FMCW radar static clutter suppression method and system based on phase feature adaptation

Abstract

This invention discloses a static clutter suppression method for FMCW radar based on phase feature adaptation, comprising: acquiring the zero-Doppler signal of the target range cell in the current frame, wherein the zero-Doppler signal is the complex signal of the zero-Doppler channel in the radar range-Doppler matrix of the current frame; obtaining the residual signal of the current frame based on the zero-Doppler signal and the static clutter estimate of the target range cell in the previous frame, wherein the residual signal is the signal after static clutter has been filtered out; calculating the phase instability index of the target range cell based on the residual signal of the current frame and the residual signal of the previous frame; determining the static clutter update weight of the target range cell based on the index and the mapping function; and obtaining the static clutter estimate for filtering the zero-Doppler signal of the target range cell in the next frame based on the weight, the zero-Doppler signal and the static clutter estimate of the previous frame. This invention can achieve rapid suppression of static clutter while ensuring low computational complexity.

Description

Technical Field

[0001] This invention belongs to the field of radar signal processing technology, specifically relating to a static clutter suppression method and system for FMCW radar based on phase characteristic adaptive design. Background Technology

[0002] With the deep integration of IoT and AI technologies, millimeter-wave radar has been widely used in indoor target detection due to its excellent range and velocity resolution and non-contact sensing advantages. However, in frequency modulated continuous wave (FMCW) radar systems, stationary objects in the indoor environment (such as walls and furniture) generate strong static clutter, which seriously affects the signal-to-clutter ratio and may cause weak targets to be obscured or false targets to be generated. Especially in application scenarios that require simultaneous static clutter suppression and detection of stationary human movement, traditional methods struggle to achieve both, resulting in significant technical contradictions.

[0003] In existing technologies, methods such as sliding window averaging or weighted averages of old and new means are commonly used to estimate and suppress static clutter. However, these methods typically employ fixed window lengths or forgetting factors, making them unsuitable for dynamically changing scenarios. On one hand, when a stationary human target exhibits subtle movements (such as breathing), its signal energy is primarily concentrated near the Doppler zero frequency. If clutter updates too quickly or suppression is too strong, these subtle movements can be falsely filtered out, leading to missed detections. On the other hand, when the environment undergoes abrupt changes (such as furniture movement), algorithms with fixed parameters converge slowly, leaving behind false targets for extended periods, impacting system real-time performance and reliability. Furthermore, while methods based on complex adaptive filtering or deep learning offer high accuracy, they are computationally intensive and difficult to implement in real-time on resource-constrained embedded platforms.

[0004] Therefore, how to achieve rapid suppression of static clutter with low computational complexity and improve the accuracy of distinguishing between background clutter and the micro-motion signals of a stationary human body is a technical problem that urgently needs to be solved. Summary of the Invention

[0005] To address the problem of achieving rapid suppression of static clutter with low computational complexity and improving the accuracy of distinguishing between background clutter and the subtle motion signals of a stationary human body, this invention provides a static clutter suppression method and system for FMCW radar based on phase feature adaptation. The technical problem to be solved by this invention is achieved through the following technical solution:

[0006] This invention provides a static clutter suppression method for FMCW radar based on phase characteristic adaptation, comprising:

[0007] Obtain the zero-Doppler signal of the target range cell in the current frame. The zero-Doppler signal is the complex signal of the zero-Doppler channel in the radar range-Doppler matrix of the current frame.

[0008] Based on the zero Doppler signal and the static clutter estimate of the target range cell in the previous frame, the residual signal of the current frame is obtained. The residual signal of the current frame is the target echo signal after static clutter has been filtered out.

[0009] Based on the residual signal of the current frame and the residual signal of the previous frame, the phase instability index of the target range cell is calculated. The phase instability index is used to characterize the degree of dispersion of the phase change of the residual signal between consecutive frames.

[0010] Based on the phase instability index and the constructed mapping function, the static clutter update weight of the target range cell is determined. The mapping function is configured such that the static clutter update weight is negatively correlated with the phase instability index.

[0011] Based on the static clutter update weights, the zero-Doppler signal, and the static clutter estimate from the previous frame, the static clutter estimate of the target range cell is obtained, which is used to filter out static clutter from the zero-Doppler signal of the target range cell in the next frame.

[0012] In one embodiment of the present invention, the residual signal of the current frame is obtained based on the zero-Doppler signal and the static clutter estimate of the target range cell in the previous frame, including:

[0013] The residual signal of the current frame is obtained by subtracting the zero Doppler signal from the static clutter estimate of the target range cell in the previous frame.

[0014] In one embodiment of the present invention, the phase instability index of the target range cell is calculated based on the residual signal of the current frame and the residual signal of the previous frame, including:

[0015] Calculate the target phase difference between the residual signal of the current frame and the residual signal of the previous frame;

[0016] Sort the target phase difference with the absolute values ​​of multiple historical phase differences in the sliding window queue to determine a preset number of first phase differences;

[0017] Remove all first phase differences from the sliding window queue to obtain the phase difference sequence after removal;

[0018] The phase trajectory is reconstructed by summing the phase difference sequence after elimination.

[0019] Based on the phase trajectory, the phase instability index of the target range cell is obtained.

[0020] In one embodiment of the present invention, the phase instability index of the target range cell is obtained based on the phase trajectory, including:

[0021] Determine the maximum and minimum values ​​of the phase trajectory;

[0022] The phase instability index of the target range cell is obtained by subtracting the maximum and minimum values.

[0023] In one embodiment of the present invention, the formula for calculating the target phase difference is as follows:

[0024] ,

[0025] in, For the target phase difference, For the current number The first frame The residual signal of each target distance cell, For the first The first frame The residual signal of each target distance cell, To obtain the complex conjugate operation, This is for phase angle taking operations.

[0026] In one embodiment of the present invention, the mapping function satisfies the following: when the phase instability index is less than or equal to the static threshold, the preset maximum update weight is determined as the static clutter update weight; when the phase instability index is greater than or equal to the motion threshold, the preset minimum update weight is determined as the static clutter update weight; when the phase instability index is less than the motion threshold and greater than the static threshold, the static clutter update weight is calculated using a linear interpolation method, where the motion threshold is greater than the static threshold.

[0027] In one embodiment of the present invention, the expression of the mapping function is:

[0028] ,

[0029] in, For the current number The first frame Static clutter update weights for each target range cell. To maximize the update weight, To minimize the update weight, For motion threshold, This is a static threshold. For the first Phase instability index of each target range cell.

[0030] In one embodiment of the present invention, the formula for calculating the static clutter estimate of the target range cell is as follows:

[0031] ,

[0032] in, For the current number The first frame Static clutter estimates for each target range cell. For the current number The first frame Static clutter update weights for each target range cell. For the first The first frame Static clutter estimates for each target range cell. For the current number The first frame Zero Doppler signal of each target range cell.

[0033] Another aspect of the present invention provides a phase-characteristic adaptive FMCW radar static clutter suppression system, the system comprising:

[0034] The acquisition module is used to acquire the zero-Doppler signal of the target range cell in the current frame;

[0035] The clutter filtering module is used to obtain the residual signal of the current frame based on the zero Doppler signal and the static clutter estimate of the target range cell in the previous frame. The residual signal of the current frame is the target echo signal after static clutter has been filtered out.

[0036] The calculation module is used to calculate the phase instability index of the target distance unit based on the residual signal of the current frame and the residual signal of the previous frame. The phase instability index is used to characterize the degree of dispersion of the phase change of the residual signal between consecutive frames.

[0037] The determination module is used to determine the static clutter update weight of the target range cell based on the phase instability index and the constructed mapping function. The mapping function is configured such that the static clutter update weight is negatively correlated with the phase instability index.

[0038] The estimation module is used to obtain the static clutter estimate of the target range cell based on the static clutter update weight, the zero-Doppler signal, and the static clutter estimate of the previous frame, and is used to perform static clutter filtering on the zero-Doppler signal of the target range cell in the next frame.

[0039] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0040] This invention provides a static clutter suppression method for FMCW radar based on phase feature adaptation. First, the static clutter estimate of the target range cell in the previous frame is obtained by using the zero Doppler signal and the static clutter estimate of the target range cell in the previous frame. This is the residual signal of the current frame, which is filtered out of static clutter. This effectively removes strong static background components, making the micro-moving target signal more prominent. This ensures that the breathing signal of a stationary human body will not be filtered out as clutter, and significantly reduces the false detection rate. Based on this, the phase instability index of the target range cell is further calculated by combining the residual signal from the previous frame. This phase instability index is used to characterize the dispersion of the phase change of the residual signal between consecutive frames. That is, the phase instability index can characterize the micro-motion state such as human breathing. Subsequently, this is used as the basis for adjusting the static clutter update weight. By constructing a negative correlation mapping relationship between the phase instability index and the static clutter update weight, intelligent background learning control is realized. When the phase instability index is low, the static clutter update weight is increased to quickly learn newly appearing stationary objects; when the phase instability index is high, the static clutter update weight is decreased to protect the micro-motion signal from being absorbed. This achieves rapid suppression of static clutter and accurate protection of stationary human micro-motion signals under the premise of low computational complexity. It effectively solves the contradiction between slow response to environmental changes and missed detection of micro-motion targets in traditional methods, and improves the accuracy of distinguishing between background static clutter and the micro-motion signals of stationary human bodies.

[0041] The present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0042] Figure 1 This is a flowchart of a static clutter suppression method for FMCW radar based on phase feature adaptation provided in an embodiment of the present invention;

[0043] Figure 2 This is a schematic diagram of the residual signal amplitude corresponding to a human target under the method provided by this invention and the traditional mean update method with a fixed forgetting factor, provided by an embodiment of this invention;

[0044] Figure 3 This is a schematic diagram of the residual signal amplitude corresponding to the water cup target under the method provided by this invention and the traditional mean update method with a fixed forgetting factor, provided by an embodiment of this invention;

[0045] Figure 4 This is a schematic diagram of the phase instability index corresponding to a human target under the method provided by the present invention, provided by an embodiment of the present invention;

[0046] Figure 5 This is a schematic diagram of the phase instability index corresponding to the water cup target under the method provided by the present invention.

[0047] Figure 6 This is a schematic diagram of static clutter update weights corresponding to a human target under the method provided by the present invention.

[0048] Figure 7 This is a schematic diagram of the static clutter update weight corresponding to the water cup target under the method provided by the present invention. Detailed Implementation

[0049] To further illustrate the technical means and effects adopted by the present invention to achieve the intended purpose, the following describes in detail, with reference to the accompanying drawings and specific embodiments, a static clutter suppression method for FMCW radar based on phase characteristic adaptive method proposed in accordance with the present invention.

[0050] The foregoing and other technical contents, features, and effects of the present invention will be clearly presented in the following detailed description of specific embodiments in conjunction with the accompanying drawings. Through the description of the specific embodiments, a more in-depth and concrete understanding can be gained of the technical means and effects adopted by the present invention to achieve its intended purpose. However, the accompanying drawings are for reference and illustration only and are not intended to limit the technical solutions of the present invention.

[0051] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations are intended to cover non-exclusive inclusion, such that an article or apparatus comprising a list of elements includes not only those elements but also other elements not expressly listed. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the article or apparatus that includes said element.

[0052] This invention addresses the problem of achieving rapid suppression of static clutter with low computational complexity and improving the accuracy of distinguishing between background clutter and the subtle motion signals of a stationary human body. It proposes a static clutter suppression method for FMCW radar based on phase feature adaptation. Please refer to [link to relevant documentation]. Figure 1 The method includes the following steps:

[0053] Step 1: Obtain the zero-Doppler signal of the target range cell in the current frame. The zero-Doppler signal is the complex signal of the zero-Doppler channel in the radar range-Doppler matrix of the current frame.

[0054] It should be noted that the static clutter estimate needs to be initialized before performing step 1 to obtain an initial static clutter estimate, so that in the current frame... At that time, the initial static clutter estimate is used as the static clutter estimate of the target range cell in the previous frame. Specifically, it can be collected... Frames (e.g.) The raw data is used to calculate the mean zero-Doppler signal for each target range cell, and this is used as the initial static clutter estimate. :

[0055] ,

[0056] in, For the first The first frame Zero Doppler signal of each target range cell.

[0057] It should be noted that, regarding the current [number]th For each frame, the range-Doppler matrix (RDM) obtained after its two-dimensional Fast Fourier Transform (FFT) is read. A single column of data corresponding to the zero-Doppler element (i.e., Doppler dimension index p=0) is extracted from this matrix, forming a complex signal vector. Each element in this vector corresponds sequentially to a specific target range cell. Assuming there are N target range cells, the target range cell indices are traversed... , and sequentially obtain the first in the vector The value of the nth element is the current nth element. The first frame Zero Doppler signal of each target range cell .

[0058] Step 2: Based on the zero Doppler signal and the static clutter estimate of the target range cell in the previous frame, obtain the residual signal of the current frame. The residual signal of the current frame is the target echo signal after filtering out static clutter.

[0059] Understandably, the static clutter estimate of the target range cell in the previous frame is stored in the radar's internal memory. After acquiring the zero-Doppler signal of the target range cell in the current frame, the static clutter estimate of the target range cell in the previous frame can be retrieved from the internal memory. Based on the zero-Doppler signal and the static clutter estimate of the target range cell in the previous frame, the target echo signal with static clutter filtered out, i.e., the residual signal of the current frame, can be obtained.

[0060] Specifically, the residual signal of the current frame is obtained by subtracting the zero Doppler signal from the static clutter estimate of the target range cell in the previous frame.

[0061] For example, the formula for calculating the residual signal of the current frame is as follows:

[0062] ,

[0063] in, For the current number The first frame The residual signal of each target distance cell, For the current number The first frame Zero Doppler signal of each target range cell, For the first The first frame Static clutter estimates for each target range cell.

[0064] Step 3: Based on the residual signal of the current frame and the residual signal of the previous frame, calculate the phase instability index of the target range cell. The phase instability index is used to characterize the degree of dispersion of the phase change of the residual signal between consecutive frames.

[0065] It should be noted that, after obtaining the residual signal of the current frame... After that, it can be used The phase change characteristics are used to assess the micro-motion state of the target. To overcome impulse noise and phase jumps in the radar system, this embodiment of the invention employs a "rejection-reconstruction-range" calculation logic to calculate the phase instability index of the target range cell, specifically including the following five steps:

[0066] Step (1): Calculate the target phase difference between the residual signal of the current frame and the residual signal of the previous frame stored in the internal memory.

[0067] Specifically, the formula for calculating the target phase difference is as follows:

[0068] ,

[0069] in, For the target phase difference, For the first The first frame The residual signal of each target distance cell, To obtain the complex conjugate operation, This is for phase angle taking operations.

[0070] It should be noted that, after obtaining the target phase difference Then, the target phase difference can be... Storage length is (For example, The sliding window queue, for example, the target phase difference Store it in a first-in, first-out (FIFO) queue, and denote the queue as [queue name]. .

[0071] Understandably, if it is written to the queue The number of elements in the middle exceeds the length. Then it will automatically be removed from the queue. The front end removes the oldest historical data to maintain the queue. The most recent The target phase difference of the frame.

[0072] Step (2): Sort the target phase difference with the absolute values ​​of multiple historical phase differences in the sliding window queue to determine a preset number of first phase differences.

[0073] Specifically, the absolute values ​​of the target phase difference and multiple historical phase differences in the sliding window queue are sorted. Then, the absolute value sequence is sorted in ascending order, and the number of phase differences to be removed is determined according to a preset limit. (For example, In the sorted sequence, the two phase differences with the largest absolute values ​​are identified. These two phase differences are considered to be non-physical burst noise or anomalous jumps. It should be noted that the preset number of the first phase difference depends on the duration of the change in occlusion relationship, for example, 20. fps The preset number of radars is 4.

[0074] Step (3) remove all first phase differences from the sliding window queue to obtain the phase difference sequence after removal.

[0075] Specifically, after removing all first phase differences from the sliding window queue, all remaining phase differences are... Each phase difference is restored to its temporal order in the sliding window queue, resulting in a phase difference sequence after removal. .

[0076] Step (4) involves summing the phase difference sequence after elimination to reconstruct the phase trajectory.

[0077] Specifically, after obtaining the phase difference sequence after elimination... Then, the phase difference sequence after elimination By performing cumulative summation (or integration), the phase trajectory within the window time can be reconstructed. .

[0078] For example, the formula for calculating the phase trajectory is as follows:

[0079] ,

[0080] in, For the reconstructed phase trajectory at the 1st The cumulative phase value at each time point The phase difference sequence after elimination The Middle The phase difference of each, This is a length function.

[0081] ,

[0082] Assume that the phase difference sequence after elimination There are 4 phase differences (in radians): So, phase trajectory The calculation is as follows:

[0083] :

[0084] :

[0085] :

[0086] :

[0087] The final phase trajectory for:

[0088]

[0089] Step (5): Based on the phase trajectory, the phase instability index of the target range cell is obtained.

[0090] Specifically, the maximum and minimum values ​​of the phase trajectory are determined, and the difference between the maximum and minimum values ​​is used to obtain the phase instability index of the target range cell.

[0091] For example, the formula for calculating the phase instability index of the target range cell is as follows:

[0092] ,

[0093] in, For the first Phase instability index of each target range cell This represents the maximum value of the phase trajectory. This represents the minimum value of the phase trajectory.

[0094] Step 4: Based on the phase instability index and the constructed mapping function, determine the static clutter update weight of the target range cell. The mapping function is configured such that the static clutter update weight is negatively correlated with the phase instability index.

[0095] In an embodiment of the present invention, the mapping function satisfies the following: when the phase instability index is less than or equal to the static threshold, the preset maximum update weight is determined as the static clutter update weight; when the phase instability index is greater than or equal to the motion threshold, the preset minimum update weight is determined as the static clutter update weight; when the phase instability index is less than the motion threshold and greater than the static threshold, the static clutter update weight is calculated using a linear interpolation method, where the motion threshold is greater than the static threshold.

[0096] It should be noted that the static clutter update weight is a coefficient between 0 and 1, which can be regarded as a "learning rate regulator." It directly determines the radar system's level of confidence in the current observation data, thereby controlling the speed of background model updates. Specifically, when the static clutter update weight is large, the radar system quickly learns the current signal and absorbs it as part of the background (used to suppress newly emerging static clutter). When the static clutter update weight is small, the radar system ignores the current signal, protecting the existing model (used to protect the signal of slightly moving targets from being filtered out as background).

[0097] Specifically, the expression for the mapping function is:

[0098] ,

[0099] in, For the current number The first frame Static clutter update weights for each target range cell. For maximum update weight ( For a purely static background, such as ), Minimum update weight ( Corresponding to micro-motion / moving targets, ), The motion threshold (e.g., 1.5 rad). The static threshold is (e.g., 0.1 rad). For the first Phase instability index of each target range cell.

[0100] Understandably, in the first scenario: when Below the static threshold When this occurs, it indicates that the target phase trajectory of the target distance unit is flat, and it is determined to be a wall or a stationary object. Take the preset maximum value This allows the static clutter estimate to quickly follow environmental drift or abrupt changes. The second case: when... The motion threshold is higher than the preset threshold. At this time, it indicates that the target phase trajectory of the target in the distance unit fluctuates significantly, and it is determined that there is a breathing human body or other micro-moving target. Take the preset minimum value This causes the static clutter estimate to stop updating or update very slowly, thus preventing the micro-moving target signal from being absorbed into the background clutter. The third case: when... Between and In between, Follow The value increases and then monotonically decreases. In this embodiment, linear interpolation is used to calculate the value in the third case. .

[0101] Alternatively, Gaussian smoothing, S-curves, or piecewise step functions can be used to calculate the transition region, i.e., the third case. The embodiments of the present invention do not limit this.

[0102] Step 5: Based on the static clutter update weights, the zero-Doppler signal, and the static clutter estimate from the previous frame, obtain the static clutter estimate for the target range cell, which is used to filter out static clutter from the zero-Doppler signal of the target range cell in the next frame.

[0103] Specifically, the formula for calculating the static clutter estimate of the target range cell in the current frame is as follows:

[0104] ,

[0105] in, For the current number The first frame Static clutter estimates for each target range cell. For the current number The first frame Static clutter update weights for each target range cell. For the first The first frame Static clutter estimates for each target range cell. For the current number The first frame Zero Doppler signal of each target range cell.

[0106] In summary, this invention provides a static clutter suppression method for FMCW radar based on phase feature adaptation. First, the static clutter estimate of the target range cell in the previous frame is obtained by using the zero Doppler signal and the static clutter estimate. This is the residual signal of the current frame, which is the target echo signal after static clutter has been filtered out. This effectively removes strong static background components, making the signal of slightly moving targets more prominent. This ensures that the breathing signal of a stationary human body will not be filtered out as clutter, significantly reducing the false negative rate. Based on this, the phase instability index of the target range cell is further calculated by combining the residual signal from the previous frame. This phase instability index is used to characterize the dispersion of the phase change of the residual signal between consecutive frames. That is, the phase instability index can characterize the micro-motion state such as human breathing. Subsequently, this is used as the basis for adjusting the static clutter update weight. By constructing a negative correlation mapping relationship between the phase instability index and the static clutter update weight, intelligent background learning control is realized. When the phase instability index is low, the static clutter update weight is increased to quickly learn newly appearing stationary objects; when the phase instability index is high, the static clutter update weight is decreased to protect the micro-motion signal from being absorbed. This achieves rapid suppression of static clutter and accurate protection of stationary human micro-motion signals under the premise of low computational complexity. It effectively solves the contradiction between slow response to environmental changes and missed detection of micro-motion targets in traditional methods, and improves the accuracy of distinguishing between background static clutter and the micro-motion signals of stationary human bodies.

[0107] To verify the technical effects of the method provided by this invention, it can be run in real time on a hardware platform and compared with the traditional mean update method with a fixed forgetting factor.

[0108] In the experiment, the radar was placed horizontally. Target 1 and Target 2 were a human body (e.g., an adult sitting still about 1.5 meters in front of the radar, maintaining normal breathing) and a sudden static interference (e.g., during the experiment, a water glass was placed on the table within the radar's field of view (simulating a sudden environmental change), and then the hand was removed, and the water glass remained still).

[0109] Specifically, the radar continuously observes an adult sitting still and breathing normally at a distance of approximately 1.5 meters in front of it. The residual signal amplitude corresponding to the distance cell of the adult is continuously processed and output using both the method provided in this invention and the traditional mean update method with a fixed forgetting factor. The residual signal amplitudes under these two methods are then obtained as follows: Figure 2 As shown, by Figure 2 It can be seen that, under the method provided by the present invention (corresponding to...) Figure 2(The red solid line in the image) The residual signal amplitude of the corresponding distance unit for this adult exhibits periodic fluctuations synchronized with the respiratory rhythm, which is clearly visible. This indicates that the method has an excellent protective effect on the signal of micro-moving targets, ensuring that the respiratory signal of a stationary human body is not filtered out as clutter, and significantly reducing the false negative rate; while under the traditional mean update method with a fixed forgetting factor (corresponding to...) Figure 2 (The blue dashed line in the image) indicates that the residual signal amplitude of the distance unit corresponding to this adult is suppressed to near noise levels, severely weakening the effective signal and making it highly susceptible to missed detections. Figure 2 The gray solid line in the figure represents the zero Doppler signal of the corresponding distance unit for the adult.

[0110] Furthermore, during continuous radar operation, at frame 150, a water-filled cup is quickly placed on a table within the radar's field of view (simulating a sudden environmental change), and then the hand is removed, leaving the cup stationary. The residual signal amplitude of the distance cell where the water cup is located is continuously processed and output using both the method provided in this invention and the traditional mean update method with a fixed forgetting factor. It should be noted that after the water cup stabilizes, the phase instability index rapidly drops to an extremely low level under the method provided in this invention, and then the static clutter update weight is adjusted to its maximum value. Therefore, the background model quickly learns this new static scene, and the output signal is rapidly suppressed and returns to zero within a few frames (e.g., 5-10 frames), with the "tailing" phenomenon essentially disappearing. Specifically, this is achieved by… Figure 3 As shown by the solid red line, the residual signal amplitude of the distance cell where the water cup is located is quickly suppressed and returns to zero within a few frames. However, in the traditional mean update method with a fixed forgetting factor, due to the use of a fixed and small amount of static clutter to update the weights, the learning speed for newly appearing static objects is very slow. The output signal takes tens or even hundreds of frames to slowly converge to a low level, during which a persistent high-amplitude "tail" is formed. In practical applications, this "tail" will be falsely detected as a false target, resulting in persistent false alarms. Specifically, from Figure 3 The blue dashed line indicates that the residual signal amplitude at the distance unit from the water cup will only be suppressed and return to zero after a relatively long period of time. Furthermore, from... Figure 3 As shown by the solid gray line, after the water cup was placed on the table within the radar's field of view, the zero-Doppler signal of the range cell containing the water cup exhibited a step jump and remained at a high level. This fully demonstrates the invention's extremely fast adaptation and suppression capability to sudden scene changes and newly added static clutter. Furthermore, Figure 3 The gray solid line in the figure represents the zero Doppler signal of the corresponding distance unit for the adult.

[0111] Next, see Figure 4 and Figure 5 , Figure 4This is a schematic diagram illustrating the phase instability index of a human target under the method provided by this invention, according to an embodiment of the present invention. Figure 5 This is a schematic diagram illustrating the phase instability index of a water cup target under the method provided by this invention, according to an embodiment of the invention. Figure 4 It can be seen that the phase instability index fluctuated in a high range (1.0 rad to 5.0 rad) throughout the observation period. This fluctuation was significantly correlated with human respiration and the micro-tremor cycle of the body, clearly reflecting the non-stationary phase modulation caused by vital signs. Moreover, the index value was consistently higher than the preset static threshold. This directly and objectively demonstrates from a data perspective that the method provided by this invention can correctly identify the breathing human body as a micro-motion source with high phase instability, rather than a static background. Figure 5 It can be seen that in the initial instant after the water cup is placed in the container, the indicator fluctuates slightly due to the brief disturbance caused by human intervention. However, once the water cup comes to rest and the hand is removed, the phase instability indicator rapidly decreases and converges to an extremely low level close to zero, accurately reflecting the physical nature of an absolutely stationary object. This result proves that the phase instability indicator can effectively eliminate interference from strong amplitude signals and, based solely on the physical characteristics of phase changes, fundamentally distinguish between stationary objects and slightly moving targets.

[0112] Next, see Figure 6 and Figure 7 , Figure 6 This is a schematic diagram illustrating the static clutter update weights corresponding to a human target under the method provided by this invention, according to an embodiment of the present invention. Figure 7 This is a schematic diagram illustrating the static clutter update weights corresponding to a water cup target under the method provided by this invention. Figure 6 It can be seen that throughout the experiment, the static clutter update weights were mostly suppressed to near the preset minimum weight (0), which means that the static clutter estimate stopped updating, thus preserving the human signal. Figure 7 It can be seen that after the water cup is placed in and comes to rest, the static clutter update weight quickly jumps from a low value to near the preset maximum weight (1.0). This means that the method determines the current scene as "new static background", thereby triggering the "fast learning" logic, which integrates the strong signal of the current frame into the background estimation at a high proportion.

[0113] In summary, this experiment verifies that the method provided by this invention can accurately distinguish between "a person with slight movements" and "a mutated object" through phase characteristics, perfectly resolving the contradiction between "preserving respiration" and "suppressing background".

[0114] Another embodiment of the present invention provides a static clutter suppression system for FMCW radar based on phase characteristic adaptation, the system comprising:

[0115] The acquisition module is used to acquire the zero-Doppler signal of the target range cell in the current frame. The zero-Doppler signal is the complex signal of the zero-Doppler channel in the radar range-Doppler matrix of the current frame.

[0116] The clutter filtering module is used to obtain the residual signal of the current frame based on the zero Doppler signal and the static clutter estimate of the target range cell in the previous frame. The residual signal of the current frame is the target echo signal after static clutter has been filtered out.

[0117] The calculation module is used to calculate the phase instability index of the target distance unit based on the residual signal of the current frame and the residual signal of the previous frame. The phase instability index is used to characterize the degree of dispersion of the phase change of the residual signal between consecutive frames.

[0118] The determination module is used to determine the static clutter update weight of the target range cell based on the phase instability index and the constructed mapping function. The mapping function is configured such that the static clutter update weight is negatively correlated with the phase instability index.

[0119] The estimation module is used to obtain the static clutter estimate of the target range cell based on the static clutter update weight, the zero-Doppler signal, and the static clutter estimate of the previous frame, and is used to perform static clutter filtering on the zero-Doppler signal of the target range cell in the next frame.

[0120] In the several embodiments provided by this invention, it should be understood that the systems and methods disclosed in this invention can be implemented in other ways. For example, the system embodiments described above are merely illustrative. For instance, the division of modules is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple modules or components may be combined or integrated into another system, or some features may be ignored or not executed.

[0121] Furthermore, the functional modules in the various embodiments of the present invention can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module. The integrated module can be implemented in hardware or in the form of hardware plus software functional modules.

[0122] Another embodiment of the present invention provides a storage medium storing a computer program for executing the steps of the phase feature-adaptive FMCW radar static clutter suppression method described in the above embodiments.

[0123] Another aspect of the present invention provides an electronic device, including a memory and a processor. The memory stores a computer program, and when the processor invokes the computer program in the memory, it implements the steps of the phase-feature-adaptive FMCW radar static clutter suppression method as described in the above embodiments. Specifically, the integrated modules implemented as software functional modules can be stored in a computer-readable storage medium. These software functional modules, stored in a storage medium, include several instructions to cause an electronic device (which may be a personal computer, server, or network device, etc.) or processor to execute some steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as a USB flash drive, a portable hard drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0124] The above description, in conjunction with specific preferred embodiments, provides a further detailed explanation of the present invention. It should not be construed that the specific implementation of the present invention is limited to these descriptions. For those skilled in the art, various simple deductions or substitutions can be made without departing from the concept of the present invention, and all such modifications and substitutions should be considered within the scope of protection of the present invention.

Claims

1. A static clutter suppression method for FMCW radar based on phase characteristic adaptation, characterized in that, include: Obtain the zero-Doppler signal of the target range cell in the current frame, wherein the zero-Doppler signal is the complex signal of the zero-Doppler channel in the radar range-Doppler matrix of the current frame; Based on the zero Doppler signal and the static clutter estimate of the target range cell in the previous frame, the residual signal of the current frame is obtained, and the residual signal of the current frame is the target echo signal after static clutter has been filtered out. Based on the residual signal of the current frame and the residual signal of the previous frame, the phase instability index of the target distance unit is calculated. The phase instability index is used to characterize the degree of dispersion of the phase change of the residual signal between consecutive frames. Based on the phase instability index and the constructed mapping function, the static clutter update weight of the target range cell is determined, and the mapping function is configured such that the static clutter update weight is negatively correlated with the phase instability index. Based on the static clutter update weight, the zero Doppler signal, and the static clutter estimate of the previous frame, the static clutter estimate of the target range cell is obtained, which is used to perform static clutter filtering on the zero Doppler signal of the target range cell in the next frame. The step of calculating the phase instability index of the target range unit based on the residual signal of the current frame and the residual signal of the previous frame includes: Calculate the target phase difference between the residual signal of the current frame and the residual signal of the previous frame; The target phase difference is sorted with the absolute values ​​of multiple historical phase differences in the sliding window queue to determine a preset number of first phase differences; Remove all first phase differences from the sliding window queue to obtain the phase difference sequence after removal; The phase trajectory is reconstructed by summing the phase difference sequence after elimination. Based on the phase trajectory, the phase instability index of the target range cell is obtained; The step of obtaining the phase instability index of the target range cell based on the phase trajectory includes: Determine the maximum and minimum values ​​of the phase trajectory; The phase instability index of the target range cell is obtained by subtracting the maximum value from the minimum value. The formula for calculating the target phase difference is as follows: ， in, The target phase difference, For the current number The first frame The residual signal of each target distance cell, For the first The first frame The residual signal of each target distance cell, To obtain the complex conjugate operation, This is for phase angle taking operations.

2. The FMCW radar static clutter suppression method based on phase characteristic adaptation according to claim 1, characterized in that, The process of obtaining the residual signal of the current frame based on the zero-Doppler signal and the static clutter estimate of the target range cell in the previous frame includes: The residual signal of the current frame is obtained by subtracting the zero-Doppler signal from the static clutter estimate of the target range cell in the previous frame.

3. The FMCW radar static clutter suppression method based on phase characteristic adaptation according to claim 1, characterized in that, The mapping function satisfies the following: when the phase instability index is less than or equal to the static threshold, the preset maximum update weight is determined as the static clutter update weight; when the phase instability index is greater than or equal to the motion threshold, the preset minimum update weight is determined as the static clutter update weight. When the phase instability index is less than the motion threshold and greater than the static threshold, the static clutter update weight is calculated using a linear interpolation method, where the motion threshold is greater than the static threshold.

4. The FMCW radar static clutter suppression method based on phase characteristic adaptation according to claim 3, characterized in that, The expression for the mapping function is: ， in, For the current number The first frame Static clutter update weights for each target range cell. For the maximum update weight, The minimum update weight, The motion threshold, The static threshold, For the first Phase instability index of each target range cell.

5. The FMCW radar static clutter suppression method based on phase characteristic adaptation according to claim 1, characterized in that, The formula for calculating the static clutter estimate of the target range cell is as follows: ， in, For the current number The first frame Static clutter estimates for each target range cell. For the current number The first frame Static clutter update weights for each target range cell. For the first The first frame Static clutter estimates for each target range cell. For the current number The first frame Zero Doppler signal of each target range cell.

6. A static clutter suppression system for FMCW radar based on phase characteristic adaptation, characterized in that, include: The acquisition module is used to acquire the zero-Doppler signal of the target range cell in the current frame, wherein the zero-Doppler signal is the complex signal of the zero-Doppler channel in the radar range-Doppler matrix of the current frame; The clutter filtering module is used to obtain the residual signal of the current frame based on the zero Doppler signal and the static clutter estimate of the target range cell in the previous frame. The residual signal of the current frame is the target echo signal with static clutter filtered out. The calculation module is used to calculate the phase instability index of the target distance unit based on the residual signal of the current frame and the residual signal of the previous frame. The phase instability index is used to characterize the degree of dispersion of the phase change of the residual signal between consecutive frames. The step of calculating the phase instability index of the target range unit based on the residual signal of the current frame and the residual signal of the previous frame includes: Calculate the target phase difference between the residual signal of the current frame and the residual signal of the previous frame; The target phase difference is sorted with the absolute values ​​of multiple historical phase differences in the sliding window queue to determine a preset number of first phase differences; Remove all first phase differences from the sliding window queue to obtain the phase difference sequence after removal; The phase trajectory is reconstructed by summing the phase difference sequence after elimination. Based on the phase trajectory, the phase instability index of the target range cell is obtained; The step of obtaining the phase instability index of the target range cell based on the phase trajectory includes: Determine the maximum and minimum values ​​of the phase trajectory; The phase instability index of the target range cell is obtained by subtracting the maximum value from the minimum value. The formula for calculating the target phase difference is as follows: ， in, The target phase difference, For the current number The first frame The residual signal of each target distance cell, For the first The first frame The residual signal of each target distance cell, To obtain the complex conjugate operation, This is for phase angle acquisition operations; The determination module is used to determine the static clutter update weight of the target range cell based on the phase instability index and the constructed mapping function, wherein the mapping function is configured such that the static clutter update weight is negatively correlated with the phase instability index. The estimation module is used to obtain the static clutter estimate of the target range cell based on the static clutter update weight, the zero Doppler signal, and the static clutter estimate of the previous frame, and to perform static clutter filtering on the zero Doppler signal of the target range cell in the next frame.

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