CN112213709B - Method for grouping simulated airborne data based on coherent pulse train - Google Patents
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
- G01S7/02—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00
- G01S7/41—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00 using analysis of echo signal for target characterisation; Target signature; Target cross-section
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
- G01S7/02—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00
- G01S7/41—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00 using analysis of echo signal for target characterisation; Target signature; Target cross-section
- G01S7/414—Discriminating targets with respect to background clutter
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Abstract
The invention discloses an imitation airborne data grouping method based on coherent pulse trains, which comprises the following steps: step 1, data interpretation of an original binary file: step 2, determining the pulse number in the coherent pulse train: step 3, determining the initial pulse of the first coherent pulse train: step 4, carrying out interframe data decomposition; and 5, grouping the data in the frame according to the coherent pulse train. The invention discloses an imitation airborne data grouping method based on coherent pulse trains, which takes the coherent pulse trains determined by an imitation airborne mode as basic units and carries out interframe grouping and intraframe grouping on imitation airborne data, so that the imitation airborne data is converted into data which can be directly used for sea clutter space-time two-dimensional processing, and reliable guarantee is provided for subsequent data analysis.
Description
Technical Field
The invention belongs to the field of sea clutter measurement, and particularly relates to an imitation airborne data grouping method based on a coherent pulse train in the field.
Background
When the shore-based multi-channel radar is used for realizing the simulated airborne motion, the moving distance of the transmitting subarray is limited due to the limited length of the antenna of the array, namely, the pulse for simulating the airborne motion is limited, but in the actual measurement, when the transmitting subarray moves to the tail of the array, the transmitting subarray jumps to the array to start moving when the transmitting subarray moves to the next pulse. Therefore, the actually recorded simulated airborne data is formed by combining a plurality of coherent pulse trains, and how to effectively and accurately group a plurality of groups of coherent pulse trains is one of the key technologies for performing the sea clutter space-time two-dimensional characteristic analysis.
The shore-based multi-channel radar adopts the following time sequence design when realizing the airborne imitation function: each measurement data is composed of a plurality of frames of data, each frame of data contains a plurality of pulses, the pulses in the frames are coherent, the pulses between the frames are non-coherent, and each recording data is not the beginning of one frame. At present, no platform for realizing the simulation airborne sea clutter measurement by using a shore-based multi-channel radar exists in China, no corresponding processing scheme is provided for the problem of coherent pulse trains related to simulation airborne data, the American mountain top plan only provides the simulation airborne data which is preprocessed and contains one coherent pulse train, and no description is provided for the grouping of the coherent pulse trains on the simulation airborne data. Therefore, how to orderly and completely divide the measured simulated airborne data into simulated airborne data which can be used for processing is one of the problems which need to be solved urgently.
Disclosure of Invention
The invention aims to solve the technical problem of providing an imitation airborne data grouping method based on a coherent pulse train.
The invention adopts the following technical scheme:
the improvement of an imitation airborne data grouping method based on coherent pulse trains is that the method comprises the following steps:
converting the binary file into a three-dimensional data matrix, which comprises the following specific steps:
step 11, clearing a binary file organization structure, wherein multi-channel radar data consists of data transmitted by three optical fibers, each pulse data consists of a packet header starting K code, packet header information, a plurality of distance sampling values and a packet header ending K code, and a single distance sampling value consists of I, Q two paths of data of 4 channels;
step 3, determining the initial pulse of the first coherent pulse train:
step 31, reading a pulse sequence number corresponding to the first pulse from frame header information of the data;
step 32, performing a complementation operation on the pulse serial number corresponding to the first pulse and the pulse number contained in the coherent pulse train determined in the step 2, wherein the complementation operation result is the position of the first pulse in the read data in the complete coherent pulse train;
step 33, subtracting the remainder operation value in step 32 from the number of pulses included in the coherent pulse train determined in step 2 to obtain an initial pulse sequence of the complete coherent pulse train;
and 4, performing interframe data decomposition: according to the pulse serial number corresponding to the initial pulse in the coherent pulse train determined in the step 3, the read pulse can be subjected to interframe decomposition, and the method specifically comprises the following steps:
step 41, reading the packet header information of all pulses in the read data, and separating the pulse serial number of the pulse in each frame of data from the packet header information;
and 42, decomposing first frame data: decomposing first frame data based on the two pulse counting flag bits by the initial pulse sequence number of the first coherent pulse train obtained in the step 3 and the pulse sequence number of each frame data starting from 0;
and 43, decomposing the data of the last frame: finding out the position of the pulse with the last pulse sequence being 0 from the pulse sequence numbers, and taking the last pulse from the position of the pulse to the read data as the last frame data;
and step 44, decomposing intermediate frame data: finding out all positions with the pulse serial number of 0 based on the pulse serial numbers, and performing frame data decomposition by taking the pulse number set in each frame of data as an interval from the position with the first pulse serial number of 0 to the position with the last pulse serial number of 0;
and 5, grouping intra-frame data according to the coherent pulse trains:
and (3) decomposing the pulses contained in each frame of data based on the number of pulses contained in the coherent pulse train in the step (2), and decomposing the pulses frame by frame until the final decomposition of the data is completed.
Further, in step 12, a cell structure in matlab is used for storage, each cell structure includes one channel data, and each channel data is composed of a distance unit and a pulse number.
The invention has the beneficial effects that:
the invention discloses an imitation airborne data grouping method based on coherent pulse trains, which takes the coherent pulse trains determined by an imitation airborne mode as basic units and carries out interframe grouping and intraframe grouping on imitation airborne data, so that the imitation airborne data is converted into data which can be directly used for sea clutter space-time two-dimensional processing, and reliable guarantee is provided for subsequent data analysis.
The invention discloses an imitation airborne data grouping method based on a coherent pulse train, which is characterized in that a reasonable multi-channel data structure is established, and multi-channel data are grouped according to the coherent pulse train on the basis, so that data which can be directly used for sea clutter space-time two-dimensional characteristic analysis are obtained, and a data basis is laid for the sea clutter space-time two-dimensional characteristic analysis.
Drawings
FIG. 1 is a schematic flow chart of the method disclosed in example 1 of the present invention;
FIG. 2 is a schematic diagram of a data structure of a single fiber;
FIG. 3 is a diagram illustrating the determination of the number of pulses in the coherent pulse train in step 2 in the method disclosed in embodiment 1 of the present invention;
FIG. 4 is a schematic illustration of a lightning control sequence number and a pulse sequence number.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be described in further detail below with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
in order to avoid multi-channel radar data dislocation, the current data is stored by adopting three optical fiber data, each channel contains 4 channel data, each channel data is divided into I channel data and Q channel data, and for convenience in processing, the current radar data adopts a frame design mode, namely each frame of data contains a plurality of pulse echoes, pulses in the frame are coherent, and the frames are non-coherent.
The data file interpretation is to interpret an original binary file into a file structure required to be processed, mainly to convert the binary file into a three-dimensional data matrix, and the specific steps are as follows:
step 11, clearing a binary file organization structure, wherein multi-channel radar data consists of data transmitted by three optical fibers, the data structure transmitted by each optical fiber is shown in fig. 2, the data consists of a plurality of pulse echo sampling values, each pulse data consists of a packet header start K code, packet header information, a plurality of distance sampling values and a packet header end K code, and a single distance sampling value consists of I, Q two paths of data of 4 channels;
and step 12, respectively interpreting the three optical fiber files based on the binary file organization structure, and finally storing the three optical fiber files in a three-dimensional matrix structure, wherein one dimension is a space dimension (channel), one dimension is a slow time dimension (pulse), and the other dimension is a fast time dimension (distance unit). Storing by adopting a cell structure in matlab, wherein each cell structure comprises channel data, and each channel data consists of a distance unit and a pulse number;
as shown in fig. 3, the total number of the antenna subarrays is 12, and the transmission subarray includes 6 elements, which illustrates the determination process of the complete coherent pulse number. The transmitting subarray moves a distance between subarrays in a pulse repetition period, when the transmitting subarray moves to the tail end of an antenna array, 7 pulses are in total, namely when the transmitting subarray comprises 6 subarrays and the pulses move 1 row, the number of pulses in the coherent pulse train is determined to be 7.
Step 3, determining the initial pulse of the first coherent pulse train:
since the recorded first pulse is not the first pulse corresponding to the start of the frame, the complete coherent pulse train cannot be determined, and therefore the pulse sequence number corresponding to the start pulse of the first coherent pulse train needs to be determined. And (3) reversely deducing the pulse serial number corresponding to the initial pulse of the first complete coherent pulse train according to the read pulse serial number corresponding to the first pulse and the pulse number determined in the step (2).
The number of pulses is set to 100, the number of pulses read in the interpreted data is 1300, under the above parameters, the pulse serial number of each pulse and the radar control serial number corresponding to each frame are as shown in fig. 4, the initial serial number of the pulse serial number corresponding to each pulse is 0, the maximum number of pulses is the set value minus 1, the pulse serial number shows periodic change along with the number of pulses, and the radar control serial number corresponding to a frame shows step increase along with the increase of the number of frames. Based on the data characteristics, the pulse serial number corresponding to the first pulse in the complete coherent pulse train can be determined, and the specific steps are as follows:
step 31, reading a pulse sequence number corresponding to the first pulse from frame header information of the data;
step 32, performing a complementation operation on the pulse serial number corresponding to the first pulse and the pulse number contained in the coherent pulse train determined in the step 2, wherein the complementation operation result is the position of the first pulse in the read data in the complete coherent pulse train;
step 33, subtracting the remainder operation value in step 32 from the number of pulses included in the coherent pulse train determined in step 2 to obtain an initial pulse sequence of the complete coherent pulse train;
and 4, performing interframe data decomposition:
because radar data is stored according to a frame mode, the first pulse of read data is not a frame starting pulse, inter-frame data needs to be decomposed when the number of pulses set in a frame is large, and the first task of data decomposition is to separate the number of pulses from frame to frame because the pulses between frames are not related. According to the pulse serial number corresponding to the initial pulse in the coherent pulse train determined in the step 3, the read pulse can be subjected to interframe decomposition, and the method specifically comprises the following steps:
step 41, reading the packet header information of all pulses in the read data, and separating the relative number of the pulses in each frame of data from the packet header information: a pulse sequence number;
and 42, decomposing first frame data: decomposing first frame data based on the two pulse counting flag bits by the initial pulse sequence number of the first coherent pulse train obtained in the step 3 and the pulse sequence number of each frame data starting from 0;
and 43, decomposing the data of the last frame: finding out the position of the pulse with the last pulse sequence being 0 from the pulse sequence numbers, and taking the last pulse from the position of the pulse to the read data as the last frame data;
and step 44, decomposing intermediate frame data: finding out all positions with the pulse serial number of 0 based on the pulse serial numbers, and performing frame data decomposition by taking the pulse number set in each frame of data as an interval from the position with the first pulse serial number of 0 to the position with the last pulse serial number of 0;
as can be seen from the above decomposition results, the numbers of coherent bursts included in the first frame data, the last frame data, and the middle frame data are not the same.
And 5, grouping intra-frame data according to the coherent pulse trains:
the complete coherent pulse number in the imitation airborne mode is determined by the length of the antenna, and the movement of the sub-array of the multi-channel radar is carried out in a frame, so the setting of the pulse number in the frame is required to be larger than the complete coherent pulse number corresponding to the imitation airborne sub-mode.
As can be seen from step 4, the initial pulse of the data in each frame is the echo pulse corresponding to the first transmit subarray, so that the pulses included in each frame of data are decomposed on the basis of the number of pulses included in the coherent pulse train in step 2, and the decomposition is performed frame by frame until the final decomposition of the data is completed.
Claims (2)
1. An imitation airborne data grouping method based on coherent pulse trains is characterized by comprising the following steps:
step 1, data interpretation of an original binary file:
converting the binary file into a three-dimensional data matrix, which comprises the following specific steps:
step 11, clearing a binary file organization structure, wherein multi-channel radar data consists of data transmitted by three optical fibers, each pulse data consists of a packet header starting K code, packet header information, a plurality of distance sampling values and a packet header ending K code, and a single distance sampling value consists of I, Q two paths of data of 4 channels;
step 12, based on the binary file organization structure, respectively interpreting the three optical fiber files, and finally storing the three optical fiber files in a three-dimensional matrix structure, wherein one dimension is a space dimension, one dimension is a slow time dimension, and one dimension is a fast time dimension;
step 2, determining the pulse number in the coherent pulse train: after the transmitting subarray moves from the array start to the array end, the transmitting subarray jumps to the array start again to move, and the number of pulses is determined by the length of the array antenna and the number of columns contained in the transmitting subarray;
step 3, determining the initial pulse of the first coherent pulse train:
step 31, reading a pulse sequence number corresponding to the first pulse from frame header information of the data;
step 32, performing a complementation operation on the pulse serial number corresponding to the first pulse and the pulse number contained in the coherent pulse train determined in the step 2, wherein the complementation operation result is the position of the first pulse in the read data in the complete coherent pulse train;
step 33, subtracting the remainder operation value in step 32 from the number of pulses included in the coherent pulse train determined in step 2 to obtain an initial pulse sequence of the complete coherent pulse train;
and 4, performing interframe data decomposition: according to the pulse serial number corresponding to the initial pulse in the coherent pulse train determined in the step 3, the read pulse can be subjected to interframe decomposition, and the method specifically comprises the following steps:
step 41, reading the packet header information of all pulses in the read data, and separating the pulse serial number of the pulse in each frame of data from the packet header information;
and 42, decomposing first frame data: decomposing first frame data based on the two pulse counting flag bits by the initial pulse sequence number of the first coherent pulse train obtained in the step 3 and the pulse sequence number of each frame data starting from 0;
and 43, decomposing the data of the last frame: finding out the position of the pulse with the last pulse sequence being 0 from the pulse sequence numbers, and taking the last pulse from the position of the pulse to the read data as the last frame data;
and step 44, decomposing intermediate frame data: finding out all positions with the pulse serial number of 0 based on the pulse serial numbers, and performing frame data decomposition by taking the pulse number set in each frame of data as an interval from the position with the first pulse serial number of 0 to the position with the last pulse serial number of 0;
and 5, grouping intra-frame data according to the coherent pulse trains:
and (3) decomposing the pulses contained in each frame of data based on the number of pulses contained in the coherent pulse train in the step (2), and decomposing the pulses frame by frame until the final decomposition of the data is completed.
2. The method for grouping airborne-imitation data based on coherent pulse trains according to claim 1, characterized in that: in step 12, cell structures in matlab are used for storage, each cell structure contains one channel data, and each channel data is composed of a distance unit and a pulse number.
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Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US4593286A (en) * | 1983-04-25 | 1986-06-03 | Westinghouse Electric Corp. | Method of operating an agile beam coherent radar |
| EP0932836A1 (en) * | 1997-07-22 | 1999-08-04 | Thomson Csf | Method for pulse compression with a stepped frequency waveform |
| WO2002044752A1 (en) * | 2000-12-01 | 2002-06-06 | Klaus Bibl | Iterative spectrum analysis |
| CN1496015A (en) * | 2002-08-08 | 2004-05-12 | 汤姆森许可贸易公司 | Regulating method for signal receiver and relevant receiver |
| WO2018045566A1 (en) * | 2016-09-09 | 2018-03-15 | 深圳大学 | Random pulse doppler radar angle-doppler imaging method based on compressed sensing |
| CN108318866A (en) * | 2018-01-22 | 2018-07-24 | 西安电子科技大学 | Ocean clutter cancellation method based on the joint accumulation of multiframe echo |
| CN108919213A (en) * | 2018-08-06 | 2018-11-30 | 中国航空工业集团公司雷华电子技术研究所 | A kind of airborne radar synchronization on-line analysis system |
| CN110058194A (en) * | 2019-04-01 | 2019-07-26 | 四川九洲防控科技有限责任公司 | The orientation of target determines method and computer readable storage medium |
| CN110501685A (en) * | 2019-08-23 | 2019-11-26 | 北京电子工程总体研究所 | A kind of multiframe correlative accumulation method based on radar signal phase compensation |
| CN110907907A (en) * | 2019-10-19 | 2020-03-24 | 中国电波传播研究所（中国电子科技集团公司第二十二研究所） | Sea clutter Doppler spectrum characteristic analysis and comparison method |
-
2020
- 2020-10-17 CN CN202011113823.7A patent/CN112213709B/en active Active
Patent Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US4593286A (en) * | 1983-04-25 | 1986-06-03 | Westinghouse Electric Corp. | Method of operating an agile beam coherent radar |
| EP0932836A1 (en) * | 1997-07-22 | 1999-08-04 | Thomson Csf | Method for pulse compression with a stepped frequency waveform |
| WO2002044752A1 (en) * | 2000-12-01 | 2002-06-06 | Klaus Bibl | Iterative spectrum analysis |
| CN1496015A (en) * | 2002-08-08 | 2004-05-12 | 汤姆森许可贸易公司 | Regulating method for signal receiver and relevant receiver |
| WO2018045566A1 (en) * | 2016-09-09 | 2018-03-15 | 深圳大学 | Random pulse doppler radar angle-doppler imaging method based on compressed sensing |
| CN108318866A (en) * | 2018-01-22 | 2018-07-24 | 西安电子科技大学 | Ocean clutter cancellation method based on the joint accumulation of multiframe echo |
| CN108919213A (en) * | 2018-08-06 | 2018-11-30 | 中国航空工业集团公司雷华电子技术研究所 | A kind of airborne radar synchronization on-line analysis system |
| CN110058194A (en) * | 2019-04-01 | 2019-07-26 | 四川九洲防控科技有限责任公司 | The orientation of target determines method and computer readable storage medium |
| CN110501685A (en) * | 2019-08-23 | 2019-11-26 | 北京电子工程总体研究所 | A kind of multiframe correlative accumulation method based on radar signal phase compensation |
| CN110907907A (en) * | 2019-10-19 | 2020-03-24 | 中国电波传播研究所（中国电子科技集团公司第二十二研究所） | Sea clutter Doppler spectrum characteristic analysis and comparison method |
Non-Patent Citations (15)
| Title |
|---|
| Applicability of sea clutter models in nonequilibrium sea conditions;Zhang Yu-shi 等;《IET International Radar Conference 2009》;20091231;第1-4页 * |
| Jamming wideband radar using interrupted-sampling repeater;Dejun Feng 等;《IEEE Transactions on Aerospace and Electronic Systems》;20170217;第1341-1354页 * |
| Performance assessment of multi-channel radars using simulated sea clutter;Stéphane Kemkemian 等;《2015 IEEE Radar Conference (RadarCon)》;20150625;第1015-1020页 * |
| Radar signals with ZACZ based on pairs of D-code sequences and their compression algorithm;Roman N. Ipanov 等;《IEEE Signal Processing Letters》;20180829;20180829 * |
| 一种快速的基于压缩感知的多普勒高分辨方法;刘寅 等;《西安电子科技大学学报》;20140615;第38卷(第2期);第82-87页 * |
| 临近空间高超声速目标点迹处理研究;李定山 等;《航天电子对抗》;20121031;第28卷(第5期);第16-19页 * |
| 基于单帧调频步进脉冲串的高速目标运动补偿算法;刘二平 等;《无线电工程》;20170831;第14-17、26页 * |
| 基于奇异值分解的雷达微小目标检测方法;吴琳拥 等;《电子科技大学学报》;20190531;第48卷(第3期);第326-330页 * |
| 基于相参脉冲序列的频率精测量;熊波 等;《电子信息对抗技术》;20190531;第19-23页 * |
| 基于集群架构的地海杂波数据存储系统;张浙东 等;《现代雷达》;20190531;第41卷(第5期);第52-57、85页 * |
| 多帧相参积累的检测前跟踪方法;王瑞军 等;《计算机工程与应用》;20111231;第134-136、139页 * |
| 相参脉冲串多普勒频率变化率估计算法;张刚兵 等;《数据采集与处理》;20100930;第25卷(第5期);第560-564页 * |
| 相参雷达K分布海杂波背景下非相干积累恒虚警检测方法;张坤 等;《电子与信息学报》;20200731;第42卷(第7期);第1627-1635页 * |
| 雷达信号相参脉冲串频率测量方法研究;卢鑫 等;《航天电子对抗》;20091231;第25卷(第6期);第30-32、61页 * |
| 高速大容量数据记录仪的设计与实现;杨政军;《中国优秀博硕士学位论文全文数据库(硕士)信息科技辑》;20140615;第1-67页 * |
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