A signal processing method and device for eliminating azimuth ambiguity, equipment and medium

Abstract

The application discloses a signal processing method and device for eliminating azimuth ambiguity, equipment and medium, relates to the technical field of radar signal processing, and the method acquires a moving track point set of a receiver and an aliasing signal sequence received by the receiver; determines a longitude range and a latitude range of the moving track according to the moving track point set; divides the longitude range to obtain a longitude ambiguity region set, and divides the latitude range to obtain a latitude ambiguity region set; constructs an ambiguous straight line of each pair of signal sources, samples the ambiguous straight line according to the longitude ambiguity region set or the latitude ambiguity region set to generate a corresponding ambiguity point set, and merges the corresponding ambiguity point sets of all signal source combinations to obtain a total ambiguity point set; merges overlapping points between the moving track point set and the total ambiguity point set to construct a track overlapping point set; removes the aliasing signals corresponding to all overlapping points in the track overlapping point set from the aliasing signal sequence to obtain a pulse signal sequence with the azimuth ambiguity eliminated, so that the accuracy of subsequent azimuth pulse sorting is improved.

Description

Technical Field

[0001] This invention relates to the field of radar signal processing technology, and specifically to a signal processing method, apparatus, device, and medium for eliminating azimuth ambiguity. Background Technology

[0002] In the fields of radar reconnaissance, electronic warfare, and signal processing in complex electromagnetic environments, pulse sorting is a key technology used to effectively separate received aliased signals based on their source. Traditional pulse sorting methods mainly rely on conventional parameters such as arrival time, pulse width, and carrier frequency, but their performance degrades significantly in environments with dense signals and overlapping parameters.

[0003] To improve sorting accuracy, pulse sorting methods based on azimuth information have been developed in recent years. Since different signal sources typically have different spatial distributions, this method combines the spatial information of the signal with its time-frequency characteristics to construct a multi-dimensional feature space. By utilizing these features, not only can signals be grouped more accurately, but interference signals from specific directions can also be effectively eliminated, thereby enhancing the robustness and reliability of the sorting process.

[0004] However, azimuth-based pulse sorting has inherent defects in practical applications: when multiple signal sources are on the same or similar angle lines as the receiver, the azimuth angles of these signal sources relative to the receiver will tend to be consistent. The receiver will find it difficult to distinguish the signal sources by azimuth differences, resulting in "azimuth ambiguity". This problem is particularly prominent in multi-target dense scenes and multipath propagation environments, which seriously restricts the effectiveness of azimuth sorting technology in practical applications.

[0005] Therefore, there is an urgent need for a method that can effectively identify and eliminate azimuth ambiguity in order to improve the reliability of azimuth pulse sorting under complex conditions. Summary of the Invention

[0006] The technical problem to be solved by the present invention is how to solve the azimuth ambiguity problem in azimuth pulse sorting. The purpose is to provide a signal processing method, device, equipment and medium for eliminating azimuth ambiguity, thereby solving the above-mentioned problem.

[0007] This invention is achieved through the following technical solution:

[0008] In a first aspect, the present invention provides a signal processing method for eliminating orientation ambiguity, comprising:

[0009] The receiver acquires a set of moving trajectory points and a sequence of aliased signals received from them; the aliased signal sequence originates from pulse sequences emitted by multiple signal sources at multiple consecutive moments.

[0010] Based on the set of movement trajectory points, determine the longitude and latitude range of the receiver's movement trajectory;

[0011] The longitude range is divided into a longitude fuzzy region set, and the latitude range is divided into a latitude fuzzy region set;

[0012] For any two signal sources among the plurality of signal sources, a fuzzy straight line is constructed. Based on the longitude fuzzy region set or the latitude fuzzy region set, the fuzzy straight line is sampled to generate a corresponding fuzzy point set. The fuzzy point sets corresponding to all signal source combinations are merged to obtain the total fuzzy point set.

[0013] Merge the overlapping points between the set of moving trajectory points and the total fuzzy point set to construct a trajectory overlapping point set;

[0014] Remove the aliased signals corresponding to all overlapping points in the trajectory overlap point set from the aliased signal sequence to obtain a pulse signal sequence with eliminated orientation ambiguity.

[0015] Optionally, constructing a fuzzy straight line for any two of the plurality of signal sources includes:

[0016] Obtain the longitude and latitude coordinates of any two signal sources from the plurality of signal sources;

[0017] The ratio of the latitude difference to the longitude difference between the two signal sources is used as the slope of the fuzzy line.

[0018] The intercept of the fuzzy line is obtained by subtracting the longitude of one of the signal sources from the product of the slope and the latitude of that signal source.

[0019] Based on the slope and the intercept, construct the standard equation of the fuzzy line.

[0020] Optionally, the step of sampling the fuzzy straight line to generate a corresponding fuzzy point set based on the longitude fuzzy region set or the latitude fuzzy region set includes:

[0021] If the slope of the fuzzy line is greater than or equal to 1, then each latitude value is selected sequentially from the set of fuzzy latitude regions, substituted into the standard equation of the fuzzy line to calculate the corresponding longitude value, and the obtained latitude and longitude coordinate points are used as the fuzzy point set corresponding to the two signal sources.

[0022] If the slope of the fuzzy line is less than 1, then each longitude value is selected sequentially from the longitude fuzzy region set, substituted into the standard equation of the fuzzy line to calculate the corresponding latitude value, and the obtained longitude and latitude coordinate points are used as the fuzzy point set corresponding to the two signal sources.

[0023] Optionally, merging the overlapping points between the set of moving trajectory points and the total fuzzy point set to construct a trajectory overlapping point set includes:

[0024] Calculate the distance between each trajectory point in the set of moving trajectory points and each point in the total fuzzy point set;

[0025] Trajectory points whose distance is less than or equal to the error threshold are marked as overlapping points;

[0026] Merge all overlapping points to form a set of trajectory overlapping points.

[0027] Optionally, the error threshold is determined based on at least one of the following factors: the positioning accuracy of the receiver, the distance between the plurality of signal sources, and the distribution density of the movement trajectory; wherein the error threshold is negatively correlated with the positioning accuracy; the error threshold is negatively correlated with the distance; and the error threshold is negatively correlated with the distribution density.

[0028] Optionally, the step of dividing the longitude range into a longitude fuzzy region set and dividing the latitude range into a latitude fuzzy region set includes:

[0029] The longitude range is evenly divided according to a preset number of division points to obtain multiple longitude sampling points. The multiple longitude sampling points are then merged to form a longitude fuzzy region set.

[0030] The latitude range is evenly divided according to the number of division points to obtain multiple latitude sampling points. The multiple latitude sampling points are then merged to form a set of latitudinal fuzzy regions.

[0031] Optionally, the number of division points is determined based on at least one of the factors: the distance between the plurality of signal sources and the straight-line span of the movement trajectory; wherein the number of division points is negatively correlated with the distance and positively correlated with the straight-line span.

[0032] In a second aspect, the present invention provides a signal processing apparatus for eliminating orientation ambiguity, comprising:

[0033] The data acquisition module is used to acquire the set of moving trajectory points of the receiver and the aliased signal sequence received therefrom; the aliased signal sequence is derived from pulse sequences emitted by multiple signal sources at multiple consecutive times;

[0034] The latitude and longitude range determination module is used to determine the longitude and latitude range of the receiver's movement trajectory based on the set of movement trajectory points.

[0035] The fuzzy region division module is used to divide the longitude range into a longitude fuzzy region set and the latitude range into a latitude fuzzy region set.

[0036] The fuzzy point set generation module is used to construct a fuzzy straight line for any two signal sources among the multiple signal sources, sample the fuzzy straight line according to the longitude fuzzy region set or the latitude fuzzy region set to generate the corresponding fuzzy point set, and merge the fuzzy point sets corresponding to all signal source combinations to obtain the total fuzzy point set.

[0037] The overlapping point set construction module is used to merge the overlapping points between the moving trajectory point set and the total fuzzy point set to construct the trajectory overlapping point set;

[0038] The elimination module is used to remove the aliased signals corresponding to all overlapping points in the trajectory overlap point set from the aliased signal sequence to obtain a pulse signal sequence with eliminated azimuth ambiguity.

[0039] Thirdly, the present invention provides a computer device, the computer device including a memory and a processor, the memory storing a computer program, the processor executing the computer program to implement a signal processing method for eliminating orientation ambiguity as described in any one of the first aspects.

[0040] Fourthly, the present invention provides a computer-readable storage medium storing a computer program, wherein a processor executes the computer program to implement a signal processing method for eliminating orientation ambiguity as described in any one of the first aspects.

[0041] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0042] This application provides a signal processing method for eliminating azimuth ambiguity. The method acquires the receiver's movement trajectory and the geographical locations of multiple signal sources, constructs an ambiguous straight line for each pair of signal sources, and samples the ambiguous straight lines using a set of ambiguous latitude and longitude regions uniformly divided from the trajectory's latitude and longitude range to generate a total ambiguous point set. This total ambiguous point set characterizes all spatial locations where the receiver may experience azimuth ambiguity. The receiver's trajectory points are then overlapped with the total ambiguous point set for matching, identifying and eliminating aliased pulse signal samples corresponding to trajectory points located within the overlap region. This eliminates azimuth ambiguity caused by multiple signal sources being collinear or nearly collinear with the receiver. Subsequent azimuth pulse sorting based on the pulse signal sequence with eliminated azimuth ambiguity significantly improves sorting accuracy and effectively reduces false alarms and missed alarms caused by azimuth ambiguity. Attached Figure Description

[0043] To more clearly illustrate the technical solutions of the exemplary embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly described below. It should be understood that the following drawings only show some embodiments of the present invention and should not be considered as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort. In the drawings:

[0044] Figure 1 This is a schematic diagram illustrating an application scenario provided in the embodiments of this application;

[0045] Figure 2 A schematic flowchart of a signal processing method for eliminating orientation ambiguity provided in an embodiment of this application;

[0046] Figure 3 Another schematic flowchart of the signal processing method for eliminating orientation ambiguity provided in the embodiments of this application;

[0047] Figure 4 This is a schematic diagram of a signal processing device for eliminating orientation ambiguity provided in an embodiment of this application. Detailed Implementation

[0048] To make the objectives, technical solutions, and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the embodiments and accompanying drawings. The illustrative embodiments and descriptions of the present invention are only used to explain the present invention and are not intended to limit the present invention.

[0049] Please refer to Figure 1 This is a schematic diagram illustrating an application scenario provided by an embodiment of this application. Assume a mobile receiver operates within a discrete time interval. Received from Pulse signals emitted by signal sources at different locations form an aliased signal sequence. ,in, Representing the characteristic dimensions of the signal, at each time point One 3D feature vector ,Right now For a moment The aliased signal. Typically, after high-precision sampling, At any moment The signal originates from a single location. However, in real-world scenarios, due to issues such as path overlap, At any moment Signals can originate from multiple locations, a phenomenon known as azimuth ambiguity. In this situation, the receiver cannot effectively distinguish between signal sources from different locations, thus affecting the accuracy of azimuth pulse sorting.

[0050] Therefore, embodiments of this application provide a signal processing method for eliminating orientation ambiguity. Please refer to... Figure 2 This is a schematic flowchart of a signal processing method for eliminating orientation ambiguity provided in an embodiment of this application. The following is a description of... Figure 2 The signal processing method for eliminating orientation ambiguity is introduced.

[0051] S101. Obtain the set of moving trajectory points of the receiver and the sequence of aliased signals received.

[0052] In the specific implementation process, the receiver's movement trajectory, after sampling, forms a discrete set of points within the time interval T, namely the movement trajectory point set, which is expressed as follows:

[0053]

[0054] in, Represents a set of points on the movement trajectory; This refers to the nth sampling moment within the time interval T; Indicates the receiver at time latitude and longitude coordinates; For the receiver at time The longitude value; For the receiver at time The latitude value.

[0055] The sequence of aliased signals received by the receiver is Assuming time aliasing signal Influenced by multiple location signal sources, it can be represented as follows:

[0056]

[0057] in, Indicates from the The signal components of each signal source; Indicates satisfaction The noise; k is the simultaneous influence under path overlap. Number of signal sources (of which) ).

[0058] S102. Based on the set of moving trajectory points, determine the longitude and latitude range of the receiver's moving trajectory.

[0059] In the specific implementation process, based on the set of movement trajectory points Record the maximum longitude of the receiver's movement trajectory: Minimum longitude: Maximum latitude: Minimum latitude: The longitude range is obtained based on the minimum and maximum longitude. The latitude range is obtained based on the minimum and maximum latitude. .

[0060] S103. The longitude range is uniformly divided to obtain a longitude fuzzy region set, and the latitude range is uniformly divided to obtain a latitude fuzzy region set.

[0061] In one possible embodiment, the longitude range is uniformly divided according to a preset number of division points to obtain multiple longitude sampling points, and the multiple longitude sampling points are merged to form a longitude fuzzy region set; the latitude range is uniformly divided according to a preset number of division points to obtain multiple latitude sampling points, and the multiple latitude sampling points are merged to form a latitude fuzzy region set.

[0062] In the specific implementation process, let the preset number of division points be P, and calculate the division step size of the longitude range according to the following formula. :

[0063]

[0064] Longitude coordinates of the i-th sampling point for:

[0065]

[0066] By merging the above P longitude sampling points, a longitude ambiguity region set is formed. Its expression is as follows:

[0067]

[0068] Calculate the step size for dividing the latitude range using the following formula. :

[0069]

[0070] Latitude coordinates of the i-th sampling point for:

[0071]

[0072] By merging the above P latitude sampling points, a latitude fuzzy region set is formed. Its expression is as follows:

[0073]

[0074] In this embodiment, the longitude and latitude ranges are uniformly divided according to a preset number of division points to construct a longitude fuzzy region set and a latitude fuzzy region set. This discretizes the continuous space into a finite number of sampling points, which not only ensures the uniformity of sampling and the integrity of spatial coverage, but also significantly reduces the complexity of subsequent calculations.

[0075] In one possible embodiment, the number of division points is determined based on at least one of the factors of the distance between multiple signal sources and the straight-line span of the movement trajectory; wherein the number of division points is negatively correlated with the distance and positively correlated with the straight-line span.

[0076] In the specific implementation process, firstly, the Euclidean distance between any two signal sources is calculated, and the maximum, minimum, or average distance is taken as the distance between multiple signal sources. Next, the longitude and latitude spans of the receiver's movement trajectory are calculated, and the larger of the two is taken as the straight-line span; where the longitude span is the difference between the maximum and minimum longitude, and the latitude span is the difference between the maximum and minimum latitude. Finally, based on the distances between signal sources and / or the straight-line span, the number of division points is determined.

[0077] In this embodiment, when the distance between signal sources is small or the straight-line span is large, the number of division points is increased to ensure sampling accuracy; when the distance between signal sources or the straight-line span is small, the number of division points is decreased to improve computational efficiency. Through this adaptive adjustment mechanism, a dynamic balance between sampling accuracy and computational efficiency can be achieved in different application scenarios.

[0078] S104. For any two signal sources among multiple signal sources, construct a fuzzy straight line. Based on the longitude fuzzy region set or the latitude fuzzy region set, sample the fuzzy straight line to generate the corresponding fuzzy point set. Combine the fuzzy point sets corresponding to all signal source combinations to obtain the total fuzzy point set.

[0079] In one possible embodiment, the step of constructing a fuzzy straight line for any two signal sources from a plurality of signal sources includes:

[0080] Obtain the longitude and latitude coordinates of any two signal sources; use the ratio of the latitude difference to the longitude difference between the two signal sources as the slope of the fuzzy line; subtract the product of the longitude and the slope of one of the signal sources from its latitude as the intercept of the fuzzy line; construct the standard equation of the fuzzy line based on the slope and the intercept.

[0081] In the specific implementation process, for any two different signal sources and Their longitude coordinates are respectively and The latitude coordinates are respectively and An ambiguous straight line can be determined using these two signal sources, and its standard equation is as follows:

[0082]

[0083] in, Represents the longitude variable on the fuzzy straight line; Represents the latitude variable on the fuzzy line; The slope of the fuzzy line is calculated using the following formula:

[0084]

[0085] The intercept of the fuzzy line is calculated using the following formula:

[0086]

[0087] In this embodiment, the slope is determined by calculating the ratio of the latitude difference to the longitude difference between the two signal sources, and the intercept is calculated accordingly to construct the standard equation of the fuzzy line. This method accurately characterizes the geometric relationship of each pair of signal sources in space with extremely low computational cost. Any point on the line satisfies the condition of being collinear with both signal sources, providing an accurate geometric reference for subsequent adaptive sampling and fuzzy point set generation.

[0088] In one possible embodiment, the step of sampling fuzzy lines to generate corresponding fuzzy point sets based on a longitude fuzzy region set or a latitude fuzzy region set includes:

[0089] If the slope of the fuzzy line is greater than or equal to 1, then select each latitude value sequentially from the latitude fuzzy region set, substitute it into the standard equation of the fuzzy line to calculate the corresponding longitude value, and use the obtained latitude and longitude coordinate points as the fuzzy point set corresponding to the two signal sources.

[0090] If the slope of the fuzzy line is less than 1, then select each longitude value sequentially from the longitude fuzzy region set, substitute it into the standard equation of the fuzzy line to calculate the corresponding latitude value, and use the obtained longitude and latitude coordinate points as the fuzzy point set corresponding to the two signal sources.

[0091] In the specific implementation process, different fuzzy region sets are selected as sampling benchmarks based on the slope of the fuzzy line, as follows:

[0092] Scenario 1: When At that time, a latitudinal fuzzy region set was adopted. As a sampling reference, latitude values ​​are selected sequentially from the set of latitude ambiguity regions:

[0093]

[0094] Substitute the selected latitude value into the fuzzy linear equation to calculate the corresponding longitude value:

[0095]

[0096] in, Represents the fuzzy point set. The longitude coordinates of each sampling point; To represent the fuzzy point set of the first Latitude coordinates of each sampling point.

[0097] Scenario 2: When At that time, the longitude fuzzy region set was adopted. As a sampling benchmark, longitude values ​​are selected sequentially from the longitude ambiguity region set:

[0098]

[0099] Substitute the selected longitude value into the fuzzy straight line equation to calculate the corresponding latitude value:

[0100]

[0101] in, Represents the fuzzy point set. The longitude coordinates of each sampling point; Represents the fuzzy point set. Latitude coordinates of each sampling point.

[0102] In this embodiment, when the slope is greater than or equal to 1, it indicates that the latitude of the line changes rapidly. In this case, uniform sampling along the latitude direction followed by calculation of the corresponding longitude ensures that the sampling points are uniformly distributed along the latitude direction on the line. When the slope is less than 1, it indicates that the longitude of the line changes rapidly. In this case, uniform sampling along the longitude direction followed by calculation of the corresponding latitude ensures that the sampling points are uniformly distributed along the longitude direction on the line. This adaptive strategy ensures that the spacing between sampling points in the projection direction remains consistent regardless of the inclination of the line, thereby obtaining a uniformly distributed fuzzy point set. The uniformly distributed fuzzy point set can more accurately characterize the spatial position of the fuzzy line. When matching it with the receiver's moving trajectory point set, it can effectively reduce missed or false detections caused by uneven sampling, improving the accuracy and reliability of trajectory overlap point detection.

[0103] By merging the sets of ambiguities corresponding to all signal source combinations, we obtain the total set of ambiguities, which is expressed as follows:

[0104]

[0105] in, Represents the total fuzzy point set; Indicates the total number of signal sources; To indicate that by the first The signal source and the first The set of fuzzy points corresponding to the fuzzy straight line determined by each signal source; This indicates that combinations of the same signal source and itself are excluded.

[0106] S105. Merge the overlapping points between the moving trajectory point set and the total fuzzy point set to construct the trajectory overlapping point set.

[0107] In one possible embodiment, step S105 includes: calculating the distance between each trajectory point in the moving trajectory point set and each point in the total fuzzy point set; marking trajectory points whose distance is less than or equal to the error threshold as overlapping points; and merging all overlapping points to form a trajectory overlapping point set.

[0108] In the specific implementation process, for the set of mobile trajectory points Each point in the total fuzzy point set For each point in the trajectory, calculate the Euclidean distance point to obtain the set of overlapping points, which is expressed as follows:

[0109]

[0110] in, Represents the set of overlapping points of the trajectories; Indicates the receiver at time latitude and longitude coordinates; Represents the total fuzzy point set Any fuzzy point in the; This is the preset error threshold.

[0111] In practical engineering, the positioning system of the receiver has a certain positioning error, and the trajectory points of the receiver are difficult to be completely and accurately located on the fuzzy line. Therefore, in this embodiment, by setting a preset error threshold, the trajectory points are allowed to be regarded as overlapping points within a certain range near the fuzzy line, which effectively tolerates the deviation caused by positioning error and sampling discretization, and improves the robustness of the method.

[0112] In one possible embodiment, the error threshold is determined based on at least one of the following factors: the positioning accuracy of the receiver, the distance between multiple signal sources, and the distribution density of the movement trajectory; wherein the error threshold is negatively correlated with the positioning accuracy; the error threshold is negatively correlated with the distance; and the error threshold is negatively correlated with the distribution density.

[0113] In the specific implementation process, firstly, the positioning accuracy of the receiver is obtained. Secondly, the Euclidean distance between any two signal sources is calculated, and the maximum, minimum, or average distance is taken as the distance between multiple signal sources. Then, the Euclidean distance between adjacent moving trajectories is calculated to obtain the total path length of the moving trajectory. The distribution density of the moving trajectory is obtained by dividing the total number of points on the moving trajectory by the total path length. Finally, based on the positioning accuracy of the receiver, the distance between multiple signal sources, and / or the distribution density of the moving trajectories, an error threshold is determined.

[0114] In this embodiment, a smaller error threshold is used to improve detection accuracy when positioning accuracy is high, and a larger error threshold is used to avoid missed detections when positioning accuracy is low. This allows the method to adapt to positioning devices with different accuracy levels and effectively tolerate the impact of positioning errors. A smaller error threshold is used when trajectory points are dense to accurately locate overlapping points, and a larger error threshold is used when trajectory points are sparse to ensure coverage, avoiding detection deviations caused by uneven sampling intervals. A larger error threshold is used when signal sources are densely distributed to ensure the detection of all possible blurred areas, and a smaller error threshold is used when signal sources are sparsely distributed to improve detection accuracy. This allows the method to adapt to different signal source geometric layouts.

[0115] S106. Remove the aliasing signals corresponding to all overlapping points in the trajectory overlap point set from the aliasing signal sequence to obtain a pulse signal sequence with the azimuth ambiguity eliminated.

[0116] In the specific implementation process, the set of trajectory overlap points Includes all trajectory points that may be affected by multiple signal sources, and removes them from the aliased signal sequence S. The aliased signals corresponding to all overlapping points are used to obtain a pulse signal sequence without orientation ambiguity. :

[0117]

[0118] After this processing, signal samples in overlapping regions are removed, leaving the pulse signal sequence. It can be used for subsequent azimuth pulse sorting, thereby ensuring the accuracy and reliability of the sorting results.

[0119] Please refer to Figure 3 This is another flowchart illustrating the signal processing method for eliminating orientation ambiguity provided in this application embodiment. The following is a combined description of the method. Figure 3 A detailed introduction will be provided.

[0120] S201, Initialize parameters.

[0121] Suppose there is a portable receiver A with signal receiving capabilities, and One signal source The latitude and longitude of these signal sources It is fixed and distributed at different coordinate locations, among which During the movement, receiver A receives signals transmitted from these signal sources, which are then sampled to form a mixed signal sequence. The trajectory point set of receiver A is represented as follows: ,in, This indicates the change in the receiver's position at different points in time.

[0122] S202, Calculate the longitude fuzzy region set and latitude fuzzy region set :

[0123] ,

[0124]

[0125]

[0126] S203. Calculate the fuzzy line between two signal sources. :

[0127]

[0128]

[0129]

[0130] S204. Calculate the fuzzy point set corresponding to the fuzzy line. :

[0131]

[0132] like ,but ;

[0133] like ,but .

[0134] S205, Traverse all signal source combinations.

[0135] If not all signal source combinations have been traversed, then proceed to S203; if all signal source combinations have been traversed, then proceed to S206.

[0136] S206, Constructing the total fuzzy point set :

[0137]

[0138] S207, Calculate the set of overlapping points on the trajectory :

[0139]

[0140] S208. Calculate the signal sequence without orientation ambiguity. :

[0141]

[0142] In summary, to address the sorting errors caused by multiple signal sources arriving at the receiver from the same or similar angles, this application also provides a signal processing method to eliminate azimuth ambiguity. First, an ambiguous line is constructed and a total ambiguous point set is generated. Then, the ambiguous moment is identified through trajectory overlap matching. Finally, the corresponding signal samples are removed. While ensuring the signal quality after sorting, this method effectively eliminates azimuth ambiguity between different targets, significantly improving the accuracy and robustness of signal grouping. It provides an efficient and reliable solution to the problem of azimuth pulse sorting in complex electromagnetic environments.

[0143] Based on the same inventive concept, please refer to Figure 4 This application also provides a signal processing apparatus for eliminating orientation ambiguity, the apparatus comprising:

[0144] The data acquisition module is used to acquire the set of moving trajectory points of the receiver and the aliased signal sequence received by it; the aliased signal sequence comes from the pulse sequence emitted by multiple signal sources at multiple consecutive moments;

[0145] The latitude and longitude range determination module is used to determine the longitude and latitude range of the receiver's movement trajectory based on the set of movement trajectory points;

[0146] The fuzzy region division module is used to divide the longitude range to obtain a longitude fuzzy region set and the latitude range to obtain a latitude fuzzy region set.

[0147] The fuzzy point set generation module is used to construct a fuzzy straight line for any two signal sources. Based on the longitude fuzzy region set or the latitude fuzzy region set, the fuzzy straight line is sampled to generate the corresponding fuzzy point set. The fuzzy point sets corresponding to all signal sources are combined to obtain the total fuzzy point set.

[0148] The overlapping point set construction module is used to merge the overlapping points between the moving trajectory point set and the total fuzzy point set to construct the trajectory overlapping point set;

[0149] The elimination module is used to remove the aliased signals corresponding to all overlapping points in the trajectory overlap point set from the aliased signal sequence to obtain a pulse signal sequence with the azimuth ambiguity eliminated.

[0150] Optionally, the fuzzy point set generation module is specifically used for:

[0151] Obtain the longitude and latitude coordinates of any two signal sources from a set of multiple signal sources;

[0152] The ratio of the latitude difference to the longitude difference between the two signal sources is used as the slope of the fuzzy line.

[0153] The intercept of the fuzzy line is obtained by subtracting the product of the longitude and slope of one of the signal sources from its latitude.

[0154] Based on the slope and intercept, construct the standard equation of the fuzzy line.

[0155] Optionally, the fuzzy point set generation module is specifically used for:

[0156] If the slope of the fuzzy line is greater than or equal to 1, then select each latitude value sequentially from the latitude fuzzy region set, substitute it into the standard equation of the fuzzy line to calculate the corresponding longitude value, and use the obtained latitude and longitude coordinate points as the fuzzy point set corresponding to the two signal sources.

[0157] If the slope of the fuzzy line is less than 1, then select each longitude value sequentially from the longitude fuzzy region set, substitute it into the standard equation of the fuzzy line to calculate the corresponding latitude value, and use the obtained longitude and latitude coordinate points as the fuzzy point set corresponding to the two signal sources.

[0158] Optionally, the overlapping point set construction module is specifically used for:

[0159] Calculate the distance between each trajectory point in the moving trajectory point set and each point in the total fuzzy point set;

[0160] Mark trajectory points whose distance is less than or equal to the error threshold as overlapping points;

[0161] Merge all overlapping points to form a set of trajectory overlapping points.

[0162] Optionally, the error threshold is determined based on at least one of the following factors: the positioning accuracy of the receiver, the distance between multiple signal sources, and the distribution density of the movement trajectory; wherein, the error threshold is negatively correlated with the positioning accuracy; the error threshold is negatively correlated with the distance; and the error threshold is negatively correlated with the distribution density.

[0163] Optionally, the fuzzy region segmentation module is specifically used for:

[0164] The longitude range is evenly divided according to the preset number of division points to obtain multiple longitude sampling points. Multiple longitude sampling points are merged to form a longitude fuzzy region set.

[0165] The latitude range is evenly divided according to the number of division points to obtain multiple latitude sampling points. These multiple latitude sampling points are then merged to form a set of fuzzy latitude regions.

[0166] Optionally, the number of division points is determined based on at least one of the factors: the distance between multiple signal sources and the straight-line span of the movement trajectory; wherein, the number of division points is negatively correlated with the distance and positively correlated with the straight-line span.

[0167] It should be noted that each module in the signal processing device for eliminating azimuth ambiguity in this embodiment corresponds one-to-one with each step in the signal processing method for eliminating azimuth ambiguity in the aforementioned embodiment. Therefore, the specific implementation of this embodiment can refer to the implementation of the aforementioned signal processing method for eliminating azimuth ambiguity, and will not be repeated here.

[0168] Based on the same inventive concept, this application also provides a computer device, which includes a processor, a memory, and a computer program stored in the memory. The computer program is executed by the processor to implement the aforementioned signal processing method for eliminating orientation ambiguity.

[0169] Based on the same inventive concept, this application also provides a computer storage medium storing a computer program, which is executed by a processor to implement the aforementioned signal processing method for eliminating orientation ambiguity.

[0170] In some embodiments, the computer-readable storage medium may be a memory such as FRAM, ROM, PROM, EPROM, EEPROM, flash memory, magnetic surface memory, optical disk, or CD-ROM; or it may be various devices including one or any combination of the above-mentioned memories. The computer may be various computing devices, including smart terminals and servers.

[0171] In some embodiments, executable instructions may take the form of a program, software, software module, script, or code, written in any form of programming language (including compiled or interpreted languages, or declarative or procedural languages), and may be deployed in any form, including as a standalone program or as a module, component, subroutine, or other unit suitable for use in a computing environment.

[0172] As an example, executable instructions may, but do not necessarily, correspond to files in the file system. They may be stored as part of a file that holds other programs or data, for example, in one or more scripts in a HyperText Markup Language (HTML) document, in a single file dedicated to the program in question, or in multiple collaborative files (e.g., a file that stores one or more modules, subroutines, or code sections).

[0173] As an example, executable instructions can be deployed to execute on a single computing device, or on multiple computing devices located in one location, or on multiple computing devices distributed across multiple locations and interconnected via a communication network.

[0174] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or system that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or system. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or system that includes that element.

[0175] The sequence numbers of the embodiments in this application are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0176] The above specific embodiments further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above are merely specific embodiments of the present invention and are not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A signal processing method for eliminating orientation ambiguity, characterized in that, include: The receiver acquires a set of moving trajectory points and a sequence of aliased signals received from them; the aliased signal sequence originates from pulse sequences emitted by multiple signal sources at multiple consecutive moments. Based on the set of movement trajectory points, determine the longitude and latitude range of the receiver's movement trajectory; The longitude range is divided into a longitude fuzzy region set, and the latitude range is divided into a latitude fuzzy region set; For any two signal sources among the plurality of signal sources, a fuzzy straight line is constructed. Based on the longitude fuzzy region set or the latitude fuzzy region set, the fuzzy straight line is sampled to generate a corresponding fuzzy point set. The fuzzy point sets corresponding to all signal source combinations are merged to obtain the total fuzzy point set. Merge the overlapping points between the set of moving trajectory points and the total fuzzy point set to construct a trajectory overlapping point set; Remove the aliased signals corresponding to all overlapping points in the trajectory overlap point set from the aliased signal sequence to obtain a pulse signal sequence with eliminated orientation ambiguity.

2. The signal processing method for eliminating orientation ambiguity according to claim 1, characterized in that, The step of constructing a fuzzy straight line for any two of the plurality of signal sources includes: Obtain the longitude and latitude coordinates of any two signal sources from the plurality of signal sources; The ratio of the latitude difference to the longitude difference between the two signal sources is used as the slope of the fuzzy line. The intercept of the fuzzy line is obtained by subtracting the longitude of one of the signal sources from the product of the slope and the latitude of that signal source. Based on the slope and the intercept, construct the standard equation of the fuzzy line.

3. The signal processing method for eliminating orientation ambiguity according to claim 1, characterized in that, The step of sampling the fuzzy straight line to generate a corresponding fuzzy point set based on the longitude fuzzy region set or the latitude fuzzy region set includes: If the slope of the fuzzy line is greater than or equal to 1, then each latitude value is selected sequentially from the set of fuzzy latitude regions, substituted into the standard equation of the fuzzy line to calculate the corresponding longitude value, and the obtained latitude and longitude coordinate points are used as the fuzzy point set corresponding to the two signal sources. If the slope of the fuzzy line is less than 1, then each longitude value is selected sequentially from the longitude fuzzy region set, substituted into the standard equation of the fuzzy line to calculate the corresponding latitude value, and the obtained longitude and latitude coordinate points are used as the fuzzy point set corresponding to the two signal sources.

4. The signal processing method for eliminating orientation ambiguity according to claim 1, characterized in that, The step of merging the overlapping points between the set of moving trajectory points and the total fuzzy point set to construct a trajectory overlapping point set includes: Calculate the distance between each trajectory point in the set of moving trajectory points and each point in the total fuzzy point set; Trajectory points whose distance is less than or equal to the error threshold are marked as overlapping points; Merge all overlapping points to form a set of trajectory overlapping points.

5. The signal processing method for eliminating orientation ambiguity according to claim 4, characterized in that, The error threshold is determined based on at least one of the following factors: the positioning accuracy of the receiver, the distance between the plurality of signal sources, and the distribution density of the movement trajectory; wherein the error threshold is negatively correlated with the positioning accuracy; the error threshold is negatively correlated with the distance; and the error threshold is negatively correlated with the distribution density.

6. The signal processing method for eliminating orientation ambiguity according to claim 1, characterized in that, The process of dividing the longitude range into a longitude fuzzy region set and dividing the latitude range into a latitude fuzzy region set includes: The longitude range is evenly divided according to a preset number of division points to obtain multiple longitude sampling points. The multiple longitude sampling points are then merged to form a longitude fuzzy region set. The latitude range is evenly divided according to the number of division points to obtain multiple latitude sampling points. The multiple latitude sampling points are then merged to form a set of latitudinal fuzzy regions.

7. The signal processing method for eliminating orientation ambiguity according to claim 6, characterized in that, The number of division points is determined based on at least one of the factors: the distance between the plurality of signal sources and the straight-line span of the movement trajectory; wherein the number of division points is negatively correlated with the distance and positively correlated with the straight-line span.

8. A signal processing device for eliminating orientation ambiguity, characterized in that, include: The data acquisition module is used to acquire the receiver's moving trajectory point set and the sequence of aliased signals it receives; The aliased signal sequence originates from pulse sequences emitted by multiple signal sources at multiple consecutive moments; The latitude and longitude range determination module is used to determine the longitude and latitude range of the receiver's movement trajectory based on the set of movement trajectory points. The fuzzy region division module is used to divide the longitude range into a longitude fuzzy region set and the latitude range into a latitude fuzzy region set. The fuzzy point set generation module is used to construct a fuzzy straight line for any two signal sources, sample the fuzzy straight line according to the longitude fuzzy region set or the latitude fuzzy region set to generate the corresponding fuzzy point set, and merge the fuzzy point sets corresponding to all signal source combinations to obtain the total fuzzy point set. The overlapping point set construction module is used to merge the overlapping points between the moving trajectory point set and the total fuzzy point set to construct the trajectory overlapping point set; The elimination module is used to remove the aliased signals corresponding to all overlapping points in the trajectory overlap point set from the aliased signal sequence to obtain a pulse signal sequence with eliminated azimuth ambiguity.

9. A computer device, characterized in that, The computer device includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement a signal processing method for eliminating orientation ambiguity as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, and the processor executes the computer program to implement a signal processing method for eliminating orientation ambiguity as described in any one of claims 1-7.

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