Distance window parameter estimation method based on markov transition field

Abstract

The application relates to a distance window parameter estimation method based on a Markov transition field, which comprises the following steps: pre-processing obtained radar echo data to obtain a one-dimensional range image corresponding to the radar echo data; encoding the one-dimensional range image by using a Markov transition field to obtain an encoded image corresponding to the one-dimensional range image; positioning a distance window according to the encoded image to obtain a target distance window corresponding to the echo of a range expansion target; and obtaining an estimation parameter of the target distance window according to the target distance window. Through the above technical scheme, the one-dimensional range image obtained according to the radar echo data is encoded by using the Markov transition field method, the window position and the window width of the distance window of the range expansion target are estimated, the brute force sliding window detection can be effectively avoided, the calculation amount of the subsequent radar detection and identification of the target is reduced, and the operation time is reduced.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of radar, in particular to a distance window parameter estimation method and device based on Markov transition field and electronic equipment. BACKGROUND

[0002] With the continuous development of wideband radar, the radar range resolution is gradually improved, and the radar range resolution is continuously reduced. When a radar detects a large-size target, because the size of the target is much larger than the radar range resolution, the target echo no longer maintains the characteristics of a point target echo, but occupies multiple range cells in the range dimension. Such a target is called a range expansion target. A conventional point target can be regarded as a scattering point and only occupies one range cell, while a range expansion target can be regarded as multiple scattering points and thus occupies multiple range cells. The detection and recognition of range expansion targets is one of the important research problems in the field of wideband radar research.

[0003] At present, range expansion target detection algorithms, such as binary accumulation CFAR algorithm, fuzzy CFAR algorithm, and range cell screening algorithm, mostly need to obtain the range cells occupied by the range expansion target in advance, which is often difficult to achieve in practical applications. SUMMARY

[0004] In order to solve the above problems in the prior art, the present application provides a distance window parameter estimation method and device based on Markov transition field and electronic equipment.

[0005] According to a first aspect of an embodiment of the present application, a distance window parameter estimation method based on Markov transition field is provided, and the method comprises:

[0006] The obtained radar echo data is preprocessed to obtain a one-dimensional range image corresponding to the radar echo data; wherein the radar echo data contains the echo of a range expansion target which occupies multiple range cells continuously in the range dimension;

[0007] The one-dimensional range image is encoded by using a Markov transition field to obtain an encoded image corresponding to the one-dimensional range image;

[0008] The distance window is positioned according to the encoded image to obtain a target distance window corresponding to the echo of the range expansion target;

[0009] The estimated parameters of the target distance window are obtained according to the target distance window.

[0010] Optionally, the preprocessing of the obtained radar echo data to obtain the one-dimensional range image corresponding to the radar echo data comprises:

[0011] Perform an inverse discrete Fourier transform on the radar echo data to obtain the pre-processed one-dimensional range profile;

[0012] The one-dimensional distance image obtained from the initial processing is normalized.

[0013] Optionally, encoding the one-dimensional range image using a Markov migration field to obtain the encoded image corresponding to the one-dimensional range image includes:

[0014] Establish a state transition matrix based on the one-dimensional distance image;

[0015] The one-dimensional distance image is encoded according to the state transition matrix to obtain the encoded image corresponding to the one-dimensional distance image.

[0016] Optionally, establishing the state transition matrix based on the one-dimensional distance image includes:

[0017] Let the one-dimensional distance image be X = [x1, x2, ..., x]. N ]; where N is the total number of distance units in the one-dimensional distance image;

[0018] The one-dimensional distance image is divided into k states, where state S = S1, S2, ..., S... k , k≥2; where, the number of distance units contained in each state is

[0019] Based on the relationship between the current state of the distance cell and the state of the adjacent previous distance cell, the state transition matrix T is established.

[0020] Optionally, the state transition matrix may refer to the following:

[0021]

[0022] Among them, t ij Let represent the transition probability from the i-th state to the j-th state from the distance unit, where i∈[1,k] and j∈[1,k].

[0023] Alternatively, the encoded image may refer to the following:

[0024]

[0025] Among them, g i'j' Let represent the transition probability from the state of the i'th distance cell to the state of the j'th distance cell, where i'∈[1,N] and j'∈[1,N].

[0026] Optionally, the distance window positioning according to the coded image to obtain a target distance window corresponding to the target echo comprises:

[0027] According to the maximum state transition probability criterion, a maximum state transition probability in the state transition matrix is obtained.

[0028] The coded image is divided by using the maximum state transition probability to obtain a plurality of demarcation points; wherein the demarcation line of the coded image is a diagonal line of the coded image.

[0029] According to the continuity criterion, the plurality of demarcation points are determined to obtain an effective demarcation point.

[0030] Optionally, the continuity criterion is used to determine the plurality of demarcation points to obtain an effective demarcation point, comprising:

[0031] When the previous point H'(h-1, h-1) and the next point H''(h+1, h+1) of the demarcation point H(h, h) satisfy the continuity criterion, the effective demarcation point is determined according to the demarcation point H(h, h), the previous point H'(h-1, h-1) and the next point H''(h+1, h+1).

[0032] According to a second aspect of the embodiment of the present application, a distance window parameter estimation device based on Markov transition field is provided, and the device comprises:

[0033] A preprocessing module is configured to preprocess the obtained radar echo data to obtain a one-dimensional range image corresponding to the radar echo data; wherein the radar echo data contains echoes of a range expansion target occupying a plurality of range cells in a range dimension continuously.

[0034] An encoding module is configured to encode the one-dimensional range image by using a Markov transition field to obtain a coded image corresponding to the one-dimensional range image.

[0035] A distance window acquisition module is configured to perform distance window positioning according to the coded image to obtain a target distance window corresponding to the target echo.

[0036] A parameter estimation module is configured to obtain an estimated parameter of the target distance window according to the target distance window.

[0037] According to a third aspect of the embodiment of the present application, an Internet threat detection and backtracking device based on adaptive abnormal behavior analysis is provided, comprising: a processor; a memory for storing processor executable instructions.

[0038] The processor is configured to execute the executable instructions to implement the steps of the Markov transition field-based distance window parameter estimation method according to any one of the embodiments of the first aspect.

[0039] The technical scheme provided by the embodiments of the present application can have the following beneficial effects:

[0040] In the above technical scheme, the radar echo data obtained is preprocessed to obtain a one-dimensional range image corresponding to the radar echo data; the radar echo data contains echoes of a range expansion target occupying a plurality of distance units continuously in the distance dimension; a one-dimensional range image is encoded by using a Markov transition field to obtain an encoded image corresponding to the one-dimensional range image; a target distance window corresponding to the echoes of the range expansion target is obtained by distance window positioning according to the encoded image; and an estimated parameter of the target distance window is obtained according to the target distance window. Through the above technical scheme, the one-dimensional range image obtained from the radar echo data is encoded by using the Markov transition field method, the window position and window width of the range expansion target distance window are estimated, and the target signal positioning capability and distance window cutting capability are high, so that the brute force sliding window detection can be effectively avoided, the calculation amount of the subsequent radar target detection and identification is reduced, and the operation time is reduced.

[0041] Other features and advantages of the present application will be described in detail in the following detailed description. BRIEF DESCRIPTION OF DRAWINGS

[0042] The accompanying drawings are included to provide a further understanding of the present application, and constitute a part of the specification, and are used to explain the present application together with the following detailed description, but do not constitute a limitation of the present application. In the drawings:

[0043] Figure 1 is a flowchart of a Markov transition field-based distance window parameter estimation method according to an exemplary embodiment.

[0044] Figure 2a is a schematic diagram of radar echo data according to an exemplary embodiment.

[0045] Figure 2b is a schematic diagram of a one-dimensional range image after preliminary processing according to an exemplary embodiment.

[0046] Figure 2c is a schematic diagram of a one-dimensional range image according to an exemplary embodiment.

[0047] Figure 3 is a schematic diagram of an encoded image according to an exemplary embodiment.

[0048] Figure 4is a schematic diagram of a divided one-dimensional range image according to an exemplary embodiment.

[0049] Figure 5 is a state transition matrix hotspot diagram according to an exemplary embodiment.

[0050] Figure 6 is a schematic diagram of a divided coded image according to an exemplary embodiment.

[0051] Figure 7 is a schematic diagram of a simulated one-dimensional range image according to an exemplary embodiment.

[0052] Figure 8 is a schematic diagram of an estimated parameter of a target range window according to an exemplary embodiment.

[0053] Figure 9 is a schematic diagram of a relationship between an estimated parameter of a target range window and a number of division states according to an exemplary embodiment.

[0054] Figure 10 is a schematic diagram of a relationship between an estimated parameter of a target range window and a number of division states according to another exemplary embodiment.

[0055] Figure 11 is a block diagram of a distance window parameter estimation device based on a Markov transition field according to an exemplary embodiment.

[0056] Figure 12 is a block diagram of an electronic device for a distance window parameter estimation method based on a Markov transition field according to an exemplary embodiment. DETAILED DESCRIPTION

[0057] In order to make the objectives, technical solutions and advantages of the present application clearer, the present application will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and should not be used to limit the present application.

[0058] Figure 1 is a flowchart of a distance window parameter estimation method based on a Markov transition field according to an exemplary embodiment, as shown in Figure 1 the method comprises the following steps.

[0059] S101, pre-processing the obtained radar echo data to obtain a one-dimensional range image corresponding to the radar echo data; wherein the radar echo data contains echoes of a range expansion target occupying a plurality of distance units continuously in the distance dimension.

[0060] It can be understood that, in addition to containing the echoes of range expansion targets, the radar echo data can also contain noise data. After the radar echo data is preprocessed and converted into a one-dimensional range image, the distribution and characteristics of the range expansion targets in the range dimension can be intuitively displayed, so that the radar operator or algorithm can more intuitively identify the position, shape and other characteristics of the range expansion targets. This helps to quickly analyze and identify the range expansion targets. Moreover, the radar echo data is usually high-dimensional data, and converting it into a one-dimensional range image can reduce the dimensionality of the data, simplify the data analysis and processing process, and improve the interpretability of the data while preserving key information. Based on the one-dimensional range image, the range expansion targets can be more accurately located, and the distribution range of the range expansion targets in the range can be determined, thereby providing a basis for further detection and measurement.

[0061] In S102, the one-dimensional range image is encoded by using a Markov transition field to obtain an encoded image corresponding to the one-dimensional range image.

[0062] It can be understood that the Markov transition field (MTF) is usually used to encode time series data, rather than range images. However, the one-dimensional range image can be regarded as a one-dimensional time series, and then the state transition probabilities at different time steps can be calculated by using a sliding window technique. These state transition probabilities constitute a certain Markov transition field. Each window centered on a range cell in the range image can be regarded as a trajectory, and the range cell at the center of the window is the state of the trajectory at the current time, while the distribution of other range cells within the window can be regarded as a possible transition to the next state.

[0063] In S103, a range window is positioned according to the encoded image to obtain a target range window corresponding to the echo of the range expansion target.

[0064] It can be understood that after obtaining the encoded image, a range window needs to be positioned. In general, there are more parts that do not contain range expansion targets and have smaller amplitudes, and there are fewer parts that contain range expansion targets but have higher amplitudes.

[0065] In S104, estimated parameters of the target range window are obtained according to the target range window.

[0066] It can be understood that when the estimated parameters are obtained according to the target range window, the window position and window width are usually obtained, and the position of the target range window can be estimated.

[0067] Optionally, S101 can include:

[0068] The radar echo data is inversely discrete Fourier transformed to obtain a one-dimensional range image after preliminary processing.

[0069] The one-dimensional range image after preliminary processing is normalized to obtain a one-dimensional range image.

[0070] Optionally, the radar echo data is subjected to inverse discrete Fourier transform to obtain the one-dimensional range image after preliminary processing. Assuming that the radar echo data is X'(k'), k'∈[0, N'-1], the one-dimensional range image after preliminary processing can be expressed as wherein i is an imaginary number, and N is the total number of distance units in the one-dimensional range image after preliminary processing.

[0071] Since the signal amplitude received by the radar is mainly determined by the radar transmitting system power, propagation attenuation, radar receiver gain and the like, and is interfered by environmental factors such as weather, different radars receive different echo intensities of the same target, and even the same radar cannot receive consistent range images due to changes in the environment. Therefore, the one-dimensional range image after preliminary processing needs to be normalized in the preprocessing stage to overcome the intensity sensitivity, thereby obtaining the one-dimensional range image after preprocessing of the radar echo data. Figure 2a is a schematic diagram of radar echo data according to an example embodiment, Figure 2b is a schematic diagram of a one-dimensional range image after preliminary processing according to an example embodiment, Figure 2c is a schematic diagram of a one-dimensional range image according to an example embodiment, as shown in Figure 2a , Figure 2b and Figure 2c , Figure 2a is the radar echo data before preprocessing, Figure 2b is the one-dimensional range image after inverse discrete Fourier transform, Figure 2c is the one-dimensional range image after normalization. Wherein, Figure 2a , Figure 2b and Figure 2c The horizontal coordinate of the one-dimensional range image represents the number of distance units, and the vertical coordinate is the signal amplitude.

[0072] Optionally, S102 can include:

[0073] establishing a state transition matrix according to the one-dimensional range image;

[0074] encoding the one-dimensional range image according to the state transition matrix to obtain an encoded image corresponding to the one-dimensional range image.

[0075] In an embodiment, the encoded image can refer to the following:

[0076]

[0077] wherein g i'j'denotes the transition probability from the state where the i'th distance unit is located to the state where the j'th distance unit is located, i'∈[1, N], j'∈[1, N]. Figure 3 is a schematic diagram of an encoded image according to an exemplary embodiment, as Figure 3 shown, it can be seen that the encoded image obtained after encoding presents obvious partition characteristics, wherein the yellow region can be the distance window position of the distance extended target.

[0078] Optionally, the state transition matrix is established according to the one-dimensional distance image, comprising:

[0079] Let the one-dimensional distance image be X = [x1, x2, …, xN];wherein N is the total number of distance units in the one-dimensional distance image. N

[0080] The one-dimensional distance image is divided into k states, state S = S1, S2, …, Sk, k≥2;wherein the number of distance units contained in each state is k

[0081] According to the relationship between the state where the current distance unit is located and the state where the adjacent previous distance unit is located, the state transition matrix T is established.

[0082] For example, for example, taking k = 3 as an example, Figure 4 is a schematic diagram of a one-dimensional distance image after division according to an exemplary embodiment, as Figure 4 shown, the one-dimensional distance image is divided into three states by K1, K2 and K3, wherein 0~K1 is the first state S1, K1~K2 is the second state S2, and K2~K3 is the third state S3.

[0083] In an embodiment, the state transition matrix can refer to the following:

[0084]

[0085] wherein t ij denotes the transition probability from the distance unit from the i'th state to the j'th state, i∈[1, k], j∈[1, k].

[0086] Optionally, S103 can comprise:

[0087] According to the maximum state transition probability criterion, the maximum state transition probability in the state transition matrix is obtained;

[0088] The maximum state transition probability is used to divide the boundary line of the encoded image to obtain a plurality of boundary points;wherein the boundary line of the encoded image is the diagonal line of the encoded image. ​​

[0089] According to the continuity criterion and the plurality of demarcation points, an effective demarcation point is determined.

[0090] Optionally, according to the continuity criterion and the plurality of demarcation points, an effective demarcation point is determined, comprising:

[0091] When a previous point H'(h-1, h-1) and a next point H''(h+1, h+1) of a demarcation point H(h, h) satisfy the continuity criterion, the effective demarcation point is determined according to the demarcation point H(h, h), the previous point H'(h-1, h-1) and the next point H''(h+1, h+1).

[0092] In an embodiment, Figure 5 is a state transition matrix hotspot diagram according to an exemplary embodiment, as shown in Figure 5 , wherein the state transition matrix is obtained according to an encoding image. Figure 5 is a state transition matrix hotspot diagram of three states S1, S2, S3 obtained according to an encoding image when k=3, which represents the probability of transition between the three states, for example, the cell in the first column and the first row represents the probability of transition from state S1 to state S1. It is worth mentioning that the regions with relatively average distribution are mostly noise, for example Figure 5 the four cells in the upper left corner.

[0093] It can be understood that the maximum state transition probability can be obtained according to the state transition matrix, and the maximum state transition probability is taken as a threshold η to realize preliminary distance window cutting (i.e., the demarcation line of the encoding image is divided by using the maximum state transition probability), and a plurality of demarcation points are obtained, Figure 6 is a schematic diagram of dividing an encoding image according to an exemplary embodiment, as shown in Figure 6 , the diagonal line of the encoding image is divided according to the threshold η to obtain an initial left demarcation point A and an initial right demarcation point B, and the threshold η can be specifically represented as:

[0094] η = max(T);

[0095] wherein T represents a state transition matrix, and max(·) represents a maximum value.

[0096] It is worth mentioning that after obtaining the plurality of demarcation points, an effective demarcation point needs to be obtained according to the initial left demarcation point A and the initial right demarcation point B, so as to Figure 6For example, it is necessary to determine the effective boundary points. Using the initial left boundary point A and the initial right boundary point B, two effective boundary points are obtained. The two effective boundary points are the point A' before the initial left boundary point A and the point B' after the initial right boundary point B. A' and B' are the two endpoints of the diagonal of the target range window, and thus the target range window corresponding to the target echo can be obtained. When the boundary point does not meet the continuity criterion, it is determined whether the next boundary point meets the continuity criterion.

[0097] Understandably, when determining whether multiple boundary points are valid boundary points, a continuity criterion can be used. As mentioned above... Figure 6 Taking the initial left boundary point A as an example, for the initial left boundary point A(a,a), we can obtain the previous point A'(a-1,a-1) and the next point A"(a+1,a+1). Similarly, for the initial right boundary point B(b,b), we can obtain the previous point B"(b-1,b-1) and the next point B'(b+1,b+1). When the initial left boundary point A(a,a) and the previous point A'(a-1,a-1) and the next point A"(a+1,a+1) satisfy the following formula, we obtain A'(a-1,a-1) as the first valid boundary point. Similarly, we obtain B'(b+1,b+1) as the second valid boundary point, that is:

[0098] H(h-1,h-1)=t hh H(h,h)=t hh H(h+1,h+1)=t hh ;

[0099] This equation indicates that the transition probability of the point H(h-1,h-1) before the boundary point H and the point H(h+1,h+1) after the boundary point H is both t. hh That is, all are in state S h .

[0100] Specifically, the first point with the value of η and the last point with the value of η on the diagonal line of the state transition matrix are selected as the demarcation points to obtain an initial left demarcation point A and an initial right demarcation point B; then, local fine tuning is performed according to the aforementioned continuity criterion, when the initial left demarcation point A (a, a), the previous point A' (a-1, a-1) and the next point A'' (a+1, a+1) satisfy the continuity criterion, the initial right demarcation point B (b, b), the previous point B'' (b-1, b-1) and the next point B' (b+1, b+1) satisfy the continuity criterion, the previous point A' (a-1, a-1) and the next point B' (b+1, b+1) are selected as the effective demarcation points, and if the continuity criterion is not satisfied, the next point with the value of η is searched and it is determined whether it satisfies the continuity criterion, until the point with the value of η that satisfies the continuity criterion is found, the coded image is divided into three segments ①, ② and ③ according to the previous point A' (a-1, a-1) and the next point B' (b+1, b+1), and the area of the two end points of the diagonal line of the previous point A' (a-1, a-1) and the next point B' (b+1, b+1) is the target distance window. Finally, three segments of signals as shown in FIG. 8 are obtained, and the yellow part in the area 2 is the target distance window. Figure 6

[0101] It is worth mentioning that after the target distance window is obtained according to the above manner, the generalized signal-to-noise ratio of the multiple areas divided in the coded image is calculated, the area with a higher signal-to-noise ratio is determined with the target distance window obtained above, if they are the same area, it is determined that the target distance window obtained above is the final target distance window.

[0102] In an embodiment, Figure 7 FIG. 7 is a schematic diagram of a simulated one-dimensional range image according to an example embodiment, Figure 8 FIG. 8 is a schematic diagram of an estimated parameter of a target distance window according to an example embodiment, Figure 9 FIG. 9 is a schematic diagram of a relationship between an estimated parameter of a target distance window and a number of division states according to an example embodiment, Figure 10 FIG. 10 is another schematic diagram of a relationship between an estimated parameter of a target distance window and a number of division states according to an example embodiment, and in an algorithm simulation experiment, electromagnetic software simulation data is selected for accuracy verification, Figure 7 FIG. 11 is an AH-1W helicopter one-dimensional range image and a label obtained by electromagnetic simulation software. The experiment verifies the parameter estimation accuracy of the method under different signal-to-noise ratios by setting different signal-to-noise ratios, selects k = 5, sets the signal-to-noise ratio to 0 dB, 5 dB, 10 dB, 15 dB and 20 dB respectively, repeats the experiment 100 times, respectively calculates the estimation accuracy of the parameter window position P L and the window width S, and calculates the average error, and the results are as shown in FIG. 12. Figure 8 ​As shown in the figure, with the increase of the signal-to-noise ratio, the estimation accuracy of the two parameters is obviously improved, and the error is reduced. Since there is the parameter of the number of divided states k in the method, the second group of experiments is carried out to verify the influence of different number of states on the parameter estimation result, and the other parameter settings are the same as those of the first group of experiments, and the experimental results are as follows, Figure 9 The estimation accuracy and average error of the window position P L It can be seen that the greater the number of divided states k, the higher the parameter estimation accuracy and the smaller the average error. Figure 10 The estimation accuracy and average error of the window width S Figure 9 The same trend can be seen, that is, the greater the number of divided states k, the higher the parameter estimation accuracy and the smaller the average error.

[0103] Through the above technical scheme, according to the one-dimensional range image, the one-dimensional range image is encoded by the Markov transition field method, the encoded image is divided by setting the maximum state transition probability, the parameter estimation of the window position and the window width of the extended target distance window is realized, the accurate target signal positioning and cutting are realized, and the calculation amount of subsequent target detection and recognition is reduced.

[0104] Figure 11 is a block diagram of a distance window parameter estimation device based on Markov transition field according to an example embodiment, as Figure 11 shown, the device 1100 can include:

[0105] A preprocessing module 1101 is configured to preprocess the obtained radar echo data to obtain a one-dimensional range image corresponding to the radar echo data; wherein the radar echo data contains echoes of a range extended target occupying a plurality of distance units continuously in the distance dimension;

[0106] An encoding module 1102 is configured to encode the one-dimensional range image by using a Markov transition field to obtain an encoded image corresponding to the one-dimensional range image;

[0107] A distance window acquisition module 1103 is configured to position a distance window according to the encoded image to obtain a target distance window corresponding to the echoes of the range extended target;

[0108] A parameter estimation module 1104 is configured to obtain an estimated parameter of the target distance window according to the target distance window.

[0109] Regarding the device in the above embodiment, the specific manner in which each module performs operations has been described in detail in the embodiment related to the method, and will not be described in detail here.

[0110] Figure 12is a block diagram of an electronic device for a Markov transition field based distance window parameter estimation method according to an exemplary embodiment. As shown in Figure 12 The electronic device 1200 can include one or more of a processor 1201, a memory 1202, a multimedia component 1203, an input / output (I / O) interface 1204, and a communication component 1205.

[0111] The processor 1201 is configured to control overall operations of the electronic device 1200 to complete all or part of the steps of the above-mentioned Markov transition field based distance window parameter estimation method. The memory 1202 is configured to store various types of data to support operations of the electronic device 1200, which can include, for example, instructions for operating any application or method on the electronic device 1200, and application-related data, such as contact data, transmitted and received messages, pictures, audio, video, and the like. The memory 1202 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk or optical disk. The multimedia component 1203 can include a screen and an audio component. The screen can be, for example, a touch screen, and the audio component is configured to output and / or input audio signals. For example, the audio component can include a microphone configured to receive external audio signals. The received audio signals can be further stored in the memory 1202 or transmitted through the communication component 1205. The audio component also includes at least one speaker configured to output audio signals. The I / O interface 1204 provides an interface between the processor 1201 and other interface modules, which can be a keyboard, a mouse, a button, and the like. The buttons can be virtual buttons or physical buttons. The communication component 1205 is configured to perform wired or wireless communication between the electronic device 1200 and other devices. Wireless communication, such as Wi-Fi, Bluetooth, near field communication (NFC), 2G, 3G, 4G, NB-IOT, eMTC, or other 5G, and the like, or a combination of one or more of them, is not limited herein. Therefore, the corresponding communication component 1205 can include a Wi-Fi module, a Bluetooth module, an NFC module, and the like.

[0112] In an exemplary embodiment, the electronic device 1200 can be implemented by one or more Application Specific Integrated Circuits (ASICs), Digital Signal Processors (DSPs), Digital Signal Processing Devices (DSPDs), Programmable Logic Devices (PLDs), Field Programmable Gate Arrays (FPGAs), controllers, micro-controllers, microprocessors, or other electronic elements for performing the above-mentioned Markov transition field based distance window parameter estimation method.

[0113] In another exemplary embodiment, a computer readable storage medium including program instructions is also provided, which, when executed by a processor, implement the steps of the above-mentioned Markov transition field based distance window parameter estimation method. For example, the computer readable storage medium can be the above-mentioned memory 1202 including program instructions, which can be executed by the processor 1201 of the electronic device 1200 to complete the above-mentioned Markov transition field based distance window parameter estimation method.

[0114] The preferred embodiments of the present application are described in detail above with reference to the accompanying drawings, but the present application is not limited to the specific details of the above-described embodiments. Various simple modifications can be made to the technical solutions of the present application within the technical concept of the present application, and these simple modifications all belong to the protection scope of the present application.

[0115] In addition, it should be noted that each of the specific technical features described in the above specific embodiments can be combined in any appropriate manner without contradiction, and in order to avoid unnecessary repetition, the present application will not make further descriptions on various possible combinations.

[0116] Furthermore, any combination of the various different embodiments of the present application can also be made, as long as it does not deviate from the technical concept of the present application, and it should also be considered as disclosed content of the present application.

Claims

1. A method for estimating the range window parameter based on Markov transition field, characterized in that, The method comprises: preprocessing the obtained radar echo data to obtain a one-dimensional range profile corresponding to the radar echo data; wherein the radar echo data contains echoes of a range expansion target occupying multiple range cells continuously in the range dimension; encoding the one-dimensional range profile using a Markov transition field to obtain an encoded image corresponding to the one-dimensional range profile; performing range window positioning according to the encoded image to obtain a target range window corresponding to the echoes of the range expansion target; obtaining an estimated parameter of the target range window according to the target range window; wherein the encoding the one-dimensional range profile using a Markov transition field to obtain an encoded image corresponding to the one-dimensional range profile comprises: establishing a state transition matrix according to the one-dimensional range profile; encoding the one-dimensional range profile according to the state transition matrix to obtain an encoded image corresponding to the one-dimensional range profile; the establishing a state transition matrix according to the one-dimensional range profile comprises: Let the one-dimensional range image be ; wherein, is the total number of distance cells in the one-dimensional range image; The one-dimensional range image is divided into states, state , ; wherein the number of distance units contained in each state is ; According to the relationship between the state of the current distance unit and the state of the adjacent previous distance unit, the state transition matrix is established as ; the performing range window positioning according to the encoded image to obtain a target range window corresponding to the target echoes comprises: obtaining a maximum state transition probability in the state transition matrix according to a maximum state transition probability criterion; dividing a boundary line of the encoded image using the maximum state transition probability to obtain multiple boundary points; wherein the boundary line of the encoded image is a diagonal line of the encoded image; obtaining an effective boundary point according to the multiple boundary points according to a continuity criterion.

2. The Markov-based transition field-based range window parameter estimation method of claim 1, wherein, The preprocessing the obtained radar echo data to obtain a one-dimensional range profile corresponding to the radar echo data comprises: performing inverse discrete Fourier transform on the radar echo data to obtain a preliminarily processed one-dimensional range profile; performing normalization processing on the preliminarily processed one-dimensional range profile to obtain the one-dimensional range profile.

3. The method of claim 1, wherein, The state transition matrix can refer to the following: ； wherein, denotes the transition probability from the state to the state , , .

4. The method of claim 1, wherein, The encoded image can refer to the following: ； wherein, denotes the transition probability from the state in which the distance unit is located to the state in which the distance unit is located, , .

5. The method of claim 1, wherein, The obtaining an effective boundary point according to the multiple boundary points according to a continuity criterion comprises: When the dividing point The previous point and the next point When the continuity criterion is satisfied, according to the boundary point The previous point and the latter point Determine the effective boundary point.

6. A Markov-based transition field based range window parameter estimation apparatus, characterized by, The apparatus comprises: a preprocessing module configured to preprocess obtained radar echo data to obtain a one-dimensional range profile corresponding to the radar echo data; wherein the radar echo data contains echoes of a range expansion target occupying multiple range cells continuously in the range dimension; an encoding module configured to encode the one-dimensional range profile using a Markov transition field to obtain an encoded image corresponding to the one-dimensional range profile; a range window obtaining module configured to perform range window positioning according to the encoded image to obtain a target range window corresponding to the echoes of the range expansion target; a parameter estimation module configured to obtain an estimated parameter of the target range window according to the target range window; wherein the encoding the one-dimensional range profile using a Markov transition field to obtain an encoded image corresponding to the one-dimensional range profile comprises: establishing a state transition matrix according to the one-dimensional range profile; encoding the one-dimensional range profile according to the state transition matrix to obtain an encoded image corresponding to the one-dimensional range profile; the establishing a state transition matrix according to the one-dimensional range profile comprises: Let the one-dimensional range image be ; wherein is the total number of distance cells in the one-dimensional range image; The one-dimensional range image is divided into states, state , ; wherein the number of distance units contained in each state is ; According to the relationship between the state of the current distance unit and the state of the adjacent previous distance unit, the state transition matrix is established as ; The distance window positioning according to the coded image comprises: According to the maximum state transition probability criterion, the maximum state transition probability in the state transition matrix is obtained; The maximum state transition probability is used to divide the boundary line of the coded image to obtain a plurality of boundary points; wherein the boundary line of the coded image is a diagonal line of the coded image; According to the continuity criterion, the effective boundary points are obtained from the plurality of boundary points.

7. An electronic device, comprising: Comprise: A processor; A memory for storing processor-executable instructions; Wherein, the processor is configured to execute the executable instructions to implement the steps of the distance window parameter estimation method based on the Markov transition field in any one of claims 1~5.

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