CN112241005A - Method and device for compressing radar detection data and storage medium - Google Patents
Method and device for compressing radar detection data and storage medium Download PDFInfo
- Publication number
- CN112241005A CN112241005A CN201910656547.XA CN201910656547A CN112241005A CN 112241005 A CN112241005 A CN 112241005A CN 201910656547 A CN201910656547 A CN 201910656547A CN 112241005 A CN112241005 A CN 112241005A
- Authority
- CN
- China
- Prior art keywords
- data
- key
- index
- detection
- determining
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000001514 detection method Methods 0.000 title claims abstract description 296
- 238000000034 method Methods 0.000 title claims abstract description 50
- 239000000523 sample Substances 0.000 claims abstract description 48
- 238000007906 compression Methods 0.000 claims abstract description 43
- 230000006835 compression Effects 0.000 claims abstract description 43
- 230000009466 transformation Effects 0.000 claims abstract description 26
- 239000011159 matrix material Substances 0.000 claims description 157
- 230000015654 memory Effects 0.000 claims description 19
- 238000004891 communication Methods 0.000 claims description 16
- 238000004590 computer program Methods 0.000 claims description 9
- 238000012163 sequencing technique Methods 0.000 claims description 8
- 238000013139 quantization Methods 0.000 claims description 3
- 230000005540 biological transmission Effects 0.000 abstract description 18
- 238000012545 processing Methods 0.000 abstract description 17
- 230000009286 beneficial effect Effects 0.000 abstract description 4
- 238000013144 data compression Methods 0.000 description 12
- 230000002093 peripheral effect Effects 0.000 description 10
- 230000001133 acceleration Effects 0.000 description 9
- 238000010586 diagram Methods 0.000 description 7
- 230000006870 function Effects 0.000 description 7
- 230000008569 process Effects 0.000 description 6
- 238000005070 sampling Methods 0.000 description 6
- 230000003287 optical effect Effects 0.000 description 5
- 230000003247 decreasing effect Effects 0.000 description 3
- 238000013473 artificial intelligence Methods 0.000 description 2
- 239000000919 ceramic Substances 0.000 description 2
- 238000003672 processing method Methods 0.000 description 2
- 230000002159 abnormal effect Effects 0.000 description 1
- 230000009471 action Effects 0.000 description 1
- 238000006243 chemical reaction Methods 0.000 description 1
- 238000013500 data storage Methods 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 238000000605 extraction Methods 0.000 description 1
- 230000002349 favourable effect Effects 0.000 description 1
- 230000004927 fusion Effects 0.000 description 1
- 230000003993 interaction Effects 0.000 description 1
- 230000001788 irregular Effects 0.000 description 1
- 239000004973 liquid crystal related substance Substances 0.000 description 1
- 238000010801 machine learning Methods 0.000 description 1
- 239000000463 material Substances 0.000 description 1
- 230000005055 memory storage Effects 0.000 description 1
- 238000010295 mobile communication Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000009467 reduction Effects 0.000 description 1
- 238000009877 rendering Methods 0.000 description 1
- 230000002441 reversible effect Effects 0.000 description 1
- 238000011895 specific detection Methods 0.000 description 1
- 230000006641 stabilisation Effects 0.000 description 1
- 238000011105 stabilization Methods 0.000 description 1
Images
Classifications
-
- 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
- G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
- G01S13/02—Systems using reflection of radio waves, e.g. primary radar systems; Analogous systems
- G01S13/50—Systems of measurement based on relative movement of target
-
- 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
-
- H—ELECTRICITY
- H03—ELECTRONIC CIRCUITRY
- H03M—CODING; DECODING; CODE CONVERSION IN GENERAL
- H03M7/00—Conversion of a code where information is represented by a given sequence or number of digits to a code where the same, similar or subset of information is represented by a different sequence or number of digits
- H03M7/30—Compression; Expansion; Suppression of unnecessary data, e.g. redundancy reduction
Abstract
The application discloses a compression method and device of radar detection data and a storage medium, and belongs to the field of data processing. The method comprises the following steps: generating a detection data frame according to radar detection data, wherein the detection data frame comprises K data detected by M antennas of a radar in each period of N periods, and the M, N, K are integers; performing a lossless transform on the probe data frame; acquiring key detection data and a key data index according to a target detection result of the detection data frame subjected to lossless transformation; and determining compressed radar detection data according to the key detection data and the key data index. The method and the device can improve the quality of the compressed radar detection data, and solve the problem that the data volume of the original radar detection data is large, and the data volume is not beneficial to being transmitted to the computer equipment through the data transmission interface.
Description
Technical Field
The present disclosure relates to the field of data processing, and in particular, to a method and an apparatus for compressing radar detection data, and a storage medium.
Background
Currently, radar can be used as a stand-alone sensing device. That is, the radar can detect to obtain radar detection data, process the radar detection data to obtain target point information, and output the target point information to the computer equipment. The target point information may include a speed of the target point, a distance between the target point and the radar, and azimuth information of the target point, among others. In other words, the computer device does not store the raw radar detection data, but directly stores the target point information. Once the processed target point information is abnormal, the original radar detection data cannot be traced back, and the problem is difficult to troubleshoot. However, the data amount of the radar detection data is often large, which is not beneficial to be transmitted to the computer device through the data transmission interface, and therefore, the radar detection data needs to be compressed and the compressed data needs to be output to the computer device.
Disclosure of Invention
The application provides a compression method, a compression device and a storage medium of radar detection data, which can solve the problem that the original radar detection data is not favorable for being transmitted to computer equipment through a data transmission interface due to large data volume in the related technology. The technical scheme is as follows:
in one aspect, a method for compressing radar detection data is provided, and the method includes:
generating a detection data frame according to radar detection data, wherein the detection data frame comprises K data detected by M antennas of a radar in each period of N periods, and the M, N, K are integers;
performing a lossless transform on the probe data frame;
acquiring key detection data and a key data index according to a target detection result of the detection data frame subjected to lossless transformation;
and determining compressed radar detection data according to the key detection data and the key data index.
In a possible implementation manner, the acquiring key probe data and key data index according to a target detection result of a probe data frame after lossless transform includes:
generating a two-dimensional data matrix according to the detection data frame subjected to lossless transformation;
determining a power matrix according to the two-dimensional data matrix, performing target detection on each data unit in the power matrix, and taking a position index of the data unit passing the target detection as the key data index; or, performing target detection on each data unit in the two-dimensional data matrix, and taking the position index of the data unit passing the target detection as the key data index;
and acquiring the key detection data from the detection data frame subjected to lossless transformation according to the key data index.
In one possible implementation manner, the determining compressed radar detection data according to the key detection data and the key data index includes:
and determining the key detection data and the key data index as compressed radar detection data.
In one possible implementation manner, the determining compressed radar detection data according to the key detection data and the key data index includes:
determining a signal-to-noise ratio corresponding to each key data index according to the power matrix;
sorting the key data indexes according to the sequence of the signal-to-noise ratio from large to small;
multiplying the total number of data units included in the power matrix by a reference compression ratio to obtain L;
and determining data corresponding to the first L indexes in the index sequencing result and the first L indexes as compressed radar detection data.
In one possible implementation manner, the determining compressed radar detection data according to the key detection data and the key data index includes:
and determining the key detection data, the key data index and the power matrix as compressed radar detection data.
In one possible implementation manner, the determining compressed radar detection data according to the key detection data and the key data index includes:
determining a signal-to-noise ratio corresponding to each key data index according to the power matrix;
sorting the key data indexes according to the sequence of the signal-to-noise ratio from large to small;
multiplying the total number of data units included in the power matrix by a reference compression ratio to obtain L;
compressing the power matrix according to the maximum power and the minimum power in the power matrix;
and determining key detection data corresponding to the first L indexes in the index sorting result, the first L indexes and the compressed power matrix as compressed radar detection data.
In one possible implementation, the compressing the power matrix according to the maximum power and the minimum power in the power matrix includes:
and quantizing each data unit in the power matrix according to the maximum power and the minimum power in the power matrix, and taking a matrix formed by the quantized data units as a compressed power matrix.
In another aspect, an apparatus for compressing radar detection data is provided, the apparatus including:
a generation module, configured to generate a sounding data frame according to radar sounding data, where the sounding data frame includes K data detected by M antennas of a radar in each of N periods, and M, N, K are integers;
the lossless transformation module is used for carrying out lossless transformation on the detection data frame;
the acquisition module is used for acquiring key detection data and key data indexes according to a target detection result of the detection data frame subjected to lossless transformation;
and the determining module is used for determining the compressed radar detection data according to the key detection data and the key data index.
In one possible implementation manner, the obtaining module includes:
the generating submodule is used for generating a two-dimensional data matrix according to the detection data frame subjected to lossless transformation;
the first determining submodule is used for determining a power matrix according to the two-dimensional data matrix;
the target detection submodule is used for carrying out target detection on each data unit in the power matrix and taking the position index of the data unit passing the target detection as the key data index; or, performing target detection on each data unit in the two-dimensional data matrix, and taking the position index of the data unit passing the target detection as the key data index;
and the obtaining submodule is used for obtaining the key detection data from the detection data frame subjected to the lossless transformation according to the key data index.
In one possible implementation, the determining module includes:
and the second determining submodule is used for determining the key detection data and the key data index as the compressed radar detection data.
In one possible implementation, the determining module includes:
the third determining submodule is used for determining the signal-to-noise ratio corresponding to each key data index according to the power matrix;
the first sequencing submodule is used for sequencing the key data indexes according to the sequence of the signal-to-noise ratio from large to small;
the first operation submodule is used for multiplying the total number of the data units included in the power matrix by a reference compression rate to obtain L;
and the fourth determining submodule is used for determining the data corresponding to the first L indexes in the index sorting result and the first L indexes as the compressed radar detection data.
In one possible implementation, the determining module includes:
and the fifth determining submodule is used for determining the key detection data, the key data index and the power matrix as the compressed radar detection data.
In one possible implementation, the determining module includes:
a sixth determining submodule, configured to determine, according to the power matrix, a signal-to-noise ratio corresponding to each key data index;
the second sorting submodule is used for sorting the key data indexes according to the sequence of the signal-to-noise ratio from large to small;
the second operation submodule is used for multiplying the total number of the data units included in the power matrix by a reference compression rate to obtain L;
the compression submodule is used for compressing the power matrix according to the maximum power and the minimum power in the power matrix;
and the seventh determining submodule is used for determining the key detection data corresponding to the first L indexes in the index sorting result, the first L indexes and the compressed power matrix as the compressed radar detection data.
In one possible implementation, the compression submodule includes:
and the quantization unit is used for quantizing each data unit in the power matrix according to the maximum power and the minimum power in the power matrix, and taking a matrix formed by the quantized data units as a compressed power matrix.
In another aspect, a computer device is provided, where the computer device includes a processor, a communication interface, a memory, and a communication bus, where the processor, the communication interface, and the memory complete mutual communication through the communication bus, the memory is used to store a computer program, and the processor is used to execute the program stored in the memory to implement the steps of the radar detection data compression method.
In another aspect, a computer-readable storage medium is provided, in which a computer program is stored, which, when being executed by a processor, implements the steps of the method for compressing radar probe data described above.
In another aspect, a computer program product comprising instructions is provided, which when run on a computer, causes the computer to perform the steps of the method of compressing radar detection data as described above.
The technical scheme provided by the application can at least bring the following beneficial effects:
according to the method and the device, the compressed radar detection data can be determined according to the key detection data and the key data index, and the key detection data are the information of the potential target points in the original radar detection data, so that the compressed radar detection data basically contain the information of most potential target points, namely, the compressed radar detection data is high in quality and small in information loss. Moreover, after data compression is performed by the method provided by the embodiment of the application, the compressed radar detection data can be transmitted to the computer equipment, so that the problem that the original radar detection data is unfavorable to be transmitted to the computer equipment through a data transmission interface due to large data quantity is solved.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings without creative efforts.
FIG. 1 is a schematic illustration of an implementation environment provided by an embodiment of the present application;
fig. 2 is a flowchart of a method for compressing radar detection data according to an embodiment of the present disclosure;
fig. 3 is a schematic diagram of a data frame provided in an embodiment of the present application;
fig. 4 is a schematic structural diagram of a compressing apparatus for radar detection data according to an embodiment of the present application;
fig. 5 is a schematic structural diagram of a computer device according to an embodiment of the present application.
Detailed Description
To make the objects, technical solutions and advantages of the present application more clear, embodiments of the present application will be described in further detail below with reference to the accompanying drawings.
Before explaining the method for compressing radar detection data provided by the embodiment of the present application in detail, an implementation environment provided by the embodiment of the present application is introduced.
Referring to FIG. 1, FIG. 1 is a schematic diagram illustrating an implementation environment in accordance with an example embodiment. The implementation environment includes a radar 101 and a computer device 102. The radar 101 may be communicatively coupled to a computer device 102. The communication connection may be a connection using a high-speed data transmission interface, which is not limited in this application.
The radar 101 may perform detection to obtain radar detection data. The radar 101 may further process the radar detection data to obtain target point information, and compress the radar detection data. The target point information and the compressed radar sounding data may then be transmitted to the computer device 102. As an example, the radar 101 may include M antennas, a radio frequency unit, and a data compression processing unit. The M antennas are used for receiving electromagnetic wave signals, and the radio frequency unit is used for generating radar detection data based on the electromagnetic wave signals received by the antennas. The data compression processing unit is configured to process the radar detection data to obtain target point information, compress the radar detection data, and transmit the target point information and the compressed radar detection data to the computer device 102.
The computer device 101 may receive target point information transmitted by the radar 101 and compressed radar detection data. The computer device 101 may also track the target point according to the target point information, and may also fuse the compressed radar detection data with the acquired image.
As an example, the radar 101 may be a millimeter wave radar, a microwave radar, and the like, and the Computer device 102 may be an electronic product that interacts with a user through one or more modes of a keyboard, a touch pad, a touch screen, a remote controller, a voice interaction device, or a handwriting device, such as a PC (Personal Computer), a mobile phone, a smart phone, a PDA (Personal Digital Assistant), a wearable device, a pocket PC (pocket PC), a tablet PC, a smart car machine, a smart television, a smart speaker, and the like.
Those skilled in the art will appreciate that the radar 101 and computer device 102 described above are merely exemplary, and that other existing or future radar or computer devices may be suitable for use in the present application and are intended to be included within the scope of the present application and are hereby incorporated by reference.
The following explains the method for compressing radar detection data provided in the embodiments of the present application in detail.
Referring to fig. 2, fig. 2 is a flowchart illustrating a method for compressing radar detection data according to an exemplary embodiment, which may be applied to the radar 101 in the implementation environment illustrated in fig. 1. The method may include the following steps.
Step 201: a sounding frame is generated from the radar sounding data, the sounding frame including K data detected by M antennas of the radar in each of N periods, M, N, K being integers.
The radar may include M antennas, each of which may acquire radar detection data, and each of which may be sampled K times in a period. Moreover, since the radar compresses data in units of data frames, the radar can generate a probe data frame from radar probe data according to a reference data format. The parameter data format specifies a period included in one data frame, i.e., N periods.
As an example, the radar detection data may be arranged according to three dimensions of sampling within a cycle, sampling during the cycle, and antenna, so as to form a data frame as shown in fig. 3. The size of the data frame shown in fig. 3 is N × K × M, that is, the data frame shown in fig. 3 includes N × K × M data units. As such, for a data unit in the data frame shown in fig. 3, the data unit may be labeled as s (k, n, m), and represents the data of the kth sampling point in the nth period of the mth antenna.
For example, assuming that the radar can perform 256 samples in each period, each data frame contains 128 periods, and the radar has 8 antennas. The size of each data frame is 256 × 128 × 8. If each sample point is expressed by 2 bytes (16 bits), the data amount of one data frame is 256 × 128 × 8 × 16 ═ 4 Mbps.
It should be noted that the radar detection data is ADC (Analog-to-Digital Converter) data, which is generally non-intuitive data, and the radar can process the ADC data to extract information of a target point in the radar field of view at the current time, where the information of the target point may generally include, but is not limited to, a speed of the target point, a distance between the target point and the radar, and orientation information of the target point.
Step 202: the frames of probe data are lossless transformed.
As one example, a fourier transform may be performed on each data in the probe data frame. The Fourier transform is a reversible transform, and no information loss exists in the transform process, so that no data loss is caused after the detection data frame is subjected to lossless transform according to the Fourier transform.
It should be noted that, in the embodiment of the present application, the lossless transform of the probe data frame may be implemented by fourier transform, and in some other embodiments, the lossless transform of the probe data frame may also be implemented by other transform manners, which is not limited in the embodiment of the present application.
Step 203: and acquiring key detection data and key data indexes according to the target detection result of the detection data frame subjected to lossless transformation.
In some embodiments, a two-dimensional data matrix may be generated from the lossless transformed probe data frame. A power matrix is determined from the two-dimensional data matrix. And performing target detection on each data unit in the power matrix, and determining the position index of the data unit passing the target detection in the power matrix as a key data index. And acquiring key detection data from the detection data frame subjected to lossless transformation according to the key data index. Or generating a two-dimensional data matrix according to the detection data frame subjected to lossless transformation. And carrying out target detection on each data unit in the two-dimensional data matrix, and taking the position index of the data unit passing the target detection as a key data index. And acquiring key detection data from the detection data frame subjected to lossless transformation according to the key data index.
For each data unit in the power matrix or the two-dimensional data matrix, if the data unit passes the target detection, it may be stated that the data unit may include information of the target point (e.g., the velocity of the target point, the distance between the target point and the radar, and the azimuth information of the target point, etc.), and the data unit is a data unit of a potential target point. Therefore, the position index of the data unit detected by the target is determined as the key data index, so that omission of key detection data can be avoided, and data loss is reduced.
It should be noted that, when performing target detection on each data unit in the power matrix or the two-dimensional data matrix, potential target detection may be performed by a constant false alarm detection method, for example, if a data unit of the power matrix or the two-dimensional data matrix is subjected to constant false alarm detection to determine that a potential target exists, the data unit passes through target detection, and if a data unit is subjected to constant false alarm detection to determine that a potential target does not exist, the data unit fails in target detection, and the data unit that fails in target detection may be removed to implement data compression. The potential target detection may also be performed by other detection methods, and a specific detection method is not limited in this application.
As an example, from the lossless transformed probe data frame, the operation of generating the two-dimensional data matrix may be: and (4) performing modulus extraction on each datum in the detection data frame subjected to lossless transformation, and accumulating the modulus-extracted datum according to the dimension of the antenna. That is, the data on the same antenna is accumulated. The accumulated data is data in two dimensions of a period and a sampling point. Thus, a two-dimensional data matrix can be generated from the accumulated data. For example, for the above-mentioned N × K × M-sized detection data frame, each datum in the detection data frame after lossless transformation is modulo, and a two-dimensional data matrix of N × K size may be obtained after accumulating the modulo data according to the antenna dimension.
As an example, the operation of determining the power matrix from the two-dimensional data matrix may be: each data cell in the two-dimensional data matrix is converted into dB form so that a power matrix can be obtained. In some embodiments, it may be in terms of 20log for each data element in a two-dimensional data matrix10|sFConvert each data unit into dB form. Wherein s isFIs a data element in a two-dimensional data matrix.
Since the two-dimensional data matrix comprises data in two dimensions of a period and a sampling point, and the power matrix is obtained by converting data units in the two-dimensional data matrix in a dB mode, the position index of the data units detected by targets in the power matrix can be used as the position index in the two-dimensional data matrix.
As an example, according to the key data index, the operation of obtaining the key probe data from the probe data frame after the lossless transform may be: and determining data with position indexes of two dimensions of a period and a sampling point as the key data indexes in the detection data frame after lossless transformation as key detection data. That is, in the probe data frame after lossless transform, the data on the M antennas corresponding to the key data index is determined as the key probe data. Exemplarily, assuming that the index of the 2 nd position in the 1 st row is included in the key data index, the data collected by the M antennas at this position may be determined as the key detection data.
It should be noted that, when performing target detection on each data unit in the power matrix or the two-dimensional data matrix, a detection threshold, which may also be referred to as a first detection threshold, is used to determine the data amount of the compressed radar detection data. The first detection threshold may be transmitted by the computer device to the radar. The first detection threshold may be set by a user according to actual needs in a self-defined manner, or may be set by default by a computer device, which is not limited in the embodiment of the present application. In addition, the first detection threshold is usually set to be relatively small, so that it can be ensured that most of the data units corresponding to the potential target points can be detected through the target.
Step 204: and determining the compressed radar detection data according to the key detection data and the key data index.
Since the key probe data is part of the data in the lossless transformed data frame, in some embodiments, the key probe data and the key data may be indexed to be determined as compressed radar probe data. Therefore, compared with the data frame after lossless conversion, the data volume of the compressed radar detection data is relatively small, so that the problems that the data volume of the original radar detection data is large and is not beneficial to being transmitted to computer equipment through a data transmission interface can be solved, the information loss amount in the compressed radar detection data can be reduced, and the data compression quality is improved.
In order to further reduce the data volume of the compressed radar probe data, the key probe data and the key data index may also be compressed. That is, the signal-to-noise ratio of each data corresponding to the key data index in the power matrix is determined. And sorting the key data indexes according to the sequence of the signal-to-noise ratio from large to small. And multiplying the total number of the data units included in the power matrix by the reference compression rate to obtain L. And determining the key detection data and the first L position indexes corresponding to the first L position indexes in the index sorting result as compressed radar detection data.
The reference compression rate is used to determine the data amount of the compressed radar probe data. And, the reference compression ratio can be sent to the radar by the computer equipment, and the reference compression ratio can be set by a user according to actual requirements in a self-defined manner or by default of the computer equipment, which is not limited in the embodiment of the present application.
For example, assuming that the first detection threshold is 6dB, the reference compression rate is 2%. From the lossless transformed probe data frame, the size of the generated two-dimensional data matrix is 256 × 128. Thus, after the total number of data units included in the power matrix is multiplied by the reference compression rate, the resultant L is 256 × 128 × 2% >, 655. Assuming that the number of data units detected by the target in the power matrix is 800, after sorting the key data indexes, the key probe data after the sorting is 655 may be discarded, and the key data indexes after the sorting is 655 may be discarded, and the remaining key probe data and key data indexes after the discarding may be used as the compressed radar probe data.
As an example, the operation of determining the signal-to-noise ratio of each data corresponding to the key data index in the power matrix may be: for a first data unit in the power matrix, an average of data units in the neighborhood of the first data unit is determined, a difference between the average and the first data unit is determined, and the difference is determined as the signal-to-noise ratio of the first data unit. The first data unit is any data unit in the power matrix.
The size of the neighborhood can be sent to the radar by the computer device, the size of the neighborhood can be set by a user according to actual needs in a self-defined mode, and the size of the neighborhood can also be set by the computer device in a default mode.
After the radar transmits the compressed radar detection data to the computer device, the computer device may need to fuse the radar detection data with the image. For this case, in addition to determining the key probe data and the key data index as compressed radar probe data, it is necessary to determine the power matrix as radar probe data. That is, the key detection data, the key data index, and the power matrix are determined as compressed radar detection data.
In order to further reduce the data volume of the compressed radar detection data, the key detection data, the index of the key detection data and the power matrix may also be compressed. That is, the signal-to-noise ratio of each data corresponding to the key data index in the power matrix is determined. And sorting the key data indexes according to the sequence of the signal-to-noise ratio from large to small. And multiplying the total number of the data units included in the power matrix by the reference compression rate to obtain L. And compressing the power matrix according to the maximum power and the minimum power in the power matrix. And determining the key detection data corresponding to the first L position indexes in the sequencing result, the first L position indexes and the compressed power matrix as compressed radar detection data.
As an example, according to the maximum power and the minimum power in the power matrix, the operation of compressing the power matrix may be: and quantizing each data unit in the power matrix according to the maximum power and the minimum power in the power matrix, and taking a matrix formed by the quantized data units as a compressed power matrix. In some embodiments, each data unit in the power matrix may be quantized according to the following formula based on the maximum power and the minimum power in the power matrix.
Wherein, in the above formula, PRD(n, k) denotes the data unit in the nth row and kth column of the power matrix, max (P)RD) Represents the maximum value in the power matrix, min (P)RD) Represents the minimum value in the power matrix and B represents the number of quantized bits.
B is used to determine the data amount of the compressed radar detection data. And B may be sent to the radar by the computer device. The setting of B may be customized by a user according to actual needs, or may be set by default by a computer device, which is not limited in the embodiment of the present application.
Since the key probe data is complex data, 32-bit floating-point type is usually adopted to represent the real part and the imaginary part, respectively, and assuming that B is 12, the data size of the key probe data corresponding to the first 655 position indexes is 655 × 8 × 32 × 2, the data size of the first 655 position indexes is 655 × 16, and the data size of the compressed power matrix is 256 × 128 × 12, so that when the compressed radar probe data includes the key probe data corresponding to the first 655 position indexes and the first 655 position indexes, the data size of the compressed radar probe data is 0.17 Mbps. When the compressed radar sounding data includes the key sounding data corresponding to the first 655 position indexes, and the compressed power matrix, the data amount of the compressed radar sounding data is 0.545 Mbps.
In summary, the data transmission between the radar and the computer device is realized through a data transmission interface, the data transmission interface has a certain transmission rate, and the first detection threshold, the reference compression ratio and B are used to determine the data amount of the compressed radar detection data, so that before performing data compression, the computer device can determine whether the data amount of the compressed radar detection data is less than or equal to the transmission rate of the data transmission interface between the computer device and the radar after the radar performs data compression according to the first detection threshold, the reference compression ratio and B stored in the computer device. If less than or equal to, then the first detection threshold, the reference compression ratio, and B, which are stored in itself, may be sent to the radar. If the data quantity is greater than the first detection threshold, the reference compression ratio and the second detection threshold, the computer device may further adjust the stored first detection threshold, the reference compression ratio and the second compression ratio so that the data quantity of the compressed radar detection data may be less than or equal to the transmission rate of a data transmission interface between the computer device and the radar after the radar performs data compression according to the adjusted first detection threshold, the reference compression ratio and the second compression ratio. Thereafter, the computer device may transmit the adjusted first detection threshold, the reference compression rate, and B to the radar.
In some embodiments, the data amount of the key detection data and the key data index may be reduced by increasing the first detection threshold and the reference compression rate, and the data amount of the compressed power matrix may be reduced by decreasing B, so that when the computer device adjusts the stored first detection threshold, the reference compression rate, and B, one or both of the stored first detection threshold and the reference compression rate may be increased, or B may be decreased. Of course, the computer device may also be adjusted in other manners, as long as after the radar performs data compression according to the adjusted first detection threshold, the reference compression ratio, and B, the data amount of the compressed radar detection data may be smaller than or equal to the transmission rate of the data transmission interface between the computer device and the radar, and the loss amount of the compressed radar detection data is small.
By this point, compression of the radar probe data has been completed. After the radar determines the compressed radar detection data through the above-mentioned steps 201-204, the compressed radar detection data may be sent to the computer device. And the radar can also perform subsequent processing on the compressed radar detection data. Since the compressed radar detection data may be different in different cases, the following description will be given in a variety of cases.
In the first case, when the compressed radar detection data is the key detection data and the key data index, the data belonging to the same antenna in the key detection data may be processed according to the azimuth estimation algorithm to obtain azimuth information of a plurality of points. Clustering and tracking the plurality of points according to the distances between the plurality of points and the radar, the speeds of the plurality of points and the azimuth information of the plurality of points to obtain a tracking list of the target point, wherein the tracking list comprises the distances, the speeds and the azimuth information between the target point and the radar at different moments. The radar may transmit a tracking list of target points to the computer device.
It should be noted that the key probe data may indicate the distances between the plurality of points and the radar, and the velocities of the plurality of points. In addition, after the key detection data is processed by the orientation estimation algorithm, a plurality of points are obtained and are scattered points, so that the plurality of points can be clustered to determine the target point.
In a second case, when the compressed radar detection data includes the key detection data and the first L position indexes corresponding to the first L position indexes in the index sorting result, the key detection data corresponding to the first L position indexes in the index sorting result may be processed according to the processing manner of the first case, so as to obtain a tracking list of the target point, and transmit the tracking list of the target point to the computer device.
In the third case, when the compressed radar detection data includes the key detection data, the key data index and the power matrix, the target detection is performed on the power matrix again, and the position index of the data passing through the target detection again is used as the azimuth data index. And acquiring data corresponding to the orientation data index from the key detection data. The acquired data may be processed according to the processing method in the first case, so as to obtain a tracking list of the target point, and the tracking list of the target point is transmitted to the computer device.
It should be noted that, when the target detection is performed on the power matrix again, a detection threshold is also used, and this detection threshold may be referred to as a second detection threshold. Since the first detection threshold is relatively small, substantially all potential target points can be detected by the target, and therefore, in order to eliminate redundancy, target detection may be performed on the power matrix again. Wherein the second detection threshold is greater than the first detection threshold.
Another point to be explained is that when the target detection is performed on the power matrix again, the detection may be performed by a constant false alarm detection method, or may be performed by other detection methods. Moreover, the method for detecting the target twice on the power matrix may be the same or different, and this is not limited in this embodiment of the present application.
It is noted that when the compressed radar probe data includes the critical probe data, the critical data index, and the power matrix, the processing may be performed not only according to the above-described method. It is also possible to directly follow the first case, i.e. the power matrix may not be used.
In a fourth case, when the compressed radar detection data includes the key detection data corresponding to the first L position indexes in the index sorting result, the first L position indexes, and the compressed power matrix, the processing may be performed according to the processing method in the third case, so as to obtain a tracking list of the target point, and transmit the tracking list of the target point to the computer device.
According to the method and the device, the compressed radar detection data can be determined according to the key detection data and the key data index, and the key detection data are the information of the potential target points in the original radar detection data, so that the compressed radar detection data basically contain the information of most of the potential target points, namely, the compressed radar detection data is high in quality and small in information loss. Moreover, after data compression is performed by the method provided by the embodiment of the application, the compressed radar detection data can be transmitted to the computer equipment, so that the problem that the original radar detection data is unfavorable to be transmitted to the computer equipment through a data transmission interface due to large data quantity is solved.
Referring to fig. 4, fig. 4 is a schematic structural diagram illustrating a compressing apparatus for radar detection data according to an exemplary embodiment, where the compressing apparatus may be implemented by software, hardware, or a combination of the two as part of or all of a radar, which may be the radar shown in fig. 1. The device includes: a generation module 401, a lossless transform module 402, an acquisition module 403, and a determination module 404.
A generating module 401, configured to generate a sounding data frame according to the radar sounding data, where the sounding data frame includes K data detected by M antennas of a radar in each of N periods, and M, N, K are integers;
a lossless transform module 402, configured to perform lossless transform on the probe data frame;
an obtaining module 403, configured to obtain key probe data and a key data index according to a target detection result of the probe data frame after the lossless transform;
a determining module 404, configured to determine compressed radar detection data according to the key detection data and the key data index.
In one possible implementation manner, the obtaining module 403 includes:
the generating submodule is used for generating a two-dimensional data matrix according to the detection data frame subjected to lossless transformation;
the first determining submodule is used for determining a power matrix according to the two-dimensional data matrix;
the target detection submodule is used for carrying out target detection on each data in the power matrix and taking the position index of the data passing through the target detection as a key data index; or, carrying out target detection on each data unit in the two-dimensional data matrix, and taking the position index of the data unit passing the target detection as a key data index;
and the acquisition submodule is used for acquiring key detection data from the detection data frame subjected to lossless transformation according to the key data index.
In one possible implementation, the determining module 404 includes:
and the second determining submodule is used for determining the key detection data and the key data index as the compressed radar detection data.
In one possible implementation, the determining module 404 includes:
the third determining submodule is used for determining the signal-to-noise ratio corresponding to each key data index according to the power matrix;
the first sequencing submodule is used for sequencing the key data indexes according to the sequence of the signal-to-noise ratio from large to small;
the first operation submodule is used for multiplying the total number of data included in the power matrix by a reference compression ratio to obtain L;
and the fourth determining submodule is used for determining the data corresponding to the first L indexes in the index sorting result and the first L indexes as the compressed radar detection data.
In one possible implementation, the determining module 404 includes:
and the fifth determining submodule is used for determining the key detection data, the key data index and the power matrix as the compressed radar detection data.
In one possible implementation, the determining module 404 includes:
a sixth determining submodule, configured to determine, according to the power matrix, a signal-to-noise ratio corresponding to each key data index;
the second sorting submodule is used for sorting the key data indexes according to the sequence of the signal-to-noise ratio from large to small;
the second operation submodule is used for multiplying the total number of data included in the power matrix by the reference compression ratio to obtain L;
the compression submodule is used for compressing the power matrix according to the maximum power and the minimum power in the power matrix;
and the seventh determining submodule is used for determining the key detection data corresponding to the first L indexes in the index sorting result, the first L indexes and the compressed power matrix as the compressed radar detection data.
In one possible implementation, the compression submodule includes:
and the quantization unit is used for quantizing each data unit in the power matrix according to the maximum power and the minimum power in the power matrix, and taking a matrix formed by the quantized data units as a compressed power matrix.
According to the method and the device, the compressed radar detection data can be determined according to the key detection data and the key data index, and the key detection data are the information of the potential target points in the original radar detection data, so that the compressed radar detection data basically contain the information of most of the potential target points, namely, the compressed radar detection data is high in quality and small in information loss. Moreover, after data compression is performed by the method provided by the embodiment of the application, the compressed radar detection data can be transmitted to the computer equipment, so that the problem that the original radar detection data is unfavorable to be transmitted to the computer equipment through a data transmission interface due to large data quantity is solved.
It should be noted that: in the compression apparatus for radar detection data provided in the above embodiment, when compressing radar detection data, only the division of the above functional modules is taken as an example, and in practical applications, the above function distribution may be completed by different functional modules according to needs, that is, the internal structure of the apparatus may be divided into different functional modules to complete all or part of the above described functions. In addition, the compressing apparatus for radar detection data provided by the above embodiment and the compressing method embodiment of radar detection data belong to the same concept, and specific implementation processes thereof are detailed in the method embodiment and are not described herein again.
Referring to fig. 5, fig. 5 is a block diagram illustrating a computer device 500 according to an example embodiment. The computer device 500 may be a portable mobile terminal such as: a smart phone, a tablet computer, an MP3 player (Moving Picture Experts Group Audio Layer III, motion video Experts compression standard Audio Layer 3), an MP4 player (Moving Picture Experts Group Audio Layer IV, motion video Experts compression standard Audio Layer 4), a notebook computer, or a desktop computer. Computer device 500 may also be referred to by other names such as user device, portable computer device, laptop computer device, desktop computer device, and so forth.
Generally, the computer device 500 includes: a processor 501 and a memory 502.
The processor 501 may include one or more processing cores, such as a 4-core processor, an 8-core processor, and so on. The processor 501 may be implemented in at least one hardware form of a DSP (Digital Signal Processing), an FPGA (Field-Programmable Gate Array), and a PLA (Programmable Logic Array). The processor 501 may also include a main processor and a coprocessor, where the main processor is a processor for Processing data in an awake state, and is also called a Central Processing Unit (CPU); a coprocessor is a low power processor for processing data in a standby state. In some embodiments, the processor 501 may be integrated with a GPU (Graphics Processing Unit), which is responsible for rendering and drawing the content required to be displayed on the display screen. In some embodiments, processor 501 may also include an AI (Artificial Intelligence) processor for processing computing operations related to machine learning.
In some embodiments, the computer device 500 may further optionally include: a peripheral interface 503 and at least one peripheral. The processor 501, the memory 502 and the peripheral interface 503 may be connected by bus or signal lines. Each peripheral device may be connected to the peripheral device interface 503 by a bus, signal line, or circuit board. Specifically, the peripheral device includes: at least one of radio frequency circuitry 504, touch screen display 505, camera 506, audio circuitry 507, positioning components 508, and power supply 509.
The peripheral interface 503 may be used to connect at least one peripheral related to I/O (Input/Output) to the processor 501 and the memory 502. In some embodiments, the processor 501, memory 502, and peripheral interface 503 are integrated on the same chip or circuit board; in some other embodiments, any one or two of the processor 501, the memory 502, and the peripheral interface 503 may be implemented on separate chips or circuit boards, which is not limited in this embodiment.
The Radio Frequency circuit 504 is used for receiving and transmitting RF (Radio Frequency) signals, also called electromagnetic signals. The radio frequency circuitry 504 communicates with communication networks and other communication devices via electromagnetic signals. The rf circuit 504 converts an electrical signal into an electromagnetic signal to transmit, or converts a received electromagnetic signal into an electrical signal. Optionally, the radio frequency circuit 504 includes: an antenna system, an RF transceiver, one or more amplifiers, a tuner, an oscillator, a digital signal processor, a codec chipset, a subscriber identity module card, and so forth. The radio frequency circuitry 504 may communicate with other computer devices via at least one wireless communication protocol. The wireless communication protocols include, but are not limited to: the world wide web, metropolitan area networks, intranets, generations of mobile communication networks (2G, 3G, 4G, and 5G), Wireless local area networks, and/or WiFi (Wireless Fidelity) networks. In some embodiments, the rf circuit 504 may further include NFC (Near Field Communication) related circuits, which are not limited in this application.
The display screen 505 is used to display a UI (User Interface). The UI may include graphics, text, icons, video, and any combination thereof. When the display screen 505 is a touch display screen, the display screen 505 also has the ability to capture touch signals on or over the surface of the display screen 505. The touch signal may be input to the processor 501 as a control signal for processing. At this point, the display screen 505 may also be used to provide virtual buttons and/or a virtual keyboard, also referred to as soft buttons and/or a soft keyboard. In some embodiments, the display screen 505 may be one, providing the front panel of the computer device 500; in other embodiments, the display screens 505 may be at least two, each disposed on a different surface of the computer device 500 or in a folded design; in still other embodiments, the display screen 505 may be a flexible display screen, disposed on a curved surface or on a folded surface of the computer device 500. Even more, the display screen 505 can be arranged in a non-rectangular irregular figure, i.e. a shaped screen. The Display screen 505 may be made of LCD (Liquid Crystal Display), OLED (Organic Light-Emitting Diode), and other materials.
The camera assembly 506 is used to capture images or video. Optionally, camera assembly 506 includes a front camera and a rear camera. Generally, a front camera is disposed on a front panel of a computer apparatus, and a rear camera is disposed on a rear surface of the computer apparatus. In some embodiments, the number of the rear cameras is at least two, and each rear camera is any one of a main camera, a depth-of-field camera, a wide-angle camera and a telephoto camera, so that the main camera and the depth-of-field camera are fused to realize a background blurring function, the main camera and the wide-angle camera are fused to realize panoramic shooting and a VR (Virtual Reality) shooting function or other fusion shooting functions. In some embodiments, camera assembly 506 may also include a flash. The flash lamp can be a monochrome temperature flash lamp or a bicolor temperature flash lamp. The double-color-temperature flash lamp is a combination of a warm-light flash lamp and a cold-light flash lamp and can be used for light compensation at different color temperatures.
The Location component 508 is used to locate the current geographic Location of the computer device 500 for navigation or LBS (Location Based Service). The Positioning component 508 may be a Positioning component based on the Global Positioning System (GPS) in the united states, the beidou System in china, or the galileo System in russia.
The power supply 509 is used to power the various components in the computer device 500. The power source 509 may be alternating current, direct current, disposable or rechargeable. When power supply 509 includes a rechargeable battery, the rechargeable battery may be a wired rechargeable battery or a wireless rechargeable battery. The wired rechargeable battery is a battery charged through a wired line, and the wireless rechargeable battery is a battery charged through a wireless coil. The rechargeable battery may also be used to support fast charge techniques.
In some embodiments, the computer device 500 also includes one or more sensors 510. The one or more sensors 510 include, but are not limited to: acceleration sensor 511, gyro sensor 512, pressure sensor 513, fingerprint sensor 514, optical sensor 515, and proximity sensor 516.
The acceleration sensor 511 may detect the magnitude of acceleration in three coordinate axes of a coordinate system established with the computer apparatus 500. For example, the acceleration sensor 511 may be used to detect components of gravitational acceleration in three coordinate axes. The processor 501 may control the touch screen 505 to display the user interface in a landscape view or a portrait view according to the gravitational acceleration signal collected by the acceleration sensor 511. The acceleration sensor 511 may also be used for acquisition of motion data of a game or a user.
The gyro sensor 512 may detect a body direction and a rotation angle of the computer device 500, and the gyro sensor 512 may cooperate with the acceleration sensor 511 to acquire a 3D action of the user on the computer device 500. The processor 501 may implement the following functions according to the data collected by the gyro sensor 512: motion sensing (such as changing the UI according to a user's tilting operation), image stabilization at the time of photographing, game control, and inertial navigation.
The pressure sensors 513 may be disposed on a side bezel of the computer device 500 and/or underneath the touch display screen 505. When the pressure sensor 513 is disposed on the side frame of the computer device 500, the holding signal of the user to the computer device 500 can be detected, and the processor 501 performs left-right hand recognition or shortcut operation according to the holding signal collected by the pressure sensor 513. When the pressure sensor 513 is disposed at the lower layer of the touch display screen 505, the processor 501 controls the operability control on the UI interface according to the pressure operation of the user on the touch display screen 505. The operability control comprises at least one of a button control, a scroll bar control, an icon control and a menu control.
The fingerprint sensor 514 is used for collecting a fingerprint of the user, and the processor 501 identifies the identity of the user according to the fingerprint collected by the fingerprint sensor 514, or the fingerprint sensor 514 identifies the identity of the user according to the collected fingerprint. Upon identifying that the user's identity is a trusted identity, the processor 501 authorizes the user to perform relevant sensitive operations including unlocking the screen, viewing encrypted information, downloading software, paying, changing settings, and the like. The fingerprint sensor 514 may be disposed on the front, back, or side of the computer device 500. When a physical key or vendor Logo is provided on the computer device 500, the fingerprint sensor 514 may be integrated with the physical key or vendor Logo.
The optical sensor 515 is used to collect the ambient light intensity. In one embodiment, the processor 501 may control the display brightness of the touch display screen 505 based on the ambient light intensity collected by the optical sensor 515. Specifically, when the ambient light intensity is higher, the display brightness of the touch display screen 505 is increased; when the ambient light intensity is low, the display brightness of the touch display screen 505 is turned down. In another embodiment, processor 501 may also dynamically adjust the shooting parameters of camera head assembly 506 based on the ambient light intensity collected by optical sensor 515.
A proximity sensor 516, also known as a distance sensor, is typically disposed on the front panel of the computer device 500. The proximity sensor 516 is used to capture the distance between the user and the front of the computer device 500. In one embodiment, the touch display screen 505 is controlled by the processor 501 to switch from a bright screen state to a dark screen state when the proximity sensor 516 detects that the distance between the user and the front face of the computer device 500 is gradually decreased; when the proximity sensor 516 detects that the distance between the user and the front of the computer device 500 becomes gradually larger, the touch display screen 505 is controlled by the processor 501 to switch from the breath screen state to the bright screen state.
Those skilled in the art will appreciate that the configuration shown in FIG. 5 does not constitute a limitation of the computer device 500, and may include more or fewer components than those shown, or may combine certain components, or may employ a different arrangement of components.
The present application shows a schematic structural diagram of a radar, which may generate relatively large differences due to different configurations or performances, and may include one or more processors (CPUs) and one or more memories, where at least one instruction is stored in the memory, and the at least one instruction is loaded and executed by the processors to implement the method for compressing radar detection data in the foregoing embodiments. Of course, the radar may also have components such as a wired or wireless network interface, a keyboard, and an input/output interface, so as to perform input/output, and may also include other components for implementing the functions of the device, which are not described herein again.
In some embodiments, a computer-readable storage medium is also provided, in which a computer program is stored, which, when being executed by a processor, implements the steps of the method for compressing radar detection data in the above-mentioned embodiments. For example, the computer readable storage medium may be a ROM, a RAM, a CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, and the like.
It is noted that the computer-readable storage medium referred to herein may be a non-volatile storage medium, in other words, a non-transitory storage medium.
It should be understood that all or part of the steps for implementing the above embodiments may be implemented by software, hardware, firmware or any combination thereof. When implemented in software, may be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. The computer instructions may be stored in the computer-readable storage medium described above.
That is, in some embodiments, there is also provided a computer program product containing instructions which, when run on a computer, cause the computer to perform the steps of the method of compressing radar detection data described above.
The above-mentioned embodiments are not intended to limit the present application, and any modifications, equivalents, improvements, etc. made within the spirit and scope of the present application should be included in the protection scope of the present application.
Claims (16)
1. A method of compressing radar probe data, the method comprising:
generating a detection data frame according to radar detection data, wherein the detection data frame comprises K data detected by M antennas of a radar in each period of N periods, and the M, N, K are integers;
performing a lossless transform on the probe data frame;
acquiring key detection data and a key data index according to a target detection result of the detection data frame subjected to lossless transformation;
and determining compressed radar detection data according to the key detection data and the key data index.
2. The method of claim 1, wherein obtaining key probe data and key data indices from target detection results of the lossless transformed probe data frames comprises:
generating a two-dimensional data matrix according to the detection data frame subjected to lossless transformation;
determining a power matrix according to the two-dimensional data matrix, performing target detection on each data unit in the power matrix, and taking a position index of the data unit passing the target detection as the key data index; or, performing target detection on each data unit in the two-dimensional data matrix, and taking the position index of the data unit passing the target detection as the key data index;
and acquiring the key detection data from the detection data frame subjected to lossless transformation according to the key data index.
3. The method of claim 1 or 2, wherein determining compressed radar detection data from the key detection data and the key data index comprises:
and determining the key detection data and the key data index as compressed radar detection data.
4. The method of claim 2, wherein determining compressed radar sounding data based on the critical sounding data and the critical data index comprises:
determining a signal-to-noise ratio corresponding to each key data index according to the power matrix;
sorting the key data indexes according to the sequence of the signal-to-noise ratio from large to small;
multiplying the total number of data units included in the power matrix by a reference compression ratio to obtain L;
and determining data corresponding to the first L indexes in the index sorting result and the first L indexes as compressed radar detection data.
5. The method of claim 2, wherein determining compressed radar sounding data based on the critical sounding data and the critical data index comprises:
and determining the key detection data, the key data index and the power matrix as compressed radar detection data.
6. The method of claim 2, wherein determining compressed radar sounding data based on the critical sounding data and the critical data index comprises:
determining a signal-to-noise ratio corresponding to each key data index according to the power matrix;
sorting the key data indexes according to the sequence of the signal-to-noise ratio from large to small;
multiplying the total number of data units included in the power matrix by a reference compression ratio to obtain L;
compressing the power matrix according to the maximum power and the minimum power in the power matrix;
and determining key detection data corresponding to the first L indexes in the index sorting result, the first L indexes and the compressed power matrix as compressed radar detection data.
7. The method of claim 6, wherein the compressing the power matrix according to the maximum power and the minimum power in the power matrix comprises:
and quantizing each data unit in the power matrix according to the maximum power and the minimum power in the power matrix, and taking a matrix formed by the quantized data units as a compressed power matrix.
8. An apparatus for compressing radar probe data, the apparatus comprising:
a generation module, configured to generate a sounding data frame according to radar sounding data, where the sounding data frame includes K data detected by M antennas of a radar in each of N periods, and M, N, K are integers;
the lossless transformation module is used for carrying out lossless transformation on the detection data frame;
the acquisition module is used for acquiring key detection data and key data indexes according to a target detection result of the detection data frame subjected to lossless transformation;
and the determining module is used for determining the compressed radar detection data according to the key detection data and the key data index.
9. The apparatus of claim 8, wherein the acquisition module comprises:
the generating submodule is used for generating a two-dimensional data matrix according to the detection data frame subjected to lossless transformation;
the first determining submodule is used for determining a power matrix according to the two-dimensional data matrix;
the target detection submodule is used for carrying out target detection on each data unit in the power matrix and taking the position index of the data unit passing the target detection as the key data index; or, performing target detection on each data unit in the two-dimensional data matrix, and taking the position index of the data unit passing the target detection as the key data index;
and the obtaining submodule is used for obtaining the key detection data from the detection data frame subjected to the lossless transformation according to the key data index.
10. The apparatus of claim 8 or 9, wherein the determining module comprises:
and the second determining submodule is used for determining the key detection data and the key data index as the compressed radar detection data.
11. The apparatus of claim 9, wherein the determining module comprises:
the third determining submodule is used for determining the signal-to-noise ratio corresponding to each key data index according to the power matrix;
the first sequencing submodule is used for sequencing the key data indexes according to the sequence of the signal-to-noise ratio from large to small;
the first operation submodule is used for multiplying the total number of the data units included in the power matrix by a reference compression ratio to obtain L;
and the fourth determining submodule is used for determining the data corresponding to the first L indexes in the index sorting result and the first L indexes as the compressed radar detection data.
12. The apparatus of claim 9, wherein the determining module comprises:
and the fifth determining submodule is used for determining the key detection data, the key data index and the power matrix as the compressed radar detection data.
13. The apparatus of claim 9, wherein the determining module comprises:
a sixth determining submodule, configured to determine, according to the power matrix, a signal-to-noise ratio corresponding to each key data index;
the second sorting submodule is used for sorting the key data indexes according to the sequence of the signal-to-noise ratio from large to small;
the second operation submodule is used for multiplying the total number of the data units included in the power matrix by a reference compression ratio to obtain L;
the compression submodule is used for compressing the power matrix according to the maximum power and the minimum power in the power matrix;
and the seventh determining submodule is used for determining the key detection data corresponding to the first L indexes in the index sorting result, the first L indexes and the compressed power matrix as the compressed radar detection data.
14. The apparatus of claim 13, wherein the compression submodule comprises:
and the quantization unit is used for quantizing each data unit in the power matrix according to the maximum power and the minimum power in the power matrix, and taking a matrix formed by the quantized data units as a compressed power matrix.
15. A computer device comprising a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory communicate with each other via the communication bus, the memory stores a computer program, and the processor executes the program stored in the memory to implement the steps of the method according to any one of claims 1-7.
16. A computer-readable storage medium, in which a computer program is stored which, when being executed by a processor, carries out the steps of the method according to any one of claims 1 to 7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910656547.XA CN112241005A (en) | 2019-07-19 | 2019-07-19 | Method and device for compressing radar detection data and storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910656547.XA CN112241005A (en) | 2019-07-19 | 2019-07-19 | Method and device for compressing radar detection data and storage medium |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN112241005A true CN112241005A (en) | 2021-01-19 |
Family
ID=74168152
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910656547.XA Pending CN112241005A (en) | 2019-07-19 | 2019-07-19 | Method and device for compressing radar detection data and storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112241005A (en) |
Citations (17)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US5991454A (en) * | 1997-10-06 | 1999-11-23 | Lockheed Martin Coporation | Data compression for TDOA/DD location system |
| GB0310429D0 (en) * | 2003-05-07 | 2003-06-11 | Broadway David V | Data compression and encryption software |
| FR2863797A1 (en) * | 2003-12-15 | 2005-06-17 | Cit Alcatel | Data compression device for e.g. ATM network, has compression unit generating compressed frame subdivided into sections having critical data compressed in synchronous manner and non-critical data compressed in asynchronous manner |
| CN1750164A (en) * | 2005-07-20 | 2006-03-22 | 杭州海康威视数字技术有限公司 | Digital hard disc recorder file managing method for preventing file fragment production |
| CN101075261A (en) * | 2007-06-12 | 2007-11-21 | 腾讯科技（深圳）有限公司 | Method and device for compressing index |
| CN101178424A (en) * | 2007-12-12 | 2008-05-14 | 北京四方继保自动化股份有限公司 | Dynamic data compression storage method in electric network wide-area measuring systems (WAMS) |
| CN102857727A (en) * | 2012-10-11 | 2013-01-02 | 南京莱斯信息技术股份有限公司 | Lossless compression and decompression processing method for video data |
| CN103092842A (en) * | 2011-10-28 | 2013-05-08 | 浙江大华技术股份有限公司 | Method for storing and retrieving key area information in video image data file |
| GB201416631D0 (en) * | 2014-09-19 | 2014-11-05 | Gurulogic Microsystems Oy | Encoder, decoder and methods employing partial data encryption |
| US20160099723A1 (en) * | 2014-10-07 | 2016-04-07 | Doron Kletter | Enhanced data compression for sparse multidimensional ordered series data |
| CN105513099A (en) * | 2015-11-27 | 2016-04-20 | 北京像素软件科技股份有限公司 | Compression method and apparatus for bone animation data |
| CN106021985A (en) * | 2016-05-17 | 2016-10-12 | 杭州和壹基因科技有限公司 | Genome data compression method |
| CN106790662A (en) * | 2017-01-20 | 2017-05-31 | 济南浪潮高新科技投资发展有限公司 | A kind of HTTP data compression method of use key frame compression |
| CN107450054A (en) * | 2017-07-14 | 2017-12-08 | 浙江省交通规划设计研究院 | A kind of adaptive Coherent Noise in GPR Record denoising method |
| CN109116311A (en) * | 2018-09-19 | 2019-01-01 | 西安电子科技大学 | Knowledge based assists the clutter suppression method of sparse iteration covariance estimation |
| CN109711839A (en) * | 2018-12-13 | 2019-05-03 | 平安科技（深圳）有限公司 | Block chain storage method, device, equipment and storage medium based on data compression |
| CN109952708A (en) * | 2016-12-12 | 2019-06-28 | 德州仪器公司 | Lossless data compression |
-
2019
- 2019-07-19 CN CN201910656547.XA patent/CN112241005A/en active Pending
Patent Citations (17)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US5991454A (en) * | 1997-10-06 | 1999-11-23 | Lockheed Martin Coporation | Data compression for TDOA/DD location system |
| GB0310429D0 (en) * | 2003-05-07 | 2003-06-11 | Broadway David V | Data compression and encryption software |
| FR2863797A1 (en) * | 2003-12-15 | 2005-06-17 | Cit Alcatel | Data compression device for e.g. ATM network, has compression unit generating compressed frame subdivided into sections having critical data compressed in synchronous manner and non-critical data compressed in asynchronous manner |
| CN1750164A (en) * | 2005-07-20 | 2006-03-22 | 杭州海康威视数字技术有限公司 | Digital hard disc recorder file managing method for preventing file fragment production |
| CN101075261A (en) * | 2007-06-12 | 2007-11-21 | 腾讯科技（深圳）有限公司 | Method and device for compressing index |
| CN101178424A (en) * | 2007-12-12 | 2008-05-14 | 北京四方继保自动化股份有限公司 | Dynamic data compression storage method in electric network wide-area measuring systems (WAMS) |
| CN103092842A (en) * | 2011-10-28 | 2013-05-08 | 浙江大华技术股份有限公司 | Method for storing and retrieving key area information in video image data file |
| CN102857727A (en) * | 2012-10-11 | 2013-01-02 | 南京莱斯信息技术股份有限公司 | Lossless compression and decompression processing method for video data |
| GB201416631D0 (en) * | 2014-09-19 | 2014-11-05 | Gurulogic Microsystems Oy | Encoder, decoder and methods employing partial data encryption |
| US20160099723A1 (en) * | 2014-10-07 | 2016-04-07 | Doron Kletter | Enhanced data compression for sparse multidimensional ordered series data |
| CN105513099A (en) * | 2015-11-27 | 2016-04-20 | 北京像素软件科技股份有限公司 | Compression method and apparatus for bone animation data |
| CN106021985A (en) * | 2016-05-17 | 2016-10-12 | 杭州和壹基因科技有限公司 | Genome data compression method |
| CN109952708A (en) * | 2016-12-12 | 2019-06-28 | 德州仪器公司 | Lossless data compression |
| CN106790662A (en) * | 2017-01-20 | 2017-05-31 | 济南浪潮高新科技投资发展有限公司 | A kind of HTTP data compression method of use key frame compression |
| CN107450054A (en) * | 2017-07-14 | 2017-12-08 | 浙江省交通规划设计研究院 | A kind of adaptive Coherent Noise in GPR Record denoising method |
| CN109116311A (en) * | 2018-09-19 | 2019-01-01 | 西安电子科技大学 | Knowledge based assists the clutter suppression method of sparse iteration covariance estimation |
| CN109711839A (en) * | 2018-12-13 | 2019-05-03 | 平安科技（深圳）有限公司 | Block chain storage method, device, equipment and storage medium based on data compression |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN111127509B (en) | Target tracking method, apparatus and computer readable storage medium | |
| CN111753784A (en) | Video special effect processing method and device, terminal and storage medium | |
| CN111027490B (en) | Face attribute identification method and device and storage medium | |
| CN111754386B (en) | Image area shielding method, device, equipment and storage medium | |
| CN112084811A (en) | Identity information determining method and device and storage medium | |
| CN109102811B (en) | Audio fingerprint generation method and device and storage medium | |
| CN110705614A (en) | Model training method and device, electronic equipment and storage medium | |
| CN111931712B (en) | Face recognition method, device, snapshot machine and system | |
| CN111860064B (en) | Video-based target detection method, device, equipment and storage medium | |
| CN111797017A (en) | Method and device for storing log, test equipment and storage medium | |
| CN107944024B (en) | Method and device for determining audio file | |
| CN113343709B (en) | Method for training intention recognition model, method, device and equipment for intention recognition | |
| CN111988664B (en) | Video processing method, video processing device, computer equipment and computer-readable storage medium | |
| CN111063372B (en) | Method, device and equipment for determining pitch characteristics and storage medium | |
| CN112243083B (en) | Snapshot method and device and computer storage medium | |
| CN113824902A (en) | Method, device, system, equipment and medium for determining time delay of infrared camera system | |
| CN111723615B (en) | Method and device for judging matching of detected objects in detected object image | |
| CN111757146B (en) | Method, system and storage medium for video splicing | |
| CN109344284B (en) | Song file playing method, device, equipment and storage medium | |
| CN112241005A (en) | Method and device for compressing radar detection data and storage medium | |
| CN111464829A (en) | Method, device and equipment for switching media data and storage medium | |
| CN111402873A (en) | Voice signal processing method, device, equipment and storage medium | |
| CN112861565A (en) | Method and device for determining track similarity, computer equipment and storage medium | |
| CN112308104A (en) | Abnormity identification method and device and computer storage medium | |
| CN111246240A (en) | Method and apparatus for storing media data |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |