimensional fast Fourier transform process on the ultra-wideband radar data for imaging obtained in step S2, converting the ultra-wideband radar data from the time domain to the frequency domain, and obtaining frequency domain ultra-wideband radar data; S4: setting a reference distance and calculating a reference function; and then calculating a phase compensation according to the set reference distance and the reference function so that the target at the reference distance is focused; S5: Stolt interpolation is achieved by mapping or bending the range-frequency axis, and finally the basic imaging result is obtained after two-dimensional inverse fast Fourier transform; S6: Setting a scaling factor function on the depth axis, combining the scaling factor function with the basic imaging result obtained in step S5, achieving depth-based energy compensation for the plane image, and finally constructing the environmental model contour by extreme value selection.

2. The ultra-wideband synthetic aperture radar imaging method according to claim 1, characterized in that: In step S1, the background noise of the ultra-wideband radar is obtained by averaging the ultra-wideband radar baseband data, and the formula is: a n =mean(a) (1); Among them, a is the data collected by the ultra-wideband radar in an open environment, mean(·) is the averaging function, and a n is the background noise of the ultra-wideband radar.

3. The ultra-wideband synthetic aperture radar imaging method according to claim 1, characterized in that: The preprocessing in step S2 is to perform background noise removal, normalization, absolute value taking, statistical filtering and Savitzky-Golay filtering in sequence.

4. The ultra-wideband synthetic aperture radar imaging method according to claim 3, characterized in that: The specific steps of step S2 are: S21: First, remove the background noise in the ultra-wideband radar baseband data. The formula is: c=b-a n (2); Among them, b is the ultra-wideband radar baseband data, a n is the background noise of the ultra-wideband radar, and c is the baseband data of the ultra-wideband radar after removing the background noise; S22: normalizing the ultra-wideband radar baseband data after removing the background noise in step S21, the formula is: Wherein, c represents the ultra-wideband radar baseband data after removing the background noise, μ c represents the mean value of the ultra-wideband radar baseband data after removing the background noise, σ c represents the standard deviation of the ultra-wideband radar baseband data after background noise removal, and d represents the ultra-wideband radar baseband data after background noise removal and normalization; S23: taking the absolute value and statistical filtering of the ultra-wideband radar baseband data processed in step S22, the formula is: Among them, μ d represents the average value of the background noise removed and normalized ultra-wideband radar baseband data, |d j | represents the absolute value of the jth value in the normalized ultra-wideband radar baseband data after background noise removal, e j The ultra-wideband radar base station data is the result after background noise removal, normalization, absolute value taking and statistical filtering; S24: using an SG filter to perform secondary smoothing on the ultra-wideband radar data after background noise is removed, normalized, absolute valued, and statistically filtered in step S23, to obtain ultra-wideband radar data for imaging.

5. The ultra-wideband synthetic aperture radar imaging method according to claim 3, characterized in that: The specific steps of performing two-dimensional fast Fourier transform processing in step S3 to convert ultra-wideband radar data from the time domain to the frequency domain are as follows: S31: Selecting an echo signal model of ultra-wideband radar data for imaging; S32: performing fast Fourier transform on the ultra-wideband radar data used for imaging in the range direction; S33: The stationary phase principle is used in the azimuth direction to obtain an approximation of the azimuth fast Fourier transform result, thereby obtaining frequency domain ultra-wideband radar data.

6. The ultra-wideband synthetic aperture radar imaging method according to claim 5, characterized in that: The echo signal model in step S31 is expressed as: Among them, A p is the signal gain, which is related to the target material. is a Gaussian model with a mean of The standard deviation is the pulse width σ, r p (t) is the slant distance between the target and the radar, c is the speed of light, and f c is the radar center frequency, τ is the time corresponding to the range direction, also known as the fast time, and t is the azimuth time, also known as the slow time; the formula for the fast Fourier transform in step S32 is: Among them, A p represents the signal gain, which is related to the target material, t represents the azimuth time, also known as slow time, and f τ represents the frequency corresponding to the distance, σ represents the pulse width, The standard deviation of the Gaussian model, f c represents the radar center frequency, r p (t) represents the slant range between the target and the radar, and c represents the speed of light; The formula using the static phase principle in step S33 is: Among them, A p represents the signal gain, which is related to the target material, f τ represents the frequency corresponding to the distance, σ represents the pulse width, The standard deviation of the Gaussian model, f t represents the frequency corresponding to the azimuth, v represents the speed of the mobile car, x represents the azimuth distance, r represents the depth in the distance, and f c represents the radar center frequency, and c represents the speed of light.

7. The ultra-wideband synthetic aperture radar imaging method according to claim 5, characterized in that: The specific steps of calculating the phase compensation according to the set reference distance and the reference function in step S4 are: S41: firstly, the distance and the radar equivalent speed are set at the reference distance, and the phase compensation of the ultra-wideband radar data in the two-dimensional frequency domain is calculated; S42: Calculate the residual phase in the two-dimensional frequency domain based on the phase of the ultra-wideband data for imaging in the two-dimensional frequency domain and the phase compensation at the reference distance to achieve focusing of the target at the reference distance.

8. The ultra-wideband synthetic aperture radar imaging method according to claim 7, characterized in that: The formula for calculating the phase compensation in step S41 is: Among them, θ ref represents the phase compensation at the reference distance, f t Indicates the frequency corresponding to the azimuth, f τ represents the frequency corresponding to the distance, v represents the speed of the moving car, x ref is the reference distance set in the azimuth direction, r ref is the reference depth set in the distance direction, f c represents the radar center frequency, c represents the speed of light; The formula for calculating the residual phase in the two-dimensional frequency domain in step S42 is: Among them, θ RFM represents the residual phase in the two-dimensional frequency domain, f t Indicates the frequency corresponding to the azimuth, f τ represents the frequency corresponding to the distance, v represents the speed of the moving car, x ref is the reference distance set in the azimuth direction, r ref is the reference depth set in the distance direction, f c represents the radar center frequency, c represents the speed of light, x represents the distance in azimuth, and r represents the depth in distance.

9. The ultra-wideband synthetic aperture radar imaging method according to claim 6, characterized in that: The specific steps of performing depth-based energy compensation on the basic imaging result and obtaining the contour of the environment model by extreme value extraction in step S6 are: S61: Depth-based energy compensation reduces the noise impact of targets within 4 meters and enhances the energy of targets at a depth of 4-8 meters by setting a proportional factor; S62: When constructing the environmental model profile, a fixed percentage of energy value is selected as the adaptive threshold to reduce the impact of low-power multipath noise and Gaussian white noise and improve the adaptability of the system.

10. The ultra-wideband synthetic aperture radar imaging method according to claim 9, characterized in that: The proportional factor calculation formula in step S61 is: Among them, r is the depth of the target range, max is the maximum value of the target range depth, and thre is the set threshold.

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