Radar signal plot condensation method based on GPU and sparse table

Abstract

The invention specifically relates to a radar signal trace point condensation method based on a GPU and a sparse table, and the method comprises the steps: enabling the method to be operated on the GPU, firstly carrying out the rearrangement of trace points according to a distance ascending order, extracting the distance and the signal-to-noise ratio in parallel, constructing the sparse table, carrying out the parallel processing between columns, and carrying out the serial processing between rows, and carrying out the calculation of the sparse table. Recording the maximum signal-to-noise ratio in each target interval starting from the current trace point, then searching left and right boundary indexes of each trace point in parallel by adopting a dichotomy according to a distance threshold value, respectively dividing the left and right intervals of the trace point into two longest sub-intervals with the length being the power of 2, searching the corresponding position of each sub-interval in the sparse table, and calculating the maximum signal-to-noise ratio of each target interval; and the maximum signal-to-noise ratios of the left and right boundaries are obtained. And finally, comparing the maximum signal-to-noise ratios of the left and right boundaries with the signal-to-noise ratio of the trace point, and judging the effectiveness of the trace point. According to the method, for a scene with a large trace point number, effective trace points can be efficiently distinguished, and meanwhile, false alarms possibly occurring when a greedy algorithm is adopted are avoided.

Description

Technical Field

[0001] This invention relates to the field of radar signal processing technology, and specifically to a radar signal point aggregation method based on GPU and sparse tables. Background Technology

[0002] Radar signal clustering algorithms implemented using DSP or CPU typically employ a greedy algorithm for the number of clusters N, with a time complexity of O(n log n). As the number of data points increases, the algorithm's processing time will increase significantly, leading to overall signal processing lag. Furthermore, at high data rates, the number of processable data points needs further compression. Additionally, if, during processing, a current data point falls within the right boundary of another data point and has a low signal-to-noise ratio, it may be marked as an invalid data point, resulting in a false alarm.

[0003] It should be noted that the information disclosed in the background section above is only used to enhance the understanding of the background of the present invention, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0004] This invention provides a radar signal point aggregation method based on GPU and sparse table, a computer-readable storage medium, and a computer program product, which can effectively overcome the defects existing in the prior art.

[0005] Other features and advantages of the invention will become apparent from the following detailed description, or may be learned in part by practice of the invention.

[0006] According to a first aspect of the present invention, a radar signal point aggregation method based on GPU and sparse tables is provided, the method comprising: Acquire a set of radar signal points; wherein the set of points includes: the distance and signal-to-noise ratio of each point; Based on the GPU, the point data is sorted according to distance to obtain a distance sequence and a signal-to-noise ratio sequence; Based on the distance sequence, the left and right boundary indices of each point are extracted in parallel using GPU to obtain the left and right distance neighborhood intervals corresponding to each point. The signal-to-noise ratio of each point in the signal-to-noise ratio sequence is recursively calculated layer by layer according to the increasing interval length to construct a sparse signal-to-noise ratio table; The maximum signal-to-noise ratio (SNR) of each point is obtained in the corresponding left and right distance neighborhood intervals using a sparse SNR table, and the query results are obtained. The valid points are then determined based on the query results and the SNR of each point.

[0007] In some exemplary embodiments, the step of sorting the point data according to distance based on the GPU to obtain a distance sequence and a signal-to-noise ratio sequence includes: The distance between each point is used as the sorting criterion, and the signal-to-noise ratio of each point is used as the accompanying data. Based on the distance of each point, the GPU is used to perform parallel ascending sorting to obtain a distance sequence arranged by distance; Based on the sorting sequence of distances to each point, the signal-to-noise ratio of each point is adjusted synchronously to obtain a signal-to-noise ratio sequence corresponding to the distance sequence.

[0008] In some exemplary embodiments, the step of using a GPU to extract the left and right boundary indices of each point trace in parallel based on the distance sequence, to obtain the left and right distance neighborhood intervals corresponding to each point trace, includes: Allocate one GPU thread for each point in the distance sequence; The allocated GPU threads are used to execute the binary search algorithm in parallel to find the positions of the farthest left and right points whose distance difference from the current point is within a preset threshold range, and their indices are determined as the left boundary index and the right boundary index, respectively. Based on the left boundary index and the right boundary index, determine the left distance neighborhood interval and the right distance neighborhood interval corresponding to each point.

[0009] In some exemplary embodiments, the step of recursively extrapolating the signal-to-noise ratio (SNR) of each point in the SNR sequence layer by layer in ascending order of interval length to construct a sparse SNR table includes: The signal-to-noise ratio of each point in the signal-to-noise ratio sequence is directly written into the initial layer of the sparse table, with each table entry corresponding to the signal-to-noise ratio of a point. For each entry in each layer, the maximum signal-to-noise ratio of the two adjacent sub-intervals in the previous layer is queried, and the larger value is taken as the entry in the current layer; wherein, the interval length of each layer is twice the interval length of the previous layer; The data construction of all layers of the sparse table is completed by recursively calculating layer by layer; serial calculation is used between layers, and parallel calculation is used within each layer.

[0010] In some exemplary embodiments, the step of using a signal-to-noise ratio sparse table to obtain the maximum signal-to-noise ratio of each point within its corresponding left and right distance neighborhood intervals, and then obtaining the query results, includes: Construct an indexed logarithmic table based on the trace index in the trace data; Divide the left distance neighborhood interval and the right distance neighborhood interval into two sub-intervals respectively, and obtain the interval level from the index logarithm table according to the length of the sub-intervals; The maximum signal-to-noise ratio (SNR) value within each sub-interval is obtained from the SNR sparse table based on the interval hierarchy. The larger of the two maximum signal-to-noise ratio values ​​corresponding to the two sub-intervals is determined as the maximum signal-to-noise ratio within the neighborhood interval.

[0011] In some exemplary embodiments, determining the valid traces based on the query results and the signal-to-noise ratio of each trace includes: When the maximum signal-to-noise ratio (SNR) in both the left and right distance neighborhood intervals is greater than the SNR of the current point, the current point is determined as a valid point.

[0012] In some exemplary embodiments, the indexed logarithmic table includes:

[0013] in, express The element indices in the array start with 0; This represents the index of a point in the point data.

[0014] According to a second aspect of the present invention, a computer-readable storage medium is provided, the computer-readable storage medium including a stored executable program, wherein, when the executable program is running, it controls the device where the storage medium is located to execute the above-described radar signal trace aggregation method based on GPU and sparse table.

[0015] According to a third aspect of the present invention, a computer program product is provided, comprising a computer program that, when executed by a processor, implements the aforementioned method for radar signal point aggregation based on a GPU and a sparse table.

[0016] According to a fourth aspect of the present invention, an electronic device is provided, comprising: Processor; and Memory for storing the executable instructions of the processor; The processor is configured to implement the aforementioned radar signal point aggregation method based on GPU and sparse tables by executing the executable instructions.

[0017] The radar signal point aggregation method based on GPU and sparse table provided by the embodiments of the present invention first rearranges the points in ascending order of distance using GPU, extracts the distance sequence and signal-to-noise ratio (SNR) sequence in parallel, and constructs a sparse table using the SNR sequence, wherein columns are processed in parallel and rows are processed serially; then, based on the distance threshold, a binary search method is used to find the left and right boundary indices of each point in parallel, and the left and right intervals of the current point are divided into two longest sub-intervals of length 2; then, a logarithmic index table is used to find the corresponding maximum SNR of each sub-interval in the sparse table, thereby obtaining the maximum SNR of each left and right boundary; finally, the maximum SNR of each left and right boundary is compared with the SNR of the current point to determine the validity of the point. For scenarios with a large number of points, it can efficiently distinguish valid points, while avoiding false alarms that may occur when using a greedy algorithm.

[0018] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit the invention. Attached Figure Description

[0019] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention. It is obvious that the drawings described below are merely some embodiments of the invention, and those skilled in the art can obtain other drawings based on these drawings without any inventive effort.

[0020] Figure 1 The flowchart illustrates an exemplary embodiment of the present invention: a radar signal point aggregation method based on GPU and sparse tables. Figure 2 The illustration shows a schematic diagram of an embodiment of the radar signal point aggregation method based on GPU and sparse table according to an exemplary embodiment of the present invention. Figure 3 This schematic diagram illustrates an exemplary embodiment of the present invention and a performance comparison analysis of the present invention and a greedy algorithm under different data scales. Figure 4 This diagram schematically illustrates the original distribution of dots in an exemplary embodiment of the present invention and a comparison of the dot aggregation results of the two algorithms. Figure 5 This schematic diagram illustrates the composition of an electronic device according to an exemplary embodiment of the present invention. Detailed Implementation

[0021] Exemplary embodiments will now be described more fully with reference to the accompanying drawings. However, these exemplary embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, they are provided so that the invention will be more comprehensive and complete, and will fully convey the concept of the exemplary embodiments to those skilled in the art. The described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.

[0022] Furthermore, the accompanying drawings are merely illustrative of the invention and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and therefore repeated descriptions of them will be omitted. Some block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. These functional entities can be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor devices and / or microcontroller devices.

[0023] To address the shortcomings and deficiencies of existing technologies, this example implementation provides a radar signal point aggregation method based on GPU and sparse tables. (Reference) Figure 1 As shown, it can specifically include: Step S10: Obtain the radar signal point set; wherein, the point set includes: the distance and signal-to-noise ratio of each point; Step S12: Sort the point data according to distance based on the GPU to obtain the distance sequence and the signal-to-noise ratio sequence; Step S14: Based on the distance sequence, use the GPU to extract the left boundary index and right boundary index of each point in parallel to obtain the left distance neighborhood interval and right distance neighborhood interval corresponding to each point. Step S16: The signal-to-noise ratio of each point in the signal-to-noise ratio sequence is recursively calculated layer by layer according to the increasing interval length to construct a sparse signal-to-noise ratio table. Step S18: Use the signal-to-noise ratio sparse table to obtain the maximum signal-to-noise ratio of each point in the corresponding left distance neighborhood interval and right distance neighborhood interval to obtain the query result; and determine the valid points based on the query result and the signal-to-noise ratio of each point.

[0024] The following will describe in more detail each step of a radar signal point aggregation method based on GPU and sparse tables in this exemplary embodiment, with reference to the accompanying drawings and embodiments.

[0025] For example, in step S10, the radar signal point set is a data set consisting of multiple point traces, each point trace containing the distance and signal-to-noise ratio of that point trace.

[0026] The distance of each point is used to characterize the distance position of the corresponding point relative to the radar, and the signal-to-noise ratio is used to characterize the ratio between the signal strength and noise strength of the corresponding point.

[0027] For example, a radar detection system obtains 8 points after one echo processing, with the following distance and signal-to-noise ratio: Dot numbers: 1, 2, 3, 4, 5, 6, 7, 8 Distance / m: 100, 108, 115, 130, 142, 150, 168, 180 Signal-to-noise ratio (SNR) / dB: 8, 12, 6, 15, 7, 13, 9, 11.

[0028] For example, in step S12, the step of sorting the point data according to distance based on the GPU to obtain a distance sequence and a signal-to-noise ratio sequence includes: Step S121: The distance between each point is used as the sorting criterion, and the signal-to-noise ratio of each point is used as the accompanying data. Step S122: Based on the distance of each point, use the GPU to perform parallel ascending sorting to obtain a distance sequence arranged by distance; Step S123: Based on the sorting sequence of distances between each point, the signal-to-noise ratio of each point is adjusted synchronously to obtain a signal-to-noise ratio sequence corresponding to the distance sequence.

[0029] Specifically, data including distance and signal-to-noise ratio has been obtained. Individual trace information The dots are randomly arranged. First, a parallel sorting algorithm optimized by CUDA is used. Sort by distance in ascending order. Then use Parallel extraction The distance and signal-to-noise ratio information of each point in the data are collected and integrated to obtain a distance sequence. With signal-to-noise ratio sequence .in, The block size can be set to The grid size is [value], and during extraction, each thread corresponds to a specific point.

[0030] in, This is used to perform parallel traversal of the sorted set of points and extract the distance and signal-to-noise ratio information of each point. For each point, a thread is allocated to read the distance and signal-to-noise ratio of that point and write them into the distance sequence and signal-to-noise ratio sequence respectively, so as to obtain the distance sequence and signal-to-noise ratio sequence that correspond one-to-one with the sorting result.

[0031] For example, in step S14, the step of extracting the left and right boundary indices of each point trace using the GPU in parallel based on the distance sequence to obtain the left and right distance neighborhood intervals corresponding to each point trace includes: Step S141: Allocate a GPU thread for each point in the distance sequence; Step S142: Utilize the allocated GPU threads to execute the binary search algorithm in parallel, find the positions of the farthest left and right points whose distance difference from the current point is within a preset threshold range, and determine their indices as the left boundary index and the right boundary index, respectively. Step S143: Determine the left distance neighborhood interval and the right distance neighborhood interval corresponding to each point based on the left boundary index and the right boundary index, respectively.

[0032] Specifically, using Parallel search The left and right boundary indices of each point in the trace are obtained and integrated. and .in, and The configurations are the same, and during extraction, each thread corresponds to a specific point.

[0033] For the a dot trace A binary search method is used when searching for the left boundary index. First, let... ,like The distance is less than Then let Otherwise Update mid, repeat this operation until... , obtained the dot mark left boundary index .in, This is a preset threshold range. For the right boundary index... The calculation steps are as follows: left boundary index The calculation method.

[0034] For example, in step S16, the step of recursively calculating the signal-to-noise ratio of each point in the signal-to-noise ratio sequence layer by layer according to the increasing interval length to construct a sparse signal-to-noise ratio table includes: Step S161: The signal-to-noise ratio of each point in the signal-to-noise ratio sequence is directly written into the initial layer of the sparse table, and each table entry corresponds to the signal-to-noise ratio of a point. Step S162: For each entry in each layer, query the maximum signal-to-noise ratio of the two adjacent sub-intervals in the previous layer, and take the larger value as the entry in the current layer; wherein, the interval length of each layer is twice the interval length of the previous layer. Step S163: The data construction of all layers of the sparse table is completed by recursive calculation layer by layer; wherein, serial calculation is used between layers and parallel calculation is used within each layer.

[0035] Specifically, signal-to-noise ratio sparse table middle, Representing an interval The maximum value of the signal-to-noise ratio in the initial layer. For dots The signal-to-noise ratio, For dots and The maximum value of the signal-to-noise ratio, that is For each entry in each layer, the maximum signal-to-noise ratio of the two adjacent sub-intervals in the previous layer is queried, and the larger value is taken as the entry for the current layer.

[0036] It can be obtained from the following formula:

[0037] Specifically, will Divided into 2 sub-intervals and The regions have no overlapping areas and contain the complete original interval, so a recursive approach is used to construct... . use Parallel computation yields The value of each row in the text. And because the... The value of the row depends on the first row. The rows need to be processed sequentially. To be executed Second-rate.

[0038] For example, in step S18, the maximum signal-to-noise ratio of each point trace in the corresponding left and right distance neighborhood intervals is obtained using a signal-to-noise ratio sparse table to obtain the query results, including: Step S181: Construct an indexed logarithmic table based on the point index in the point data; Step S182: Divide the left distance neighborhood interval and the right distance neighborhood interval into two sub-intervals respectively, and obtain the interval level from the index logarithm table according to the length of the sub-intervals; Step S183: Obtain the maximum signal-to-noise ratio (SNR) value in each sub-interval from the SNR sparse table according to the interval hierarchy; Step S184: Determine the larger of the two maximum signal-to-noise ratio values ​​corresponding to the two sub-intervals as the maximum signal-to-noise ratio within the distance neighborhood interval.

[0039] Specifically, construct an indexed logarithmic table. The dimension is Calculate according to the following formula. :

[0040] in, express The element indices in the array start with 0; This represents the index of a point in the point data.

[0041] The indexed logarithmic table is used in the sparse table query phase to quickly obtain the satisfying interval based on any interval length len. Find the largest integer k so that the query interval can be divided into two sub-intervals of the same length that are powers of 2, thereby enabling fast lookup of the maximum value in the interval.

[0042] Specifically, using The maximum signal-to-noise ratio (SNR) at the left boundary and the maximum SNR at the right boundary of each point are obtained in parallel. For each point... The left boundary is Divide it into and ,in, .

[0043] The two sub-intervals contain the complete original interval, respectively in the signal-to-noise ratio sparse table. The maximum value of each is retrieved from the two values, and the larger of the two values ​​is taken to obtain the trace. Maximum signal-to-noise ratio in the left interval Similarly, the maximum signal-to-noise ratio in the right interval can be obtained. .

[0044] For example, in step S18, determining the valid traces based on the query results and the signal-to-noise ratio of each trace includes: Step S185: When the maximum signal-to-noise ratio in both the left and right distance neighborhood intervals is greater than the signal-to-noise ratio of the current point, the current point is determined as a valid point.

[0045] Specifically, using The validity of each point is determined in parallel. If and All are greater than the dot. signal-to-noise ratio Then mark If it is valid, then it is invalid; otherwise, it is invalid. The final result is... .

[0046] Exemplary, this application provides an implementation method for radar signal point aggregation based on GPU and sparse tables, referring to... Figure 2 As shown, the implementation steps are as follows: (1) Obtain information including distance and signal-to-noise ratioN Individual trace information ,according to N Construct an indexed logarithmic table log_table; (2) Based on the distance of the dots, Reorder the dots in ascending order and process them in parallel to obtain the results. ; (3) Utilization Parallel extraction The distance and signal-to-noise ratio information of each point in the trace are obtained and integrated to obtain and , (4) According to and A sparse table S is constructed; (5) According to and For each point, the left L and right R boundary indices are extracted in parallel. (6) Based on the left boundary index (L) and right boundary index (R) of each point, find the maximum signal-to-noise ratio within the left and right boundaries of S in parallel. and ; (7) If the signal-to-noise ratio of the point is greater than the maximum value of both the left and right boundaries, then its mark is valid; otherwise, it is invalid. Finally, we obtain... .

[0047] For example, this application provides an implementation method for radar signal point aggregation based on GPU and sparse tables, as follows: Suppose a radar detection system obtains 8 points after one echo processing, with the following range and signal-to-noise ratio: Dot numbers: 1, 2, 3, 4, 5, 6, 7, 8 Distance / m: 100, 108, 115, 130, 142, 150, 168, 180 Signal-to-noise ratio (SNR) / dB: 8, 12, 6, 15, 7, 13, 9, 11 Set the distance threshold to 15m.

[0048] First, sort the points in ascending order of distance. Since the points in this embodiment are already sorted in ascending order of distance, the sorted distance sequence remains as follows: 100, 108, 115, 130, 142, 150, 168, 180.

[0049] The corresponding signal-to-noise ratio sequence is: 8, 12, 6, 15, 7, 13, 9, 11.

[0050] Next, a logarithmic table is constructed based on the total number of dots N=8. This is used to quickly determine the power of 2 corresponding to the interval length during subsequent sparse table queries.

[0051] Then, a sparse table S is constructed in parallel on the GPU to record the maximum signal-to-noise ratio under different starting points and different interval lengths.

[0052] Taking the third point at a distance of 115m as an example, under the condition of a distance threshold of 15m, the points to its left that fall within the threshold range include points at 100m and 108m, and the points to its right that fall within the threshold range include a point at 130m. Therefore, its left and right boundaries can be determined separately, and the maximum signal-to-noise ratio within the left and right intervals can be queried using a sparse table.

[0053] For this point, its current signal-to-noise ratio is 6dB, while the maximum signal-to-noise ratio in the left interval is 12dB and the maximum signal-to-noise ratio in the right interval is 15dB. Therefore, this point does not meet the condition of being greater than the maximum value of both the left and right intervals at the same time, and is judged as an invalid point.

[0054] Taking the fourth point at a distance of 130m as an example, its current signal-to-noise ratio is 15dB. The maximum signal-to-noise ratio of the corresponding interval within the left and right distance threshold range is less than 15dB. Therefore, this point is determined to be a valid point.

[0055] Through the above process, aggregation discrimination can be completed in parallel for all points, thereby significantly improving the speed of large-scale point post-processing in radar systems and reducing the false alarm problem that may be caused by traditional greedy processing methods.

[0056] The method provided in the embodiments of the present invention is referred to Figure 3 As shown, Figure 3 This diagram illustrates the performance comparison between the proposed algorithm and the greedy algorithm under different data scales. It is composed of two sub-graphs, left and right.

[0057] The left figure shows a comparison of algorithm processing times, with the horizontal axis representing data size and the vertical axis representing processing time (ms). The blue line represents the greedy algorithm, and the orange dashed line represents the algorithm proposed in this invention. As can be seen from the left figure, with smaller data sizes, the processing time difference between the two algorithms is relatively small; as the data size increases, the processing time of the greedy algorithm rises rapidly; in contrast, the processing time of the algorithm proposed in this invention increases more slowly, remaining at a consistently low level.

[0058] For example, when the data size increases to 51200 and 102400, the processing time of the greedy algorithm increases significantly, while the processing time of the algorithm proposed in this invention remains low, indicating that the algorithm proposed in this invention has higher computational efficiency and better stability in large-scale point data processing scenarios.

[0059] The right figure shows the speedup ratio of the proposed algorithm compared to the greedy algorithm. The horizontal axis represents the data size index, and the vertical axis represents the speedup ratio. The dashed line in the figure corresponds to a speedup ratio of 1, indicating that the two algorithms have comparable processing speeds. The bar chart represents the speedup effect of the proposed algorithm compared to the greedy algorithm. Typical speedup ratios given in the figure are: approximately 5.32 for data size index 3; approximately 9.33 for data size index 4; approximately 17.27 for data size index 5; approximately 35.93 for data size index 6; and approximately 32.57 for data size index 7. As can be seen from the right figure, when the data size is small, the speedup ratio is close to 1, indicating that the difference in processing efficiency between the two algorithms is not significant at this point. As the data size increases, the speedup ratio continuously rises. Under medium to large-scale data, the proposed algorithm exhibits a significant speedup effect.

[0060] This shows that as the scale of data increases, the advantages of the algorithm proposed in this invention over the greedy algorithm become more and more obvious, especially in large-scale data processing, where it can achieve a speedup of tens of times.

[0061] Further, refer to Figure 4 As shown, Figure 4 This diagram illustrates the original distribution of the dots and a comparison of the dot aggregation results from the two algorithms. The horizontal axis represents distance in meters; the vertical axis represents the signal-to-noise ratio in decibels; gray dots represent the original dot information; red squares represent the results of the greedy algorithm; blue triangles represent the results of the sparse table-based algorithm; and yellow hollow circles indicate locations where the results from the two algorithms differ.

[0062] As can be seen from the figure, the original point traces are widely distributed across the entire distance range, and the signal-to-noise ratio coverage is relatively wide, extending roughly from a low signal-to-noise ratio region to a high signal-to-noise ratio region approaching 100dB. The high signal-to-noise ratio point traces are mainly concentrated in the upper region of the figure.

[0063] The results selected by both algorithms are mainly distributed in high signal-to-noise ratio regions, indicating that both methods can extract more representative strong points from a large number of original points for subsequent agglomeration processing. It can also be observed that most red squares and blue triangles are close to or largely overlap, indicating a high overall consistency between the sparse table-based algorithm and the greedy algorithm. A small number of areas marked with yellow hollow circles indicate differences in the two algorithms' judgments on individual points. This suggests that the sparse table-based algorithm achieves more efficient point processing while maintaining high consistency with the greedy algorithm.

[0064] The beneficial effects of this invention are as follows: (1) Significantly improves the processing efficiency of large-scale point data. This invention introduces a GPU parallel processing mechanism to parallelize processes such as point sorting, boundary index extraction, sparse table construction, and interval maximum value query. Compared with traditional serial or greedy processing methods, it can significantly shorten the time required for point aggregation, and is especially suitable for rapid processing scenarios of large-scale radar point data. (2) Achieve fast query of the maximum signal-to-noise ratio within the neighborhood interval. This invention constructs a signal-to-noise ratio sparse table and combines it with an index logarithm table to determine the query level. This eliminates the need for point-to-point traversal when obtaining the maximum signal-to-noise ratio within the left and right distance neighborhood intervals, thereby effectively reducing query complexity and improving point aggregation efficiency.

[0065] (3) Maintain high consistency of results while ensuring processing efficiency. Experimental results show that the algorithm proposed in this invention has a high degree of consistency with the greedy algorithm in terms of point aggregation results. The judgment results of most valid points are basically the same, indicating that the present invention can maintain good aggregation accuracy and result reliability while improving processing speed.

[0066] It should be noted that the above figures are merely illustrative of the processes included in the method according to exemplary embodiments of the present invention, and are not intended to be limiting. It is readily understood that the processes shown in the above figures do not indicate or limit the temporal order of these processes. Furthermore, it is readily understood that these processes may, for example, be executed synchronously or asynchronously in multiple modules.

[0067] It should be noted that although several modules or units of the device for performing actions have been mentioned in the detailed description above, this division is not mandatory. In fact, according to embodiments of the present invention, the features and functions of two or more modules or units described above can be embodied in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided and embodied by multiple modules or units.

[0068] Figure 5 A schematic diagram of an electronic device suitable for implementing embodiments of the present invention is shown.

[0069] It should be noted that, Figure 5 The electronic device 1000 shown is merely an example and should not be construed as limiting the functionality and scope of use of the embodiments of the present invention.

[0070] like Figure 5 As shown, the electronic device 1000 includes a Central Processing Unit (CPU) 1001, which can perform various appropriate actions and processes based on programs stored in Read-Only Memory (ROM) 1002 or programs loaded from storage section 1008 into Random Access Memory (RAM) 1003. The RAM 1003 also stores various programs and data required for system operation. The CPU 1001, ROM 1002, and RAM 1003 are interconnected via a bus 1004. An Input / Output (I / O) interface 1005 is also connected to the bus 1004. Furthermore, the electronic device 1000 also includes an FPGA device and a System-on-a-Chip (SoC) device.

[0071] The following components are connected to I / O interface 1005: an input section 1006 including a keyboard, mouse, etc.; an output section 1007 including a cathode ray tube (CRT), liquid crystal display (LCD), etc., and speakers, etc.; a storage section 1008 including a hard disk, etc.; and a communication section 1009 including a network interface card such as a LAN (Local Area Network) card, modem, etc. The communication section 1009 performs communication processing via a network such as the Internet. A drive 1010 is also connected to I / O interface 1005 as needed. Removable media 1011, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., are installed on drive 1010 as needed so that computer programs read from them can be installed into storage section 1008 as needed.

[0072] In particular, according to embodiments of the present invention, the processes described below with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of the present invention include a computer program product comprising a computer program carried on a storage medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication section 1009, and / or installed from removable medium 1011. When the computer program is executed by central processing unit (CPU) 1001, it performs various functions defined in the system of this application.

[0073] Specifically, the aforementioned electronic devices can be airborne intelligent electronic devices.

[0074] It should be noted that the storage medium shown in the embodiments of the present invention can be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. A computer-readable storage medium can be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), flash memory, optical fiber, portable compact disc read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In the present invention, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In the present invention, a computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, wherein computer-readable program code is carried. Such transmitted data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. The computer-readable signal medium can also be any storage medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the storage medium can be transmitted using any suitable medium, including but not limited to wireless, wired, etc., or any suitable combination thereof.

[0075] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram or flowchart, and combinations of blocks in a block diagram or flowchart, may be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0076] The units described in the embodiments of the present invention can be implemented in software or hardware, and the described units can also be located in a processor. The names of these units do not necessarily limit the specific unit itself.

[0077] It should be noted that, as another aspect, this application also provides a storage medium, which may be included in an electronic device or may exist independently without being assembled into the electronic device. The aforementioned storage medium carries one or more programs, which, when executed by an electronic device, cause the electronic device to perform the methods described in the following embodiments. For example, the electronic device may perform... Figure 1 The steps of the method shown.

[0078] In one embodiment, this application provides a computer program product including a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.

[0079] Furthermore, the above figures are merely illustrative of the processes included in the method according to exemplary embodiments of the present invention, and are not intended to be limiting. It is readily understood that the processes shown in the above figures do not indicate or limit the temporal order of these processes. Additionally, it is readily understood that these processes may be executed synchronously or asynchronously, for example, in multiple modules.

[0080] Other embodiments of the invention will readily occur to those skilled in the art upon consideration of the specification and practice of the invention herein. This application is intended to cover any variations, uses, or adaptations of the invention that follow the general principles of the invention and include common knowledge or customary techniques in the art not disclosed herein. The specification and embodiments are to be considered exemplary only, and the true scope and spirit of the invention are indicated by the claims.

[0081] It should be understood that the present invention is not limited to the precise structure described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of the invention is limited only by the appended claims.

Claims

1. A radar signal point aggregation method based on GPU and sparse tables, characterized in that, The method includes: Acquire a set of radar signal points; wherein the set of points includes: the distance and signal-to-noise ratio of each point; Based on the GPU, the point data is sorted according to distance to obtain a distance sequence and a signal-to-noise ratio sequence; Based on the distance sequence, the left and right boundary indices of each point are extracted in parallel using GPU to obtain the left and right distance neighborhood intervals corresponding to each point. The signal-to-noise ratio of each point in the signal-to-noise ratio sequence is recursively calculated layer by layer according to the increasing interval length to construct a sparse signal-to-noise ratio table; The maximum signal-to-noise ratio (SNR) of each point is obtained in the corresponding left and right distance neighborhood intervals using a sparse SNR table, and the query results are obtained. The valid points are then determined based on the query results and the SNR of each point.

2. The method according to claim 1, characterized in that, The method of sorting the point data according to distance based on GPU to obtain a distance sequence and a signal-to-noise ratio sequence includes: The distance between each point is used as the sorting criterion, and the signal-to-noise ratio of each point is used as the accompanying data. Based on the distance of each point, the GPU is used to perform parallel ascending sorting to obtain a distance sequence arranged by distance; Based on the sorting sequence of distances to each point, the signal-to-noise ratio of each point is adjusted synchronously to obtain a signal-to-noise ratio sequence corresponding to the distance sequence.

3. The method according to claim 1, characterized in that, The method based on distance sequences utilizes GPU parallel extraction of the left and right boundary indices of each point to obtain the left and right distance neighborhood intervals corresponding to each point, including: Allocate one GPU thread for each point in the distance sequence; The allocated GPU threads are used to execute the binary search algorithm in parallel to find the positions of the farthest left and right points whose distance difference from the current point is within a preset threshold range, and their indices are determined as the left boundary index and the right boundary index, respectively. Based on the left boundary index and the right boundary index, determine the left distance neighborhood interval and the right distance neighborhood interval corresponding to each point.

4. The method according to claim 1, characterized in that, The signal-to-noise ratio (SNR) of each point in the SNR sequence is recursively calculated layer by layer according to the increasing interval length to construct a sparse SNR table, including: The signal-to-noise ratio of each point in the signal-to-noise ratio sequence is directly written into the initial layer of the sparse table, with each table entry corresponding to the signal-to-noise ratio of a point. For each entry in each layer, the maximum signal-to-noise ratio of the two adjacent sub-intervals in the previous layer is queried, and the larger value is taken as the entry in the current layer; wherein, the interval length of each layer is twice the interval length of the previous layer; The data construction of all layers of the sparse table is completed by recursively calculating layer by layer; serial calculation is used between layers, and parallel calculation is used within each layer.

5. The method according to claim 1, characterized in that, The maximum signal-to-noise ratio (SNR) of each point within its corresponding left and right distance neighborhood intervals is obtained using a sparse SNR table, resulting in query results including: Construct an indexed logarithmic table based on the trace index in the trace data; Divide the left distance neighborhood interval and the right distance neighborhood interval into two sub-intervals respectively, and obtain the interval level from the index logarithm table according to the length of the sub-intervals; The maximum signal-to-noise ratio (SNR) value within each sub-interval is obtained from the SNR sparse table based on the interval hierarchy. The larger of the two maximum signal-to-noise ratio values ​​corresponding to the two sub-intervals is determined as the maximum signal-to-noise ratio within the neighborhood interval.

6. The method according to claim 1, characterized in that, The step of determining valid traces based on the query results and the signal-to-noise ratio of each trace includes: When the maximum signal-to-noise ratio (SNR) in both the left and right distance neighborhood intervals is greater than the SNR of the current point, the current point is determined as a valid point.

7. The method according to claim 5, characterized in that, The indexed logarithmic table includes: in, express The element indices in the array start from 0; This represents the index of a point in the point data.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored executable program, wherein, when the executable program is executed, it controls the device on which the storage medium is located to perform the method according to any one of claims 1 to 7.

9. A computer program product, characterized in that, Includes a computer program that, when executed by a processor, implements the method according to any one of claims 1 to 7.

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