A multi-angle detection method based on single radar and multi-path dealiasing

Abstract

A kind of multi-angle detection method based on single radar realization of multi-path dealiasing, comprising the following steps: step one: based on echo dealiasing algorithm of multi-path hypothesis, realize to eliminate the false target produced by multi-path echo in radar echo data, utilize multi-path echo to strengthen the signal strength of real target, correctly find target position, obtain the real image of target;Step two: utilize the relationship that direct wave component and multi-path echo component after dealiasing both contain part target information, convert the false target produced by multi-path echo into the multi-path image produced by multiple virtual radars under multi-angle and multi-distance, observe real target position, carry out image fusion to multi-path image under the same coordinate system, obtain higher resolution target image.The present application not only can eliminate a large number of false targets produced by multi-path echo, but also make single radar have multi-angle cooperative detection capability, realize to enhance target echo energy, expand detection visual angle, high-resolution imaging, obtain the effect of real target speed.

Description

Technical Field

[0001] This invention belongs to the field of radar detection technology, specifically relating to a method for multi-angle detection using a single radar based on multipath dealiasing. Background Technology

[0002] The problem of a large number of false targets generated by multipath echoes in radar detection: Existing methods associate the target image with the multipath image after detection, or only apply to the known environment of the multipath channel, which cannot adapt to the complex superimposed multipath environment with multiple targets, multiple reflective surfaces, and multiple reflections in actual applications.

[0003] Effective detection of weak, non-cooperative targets in complex multipath aliasing environments involves three aspects: filtering out false targets from multipath paths, detecting weak targets, and suppressing noisy false alarm targets. For example... Figure 1 As shown, this illustrates the problems encountered when performing high-confidence detection of small targets such as drones in complex, multipath-overlapping urban environments.

[0004] The main shortcoming of existing methods is that they have not overcome the challenge of detecting weak targets in complex multipath aliasing environments. One such challenge is the complex aliasing multipath problem, which is complex because the radar beam is reflected multiple times by multiple unknown reflective surfaces. After the echoes are aliased, it is difficult to distinguish between the target and the multipath echoes, resulting in false multipath targets. At the same time, the detection angle is limited by the obstruction of reflective surfaces.

[0005] Current methods for dealing with multipath echoes include using matched filters to filter out multipath echoes and using fractional Fourier transforms to retrieve the original target echo.

[0006] However, the current method has the following problems: (1) It ignores the target information in the multipath echo. (2) It requires prior knowledge or learning samples of the target echo and the detection environment, which limits its practicality. (3) In practical applications, the current method is not suitable for situations where the parameters of the reflecting surface are unknown, there are multiple reflecting surfaces, and multiple reflections occur in complex aliased multipath environments. Summary of the Invention

[0007] In order to overcome the shortcomings of the existing technology, the present invention aims to provide a single radar method for multi-angle detection based on multipath dealiasing. This method can not only eliminate a large number of false targets generated by multipath echoes, but also utilize multipath echoes to enable a single radar to have multi-angle collaborative detection capabilities, thereby enhancing target echo energy, expanding the detection angle, achieving high-resolution imaging, and obtaining the true speed of the target.

[0008] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0009] A method for multi-angle detection using a single radar based on multipath dealiasing includes the following steps:

[0010] Step 1: Based on the multipath assumption, the echo dealiasing algorithm eliminates false targets generated by multipath echoes in radar echo data, enhances the signal strength of real targets by utilizing multipath echoes, correctly detects the target location, and obtains the true image of the target.

[0011] Step 2: Based on the multi-path echo high-resolution imaging algorithm, the relationship between the target information in the direct wave component after dealiasing and the multipath echo component is used to convert the false target generated by the multipath echo into a multipath image generated by multiple virtual radars at multiple angles and distances. The real target position is observed, and the multipath images are fused in the same coordinate system to improve the target imaging quality and obtain a target image with higher resolution.

[0012] The specific steps of step one are as follows:

[0013] Step 1: Detect multipath reflectors based on radar echo data;

[0014] The radar echo data with low Doppler velocity, high echo intensity, and short distance is processed as a stationary echo to obtain a multipath reflection plane.

[0015] Step 2: Establish a set of multipath assumptions for multiple reflections from a multipath reflecting plane, and estimate the multipath propagation paths under the multipath assumptions;

[0016] Step 3: Mapping multipath echoes to the actual measured area;

[0017] Based on the multipath propagation path under the multipath assumption, the radar echo in the non-direct area is mapped to obtain the actual area measurement value detected by the multipath echo. The image of the actual area after mapping is the multipath dealiasing detection image. Under the multipath assumption, the target multipath image will be mapped to the actual position of the target.

[0018] Step 4: Dealiasing and echo superposition under the multipath assumption;

[0019] Under each multipath assumption, the target multipath image stably appears at the actual location of the target. The multipath energy is accumulated by superimposing the multipath dealiasing detection images in space. Among the multipath dealiasing detection images, the corresponding multipath image will stably appear in the target region (within the dashed ellipse), while other multipath images are discrete and random and cannot be accumulated in space.

[0020] Step 5: Perform Doppler velocity consistency analysis on the superimposed multipath dealiasing probe images.

[0021] The specific steps in step 1 are as follows:

[0022] Step (1): Filter the radar echo data for measurements with low Doppler velocity, high echo intensity, and close distance;

[0023] Step (2): Use superpixel segmentation to process the measured values ​​and obtain individual pixel blocks;

[0024] Step (3): Convert the coordinate system of the pixel block from polar coordinate system to rectangular coordinate system to facilitate feature plane detection;

[0025] Step (4): Use the feature plane detection method to filter out the parts of the pixel block that can form a plane, which are multipath reflection planes (walls, ceilings, floors, etc.).

[0026] In step 2, a 3×3 multipath reflection hypothesis set is established based on the multipath reflection plane, and the multipath hypothesis T is... ij This indicates that the transmitted wave is reflected i times and the target echo is reflected j times, where i and j are 0, 1, and 2, respectively. This means that only the case of electromagnetic wave reflection twice or less is considered, and the high-order multipath reflected waves with weak amplitude are filtered out.

[0027] In each set of multipath reflection assumptions, the reflection rules and electromagnetic wave propagation paths can be solved by the reflection plane, estimating the multipath propagation paths under each multipath assumption.

[0028] Step 5 specifically involves:

[0029] Under different multipath assumptions, the angles of the transmitted and reflected waves differ, resulting in different Doppler velocities. The analysis examines whether the Doppler components within a region point to the same true velocity. That is, under each multipath assumption, the target's Doppler velocity can be estimated from the actual velocity and direction of motion within that region. Therefore, within each multipath channel, a system of equations can be established based on the directions of the transmitted and received waves and the target's Doppler velocity, allowing the determination of the target's maximum likelihood velocity and direction, as shown in the formula.

[0030]

[0031] Among them, v XY V represents the Doppler velocities of the transmission and reception angles X and Y. Tar For the target's true velocity, α Tar The actual direction of motion of the target. This is an estimate of the target velocity. α is an estimate of the target's direction of motion. T Let α be the angle of the emitted electromagnetic wave. R The angle at which the electromagnetic wave is received;

[0032] If the Doppler velocity and the target's maximum likelihood velocity are consistent in direction in all multipath assumptions within the region, it indicates that the echoes in the region all originate from the same target.

[0033] The process is shown in the following formula, using... Estimate Doppler velocities under various multipath conditions If the estimated Doppler velocity is the same as the measured Doppler velocity v XY If they are similar, the multipath images in the region can corroborate each other, indicating a high probability of a target in the region, and at the same time, the true speed and direction of the suspected target can be estimated.

[0034]

[0035] Where N is the number of electromagnetic wave propagation paths. v is an estimate of the Doppler velocity. XY The measured value is the Doppler velocity. This is an estimate of the target velocity. α is an estimate of the target's direction of motion. T Let α be the angle of the emitted electromagnetic wave. R The angle at which the electromagnetic wave is received.

[0036] The specific steps of step two are as follows:

[0037] Step 1: Establish a virtual radar under the multipath assumption.

[0038] After superposition, regions with high echo amplitude and consistent Doppler velocity of multipath echoes are regarded as suspected target regions for multi-angle high-resolution imaging. Based on the multipath assumption, the virtual radar position corresponding to each multipath image is calculated. Each multipath image corresponds to a virtual radar, and the virtual radar realizes the imaging of the target from multiple angles and multiple distances.

[0039] Step 2: Multi-angle high-resolution imaging based on accumulated votes.

[0040] Multi-angle images from virtual radar are fused to improve imaging resolution. By transforming the coordinate system of the multi-angle images, the cells in the polar coordinate system are converted to rectangular coordinates to obtain the target image in the rectangular coordinate system. Image fusion is achieved through voting accumulation. The multi-angle images in the rectangular coordinate system are multiplied point-to-point to obtain the final fused image.

[0041] At this point, the resolution of the fused image in the Cartesian coordinate system is close to the radar range resolution. Compared with direct wave imaging, the fused image is closer to the true image of the target in the upper left corner. Thus, imaging resolution is improved through multipath image fusion.

[0042] The method is applied to target detection.

[0043] The method is applied to robot path planning.

[0044] The beneficial effects of this invention are:

[0045] This invention not only eliminates the problem of false targets in multipath echoes, but also utilizes the target information in multipath echoes, thereby increasing the target echo energy and improving the imaging resolution.

[0046] First, it makes full use of multipath echo information. Most existing methods find and filter out multipath echoes, while this invention makes full use of target information in multipath echoes, which not only eliminates false multipath targets but also enhances the imaging resolution of real targets.

[0047] Secondly, it has strong practicality. Existing methods require prior knowledge or learning samples of target echoes and detection environments, which limits their practicality. This invention does not require prior knowledge; it directly analyzes and processes radar echo data, resulting in high accuracy and strong practicality.

[0048] Third, it has excellent environmental adaptability. Existing methods cannot adapt to the complex aliased multipath environment in practical applications, where multiple targets, multiple reflective surfaces, and multiple reflections overlap. This invention has good adaptability in such complex aliased multipath environments and can accurately identify real targets. Attached Figure Description

[0049] Figure 1 This is a schematic diagram illustrating high-confidence detection of small, unmanned aerial vehicle (UAV) targets in complex, multipath-overlapping urban environments.

[0050] Figure 2 This refers to measurements of static objects and multipath echoes of moving targets.

[0051] Figure 3 This is a schematic diagram illustrating the principle of multipath reflection surface parameter estimation based on still object echoes.

[0052] Figure 4 This is a schematic diagram illustrating the multipath propagation path calculated using the multipath hypothesis set.

[0053] Figure 5 This is a schematic diagram of the echo obtained from the actual measured area based on the multipath assumption.

[0054] Figure 6 This is a schematic diagram of the multipath dealiasing detection image under various multipath assumptions.

[0055] Figure 7 This is a schematic diagram of the consistency analysis of Doppler velocities from multiple angles.

[0056] Figure 8 This is a schematic diagram of a virtual radar based on the multipath assumption.

[0057] Figure 9 This is a schematic diagram illustrating the principle of multi-angle image fusion based on vote accumulation. Detailed Implementation

[0058] The present invention will now be described in further detail with reference to the accompanying drawings.

[0059] This invention discloses a multi-angle detection method based on multipath dealiasing using a single radar. The multipath echo dealiasing multi-angle detection method of this invention is based on existing literature and verification experiments, taking the fact that the target multipath echo contains target information and that the target multipath path conforms to specular reflection as the basis.

[0060] Overall, it is divided into two parts: multipath hypothesis solution multipath echo dealiasing and multipath echo to achieve multi-angle high-resolution imaging.

[0061] In principle, information such as multipath echoes and reflector positions is used to map false targets generated by multipath echoes to the positions of real targets, thereby eliminating false targets. These false targets are then spatially superimposed to accumulate multipath energy. The Doppler velocities of the multipath components are then analyzed to confirm the target's location. Based on the multipath assumption, the virtual radar position corresponding to each multipath image can be calculated. These virtual radars constitute multi-angle, multi-range imaging of the target. Finally, these multi-angle, multi-range target images are fused to create an image closer to the real target, thus improving imaging resolution through multipath image fusion.

[0062] In complex aliased multipath environments, radar detection echoes include the true image of the target, echoes from stationary objects, multipath images of the target, and false alarms from noise. For example... Figure 2 As shown, the fixed object echo has high amplitude, a large number of multipath images, and Doppler velocity. The multi-angle detection method for multipath echo dealiasing of the present invention specifically involves seven steps: multipath reflector detection, establishment of a multipath hypothesis set, multi-radial measurement region mapping, echo superposition after dealiasing, Doppler velocity consistency analysis of multipath components, establishment of a multipath virtual radar, and multi-angle multipath image fusion.

[0063] This invention was developed to address complex aliased multipath environments characterized by multiple targets, multiple reflective surfaces, and overlapping reflections, and is applicable to complex urban multipath environments. Furthermore, this invention possesses strong scalability and portability, making it valuable for various applications such as drones, vehicles, and human detection.

[0064] A method for multi-angle detection using a single radar based on multipath dealiasing includes the following steps;

[0065] Step 1: Echo Dealiasing Algorithm Based on Multipath Assumption

[0066] The present invention utilizes the multipath hypothesis to solve the multipath echo dealiasing portion, primarily to eliminate false targets generated by multipath echoes, and to enhance the signal strength of the real target using multipath echoes, thereby correctly detecting the target's location. The specific steps are as follows:

[0067] Step 1: Detect multipath reflectors based on radar echo data;

[0068] Measurements with low Doppler velocity, high echo intensity, and close distance are treated as static echoes.

[0069] like Figure 3 The area for still-object echo measurement is extracted as shown. After superpixel segmentation, coordinate system transformation, and feature plane detection, potential multipath reflection planes can be detected.

[0070] Step 2: Establish a set of multipath assumptions based on multiple reflections from multiple reflective surfaces;

[0071] Based on the current reflective surface, a 3×3 multipath reflection hypothesis set is established, with multipath hypothesis T. ij This represents the transmitted wave reflecting i times and the target echo reflecting j times, where i and j are 0, 1, and 2, respectively. This means only cases where the electromagnetic wave reflects twice or less are considered, filtering out weaker, higher-order multipath reflections. The set of multipath reflection assumptions is as follows: Figure 4 As shown, within each multipath hypothesis space, the reflection rule and electromagnetic wave propagation path can be solved by the reflection plane to estimate the multipath propagation path under each multipath hypothesis.

[0072] Figure 4 The left side shows the set of multipath reflection hypotheses, divided into 3×3 cases based on the different propagation paths (number of reflections) of electromagnetic waves during transmission and reception. Figure 4 Listed on the left;

[0073] Figure 4 The right side shows the target imaging positions for different propagation paths, which are solved using the propagation path and reflection relationship (that is, all echoes are treated as direct waves). Therefore, a target will produce multiple false images due to the multipath effect. The diagram on the right shows all the cases of these false images and the real target image.

[0074] Step 3: Mapping multipath echoes to the actual measured area;

[0075] Based on the multipath propagation path under the multipath assumption, the echo from the non-direct radar region is mapped to obtain the actual measurement value of the region detected by the multipath echo. The image of the actual region after mapping is the multipath dealiasing detection image.

[0076] like Figure 5 As shown, under the multipath assumption, the multipath image of the target will be mapped to the actual location of the target. Figure 5This example illustrates how to perform target multipath echo superposition. Given a multipath propagation path and the target multipath image of that path, when the electromagnetic wave is received (echo), it is reflected only once on the left wall. Using the principle of mirror reflection, the multipath image is mapped with the left wall as a mirror, thus mapping the position of the target multipath image to the target's true position.

[0077] Step 4: Dealiasing and echo superposition under the multipath assumption;

[0078] Because the target multipath image stably appears at the actual target location within the nine multipath assumptions, multipath energy accumulation is achieved by spatially stacking the de-aliased detection images from each multipath path. For example... Figure 6 In the multipath dealiasing detection images, the corresponding multipath image will stably appear in the target region (within the dashed ellipse), while other multipath images are discretely and randomly distributed and cannot be accumulated in space.

[0079] and Figure 5 The principle is the same, the difference is... Figure 5 Mapping is performed only for a single path and its corresponding target multipath image. Figure 6 It is Figure 4 All multipath images shown on the right are mapped. Since there are 9 propagation paths, all multipath images are mapped under each propagation path. Only the multipath image corresponding to the path can be mapped to the real target position (within the dashed ellipse). At this time, other multipath images will not be mapped to the target position, ensuring that each multipath image is consistent with the information carried before mapping when it is mapped to the target position (other multipath images will not be superimposed on this multipath image), which is used for fusion in step 2.

[0080] Figure 6 Ensuring that each multipath image can be mapped to the true location of the target based on the mapping relationship is a prerequisite step for fusion, and it is proven that this method can be used to fuse and superimpose multipath images.

[0081] Step 5: Multipath component Doppler velocity consistency analysis.

[0082] Doppler velocity consistency analysis is performed on the superimposed multipath dealiasing probe images to determine whether regional echoes originate from the same target and to predict the target's true velocity and direction of motion.

[0083] Under different multipath assumptions, the angles of the emitted and reflected waves differ, thus yielding different Doppler velocities. The analysis determines whether the Doppler components within the region point to the same true velocity; that is, under each multipath assumption, the target's Doppler velocity can be estimated using the actual velocity and direction of motion within that region.

[0084] like Figure 7As shown, a set of equations can be established within each multipath channel based on the directions of the transmitted and received waves and the target's Doppler velocity, and the target's maximum likelihood velocity and direction can be calculated. Figure 7 in the formula

[0085] If the Doppler velocity and the target's maximum likelihood velocity are consistent in direction among the various multipath assumptions within the region, it indicates that all echoes in the region originate from the same target. This process is illustrated in the following equation, using... Estimate Doppler velocities under various multipath conditions If the estimated Doppler velocity is the same as the measured Doppler velocity v XY If they are similar, the multipath images within the region can corroborate each other, indicating a high probability of a target existing in the region. At the same time, the true speed and direction of the suspected target can be estimated.

[0086]

[0087] Step 2: Multi-angle high-resolution imaging algorithm based on multipath echo

[0088] The multipath echo high-resolution imaging part of this invention mainly utilizes multipath information to convert false targets generated by multipath echoes into multiple virtual radars observing the real target position from multiple angles and distances, generating multipath images, and then fuses these multipath images into a more accurate image that is closer to the real target.

[0089] The specific steps are as follows:

[0090] Step 1: Establish a virtual radar under the multipath assumption.

[0091] Regions with high echo amplitude and consistent Doppler velocities in the multipath echoes after superposition are considered as suspected target areas for multi-angle high-resolution imaging. Based on the multipath assumption, the virtual radar position corresponding to each multipath image is calculated, such as... Figure 8 As shown, each multipath image corresponds to a virtual radar, and these virtual radars enable imaging of the target from multiple angles and distances.

[0092] Similar to Figure 4 The difference in the diagram on the right is that the radar is mapped to the corresponding position based on the target location and the path relationship and mirror reflection. This is equivalent to having radars at multiple angles in different positions to detect the target.

[0093] Path and Figure 4 The same applies, with 9 types in total, but they are simplified to 4 types here because they are not all drawn.

[0094] The multipath path of wall A is the path along which an electromagnetic wave is reflected once at wall A;

[0095] The B-wall multipath is the path along which an electromagnetic wave is reflected once at the B-wall.

[0096] The AB wall multipath is the path in which electromagnetic waves are reflected once at each of the AB walls;

[0097] The direct wave path is the path along which the electromagnetic wave is not emitted.

[0098] Step 2: Multi-angle high-resolution imaging based on accumulated votes.

[0099] Multi-angle images from virtual radar are fused to improve imaging resolution. By transforming the coordinate systems of the multi-angle images, converting the polar coordinates to Cartesian coordinates, the desired image resolution can be obtained. Figure 9 The target image is shown in a Cartesian coordinate system. Image fusion is achieved through voting accumulation, which multiplies the amplitude of multiple angle images in the Cartesian coordinate system point-to-point, ultimately obtaining the final image. Figure 9 The fused image is shown in the bottom right corner.

[0100] One is the transformation of the coordinate position of the multipath image to the actual position of the target (using the principle of mirror reflection), and the other is the transformation of polar coordinates to a rectangular coordinate system (radar information is in polar coordinates).

[0101] At this point, the resolution of the fused image in the Cartesian coordinate system is close to the radar range resolution. Compared with direct wave imaging, the fused image is closer to the true image of the target in the upper left corner. Thus, imaging resolution is improved through multipath image fusion. In summary, the multi-angle cooperative detection capability is brought about by step 1 in step 2. The multipath images of the target generated by different propagation paths are converted into virtual radars at different angles at the corresponding positions by taking advantage of the different propagation paths of electromagnetic waves. Therefore, the multipath images are equivalent to the direct wave imaging of these virtual radars at their own positions (imaging of electromagnetic waves without specular reflection).

[0102] The enhanced target echo energy is achieved by step 4 in step one, which involves superimposing the de-aliased echoes to enhance the target echo energy.

[0103] Expanding the detection angle is achieved through steps 2 and 3 in step one. By utilizing the principles of steps 2 and 3, echo information of non-direct-view targets (where there is a barrier between the radar and the target that prevents electromagnetic waves from passing through) can be obtained, which is achieved by using multipath reflection imaging.

[0104] Step 2 in the high-resolution imaging process results in a target image that combines direct wave (direct wave) and multipath echo, which has a higher resolution than a single direct wave image.

[0105] The target's true velocity is obtained from step 5 in step one. By using the target's Doppler velocity components at different angles, the target's true velocity and direction of motion are estimated.

Claims

1. A method for multi-angle detection using a single radar based on multipath dealiasing, characterized in that, Includes the following steps: Step 1: Based on the multipath assumption, the echo dealiasing algorithm eliminates false targets generated by multipath echoes in radar echo data, enhances the signal strength of real targets by utilizing multipath echoes, correctly detects the target location, and obtains the true image of the target. Step 2: Based on the multi-path echo high-resolution imaging algorithm, the relationship between the target information in the direct wave component after dealiasing and the multipath echo component is used to convert the false target generated by the multipath echo into a multipath image generated by multiple virtual radars at multiple angles and distances. The real target position is observed, and the multipath images are fused in the same coordinate system to obtain a target image with higher resolution.

2. The method for multi-angle detection using a single radar based on multipath dealiasing according to claim 1, characterized in that, The specific steps of step one are as follows: Step 1: Detect multipath reflectors based on radar echo data; The radar echo data with low Doppler velocity, high echo intensity, and short distance is processed as a stationary echo to obtain a multipath reflection plane. Step 2: Establish a set of multipath assumptions for multiple reflections from a multipath reflecting plane, and estimate the multipath propagation paths under the multipath assumptions; Step 3: Mapping multipath echoes to the actual measured area; Based on the multipath propagation path under the multipath assumption, the radar echo in the non-direct area is mapped to obtain the actual area measurement value detected by the multipath echo. The image of the actual area after mapping is the multipath dealiasing detection image. Under the multipath assumption, the target multipath image will be mapped to the actual position of the target. Step 4: Dealiasing and echo superposition under the multipath assumption; Under each multipath assumption, the target multipath image stably appears at the actual location of the target. The multipath energy is accumulated by superimposing the multipath dealiasing detection images in space. In each multipath dealiasing detection image, the corresponding multipath image will stably appear in the target area, while other multipath images are discrete and random, and cannot be accumulated in space. Step 5: Perform Doppler velocity consistency analysis on the superimposed multipath dealiasing probe images.

3. The method for multi-angle detection using a single radar based on multipath dealiasing according to claim 2, characterized in that, The specific steps in step 1 are as follows: Step (1): Filter the radar echo data for measurements with low Doppler velocity, high echo intensity, and close distance; Step (2): Use superpixel segmentation to process the measured values ​​to obtain a pixel block; Step (3): Convert the coordinate system of the pixel block from polar coordinate system to rectangular coordinate system; Step (4): Use the feature plane detection method to filter out the parts of the pixel block that can form a plane, which are the multipath reflection planes.

4. The method for multi-angle detection using a single radar based on multipath dealiasing according to claim 2, characterized in that, In step 2, a 3×3 multipath reflection hypothesis set is established based on the multipath reflection plane, and the multipath hypothesis T is... ij This indicates that the transmitted wave is reflected i times and the target echo is reflected j times, where i and j are 0, 1, and 2, respectively. This means that only the case of electromagnetic wave reflection twice or less is considered, and the high-order multipath reflected waves with weak amplitude are filtered out. In each set of multipath reflection assumptions, the reflection rules and electromagnetic wave propagation paths are solved by the reflection plane to estimate the multipath propagation paths under each multipath assumption.

5. A method for multi-angle detection using a single radar based on multipath dealiasing according to claim 2, characterized in that, Step 5 specifically involves: Under different multipath assumptions, the angles of the transmitted and reflected waves differ, resulting in different Doppler velocities. The analysis examines whether the Doppler components within a region point to the same true velocity. That is, under each multipath assumption, the target's Doppler velocity is estimated using the actual velocity and direction of motion. Therefore, within each multipath channel, a system of equations is established based on the directions of the transmitted and received waves and the target's Doppler velocity. The target's maximum likelihood velocity and direction are then calculated, as shown in the formula. Among them, v XY V represents the Doppler velocities of the transmission and reception angles X and Y. Tar For the target's true velocity, α Tar The actual direction of motion of the target. This is an estimate of the target velocity. α is an estimate of the target's direction of motion. T Let α be the angle of the emitted electromagnetic wave. R The angle at which the electromagnetic wave is received.

6. A method for multi-angle detection using a single radar based on multipath dealiasing according to claim 5, characterized in that, If the direction of the Doppler velocity in each multipath hypothesis within the region is consistent with the direction of the target's maximum likelihood velocity, it indicates that the echoes in the region all originate from the same target. As shown in the following formula, use Estimate Doppler velocities under various multipath conditions If the estimated Doppler velocity is the same as the measured Doppler velocity v XY If they are similar, the multipath images within the region corroborate each other, indicating a high probability of a target being present in the region. At the same time, the true speed and direction of the suspected target are estimated. Where N is the number of electromagnetic wave propagation paths. v is an estimate of the Doppler velocity. XY The measured value is the Doppler velocity. This is an estimate of the target velocity. α is an estimate of the target's direction of motion. T Let α be the angle of the emitted electromagnetic wave. R The angle at which the electromagnetic wave is received.

7. A method for multi-angle detection using a single radar based on multipath dealiasing according to claim 6, characterized in that, The specific steps of step two are as follows: Step 1: Establish a virtual radar under the multipath assumption; After superposition, regions with high echo amplitude and consistent Doppler velocity of multipath echoes are regarded as suspected target regions for multi-angle high-resolution imaging. Based on the multipath assumption, the virtual radar position corresponding to each multipath image is calculated. Each multipath image corresponds to a virtual radar, and the virtual radar realizes the imaging of the target from multiple angles and multiple distances. Step 2: Multi-angle high-resolution imaging based on accumulated votes; Multi-angle images from virtual radar are fused to improve imaging resolution. By transforming the coordinate system of the multi-angle images, the cells in the polar coordinate system are converted into rectangular coordinates, and the target image in the rectangular coordinate system can be obtained. Image fusion is achieved by accumulating votes. The multi-angle images in the rectangular coordinate system are multiplied point-to-point to obtain the fused image.

8. The application of a single radar method for multi-angle detection based on multipath dealiasing according to any one of claims 1-7, characterized in that, The method is applied to target detection.

9. The application of a single radar method for multi-angle detection based on multipath dealiasing according to any one of claims 1-7, characterized in that, The method is applied to robot path planning.

Images