Three-dimensional super-resolution detection method and device for pulse Doppler radar

Abstract

The invention discloses a three-dimensional super-resolution detection method for a pulse Doppler radar, and the method comprises the steps: 1, obtaining an echo signal, so as to obtain corresponding detection information; 2, performing discrete linear chirp transformation on the detection information, and taking an amplitude maximum value point in a transformation result as a detection unit; 3, correcting the detection parameters by adopting a Newton method, and adding the corrected detection parameters as a first effective target into an effective target set; 4, removing the detection information corresponding to the first effective target from the echo information signal, repeatedly executing the above steps to obtain a second effective target, and performing cyclic correction on the first effective target based on the second effective target; and step 5, repeating the step 2 to the step 4 to obtain a final effective target set. The invention further provides a three-dimensional super-resolution detection device. According to the method provided by the invention, the problem that the number of the cluster targets cannot be accurately estimated due to energy cross in the Doppler dimension can be solved.

Description

Technical Field

[0001] The present invention belongs to the technical field of radar signal processing, and in particular relates to a three-dimensional super-resolution detection method and device for pulse Doppler radar. Background Art

[0002] Conventional radar systems use linear signal processing methods, namely pulse compression and moving targets detection (MTD). Their resolution is limited by the Rayleigh resolution, and the acceleration of nearby targets will cause energy crossover in the Doppler dimension, making it difficult to accurately distinguish the number of targets, resulting in the inability to implement accurate identification.

[0003] Low, slow and small targets mainly refer to targets with low flying altitude, slow flying speed and small radar reflection area. In the process of using linear frequency modulation pulse Doppler radar to detect low, slow, small and highly maneuverable targets such as drones, the use of long-term coherent accumulation can effectively improve the probability of target detection. Generally speaking, early warning radars can use the MTD method to detect traditional targets such as aircraft, ships, and missiles. However, for low, slow, small and highly maneuverable targets, the use of the MTD method is easily affected by clutter or interference between strong and weak targets, resulting in missed detection of weak targets. At the same time, in scenarios with a large number of pulses, the acceleration of highly maneuverable targets themselves can easily cause serious Doppler frequency shifts, resulting in the inability of the MTD-based coherent accumulation method to focus the target energy.

[0004] Patent document CN118191844A discloses a dynamic risk situation awareness method for UAV operation in low-altitude airspace. It uses multiple radars to dynamically monitor the operation trajectory of UAVs in the entire low-altitude airspace, and calculates the position, speed, acceleration, and displacement of multiple UAVs at any time based on radar fusion calculation. Based on this, deep learning is used to deduce the spatiotemporal evolution trend of each UAV, and the collision risk index between any two UAVs now and in the future is calculated from a microscopic perspective. The collision risk index of the entire low-altitude airspace now and in the future is calculated from a macroscopic perspective. According to the safety threshold, dynamic warning of the micro and macro collision risk levels of the low-altitude airspace is given, thereby providing data support for UAV monitoring and command.

[0005] Patent document CN118426014A discloses an inertial multi-source fusion unmanned system global positioning method and system, including: in the initialization stage, using GNSS and IMU for combined positioning to realize the initialization of the initial position, attitude and speed of the positioning system; in the motion positioning stage, GNSS signals are obtained, and in areas where GNSS signals are present, IMU and GNSS combined positioning is used, and internal and external parameters between the IMU, laser radar and camera are calibrated online. In areas without GNSS signals, IMU and laser radar are used for tight coupling to realize autonomous positioning, and the camera is used to identify landmarks of known positions for absolute position correction to realize global seamless positioning of the unmanned system. Summary of the invention

[0006] The purpose of the present invention is to provide a three-dimensional super-resolution detection method and device for pulse Doppler radar, which can solve the problem of mutual masking or mutual interference between targets when detecting multiple clustered low, slow and small targets in complex scenes, as well as the problem of Doppler frequency ambiguity of highly maneuverable targets.

[0007] In order to achieve the first object of the present invention, the following technical solution is provided: a three-dimensional super-resolution detection method for pulse Doppler radar, comprising the following steps: Step 1: Acquire the echo signal emitted by the pulse Doppler radar, and perform linear processing on the echo signal based on a preset unit distance to obtain detection information corresponding to each unit distance; Step 2: Perform discrete linear chirp transformation on the detection information corresponding to each distance unit, and use the point with the highest amplitude value in the transformation result as the detection unit, and use the detection parameters in the detection unit as a judgment basis to determine whether the detection unit is a valid target, wherein the detection parameters include three-dimensional data consisting of distance, velocity angular frequency, acceleration angular frequency, and amplitude value; Step 3: for the detection unit determined as a valid target in step 2, the detection parameter is corrected by using the Newton method, and the corrected detection parameter is added to the valid target set as the first valid target; Step 4: remove the detection information corresponding to the first valid target from the echo information number, and perform steps 2-3 once for the echo information that has completed the removal operation, so as to obtain the corresponding second valid target to be added to the valid target set; Based on the detection parameters corresponding to the second valid target, a first valid target previously added to the valid target set is cyclically corrected by Newton's method to obtain a final first valid target; Step 5: Repeat steps 2 to 4 until the maximum number of cycles is reached to obtain the final valid target set.

[0008] The present invention introduces Newton's method to perform three-dimensional joint estimation of the echo signal of the pulse Doppler radar, and updates the amplitude value after parallel Newton correction of the distance, velocity angular frequency and acceleration angular frequency; at the same time, by eliminating the influence of the estimated target and fixing some of the detected results to perform cyclic correction on the remaining detection results, the problem that the number of cluster targets cannot be accurately estimated in the Doppler dimension due to energy crossover is solved.

[0009] Specifically, the pulse Doppler radar is a shore-based radar, an airborne radar or a ship-based radar.

[0010] Specifically, in step 1, the linear processing uses an oversampling matched filtering method to perform pulse compression on the echo signal to obtain detection information after pulse compression.

[0011] Specifically, in step 2, the expression of the discrete linear chirp transform is as follows: ;in, is the frequency matching factor, is the chirp rate matching factor, For the corresponding The transformation information of distance units, For the corresponding The detection information of each distance unit, is the number of pulses; according to and The scope of this is limited to discrete finite sets and In, and and Every two adjacent and The interval satisfies: ;in, express Every two adjacent The interval, express Every two adjacent interval.

[0012] Specifically, the expression of the three-dimensional data in the detection parameters is as follows: ;in, ; ; ; ; ; ;

[0013] in, Represents three-dimensional data of distance-velocity angular frequency-acceleration angular frequency, Indicates distance, represents the velocity angular frequency, represents the acceleration angular frequency, Indicates returning the variable value corresponding to the maximum value. is the sampling interval, Indicates the distance The manifold vector of Indicates the angular frequency of velocity An array of manifold vectors, Indicates the angular frequency of acceleration Array manifold vector of ; Indicates the amplitude value, Indicates the residual of the echo data of all detected units corresponding to the target is eliminated. When detecting the detection unit ; The superscript H represents the conjugate transpose operation, the superscript T represents the transpose operation, and the superscript represents the conjugation operation.

[0014] Specifically, in step 4, the cyclic correction is performed by performing Newton correction on the velocity angular frequency and the acceleration angular frequency in parallel to update the amplitude value.

[0015] Specifically, the specific process of determining whether the detection unit is a valid target is as follows: By eliminating the protection unit near the corresponding distance unit in the detection information, and calculating the average value of the square of all numerical modulus values ​​within the number of training units as the background noise estimation value, the threshold value of the effective target is set based on the background noise estimation value; Compare the calculated detection amount with the threshold value, if it is greater than the threshold value, it is judged that there is a valid target, otherwise it is judged that there is an invalid target; The expression of the detected amount is as follows: ;in, is the detection amount, is the signal length.

[0016] In order to achieve the second objective of the present invention, the following technical solution is provided: a three-dimensional super-resolution detection device, used to execute the steps of the above-mentioned three-dimensional super-resolution detection method for pulse Doppler radar.

[0017] Compared with the prior art, the present invention has the following beneficial effects: The three-dimensional joint estimation of the distance, velocity angular frequency and acceleration angular frequency in the echo signal can overcome the Doppler frequency ambiguity and estimate the acceleration of highly maneuverable targets. The amplitude value is updated by using the distance, velocity angular frequency and acceleration angular frequency in parallel with Newton correction, which overcomes the limitation of Rayleigh resolution and improves the estimation accuracy of the distance, velocity and acceleration of the nearby target. By eliminating the influence of estimated targets and fixing some detected results to perform cyclic correction on the remaining detection results, the accuracy of the cluster target number estimation result is improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 A flow chart of a three-dimensional super-resolution detection method for pulse Doppler radar provided in this embodiment; Figure 2 A schematic diagram of a three-dimensional super-resolution detection device provided in this embodiment; Figure 3 A schematic diagram of the detection results of a scene close to a high-mobility target provided by this embodiment; Figure 4 A schematic diagram of the detection results of the cluster high-mobility target scene provided in this embodiment; Figure 5 A schematic diagram showing the relationship between the residual energy and the number of Newton method iterations provided in this embodiment. DETAILED DESCRIPTION

[0019] In order to make the purpose, technical scheme and advantages of the embodiments of the present invention clearer, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. The components of the embodiments of the present invention generally described and shown in the drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the drawings is not intended to limit the scope of the claimed invention, but merely represents selected embodiments of the present invention. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0020] like Figure 1 As shown, the three-dimensional super-resolution detection method for pulse Doppler radar provided in this embodiment includes the following steps: Step 1: Acquire the echo signal emitted by the pulse Doppler radar, and perform linear processing on the echo signal based on a preset unit distance to obtain detection information corresponding to each unit distance; More specifically, the pulse Doppler radar is obtained by down-conversion demodulation. K Target echo signal , expressed as: ;in, represents noise, Indicates the amplitude value, represents the transpose operation, Indicates the distance The manifold vector of Indicates the angular frequency of velocity An array of manifold vectors, Indicates the angular frequency of acceleration The array manifold vector is expressed as: ; ; ; ; ;in, is the sampling interval, Indicates distance, represents the speed of light, Represents a pulse signal, represents the number of distance dimension samples, Indicates the number of pulses, Indicates the carrier frequency, represents the pulse repetition period, represents the target speed, Indicates the target acceleration.

[0021] Use the oversampling matched filter method to compress the pulse and obtain the data after pulse compression. Restricted to finite discrete sets In the formula, it is expressed as:

[0022] ;in, Indicates distance, represents a finite discrete set, represents the fast time index, represents the speed of light, represents the fast-time sampling frequency, represents the number of distance dimension samples, Indicates the number of samples corresponding to the pulse width, represents the oversampling factor relative to the conventional pulse compression grid.

[0023] Step 2: Perform discrete linear chirp transformation on the detection information corresponding to each distance unit, and use the point with the highest amplitude value in the transformation result as the detection unit, and use the detection parameters in the detection unit as a judgment basis to determine whether the detection unit is a valid target, wherein the detection parameters include three-dimensional data consisting of distance, velocity angular frequency, acceleration angular frequency, and amplitude value; In this embodiment, the amplitude corresponding to the first detection unit is ,distance , speed frequency and acceleration frequency The maximum likelihood estimation of can be transformed into the problem of minimizing the residual energy: It is equivalent to maximizing the following cost function : ;

[0024] in, Indicates the optimal After finding the minimum value of the entire expression, return the corresponding variable value. represents taking the real part; ; ; represents the L2 norm, that is: ;

[0025] In this embodiment, the discrete linear chirp transform is expressed by the formula: ;

[0026] in, is the frequency matching factor, is the chirp rate matching factor, For the corresponding The transformation information of distance units, For the corresponding The detection information of each distance unit, is the number of pulses. and The scope of this is limited to discrete finite sets and In, and and Every two adjacent and The interval satisfies:

[0027] ;in, express Every two adjacent The interval, express Every two adjacent interval.

[0028] Pulse pressure information corresponding to each distance unit The transformed detection quantity Take the highest point corresponding to and The value is used as a rough estimate of the distance unit , speed angular frequency unit and acceleration angular frequency unit , expressed as: ; According to the corresponding relationship , , Get the rough estimated distance, velocity angular frequency and acceleration angular frequency.

[0029] For a given , and ,make The largest amplitude for: ;in .

[0030] Will Substitution You can get The generalized likelihood ratio test (GLRT) estimate of is to solve the following optimization problem:

[0031] ;in, is the cost function of GLRT, , .

[0032] In this embodiment, the method for determining that the first detection unit is a valid target is to remove the protection unit near the distance unit corresponding to the target in the detection information, and calculate the average value of the square of all data modulus values ​​within the number of training units as the estimated value of the background noise. . Determine the threshold of effective targets based on the size of the background noise and calculate the detection amount , compared with the threshold, if the ratio is greater than the threshold, it is considered that there is a valid target, otherwise it is considered that there is an invalid target.

[0033] Step 3: For the detection unit determined as a valid target in step 2, the detection parameter is corrected using the Newton method, and the corrected detection parameter is added to the valid target set as the first valid target.

[0034] In this embodiment, if the detection result of the first detection unit is determined to be a valid target, the distance, velocity angular frequency, acceleration angular frequency and amplitude value of the unit are corrected using the Newton method. The correction process is as follows: The cost function and Newton's method are used to perform parallel corrections on the distance, velocity angular frequency, and acceleration angular frequency corresponding to the first detection unit, which can be expressed as follows: ;

[0035] Based on the corrected distance, velocity angular frequency and acceleration angular frequency, the amplitude of the detection unit is obtained using the least squares method, which is expressed as: ;

[0036] Among them, solving the gradient matrix and Hansen matrix of the cost function is very critical. The process is as follows: Solving the cost function The corresponding gradient matrix : ;in, Indicates that at the iteration point About distance The first derivative of Indicates that at the iteration point About the velocity angular frequency The first derivative of Indicates that at the iteration point About the acceleration angular frequency The first-order derivative of is further: ; ; ; Further: ; ; ;in, ; In the formula, , No. The elements are about The first-order derivative of is expressed as: ;

[0037] Solving the cost function The corresponding Heather matrix : ;in, Indicates that at the iteration point About distance The second-order derivative of Indicates that at the iteration point About the velocity angular frequency The second-order derivative of Indicates that at the iteration point About the acceleration angular frequency The second-order derivative of Indicates that at the iteration point About distance and velocity angular frequency The second-order derivative of Indicates that at the iteration point About distance and acceleration angular frequency The second-order derivative of Indicates that at the iteration point About the velocity angular frequency and acceleration angular frequency The second-order derivative of is further: ; ; ; ; ; ; Further: ; ; ; In the formula No. The elements are about The second-order derivative of is expressed as: ;

[0038] Finally, the corrected distance, velocity angular frequency, acceleration angular frequency and amplitude value of the first detection unit are added to the valid target set.

[0039] Step 4: remove the detection information corresponding to the first valid target from the echo information number, and perform steps 2-3 once for the echo information that has completed the removal operation, so as to obtain the corresponding second valid target to be added to the valid target set; Based on the detection parameters corresponding to the second valid target, the first valid target previously added to the valid target set is cyclically corrected by Newton's method to obtain the final first valid target.

[0040] In this embodiment, it is assumed that the currently estimated valid target set is: , the residual of the received signal is recorded as , then each time you select The corresponding residual for: ; Then according to the residual For The goal is to readjust Corrected to , then the residual Will also be updated to : ,use replace Then proceed to The target is modified until all the modifications are completed.

[0041] Step 5: Repeat steps 2 to 4 until the maximum number of cycles is reached to obtain the final valid target set.

[0042] Based on the same inventive concept, this embodiment also provides a three-dimensional super-resolution detection device, such as Figure 2 As shown, it includes a detection information acquisition unit 510, a target parameter acquisition unit 520, a Newton correction unit 530, a cycle correction unit 540, and a target output unit 550: The detection information acquisition unit 510 is used to receive the echo signal emitted by the pulse Doppler radar and obtain the detection information through a linear processing method; The target parameter acquisition unit 520 is used to perform discrete linear chirp transformation on the detection information corresponding to each distance unit, and take the point with the highest amplitude value as the first detection unit, obtain the distance, velocity angular frequency, acceleration angular frequency and amplitude value corresponding to the first detection unit, and determine whether it is a valid target; The Newton correction unit 530 is used to correct the distance, velocity angular frequency, acceleration angular frequency and amplitude value corresponding to the first detection unit determined as a valid target using the Newton method, and add the corrected first result to the valid target set; The cyclic correction unit 540 is used to remove the first result from the echo signal, and perform linear processing on the echo signal after the first result is removed to obtain the detection information after the removal, and perform discrete linear chirp transformation on the detection information after the removal to obtain the distance, velocity angular frequency, acceleration angular frequency and amplitude value corresponding to the second detection unit. After judging it as a valid target, the Newton method is used to correct the distance, velocity angular frequency, acceleration angular frequency and amplitude value corresponding to the second detection unit, and the corrected second result is added to the valid target set; at the same time, the second result is fixed, and the first result is cyclically corrected by the Newton method, and after correction, it is stored in the valid target set again;

[0043] The target output unit 550 is used to repeat the target parameter acquisition unit, the Newton correction unit and the cyclic correction unit until the set maximum number of cycles is met, output the final valid target set, and obtain the distance, speed and acceleration information of all targets in the echo signal.

[0044] In order to better illustrate the technical effect of the present invention, the following experimental contents are provided: The radar parameters in the experiment are set as follows: carrier frequency ; Pulse width ; Pulse repetition interval ;bandwidth ; Chirp rate ; Sampling frequency ; Number of pulses = 2048, then the length of a distance unit is .

[0045] Experimental Example 1: Set the actual target distance to 485, 486 (m), set the target speed to 5, 5.1 (m / s), set the target speed to 10, 7 ( ), and set the target cumulative signal-to-noise ratio to 18 dB.

[0046] In the scenario of approaching a highly maneuverable target, the target is located in the same range unit and crosses 28 and 19 velocity resolution units respectively in one coherent processing cycle, resulting in Doppler frequency shift. The test results are as follows: Figure 3 shown.

[0047] in, Figure 3 Figure a is a schematic diagram of the detection results of the traditional pulse compression method based on matched filtering. Since the adjacent targets are located in the same distance unit and a serious Doppler frequency shift occurs, the target energy is dispersed and cannot be successfully distinguished.

[0048] Figure 3 b in the figure is a schematic diagram of the detection result of the method provided by the present invention, which successfully distinguishes the nearby target and the root mean square error of the distance is , the speed root mean square error is , the acceleration root mean square error is , by comparison Figure 3 a and Figure 3 From b in FIG. 1 , it can be seen that the method provided by the present invention has greatly improved the estimation accuracy and resolution of target distance, speed and acceleration.

[0049] Experimental Example 2: Set the real target distance to 72, 74, 76, 76.8, 78.8, 80.8, 81.6, 83.6, 85.6 (m), set the target speed to 1.1, 1.2, 1.3, 1.4, 1.5, 1.6, 1.7, 1.8, 1.9 (m / s), set the target acceleration to -10, -8, -6, -4, -2, 0, 2, 4, 6 ( ), set the target cumulative signal-to-noise ratio to 18 dB. In the scenario of high-mobility cluster targets, the minimum distance between real targets is 0.8m. The target with the largest acceleration crosses 28 velocity resolution units in one coherent processing cycle, and Doppler frequency shift occurs. The test results are as follows Figure 4 shown.

[0050] in, Figure 4Figure a is a schematic diagram of the detection results of the traditional pulse compression method based on matched filtering. Due to the serious Doppler frequency offset of the target, the traditional method cannot effectively accumulate the target energy, resulting in many false alarms, and the energy of some targets crosses in the velocity dimension and cannot be successfully distinguished.

[0051] Figure 4 b in the figure is a schematic diagram of the detection results obtained by the method proposed in the present invention, which successfully distinguishes 9 targets, and the root mean square error of the distance is , the speed root mean square error is , the acceleration root mean square error is .

[0052] By comparison Figure 4 a and Figure 4 From b in FIG. 1 , it can be seen that the present invention has obvious advantages in overcoming the Doppler frequency shift phenomenon and has a significant improvement in the target number estimation.

[0053] Experimental Example 3: Set the actual target distance to 485 m, the target speed to 5 m / s, and the target acceleration to 10 , set the target cumulative signal-to-noise ratio to 18 dB, the test results are as follows Figure 5 shown.

[0054] in, Figure 5 The a in the figure is the result of using serial Newton correction: the residual energy is minimized after 100 iterations of correction.

[0055] Figure 5 b in the figure is the result of the three-dimensional super-resolution detection method proposed in the present invention using parallel Newton correction: the minimum residual energy is achieved after 20 iterative corrections.

[0056] By comparison Figure 5 a and Figure 5 From b in , it can be seen that the parallel Newton correction used in the present invention can improve the convergence speed of the Newton method and enhance the computational efficiency.

[0057] In addition, the terms "upper", "lower", "inner", "outer", "front", "rear" are used for descriptive purposes only and should not be understood as indicating or implying relative importance. Unless otherwise specifically stated, the relative steps, numerical expressions and values ​​of the components and steps described in these embodiments do not limit the scope of the present invention.

[0058] Of course, the above description is only a specific embodiment of the present invention and is not intended to limit the scope of implementation of the present invention. All equivalent changes or modifications made according to the structure, characteristics and principles described in the patent application scope of the present invention should be included in the patent application scope of the present invention.

[0059] Finally, it should be noted that the above-described embodiments are only specific implementations of the present invention, which are used to illustrate the technical solutions of the present invention, rather than to limit them. The protection scope of the present invention is not limited thereto. Although the present invention is described in detail with reference to the above-described embodiments, ordinary technicians in the field should understand that any technician familiar with the technical field can still modify the technical solutions recorded in the above-described embodiments within the technical scope disclosed by the present invention, or can easily think of changes, or make equivalent replacements for some of the technical features therein; and these modifications, changes or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.

Claims

1. A three-dimensional super-resolution detection method for pulse Doppler radar, characterized in that: The following steps are involved: Step 1: Acquire the echo signal emitted by the pulse Doppler radar, and perform linear processing on the echo signal based on a preset unit distance to obtain detection information corresponding to each unit distance; Step 2: Perform discrete linear chirp transformation on the detection information corresponding to each distance unit, take the point with the highest amplitude value in the transformation result as the detection unit, and use the detection parameters in the detection unit as a judgment basis to judge whether the detection unit is a valid target, wherein the detection parameters include three-dimensional data composed of distance, velocity angular frequency, acceleration angular frequency and amplitude value; Step 3: for the detection unit determined as a valid target in step 2, the detection parameter is corrected by using the Newton method, and the corrected detection parameter is added to the valid target set as the first valid target; Step 4: remove the detection information corresponding to the first valid target from the echo information number, and perform steps 2-3 once for the echo information that has completed the removal operation, so as to obtain the corresponding second valid target to be added to the valid target set; Based on the detection parameters corresponding to the second valid target, a first valid target previously added to the valid target set is cyclically corrected by Newton's method to obtain a final first valid target; Step 5: Repeat steps 2 to 4 until the maximum number of cycles is reached to obtain the final valid target set.

2. The three-dimensional super-resolution detection method for pulse Doppler radar according to claim 1, characterized in that: The pulse Doppler radar is a shore-based radar, an airborne radar or a ship-based radar.

3. The three-dimensional super-resolution detection method for pulse Doppler radar according to claim 1, characterized in that: In step 1, the linear processing uses an oversampling matched filtering method to perform pulse compression on the echo signal to obtain detection information after pulse compression.

4. The three-dimensional super-resolution detection method for pulse Doppler radar according to claim 1, characterized in that: In step 2, the expression of the discrete linear chirp transform is as follows: ;in, is the frequency matching factor, is the chirp rate matching factor, For the corresponding The transformation information of distance units, For the corresponding The detection information of each distance unit, is the number of pulses; according to and The scope of this is limited to discrete finite sets and In, and and Every two adjacent and The interval satisfies: ;in, express Every two adjacent The interval, express Every two adjacent interval.

5. The three-dimensional super-resolution detection method for pulse Doppler radar according to claim 1, characterized in that: The expression of the three-dimensional data in the detection parameters is as follows: ;in, ; ; ; ; ; ;in, Represents three-dimensional data of distance-velocity angular frequency-acceleration angular frequency, Indicates distance, represents the velocity angular frequency, represents the acceleration angular frequency, Indicates returning the variable value corresponding to the maximum value. is the sampling interval, Indicates the distance The manifold vector of Indicates the angular frequency of velocity An array of manifold vectors, Indicates the angular frequency of acceleration Array manifold vector of ; Indicates the amplitude value, Indicates the residual of the echo data of all detected units corresponding to the target is eliminated. When detecting the detection unit ; The superscript H represents the conjugate transpose operation, the superscript T represents the transpose operation, and the superscript represents the conjugation operation.

6. The three-dimensional super-resolution detection method for pulse Doppler radar according to claim 1, characterized in that: In step 4, the cyclic correction is performed by performing Newton correction on the velocity angular frequency and the acceleration angular frequency in parallel to update the amplitude value.

7. The three-dimensional super-resolution detection method for pulse Doppler radar according to claim 1, characterized in that: The specific process of judging whether the detection unit is a valid target is as follows: By eliminating the protection unit near the corresponding distance unit in the detection information, and calculating the average value of the square of all numerical modulus values ​​within the number of training units as the background noise estimation value, the threshold value of the effective target is set based on the background noise estimation value; Compare the calculated detection amount with the threshold value, if it is greater than the threshold value, it is judged that there is a valid target, otherwise it is judged that there is an invalid target; The expression of the detected amount is as follows: ;in, is the detection amount, is the signal length.

8. A three-dimensional super-resolution detection device, characterized in that: Used to execute the steps of the three-dimensional super-resolution detection method for pulse Doppler radar as described in any one of claims 1 to 7.

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