Environmental Imaging Method and Device Based on Millimeter-Wave Radar

Abstract

This invention relates to the field of metal environment imaging, and more particularly to an environmental imaging method and apparatus based on millimeter-wave radar. The method includes the following steps: transmitting multi-frequency signals to the metal environment to be detected using a millimeter-wave radar array, and extracting multipath signal sequences; performing interferometric phase analysis based on the multipath signal sequences to obtain the three-dimensional coordinates of the metal and the actual target reflection point; performing iterative forward calculation based on the three-dimensional coordinates of the metal and the actual target reflection point to obtain a first metal environment image; and performing iterative residual convergence on the first metal environment image to output a second metal environment image. This invention achieves high-precision and reliable three-dimensional imaging of complex metal environments.

Description

Technical Field

[0001] This invention relates to the field of metal environment imaging, and more particularly to an environment imaging method and apparatus based on millimeter-wave radar. Background Technology

[0002] In densely metal environments, millimeter-wave signals exhibit strong reflection, high-energy echoes, and severe multipath propagation. The smooth, highly conductive surfaces of metal readily induce specular reflection, scattering, and multiple reflections, resulting in complex multipath distributions in the echo paths. Traditional millimeter-wave imaging techniques, primarily designed for relatively open or weakly reflective environments, often suffer from signal path aliasing, increased false targets, and blurred image contours when facing scenes with concentrated metal structures, severely impacting image quality. In these metallic environments, multipath effects not only interfere with determining the true target location but can also cause echo energy to concentrate on non-real paths, producing artifacts, ghosting, and even target misalignment. Furthermore, with the increasingly complex geometries of metal structures—such as metal pipe corridors, cabins, containers, and equipment racks—numerous curved surfaces and angles exist, causing the reflection paths of millimeter-wave signals to increase exponentially. Conventional radar signal processing methods, such as traditional imaging algorithms relying on single-frequency or fixed-beam scanning, struggle to effectively separate multipath signals in complex metallic environments and cannot accurately recover the true geometric features of the target structure. Traditional 3D reconstruction algorithms are prone to overfitting, algorithm divergence, or difficulty in convergence in multipath environments, making the imaging results unable to meet the requirements of high precision and high reliability in engineering applications. Summary of the Invention

[0003] To address the aforementioned technical problems, this invention proposes an environmental imaging method and apparatus based on millimeter-wave radar, thereby resolving at least one of the aforementioned technical problems.

[0004] To achieve the above objectives, the present invention provides an environmental imaging method based on millimeter-wave radar, comprising the following steps: Step S1: Based on the millimeter-wave radar array, transmit multi-frequency signals to the metal environment to be detected and extract the multipath signal sequence; Step S2: Perform interferometric phase analysis based on the multipath signal sequence to obtain the three-dimensional coordinates of the metal and the actual target reflection point; Step S3: Perform iterative forward calculations based on the three-dimensional coordinates of the metal and the actual target reflection point to obtain the first metal environment image; Step S4: Iteratively converge the residuals of the first metal environment image and output the second metal environment image.

[0005] This specification provides an environmental imaging device based on millimeter-wave radar for performing the environmental imaging method based on millimeter-wave radar as described above, comprising: The signal acquisition unit is used to transmit multi-frequency signals to the metal environment under test based on the millimeter-wave radar array and extract multipath signal sequences. The interferometric analysis unit is used to perform interferometric phase analysis based on multipath signal sequences to obtain the three-dimensional coordinates of the metal and the actual target reflection point; The environmental imaging unit is used to perform iterative forward calculations based on the three-dimensional coordinates of the metal and the actual target reflection point to obtain the first metal environmental image. The residual convergence unit is used to perform iterative residual convergence on the first metal environment image and output the second metal environment image.

[0006] The specific benefits of this invention are as follows: Millimeter-wave signals have high frequency and short wavelength, enabling high-precision detection of metallic objects, especially suitable for imaging small targets or details in complex environments. Multi-frequency transmission allows for the capture of reflection features at different frequency bands, improving noise immunity and reducing signal attenuation and interference. Multipath signal sequences record multiple reflection paths of the target and environment, providing rich information for subsequent 3D coordinate reconstruction. Multi-frequency, multi-angle acquisition can obtain reflection features from different surfaces and angles of the target, improving the comprehensive perception of complex metallic environments. Interferometric phase analysis converts minute phase changes of millimeter waves into spatial position information, achieving millimeter-level or higher precision 3D coordinate acquisition. It not only obtains the target position but also distinguishes actual reflection points, reducing the impact of false targets or noise interference. 3D coordinates and actual reflection point information provide accurate initial data for subsequent iterative imaging, improving imaging accuracy and realism. It can handle multiple reflection paths in complex multipath metallic environments, ensuring the integrity of the environmental structure is captured. Visualizing target and environmental information forms the first round of environmental imaging, facilitating the judgment of environmental features. Initial imaging provides a starting point for further iterations, allowing for residual optimization in subsequent steps. Forward computation enables rapid acquisition of preliminary imaging results without requiring a single, highly complex global optimization. This preliminary imaging clearly identifies the spatial location of key metallic objects, providing guidance for refined imaging. Iterative residual analysis eliminates errors and noise, making the second-round imaging results more closely resemble the actual environment. The iterative convergence method effectively overcomes the blurring or errors in the preliminary imaging, reducing false features and misjudgments. Corrections are made for multipath interference and reflection overlap, improving the interpretability of complex metallic environments. The final output of the second metallic environment image can be used for precise environmental sensing, navigation, or other industrial applications, offering advantages such as high precision and low noise. Attached Figure Description

[0007] Figure 1 This is a schematic diagram of the steps of an environmental imaging method based on millimeter-wave radar according to the present invention; Figure 2 This is a detailed flowchart illustrating the implementation steps of step S1. Figure 3This is a detailed flowchart illustrating the implementation steps of step S2; Figure 4 , Figure 5 This is a schematic diagram of the three-dimensional spatial positioning results and the metal reflection texture map. Detailed Implementation

[0008] It should be understood that the specific embodiments described herein are for illustrative purposes only and are not intended to limit the scope of the invention.

[0009] This application provides an environmental imaging method and apparatus based on millimeter-wave radar. The execution entities of the millimeter-wave radar-based environmental imaging method and apparatus include, but are not limited to, mechanical equipment, data processing platforms, cloud server nodes, network upload devices, etc., which can be considered as general computing nodes in this application. The data processing platform includes, but is not limited to, at least one of an audio-visual management system, an information management system, and a cloud-based data management system.

[0010] Please see Figures 1 to 5 This invention provides an environmental imaging method based on millimeter-wave radar, comprising the following steps: Step S1: Based on the millimeter-wave radar array, transmit multi-frequency signals to the metal environment to be detected and extract the multipath signal sequence; Step S2: Perform interferometric phase analysis based on the multipath signal sequence to obtain the three-dimensional coordinates of the metal and the actual target reflection point; Step S3: Perform iterative forward calculations based on the three-dimensional coordinates of the metal and the actual target reflection point to obtain the first metal environment image; Step S4: Iteratively converge the residuals of the first metal environment image and output the second metal environment image.

[0011] In the embodiments of the present invention, see Figure 1 The diagram below illustrates the steps of an environmental imaging method based on millimeter-wave radar according to the present invention. In this example, the steps of the environmental imaging method based on millimeter-wave radar include: Step S1: Based on the millimeter-wave radar array, transmit multi-frequency signals to the metal environment to be detected and extract the multipath signal sequence; In this embodiment, multi-frequency signal transmission over a metallic environment is employed to capture rich multipath scattering information. Multi-frequency transmission typically covers the 76–81 GHz millimeter-wave band with a bandwidth of 1–3 GHz to provide millimeter-level range resolution. The radar array forms a virtual aperture through multiple transmission channels, achieving multi-angle coverage. The array spacing is typically λ / 2 (approximately 2–3 mm), and the number of array elements can range from 8 to 16 to ensure a spatial angular resolution of 1–2°. During transmission, each frequency sub-band employs frequency-modulated continuous wave (FMCW) or pulse compression techniques. After reflection from the metallic environment, the transmitted signal is acquired by the receiving array. Subsequently, the received echo signals are processed by channel, frequency, and time to form a multipath signal sequence. Each sequence contains amplitude, phase, and time-of-arrival information, reflecting the propagation characteristics of direct paths, first-order reflections, and higher-order multiple reflections. To enhance the detectability of higher-order reflections, the echo signals are typically subjected to window function weighting and filtering, with Hanning windows used for time-domain smoothing and bandpass filtering in the frequency domain to suppress noise.

[0012] Step S2: Perform interferometric phase analysis based on the multipath signal sequence to obtain the three-dimensional coordinates of the metal and the actual target reflection point; In this embodiment, after obtaining the multipath signal sequence, the three-dimensional coordinates of the metal surface and the position of the target reflection point are extracted through interferometric phase analysis. The analysis process involves phase expansion of each path signal to eliminate phase jumps caused by the 2π periodicity, and phase alignment of the multi-frequency sub-bands, with a typical sub-band spacing of approximately 40–60 MHz. Subsequently, the incident angle and reflection angle are calculated by combining the phase differences of different channels and angles with spatial geometric relationships to achieve path localization. Utilizing the phase gradient and amplitude consistency of high-order multipath signals, the scattering point can be precisely located in three-dimensional space, with a spatial resolution reaching the millimeter level. The analytical method typically employs multi-channel phase difference fusion or sparse reconstruction techniques, treating the complex amplitude of each potential reflection point as an unknown quantity. By minimizing the error between the observed signal and the model's predicted signal, the optimal three-dimensional coordinate solution is obtained. In areas where the metal surface is relatively flat or the texture period is stable, multi-frequency fusion further improves the localization accuracy.

[0013] Step S3: Perform iterative forward calculations based on the three-dimensional coordinates of the metal and the actual target reflection point to obtain the first metal environment image; In this embodiment, after obtaining the three-dimensional coordinates of the metal and the target reflection points, iterative forward calculations are performed to generate a first metal environment image. The forward calculation is based on geometric optics and a multipath propagation model, mapping the complex amplitude and position of each scattering point to the theoretical echo of the receiving channel. Path length and direction are discretized in millimeter-level steps, considering the superposition effect of first-order and higher-order reflections. In the calculation, the signal contributed by each scattering point is weighted according to the incident angle, reflection angle, and distance attenuation, and multi-frequency information in the frequency sub-band range of 76–81 GHz is fused to enhance spatial resolution and suppress noise. During iteration, the forward calculated signal is compared with the actual multipath echo signal using residuals, and the amplitude and position of the scattering points are adjusted based on the residuals. Iteration can employ least squares or sparse reconstruction methods, with convergence conditions set to a total residual decrease of less than 1–2% or a maximum position adjustment of less than 1 mm. After several iterations, the generated first metal environment image exhibits the macroscopic morphology, microscopic texture, and target scattering point distribution of the metal surface.

[0014] Step S4: Iteratively converge the residuals of the first metal environment image and output the second metal environment image.

[0015] In this embodiment, after the first metal environment imaging is completed, iterative residual convergence is needed to further optimize the imaging accuracy to obtain the second metal environment imaging. This process combines the dynamic characteristics of the environment and multi-frequency signal information to perform parameter compensation on the inversion field equations. Specifically, the scattering point position, complex amplitude, and phase of the first image are used as initial values. The signal contributed by each scattering point is updated using residual calculation. The iterative update rule typically employs weighted least squares or gradient descent methods, defining a sliding window length of 5–10 frames to balance dynamic response and convergence speed. In each iteration, the residual calculation includes amplitude residuals and phase residuals. For example, the amplitude residual threshold is set to 1–2%, and the phase residual threshold is set to 0.01 rad² to ensure that small deviations are corrected. During the iteration process, higher-order multipath and path drift effects are compensated through frequency sub-band fusion and path correction to maintain imaging accuracy. After multiple rounds of residual iterative convergence, the output second metal environment imaging has higher spatial accuracy, more stable reflection intensity and phase distribution, and can more realistically reflect the target scattering points and metal surface texture.

[0016] In this embodiment, see Figure 2 The diagram below illustrates the detailed implementation steps of step S1. In this embodiment, the detailed implementation steps of step S1 include: Based on millimeter-wave radar array, multi-frequency signals are transmitted to the metal environment to be detected, and multi-channel echo signals are collected; Cluster analysis of multi-channel echo signals of various signal types is performed to extract various signal types; A multidimensional reflection dataset is generated by performing joint time-frequency analysis on the various types of signals. The multidimensional reflection dataset is intelligently labeled to generate a multipath signal sequence; the intelligent labeling includes the incident angle, reflection angle, path delay, relative intensity attenuation, frequency shift and phase jump characteristics of the reflection path.

[0017] In this embodiment, during the initial stage of using a millimeter-wave radar array for imaging multipath environments in metals, a multi-channel transmit-receive structure covering a wide frequency range is required to enable millimeter-wave signals of different frequency bands to exhibit diverse reflection behaviors in the metallic medium. Millimeter-wave signals are characterized by short wavelengths, sensitive reflection characteristics, and strong response to changes in the metal surface; therefore, multi-frequency transmission can highlight subtle differences between various reflection paths. To fully capture the reflection characteristics of the metallic environment in multiple frequency dimensions, millimeter-wave frequency bands such as 60GHz and 76–81GHz are often selected. Through continuous linear frequency modulation, segmented frequency hopping, or sub-band scanning, the transmitted signal maintains a stable phase relationship at different frequency points. The multi-transmit / multi-receive antenna array can be expanded into numerous virtual channels, enriching the angular dimension information and enabling wide-area coverage from low to large angles. The amplitude, phase, time delay, and frequency response of the echoes from each channel are recorded, forming a large dataset containing multi-frequency, multi-angle, and multi-channel reflection characteristics. To address the strong reflections from metal surfaces, the signal strength of the receiving link is typically controlled dynamically to maintain high readability of the echo against a strong scattering background. Due to their high reflectivity and diverse geometries, metal surfaces often exhibit various echo structures, including direct reflection, specular reflection, angular reflection, edge diffraction, local diffuse scattering, and multiple path propagation. Therefore, relying on a single dimension is insufficient for effective separation. Three-dimensional analysis usually begins with the time dimension, obtaining a preliminary distance distribution through distance compression or frequency difference mapping. Subsequently, angular information is introduced, and angular domain spectra are formed through array aperture analysis, such as beam pointing analysis, high-resolution angular spectrum estimation, or sparse direction reconstruction, allowing the reflected energy from different directions to be expanded. Different types of signals often exhibit significant differences in angular broadening, energy peak shape, frequency dependence characteristics, and phase stability. For example, the energy distribution of direct path signals is most concentrated, while single or multiple reflection paths may exhibit angular offsets, amplitude reductions, and phase discontinuities. By correlating information from the time, angular, and frequency dimensions, the categories of various reflected signals can be gradually identified.

[0018] After different types of echo paths are grouped, further time-frequency joint analysis is needed to integrate the energy distribution, phase changes, frequency behavior, and time characteristics of each path into a richer multidimensional reflection representation. Metallic environments exhibit significant selectivity for millimeter waves of different frequencies; therefore, the energy attenuation, phase changes, frequency domain smoothness, and frequency dependence of the path may all display unique patterns, which often cannot be fully represented in a single time or frequency domain. Through short-time Fourier transform, continuous wavelet transform, multi-frequency subband analysis, or other time-frequency transform methods, the signal can be expanded in both the time and frequency domains, allowing observation of path delay variations, frequency shift trends, phase jump characteristics, and frequency band attenuation patterns. In further combined analysis, the amplitude and phase structures at different frequencies are integrated into feature vectors and correlated with angular domain information, enabling the same path to form a continuous reflection trajectory in a multidimensional coordinate system. The high range resolution provided by the wide bandwidth of millimeter waves (e.g., the 1–4 GHz defined range) allows for the identification of even minute time delay differences between different paths. Multi-angle and multi-frequency characteristics together constitute a multi-dimensional reflection data framework encompassing "time-frequency-space-amplitude-phase". The goal of labeling is to automatically identify each independent propagation path and assign it key parameters such as incidence angle, reflection angle, path delay, relative amplitude attenuation, frequency shift, and phase jump. To achieve this, feature aggregation operations are typically used to map trajectory points exhibiting consistent characteristics in the time, frequency, and angle dimensions, thereby merging continuous peak sequences in the multi-dimensional data into a single path. Differences in phase stability, energy attenuation patterns, and spectral morphology between different paths can serve as distinguishing criteria. For example, multiple reflection paths often exhibit more pronounced amplitude attenuation and phase discontinuities, while specular reflection paths show a smoother performance in the frequency domain. After path aggregation is completed, the physical parameters of the path are deduced based on the relevant characteristics: the path delay is obtained from the peak position in the time dimension, the direction-related parameters are obtained by mapping the peak in the angular domain, the relative amplitude attenuation is estimated by the amplitude difference between different frequency bands, and the phase jump and frequency shift are obtained by the continuity analysis of the phase response at multiple frequency points.

[0019] In this embodiment, see Figure 3 The diagram below illustrates the detailed implementation steps of step S2. In this embodiment, the detailed implementation steps of step S2 include: The receiving time difference of the multipath signal sequence is calculated to obtain the receiving time difference of the signal at multiple locations; the carrier phase difference of the multipath signal sequence is calculated to obtain the phase difference delay value. Geometric reverse propagation analysis is performed based on the phase difference delay value and the signal reception time difference at multiple locations to obtain the direction of the metal interface normal vector; Radial distance information is obtained by performing a squared inversion based on the first-order reflection signal; Three-dimensional coordinates of the metal are estimated based on the normal vector direction and radial distance information of the metal interface; Interferometric phase analysis was performed on the high-order multipath reflection signal to obtain the metal reflection texture map; By reverse ray tracing and scattering point filtering of the metal reflection texture map, the actual target reflection point is obtained.

[0020] In this embodiment, after obtaining the multipath signal sequence, it is necessary to calculate the reception time difference of each path based on the time positioning characteristics of different channels, spatial locations, or path peaks in the sequence. Since millimeter waves have extremely short wavelengths and propagation speeds close to the speed of light, to extract effective time differences, it is necessary to first finely locate the amplitude peak, phase peak, or pulse-compressed time peak corresponding to each path and compare them with a reference path. The multipath signal sequence itself already contains clear time delay indicators; for example, the direct path has the shortest time delay, while first-order reflection and multiple reflection paths increase sequentially. By performing secondary interpolation or curve fitting on these peaks, the time resolution can be improved to below the sampling interval, making the time difference extraction more precise. With millimeter wave bandwidths reaching 1–3 GHz, theoretically, sub-centimeter or even millimeter-level path discrimination capabilities can be achieved. Therefore, time difference extraction is not only used to distinguish the order of paths. Due to the high frequency of millimeter waves (e.g., 60 GHz corresponds to a wavelength of approximately 5 mm; 77 GHz corresponds to a wavelength of approximately 3.9 mm), phase changes are extremely sensitive to path length; even micrometer-level path differences can cause significant phase changes. Therefore, by comparing frequencies or analyzing phase differences across channels, much more detailed path differential characteristics can be obtained than coarse time differences. During processing, it is necessary to first expand the phase at each frequency or sub-band frequency to eliminate phase jumps caused by the 2π periodicity, thus maintaining the continuity of phase change curves across different paths.

[0021] The reflection behavior at a metal interface follows the geometric relationship of the reflection path. Therefore, the time delay difference of the path is subject to definite geometric constraints related to the reflection point position, interface tilt angle, and propagation angle. The inverse analysis here is based on the specular reflection characteristic of "incident angle equals reflection angle," and combines the trends of path length difference and phase difference to construct an inverse model for the path propagation direction. By comparing the differences in reception time and phase exhibited by different channels, the directional offset of the signal reflected back from the metal interface can be determined. This directional offset has a fixed geometric relationship with the normal vector direction of the interface. When there are many multi-angle channels (such as a virtual aperture containing hundreds of angle sampling points), these path differences can be combined to form a constraint set. By solving the common solution of this set, the direction estimate of the normal vector can be obtained. After obtaining the interface direction, the radial distance information of the metal interface needs to be deduced based on the propagation characteristics of the first-order reflected signal. The first-order reflected signal usually has the characteristics of strong energy, stable phase, and relatively simple path structure, making it suitable as the main reference path for distance inversion. During the inversion process, the initial path length is obtained by converting the arrival time corresponding to the first-order reflection peak into the speed of light. Subsequently, the path projection relationship is corrected based on the geometric direction (inferred from the normal vector) to accurately map the path length to the radial distance. Because the reflection of a metallic environment has high directionality, the projection relationship of the path length in space will differ depending on the interface tilt angle. Therefore, in the distance-squared inversion step, the propagation distance is corrected using the normal angle information to reflect the true interface position. After obtaining the normal vector direction and radial distance of the metallic interface, the coordinate position of the metallic surface in three-dimensional space can be further calculated. Three-dimensional coordinate estimation relies on integrating directional constraints and distance measurements into a unified spatial framework, allowing each reflection path to correspond to a specific point in three-dimensional space. The normal vector direction provides a directional constraint indicating the interface tilt angle, preventing the coordinate solutions from freely dispersing and confining them to a spatial plane or curved surface that conforms to the reflection geometry. Using the radial distance as the spatial scale input, the distance of the reflection point is projected onto the direction vector to obtain the corresponding three-dimensional coordinate point. For example, if the normal vector indicates that the interface is biased in a specific direction, the radial distance can be decomposed along that direction to obtain the corresponding coordinates of the reflection point on the x, y, and z axes. With multiple channels or multiple angular paths, multiple independent 3D points can be obtained from these paths, and then the overall morphology of the metal interface can be approximated through consistency fusion or geometric fitting. The high-frequency characteristics of millimeter waves make directional angle estimation quite sensitive, often maintaining an error range of several millimeters to centimeters; combined with a large bandwidth, radial distance estimation also has high accuracy.

[0022] In this embodiment, the specific steps for performing interference phase analysis on the high-order multipath reflection signal to obtain the metal reflection texture map are as follows: Interferometric phase analysis is performed on high-order multipath reflection signals to extract phase difference distribution data of multiple propagation paths; An interferogram is constructed based on the phase difference distribution data; Interference fringes of secondary and multiple reflections are identified based on interferogram patterns to generate multi-frequency interference fringe patterns; Interference correction is performed on the multi-frequency interference fringe pattern to obtain a metal reflection texture pattern.

[0023] In this embodiment, after initial path separation, the carrier phase of the high-order multipath reflection signal needs to be expanded, corrected, and differentially processed to obtain phase difference distribution data usable for interferometric processing. During processing, a short-time Fourier transform is typically performed within an effective bandwidth of 1–3 GHz to separate the high-order reflections from the time and frequency dimensions, ensuring each path has an independent phase trajectory at a sub-band resolution of 20–40 MHz. To avoid 2π ambiguity caused by phase jumps, a gradient stability-based phase expansion strategy is employed, which completes the expansion by detecting the phase continuity between adjacent time windows or adjacent channels. Subsequently, a multi-channel phase differential method is used to cancel out the phases of the same high-order path under different array elements (e.g., array spacing λ / 2, number of array elements 8–16), thereby extracting the phase difference purely caused by spatial propagation differences. To improve the robustness of weak reflection paths, a phase consistency weight is introduced to suppress noisy sub-bands. After obtaining the phase difference distribution of the high-order paths, these phase differences need to be converted into observable interferograms. Generally, the complex cross-spectrum between paths is first calculated within each frequency sub-band, mapping the phase difference to the interference amplitude, thus revealing the energy of the interference fringes in the frequency-angle domain. 2D-FFT is commonly used to perform a joint frequency-direction transformation on the phase distribution to generate a spatial frequency spectrum, where the fringe direction and fringe density reflect the path difference and the magnitude of the phase gradient, respectively. Under conditions of 12–24 frequency sub-bands, the interferogram can display spatial fringe variations at the millimeter to centimeter scale. To avoid texture diffusion caused by noise, bandpass filtering or multi-scale wavelet enhancement is added to make the fringes more prominent in the high-frequency band. Furthermore, the fringe direction is corrected using the array's geometric relationships (e.g., element spacing d = λ / 2, observation azimuth range ±60°) to ensure the spatial frequency axis aligns with the actual angle of arrival.

[0024] Fringe identification typically begins with peak detection, searching for highly correlated regions with concentrated energy in the spatial frequency domain, followed by directional analysis to determine fringe tilt angles. Fringe is treated as a linear or quasi-linear distribution in the spatial frequency domain, and the fringe parameters that best match the original spectrum are found using a minimum cost function. For fringe identification under multi-frequency conditions, fringes are extracted separately for each sub-band, and cross-frequency consistency checks confirm that the fringes originate from the same reflection path. For example, observing whether the relationship between the fringe frequency and the carrier frequency f follows a linear or near-linear frequency spread law. Under broadband conditions of 1–3 GHz and sub-band spacing of approximately 40–60 MHz, multi-frequency fringe patterns can show the relative displacement and density differences of fringes across multiple frequency layers. Multi-frequency interferometric fringe patterns contain true reflection textures and pseudo-fringe caused by frequency dispersion, angular errors, or phase drift. The correction process performs frequency and phase alignment of the multi-frequency fringes, constructing a phase compensation model using the sub-band center frequency and the measured phase shift, aligning fringes at different frequencies to a unified phase reference. Subsequently, the fringe direction is geometrically corrected by incorporating array geometric parameters (such as the number of array elements N and the azimuth spacing Δθ), mapping the bending and distortion caused by changes in the observation angle back to a straight structure. To eliminate noise pseudo-fringe, a coherence mask is introduced to suppress low-coherence regions and preserve and interpolate smooth high-coherence regions.

[0025] In this embodiment, the specific steps for performing interference correction on the multi-frequency interference fringe pattern to obtain the metal reflection texture pattern are as follows: Perform Fourier transform on the multi-frequency interference fringe pattern to extract the spatial frequency components; Based on the spatial frequency components, the periodic features of the metal surface texture are analyzed to generate geometric texture features; Identify mirror reflections under multipath interference based on geometric texture features and mark the position of the mirror target; Interference correction is performed based on the mirror target position to obtain a metal reflection texture map.

[0026] In this embodiment, the fringe pattern is normalized in two directions to maintain consistent spatial resolution. For example, the angular dimension is sampled at 0.2° and the distance dimension is discretized with a sampling step size of 2–5 mm to construct a regular two-dimensional sampling grid. A two-dimensional discrete Fourier transform (2D-DFT) is performed on the fringe pattern of each frequency sub-band to obtain the spatial frequency spectrum. High-frequency components correspond to denser fringe structures, while low-frequency components correspond to smoother texture changes. To improve sensitivity to detailed textures, edge regions are smoothed to reduce spectral leakage and make the fringe periodic components more concentrated in the frequency spectrum. Typically, in the millimeter-wave band (e.g., 76–81 GHz), due to the wavelength of approximately 4 mm, the spatial frequency corresponding to a typical fringe period falls within the range of 0.05–0.5 cycles / mm. Therefore, this frequency range is the focus of frequency domain analysis. For multi-frequency interference fringe patterns, the spectra of sub-bands with different center frequencies are calculated separately, and the spectra are superimposed or weighted averaged to provide macroscopic textures in the low-frequency sub-bands and detailed textures in the high-frequency sub-bands. Periodic texture features of metal surfaces in different directions can be extracted from Fourier spectra. Since the fringe period of a metal surface under multipath interference conditions is highly correlated with its surface geometry (such as microgrooves, surface roughness, and processing texture), the core of periodic analysis lies in identifying the dominant frequency peak in the spectrum and mapping it to a physical periodic scale. Periodic analysis typically begins with directional decomposition of the two-dimensional spectrum, i.e., extracting one-dimensional spectral slices in different directions, such as sampling along 0°, 30°, 60°, and 90°, to determine the dominant direction of the texture. When the amplitude energy in a certain direction of the spectrum is significantly enhanced, and a distinct dominant frequency peak appears in the range of 0.1–0.3 cycles / mm, it indicates that a stable and repeating geometric texture exists on the metal surface in that direction. Then, the frequency domain peak is converted into a spatial period length using the relationship period = 1 / spatial frequency; for example, when the dominant peak is 0.2 cycles / mm, its corresponding period is approximately 5 mm. To further ensure the stability of the identification results, a frequency bandwidth weighting method is adopted, which assigns higher weights to frequency bands with higher amplitude and stronger phase consistency. The frequency peaks of the multi-frequency sub-bands are compared. If a certain main peak presents a stable position in multiple sub-bands, it can be identified as a real surface texture period, rather than fringe interference noise or multiple reflection artifacts.

[0027] Metal surfaces exhibit significant specular reflection characteristics under multipath propagation, especially in areas with high surface flatness or long texture periods. The fringe structure typically displays consistent direction, stable period, and high coherence. The key to identifying specular reflection lies in comparing geometric texture features with theoretically predicted specular reflection patterns, namely, the equality of the incident angle and reflection angle, and the stable continuity of the phase gradient along the reflection path. First, the potential specular direction is determined based on the geometric texture direction. When the texture direction highly coincides with the interference fringe direction (angle ≤ 5°) and the texture period is large (e.g., period > 8 mm), a preliminary judgment can be made that the region has a specular reflection tendency. Then, combined with phase gradient information, the rate of change of the fringe phase in the spatial direction is observed. If the phase change is linear, it indicates a stable reflection path and small surface fluctuations, which better conforms to the physical model of specular reflection. Furthermore, the differences in the angle of arrival (AoA) and time delay (ToA) received by multiple channels are used to verify whether the reflection satisfies the geometric constraints of mirror reflection, i.e., the mirror points are symmetrically arranged relative to the array. By weighting and fusing the above features (e.g., assigning 40% weight to coherence, 30% weight to periodic stability, and 30% weight to linear phase gradient), specular reflection scores can be assigned to each point in the image, and regions with scores exceeding a set threshold (e.g., 0.75) are marked as mirror target locations. Once the mirror target locations are determined, their geometric symmetry and phase stability can be used to correct the overall interference texture, making the final texture map closer to the real reflective properties of a metal surface. Correction methods typically include three types of adjustments: phase correction, geometric correction, and frequency consistency correction. For phase correction, the specular reflection region provides a high-confidence phase reference; by calculating its phase shift and spreading it spatially, phase distortion caused by differences in multiple scattering paths can be compensated. For geometric correction, based on the rule that the incident angle = reflection angle at the mirror point, a geometric mapping based on the mirror normal vector can be constructed to reproject distorted fringes onto the correct spatial direction, thereby correcting fringe tilt, bending, or twisting. For frequency consistency correction, the phase difference between different sub-bands in the mirror region is used to calculate a uniform frequency compensation term, ensuring that multi-frequency stripes maintain consistency on the texture map and avoiding pseudo-texture caused by multi-frequency mixing. Weighted interpolation or least-squares fitting is typically used to smoothly extend the compensation value to non-mirror regions, ensuring phase continuity throughout the image.

[0028] In this embodiment, the specific steps for obtaining the actual target reflection point by reverse ray tracing and scattering point filtering of the metal reflection texture map are as follows: The mirror method is used to simulate the metal reflection texture map and mark the location of the virtual scattering source; The imaging location is obtained by performing reverse ray tracing on the virtual scattering source location; Based on the imaging location, a temporal correlation filter is performed to generate the motion trajectory of the virtual scattering source; Based on the stated motion trajectory, a trajectory rationality analysis is performed, and virtual reflection targets are marked; The scattering points are selected based on the virtual reflective target to obtain the actual target reflection points.

[0029] In this embodiment, after obtaining the metal reflection texture map, the mirror method, based on the geometric principle that the incident angle equals the reflection angle, treats each highly reflective region on the metal surface as a mirror plane and constructs virtual scattering source positions along the normal vector direction. Highly reflective regions are extracted from the texture map, and combined with the local normal vector direction (calculated from the texture period and phase gradient in the previous stage), corresponding virtual points are generated on the extension line of the normal vector. For example, for the 76–81 GHz millimeter-wave band, with a wavelength of approximately 4 mm, the fringe spacing can serve as a microscale reference for virtual source positioning; after discretizing the texture region into a 1–2 mm grid, each grid point generates a virtual scattering source point by reflecting along the normal vector. The generated virtual scattering source points retain the original phase information and amplitude characteristics. Reverse ray tracing, based on the principle of the shortest optical path, calculates the intersection point along the reverse propagation path from each virtual scattering source to the receiving array. The vector between the virtual scattering source and the center of the receiving array is used as the initial ray direction. Considering the constraints of the incident angle and reflection angle, to ensure spatial accuracy, the ray is typically discretely sampled at millimeter-level steps, and the path length and direction change at each step are calculated. Under high-frequency millimeter-wave conditions, path differences are sensitive to phase, so ray tracing preserves phase information for subsequent interferometry correction and multipath stacking.

[0030] After obtaining the set of imaging locations, temporal correlation filtering, based on path consistency and phase continuity, matches adjacent locations in consecutive time frames to the trajectories of the same virtual scattering source. Specific methods include using weighted nearest neighbor or Kalman filtering. In this method, the prediction step estimates the position of the next frame using the position and velocity of the previous frame, and the update step corrects the trajectory based on the observed imaging location. Weights can be assigned based on amplitude intensity or phase stability; for example, points with high amplitude and good phase consistency are assigned a weight of 0.7–0.8, while weak signal points are assigned a weight of 0.2–0.3. With millimeter-wave bandwidths of 1–3 GHz, the time sampling interval is typically 1–5 ms, capturing displacement changes from sub-meter to centimeter levels. Through filtering and temporal matching, discrete imaging locations can be connected into smooth, continuous trajectories, eliminating isolated or noise points and forming a complete set of virtual scattering source trajectories. After obtaining the trajectories of the virtual scattering sources, trajectory rationality analysis is required to distinguish between real multipath reflection paths and irregular noise interference. Rationality analysis is based on trajectory continuity, velocity consistency, and motion range constraints. The trajectory continuity check examines the variation in the spacing between points on each trajectory. If the displacement between adjacent points exceeds a preset threshold (e.g., 5–10 cm / ms), it is identified as an abnormal trajectory and removed. Velocity consistency analysis verifies whether the instantaneous velocity and acceleration of the trajectory points conform to the possible motion patterns of the target; for example, the acceleration of a vehicle or mechanical component is typically below 5 m / s². Finally, combined with spatial constraints, the trajectory is mapped to a reasonable range near the metal reflective surface (e.g., within a distance array of 1–10 m), excluding abnormal points far from the surface. By integrating these rules, the trajectories are scored and marked. Trajectories with high scores and conforming to physical constraints are marked as virtual reflective targets, indicating that they may correspond to real metal reflective points. Scattering points are then filtered based on the marked virtual reflective targets to obtain the actual target reflective points. Scattering point filtering is mainly based on three aspects: spatial consistency, phase stability, and amplitude intensity. Spatial consistency refers to the reflection point's proximity to the marked virtual target location on the trajectory, with allowable deviations typically on the order of millimeters to centimeters, such as 1–5 cm. Phase stability is determined by analyzing the phase continuity of the same reflection point across different frequency sub-bands or time frames; if the phase fluctuation is less than 0.1–0.2 rad, the path is considered stable. Amplitude intensity is used as a weighted reference, with high-amplitude points more likely to correspond to real metal reflections. By comprehensively scoring these indicators and setting thresholds for filtering, outliers or low-confidence points are eliminated.

[0031] In this embodiment, the specific steps of step S3 are as follows: A set of inversion field equations is constructed based on the three-dimensional coordinates of the metal and the actual target reflection point; The inversion field equations are discretized and numerically solved to obtain the target scattering position and the surrounding metal structure. The first metal environment image is obtained by iterative forward calculation based on the target scattering position and the surrounding metal structure.

[0032] In this embodiment, each metal surface point and target reflection point is considered as a scattering unit. Based on the amplitude and phase information received by the radar, the radar equations are used to establish the scattering field contribution expression for each receiving channel. The inversion field equations are mathematical equations relating the scattering field and the distribution of unknown scatterers, established based on the target scattering point and the environmental metal structure. They are typically in the form of integral equations or matrix equations, used to link the measured scattering field data with the physical properties of the target and the environment.

[0033] Each equation in the system corresponds to the received signal of one channel, with the unknowns being the reflection intensity or complex amplitude at each scattering point. For example, in the 76–81 GHz millimeter-wave band, the radar bandwidth is 1–3 GHz, the path length of each scattering point is quantized with millimeter-level resolution, and the equations include path delay, phase, attenuation, and multipath interference terms. After establishing the complete continuous inversion field equations, they need to be discretized to make them suitable for numerical solutions. Discretization typically involves dividing the metal surface into spatial grid cells; for example, under millimeter-wave conditions, the metal surface is divided into 1–2 mm grids, with each grid point serving as a discrete scattering unit. The target reflection point is also represented using the same or a finer grid. Subsequently, the continuous scattered field integral is approximated as a discrete summation, forming a linear or nonlinear system of equations in sparse matrix form. Numerical solutions can employ iterative methods, such as least-squares iteration, conjugate gradient methods, or sparse reconstruction techniques (e.g., L1 regularization), to stably solve for the complex amplitude and position of each scattering point. To ensure convergence during the iteration process, a residual threshold can be set, such as the sum of squared path phase differences being less than 0.01 rad² or the amplitude variation converging to below 1%. At this stage, a path attenuation model and a multi-frequency fusion strategy can also be introduced to enhance the stability of higher-order scattering points by integrating information from different sub-bands in the 76–81 GHz range.

[0034] After obtaining the target scattering location and the surrounding metallic structure, an initial version of the metallic environment image can be generated through iterative forward calculation. The forward calculation is based on a physical propagation model, using the obtained scattering points and the reflection intensity of the metal surface as input. According to geometric optics or electromagnetic propagation equations, the theoretical echo of each receiving channel at different times and angles is calculated. The calculation process considers the superposition effect of first-order and higher-order multiple reflection paths, quantizing path lengths with millimeter-level resolution. Reflection intensity is combined with the complex amplitude of each scattering unit, and multi-frequency superposition is performed in the frequency sub-band to simulate the actual radar received signal. The iterative process dynamically adjusts the amplitude and position of the scattering points by comparing the residuals of the forward calculation results with the actual measured signal to optimize imaging accuracy. Convergence conditions can be set for each iteration, such as a total residual decrease of less than 0.5% or a maximum displacement correction of less than 1 mm, to ensure stable calculation results.

[0035] In this embodiment, the specific steps of step S4 are as follows: The imaging of the first metallic environment is time-series tracked to extract imaging non-steady-state features; the imaging non-steady-state features include scattering intensity changes, path drift, and phase ripples; State prediction is performed on the non-steady-state features of imaging to obtain the dynamic characteristics of the environment; Based on the dynamic characteristics of the environment, the inversion field equations are input with parameter compensation, the sliding window length is defined, iterative residual convergence is performed, and the second metal environment image is output.

[0036] In this embodiment, the imaging results at consecutive time points are aligned by time frames, typically sampled at time intervals of 1–5 ms, and the amplitude versus time curve and phase versus time curve are calculated for each scattering unit or grid point. The change in scattering intensity can be obtained by calculating the standard deviation or relative rate of change of the amplitude at each point; for example, an amplitude change exceeding 10% is considered to have a significant dynamic response. Path drift is detected by comparing the spatial position changes of the same scattering point in consecutive time frames, typically with millimeter-level accuracy (1–5 mm). Phase ripple refers to the small fluctuations in phase over time, obtained by unfolding the phase and calculating the phase difference between adjacent frames. After extracting the non-steady-state features, state prediction is required to infer the dynamic characteristics of the environment. State prediction can employ a combination of temporal filtering and dynamic models, such as based on Kalman filtering or extended Kalman filtering, inputting scattering intensity, path drift, and phase ripple as observed variables into the dynamic model. The filtering step predicts the amplitude and position changes of scattering points in the current frame based on the environmental state of the previous frame, and predicts path drift and phase changes. The prediction accuracy is typically in the millimeter range and within the 0.1 rad phase accuracy range. Subsequently, the observed values ​​and predicted values ​​are weighted and fused to update the state estimate of each scattering point. During the prediction process, a noise covariance matrix can be defined, with amplitude measurement noise typically set to 5–10%, position measurement noise to 1–2 mm, and phase noise to 0.05–0.1 rad, to ensure the robustness of the prediction.

[0037] The compensation amounts corresponding to changes in scattering intensity, path drift, and phase ripples are introduced into the equations to adjust the complex amplitude and spatial position of each scattering point. A sliding time window length is defined in the calculation, typically chosen as 5–10 frames, to balance dynamic tracking accuracy and noise suppression. During the iterative solution process, the scattering point parameters are continuously adjusted by comparing the residuals of the forward-calculated signal and the actual observed signal, allowing the residuals to gradually converge. The residual convergence criterion can be set as a total amplitude residual of less than 1–2% or a sum of squared phase differences of less than 0.01 rad². The step size for updating amplitude and position in each iteration can be set at the millimeter level to ensure stability. After completing the iteration within each sliding window, the output imaging result integrates dynamic compensation information, reflecting the dynamic changes between the target scattering point and the surrounding metal surface, forming a second metal environment image.

[0038] In this embodiment, an environmental imaging device based on millimeter-wave radar is provided for performing the environmental imaging method based on millimeter-wave radar as described above, including: The signal acquisition unit is used to transmit multi-frequency signals to the metal environment under test based on the millimeter-wave radar array and extract multipath signal sequences. The interferometric analysis unit is used to perform interferometric phase analysis based on multipath signal sequences to obtain the three-dimensional coordinates of the metal and the actual target reflection point; The environmental imaging unit is used to perform iterative forward calculations based on the three-dimensional coordinates of the metal and the actual target reflection point to obtain the first metal environmental image. The residual convergence unit is used to perform iterative residual convergence on the first metal environment image and output the second metal environment image.

[0039] Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of the equivalents of the application are intended to be included within the invention.

[0040] The above description is merely a specific embodiment of the present invention, enabling those skilled in the art to understand or implement it. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein are implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the present invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features of the invention herein.

Claims

1. An environmental imaging method based on millimeter-wave radar, characterized in that, Includes the following steps: Step S1: Based on the millimeter-wave radar array, transmit multi-frequency signals to the metal environment to be detected and extract the multipath signal sequence; Step S2: Perform interferometric phase analysis based on the multipath signal sequence to obtain the three-dimensional coordinates of the metal and the actual target reflection point; Step S3: Perform iterative forward calculations based on the three-dimensional coordinates of the metal and the actual target reflection point to obtain the first metal environment image; Step S4: Iteratively converge the residuals of the first metal environment image and output the second metal environment image.

2. The environmental imaging method based on millimeter-wave radar according to claim 1, characterized in that, The specific steps of step S1 are as follows: Based on millimeter-wave radar array, multi-frequency signals are transmitted to the metal environment to be detected, and multi-channel echo signals are collected; Cluster analysis of multi-channel echo signals of various signal types is performed to extract various signal types; A multidimensional reflection dataset is generated by performing joint time-frequency analysis on the various types of signals. The multidimensional reflection dataset is intelligently labeled to generate a multipath signal sequence; the intelligent labeling includes the incident angle, reflection angle, path delay, relative intensity attenuation, frequency shift and phase jump characteristics of the reflection path.

3. The environmental imaging method based on millimeter-wave radar according to claim 2, characterized in that, The various types of signals include direct wave signals, first-order reflection signals, and higher-order multipath reflection signals.

4. The environmental imaging method based on millimeter-wave radar according to claim 1, characterized in that, The specific steps of step S2 are as follows: The receiving time difference of the multipath signal sequence is calculated to obtain the receiving time difference of the signal at multiple locations; the carrier phase difference of the multipath signal sequence is calculated to obtain the phase difference delay value. Geometric reverse propagation analysis is performed based on the phase difference delay value and the signal reception time difference at multiple locations to obtain the direction of the metal interface normal vector; Radial distance information is obtained by performing a squared inversion based on the first-order reflection signal; Three-dimensional coordinates of the metal are estimated based on the normal vector direction and radial distance information of the metal interface; Interferometric phase analysis was performed on the high-order multipath reflection signal to obtain the metal reflection texture map; By reverse ray tracing and scattering point filtering of the metal reflection texture map, the actual target reflection point is obtained.

5. The environmental imaging method based on millimeter-wave radar according to claim 4, characterized in that, The specific steps for performing interference phase analysis on high-order multipath reflection signals to obtain metal reflection texture maps are as follows: Interferometric phase analysis is performed on high-order multipath reflection signals to extract phase difference distribution data of multiple propagation paths; An interferogram is constructed based on the phase difference distribution data; Interference fringes of secondary and multiple reflections are identified based on interferogram patterns to generate multi-frequency interference fringe patterns; Interference correction is performed on the multi-frequency interference fringe pattern to obtain a metal reflection texture pattern.

6. The environmental imaging method based on millimeter-wave radar according to claim 5, characterized in that, The specific steps for interferometric correction of the multi-frequency interference fringe pattern to obtain the metal reflection texture pattern are as follows: Perform Fourier transform on the multi-frequency interference fringe pattern to extract the spatial frequency components; Based on the spatial frequency components, the periodic features of the metal surface texture are analyzed to generate geometric texture features; Identify mirror reflections under multipath interference based on geometric texture features and mark the position of the mirror target; Interference correction is performed based on the mirror target position to obtain a metal reflection texture map.

7. The environmental imaging method based on millimeter-wave radar according to claim 4, characterized in that, The specific steps for obtaining the actual target reflection point by reverse ray tracing and scattering point filtering of the metal reflection texture map are as follows: The mirror method is used to simulate the metal reflection texture map and mark the location of the virtual scattering source; The imaging location is obtained by performing reverse ray tracing on the virtual scattering source location; Based on the imaging location, a temporal correlation filter is performed to generate the motion trajectory of the virtual scattering source; Based on the stated motion trajectory, a trajectory rationality analysis is performed, and virtual reflection targets are marked; The scattering points are selected based on the virtual reflective target to obtain the actual target reflection points.

8. The environmental imaging method based on millimeter-wave radar according to claim 1, characterized in that, The specific steps of step S3 are as follows: A set of inversion field equations is constructed based on the three-dimensional coordinates of the metal and the actual target reflection point; The inversion field equations are discretized and numerically solved to obtain the target scattering position and the surrounding metal structure. The first metal environment image is obtained by iterative forward calculation based on the target scattering position and the surrounding metal structure.

9. The environmental imaging method based on millimeter-wave radar according to claim 1, characterized in that, The specific steps of step S4 are as follows: The imaging of the first metallic environment is time-series tracked to extract imaging non-steady-state features; the imaging non-steady-state features include scattering intensity changes, path drift, and phase ripples; State prediction is performed on the non-steady-state features of imaging to obtain the dynamic characteristics of the environment; Based on the dynamic characteristics of the environment, the inversion field equations are input with parameter compensation, the sliding window length is defined, iterative residual convergence is performed, and the second metal environment image is output.

10. An environmental imaging device based on millimeter-wave radar, characterized in that, An environmental imaging method based on millimeter-wave radar as described in claim 1, comprising: The signal acquisition unit is used to transmit multi-frequency signals to the metal environment under test based on the millimeter-wave radar array and extract multipath signal sequences. The interferometric analysis unit is used to perform interferometric phase analysis based on multipath signal sequences to obtain the three-dimensional coordinates of the metal and the actual target reflection point; The environmental imaging unit is used to perform iterative forward calculations based on the three-dimensional coordinates of the metal and the actual target reflection point to obtain the first metal environmental image. The residual convergence unit is used to perform iterative residual convergence on the first metal environment image and output the second metal environment image.

Images