Radar data transmission method and radar system

Abstract

The application discloses a radar data transmission method and a radar system, and belongs to the field of radar data transmission.The method comprises the following steps: S100, controlling a radar detection environment and acquiring heat map data; S200, performing data point screening on the heat map data; the operation of calculating the energy value and the curvature value of each data point in the heat map is included, and the selected data points include data points with the curvature value greater than a second threshold M2 and the energy value greater than a third threshold M3 in the heat map data; S300, forming a transmission data set based on the selected data points, and the transmission data set comprises the coordinates and the energy value of the selected data points in the heat map data; and S400, transmitting the transmission data set to a host computer, and performing pseudo-random reconstruction on the heat map data by filling a preset value with radar noise as a reference in the position of discarded data based on the data of the received transmission data set.The application can significantly reduce the data transmission amount between the radar and the host computer and reduce the data throughput pressure of the radar system under the premise of ensuring accurate transmission of effective target data.

Description

Technical Field

[0001] This invention belongs to the field of radar data transmission technology, specifically relating to a radar data transmission method and radar system. Background Technology

[0002] Taking millimeter-wave radar as an example, radar equipment is rapidly becoming more widespread in daily applications. As radar detection capabilities are enhanced and the number of parameter measurement dimensions increases, such as measuring distance, velocity, elevation angle, and azimuth angle, the scale of heat map data transmitted from the radar to the host becomes enormous. When the transmission bandwidth from the radar to the host is limited, it becomes difficult to transmit the data to the host in a timely manner.

[0003] To reduce data transmission volume, an existing technology employs the following approach: compressing the LiDAR point cloud using an octree compression algorithm and transmitting only differentiated new data via incremental transmission to improve transmission efficiency; however, the compression algorithm used in this approach is complex and consumes significant system data processing resources.

[0004] Similarly, in order to reduce the amount of data transmission, another existing technology adopts the following approach: by performing Fourier operations on the radar antenna data, selecting the received data of one antenna as a reference, obtaining the difference data between each of the remaining antennas and the selected antenna, compressing and encoding the obtained data for transmission, and transmitting only the data and difference data of one antenna, thereby improving transmission efficiency; however, this approach has almost no data compression capability in areas with thermal noise distribution. Summary of the Invention

[0005] This invention provides a radar data transmission method and radar system, aiming to reduce the data transmission volume and data throughput pressure of the radar system. The technical solution for achieving this invention is as follows:

[0006] In a first aspect, the present invention provides a radar data transmission method, comprising:

[0007] S100: Control radar to detect the environment and acquire thermal image data;

[0008] S200, Control the radar to perform data point filtering on the heat map data: including the operation of calculating the energy value and curvature value of each data point in the heat map, and the selected data points include data points in the heat map data whose curvature value is greater than the second threshold M2 and whose energy value is greater than the third threshold M3;

[0009] S300. A transmission dataset is formed based on the selected data points, wherein the transmission dataset includes the coordinates and energy values ​​of the selected data points in the heat map data;

[0010] S400: The control radar sends the transmitted dataset to the host. Based on the data in the received transmitted dataset, the host performs pseudo-random reconstruction of the heat map data by filling the locations of discarded data with preset values ​​referenced to the radar noise floor.

[0011] As a preferred technical solution, the heat map data includes: two-dimensional, three-dimensional, or four-dimensional heat map data composed of any one or any combination of the range dimension, Doppler dimension, elevation angle dimension, and azimuth angle dimension obtained by radar.

[0012] As a preferred technical solution, in step S200, when filtering the heat map data, the selected data points also include the maximum points in the heat map data whose energy values ​​are greater than the first threshold M1 and several points in their preset neighborhood, where M3≥M1.

[0013] As a preferred technical solution, in step S200, when filtering one-dimensional or two-dimensional heat map data, the operation of identifying inflection points for each data point in the heat map is also included. The selected data points also include inflection points in the heat map data whose energy values ​​are greater than the first threshold M1 and several data points in the preset neighborhood of the inflection point.

[0014] As a preferred technical solution, in step S200, when filtering heat map data of two dimensions or above, the operation of identifying saddle points for each data point in the heat map is also included. The selected data points also include saddle points in the heat map data whose energy values ​​are greater than the first threshold M1 and several data points in the preset neighborhood of the saddle point.

[0015] As a preferred technical solution, in step S300, when forming a transmission dataset based on the selected data points, the operation of compressing the coordinates and energy values ​​of the selected data points in the heat map data is also included.

[0016] As a preferred technical solution, in step S400, the operation method for pseudo-random reconstruction of the heat map data is as follows:

[0017] Fill the locations of discarded data points with random values ​​that follow a preset statistical distribution.

[0018] As a specific technical solution, in step S400, the method for determining the random value of the preset statistical distribution includes at least one of the following: ① determining the random value using a Gaussian distribution with the radar noise floor energy as the variance; ② determining the random value using a uniform distribution within a preset range; ③ determining the random value based on the energy spectrum characteristics of scintillation noise; ④ determining the random value after filtering Gaussian white noise using a filter; the random value is less than the preset first threshold M1.

[0019] Secondly, the present invention provides a radar system, including a radar and a host computer connected to the radar in communication; the radar and the host computer cooperate to perform the radar data transmission method described above.

[0020] As a preferred technical solution, the radar includes:

[0021] The heat map data acquisition module is used to detect the environment and acquire heat map data;

[0022] The data filtering module filters the heat map data, including calculating the energy value and curvature value of each data point in the heat map. The selected data points include data points in the heat map data whose curvature value is greater than the second threshold M2 and whose energy value is greater than the third threshold M3.

[0023] The dataset generation module assembles a transmission dataset based on selected data points, the transmission dataset containing the coordinates and energy values ​​of the selected data points in the heatmap data;

[0024] The data sending module is used to send the data set to the host.

[0025] The host includes:

[0026] A data receiving module is used to receive the transmitted dataset;

[0027] The heatmap reconstruction module, based on the received transmitted dataset, performs pseudo-random reconstruction of the heatmap data by filling the locations of discarded data with preset values ​​referenced to radar noise floor.

[0028] The beneficial effects of the technical solution of the present invention include: providing an efficient radar thermal image data transmission method, which, while ensuring accurate transmission of effective target data, has a simpler data processing algorithm and processes less data, which can significantly reduce the amount of data transmission between the radar and the host, and reduce the data throughput pressure of the radar system. Attached Figure Description

[0029] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0030] Figure 1 This is a flowchart of the radar data transmission method provided in an embodiment of the present invention.

[0031] Figure 2 This is a structural diagram of the radar system provided in an embodiment of the present invention. Detailed Implementation

[0032] To make the technical solution of the present invention clearer and its technical advantages more apparent, the technical solution of the present invention will be clearly and completely described below in conjunction with specific embodiments. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of the present invention.

[0033] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising a..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element. Furthermore, components, features, and elements with the same names in different embodiments of this application may have the same meaning or different meanings, the specific meaning of which must be determined by its interpretation in that specific embodiment or further in conjunction with the context of that specific embodiment.

[0034] It should be understood that although the steps in the flowcharts of this application's embodiments are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some of the steps in the figures may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times, and their execution order is not necessarily sequential, but can be performed alternately or in turn with other steps or at least a portion of the sub-steps or stages of other steps.

[0035] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of the invention. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0036] To make the objectives, technical solutions, and advantages of the present invention clearer, the embodiments of the present invention will be described in further detail below with reference to the accompanying drawings.

[0037] like Figure 1As shown, as a basic implementation method, the radar data transmission method provided in this embodiment includes:

[0038] S100: Control radar to detect the environment and acquire thermal image data;

[0039] S200, Control the radar to perform data point filtering on the heat map data: including the operation of calculating the energy value and curvature value of each data point in the heat map, and the selected data points include data points in the heat map data whose curvature value is greater than the second threshold M2 and whose energy value is greater than the third threshold M3;

[0040] S300. A transmission dataset is formed based on the selected data points, wherein the transmission dataset includes the coordinates and energy values ​​of the selected data points in the heat map data;

[0041] S400: The control radar sends the transmitted dataset to the host. Based on the data in the received transmitted dataset, the host performs pseudo-random reconstruction of the heat map data by filling the locations of discarded data with preset values ​​referenced to the radar noise floor.

[0042] In step S100 above, there are no restrictions on the radar's operating mode when controlling the radar's detection environment. Taking a frequency modulated continuous wave (FMCW) radar as an example, the radar transmits a linear frequency modulated signal to detect the environment, the receiving antenna receives the reflected echo from the environment, and after descrambling and analog-to-digital conversion sampling processing of the echo signal, the ADC sampling data matrix collected by each receiving antenna is obtained. Alternatively, taking a stepped frequency (SFCW) radar as an example, the radar transmits a stepped frequency waveform, and then performs corresponding receiving processing to obtain the ADC sampling data matrix.

[0043] The radar data transmission method described above, specifically the acquisition of heat map data in step S100, is explained as follows:

[0044] First, static clutter suppression is performed on the ADC sampling data matrix to remove interference caused by stationary objects or DC components in the environment. Methods for static clutter suppression include inter-frame differencing, mean cancellation, and high-pass filtering.

[0045] In this embodiment, a heatmap refers to a distribution map of signal echo energy in the detection environment obtained by processing radar echo signals. Based on the included parameter measurement dimensions, it includes: conventional two-dimensional heatmaps, such as range-velocity heatmaps and range-angle heatmaps; three-dimensional heatmaps, such as range-velocity-elevation angle heatmaps and range-velocity-azimuth angle heatmaps; heatmaps obtained by expanding a single measurement dimension over time, such as range-time heatmaps, where the range spectrum is combined in chronological order to form a range-time heatmap. Similar heatmaps include velocity-time heatmaps and angle-time heatmaps. In this embodiment, such heatmaps formed by expanding a single measurement dimension over time are called single-dimensional heatmaps. Furthermore, it also includes four-dimensional heatmaps such as range-velocity-elevation angle-azimuth angle heatmaps.

[0046] Unless necessary, this embodiment does not deliberately distinguish or limit the dimensions of the heatmap, and refers to them uniformly as heatmap data. That is to say, the heatmap data includes: two-dimensional, three-dimensional or four-dimensional heatmap data composed of any one or any combination of the range dimension, Doppler dimension, elevation angle dimension and azimuth angle dimension obtained by radar.

[0047] The following examples illustrate one-dimensional, two-dimensional, three-dimensional, and four-dimensional heatmap data:

[0048] ① Single-dimensional heatmap:

[0049] For example, by performing 1D-FFT processing on the ADC sampled data in the distance dimension, the modulus or square of the result data can be used to obtain a data branch of the distance-time heatmap at a certain moment; as another example, by performing 1D-FFT processing on the ADC sampled data in the velocity dimension, the modulus or square of the result data can be used to obtain a data branch of the velocity-time heatmap at a certain moment; as yet another example, by performing 1D-FFT processing on the ADC sampled data in the angle dimension (pitch or azimuth), the modulus or square of the result data can be used to obtain a data branch of the angle-time (heatmap) at a certain moment.

[0050] ② Two-dimensional heat map:

[0051] Taking the distance-velocity heatmap as an example: the distance-velocity heatmap data is obtained by performing 2D-FFT processing on the ADC sampled data, that is, first performing 1D-FFT on the distance dimension, then performing 1D-FFT processing on the result of the distance FFT in the slow time dimension, and taking the modulus or square of the result data.

[0052] Taking distance-angle heatmap as an example:

[0053] Method 1: Perform 1D-FFT processing on the distance dimension of the data collected by the receiving antennas in multiple antenna dimensions to obtain the distance FFT result, and then perform 1D-FFT / 2D-FFT on the antenna dimension. Take the modulus or square the modulus of the result data to obtain the distance-angle heat map data.

[0054] Method 2: Parameter estimation is performed using super-resolution algorithms such as MUSIC and MVDR, combined with spectral search, to obtain range-angle heatmap data. This invention does not impose restrictions on the super-resolution algorithm used for the radar.

[0055] ③ Three-dimensional heat map:

[0056] Taking the range-velocity-angle heatmap as an example: 3D-FFT processing is performed on the ADC sampling data collected by multiple receiving antennas. This involves performing a 1D-FFT on the antenna dimension of the 2D-FFT results from multiple antennas, and then taking the modulus or square of the result data to obtain the range-velocity-angle heatmap data. For example, if the selected antennas are arranged linearly in a horizontal direction, range-velocity-azimuth heatmap data is obtained; if the selected antennas are arranged linearly in a vertical direction, range-velocity-elevation heatmap data is obtained; if the selected antennas are arranged in a planar array, a 2D-FFT is performed on the antenna dimension of the 2D-FFT results from multiple antennas, and then taking the modulus or square of the result data to obtain four-dimensional range-velocity-azimuth-elevation heatmap data.

[0057] In the radar data transmission method described above, step S200 involves filtering the heat map data, as detailed below:

[0058] In a preferred embodiment, step S200 involves calculating the energy value and curvature value of each data point in the heatmap when filtering the heatmap data. The selected data points include those with a curvature value greater than a second threshold M2 and an energy value greater than a third threshold M3. Specifically, the second threshold M2 is a curvature value threshold, and the third threshold M3 is a threshold that limits the energy intensity. This preferred embodiment, by retaining non-extreme points with high curvature and high energy intensity, can more completely identify energy concentration areas and avoid missing key data related to the target.

[0059] In the preferred embodiments described above, the curvature calculation method includes calculating the curvature of a one-dimensional curve, the curvature of a two-dimensional surface, such as Gaussian curvature, average curvature, principal curvature, etc., and high-dimensional Riemann curvature, etc. The present invention does not limit the specific method of curvature calculation.

[0060] As a further preferred implementation, in step S200, when filtering the heatmap data, the selected data points also include the maximum points in the heatmap data whose energy values ​​are greater than the first threshold M1 and several points within their preset neighborhood, where M3≥M1. Unselected data points are discarded. The specific explanation is as follows:

[0061] Let the first threshold M1 be the threshold that limits the intensity of the energy value. The first threshold M1 is set according to the radar noise floor.

[0062] First, the selected data points include the maximum points in the heatmap data whose energy values ​​are greater than the first threshold M1, and several points in their preset neighborhood. At this point, unselected data points can be discarded, or unselected data points can be used for further selection operations.

[0063] Specifically, taking N-dimensional heatmap data as an example, a1, a2, ... a n ,...,a N This represents the coordinates of data point A on the heatmap, b1, b2, ... b n ,...,b N This represents the coordinates of data point B on the heatmap. For a given value of n (n=1,2,3,...,N), |b n -a n If |b| is less than or equal to 1, then B is a neighbor of A in the nth dimension of the heatmap; for all n = 1, 2, 3, ..., N, |b| n -a n If neither of the points is greater than or equal to 1, then B is a neighbor of A on the heatmap.

[0064] In this application, the method for determining maxima includes: 1) if the value of a point on the heatmap is not less than all its neighboring points on the heatmap, then the point is a maxima in all dimensions; 2) if the value of a point on the heatmap is not less than its neighboring points in a certain dimension of the heatmap, then the point is a maxima in that corresponding dimension. Maxima can be selected from all dimensions or from one or more specified dimensions. Selecting maxima from some specified dimensions is a weakened judgment, which helps retain more data in the heatmap, but increases data transmission volume. Whether to use this method depends on the actual transmission bandwidth and requirements, and this invention does not impose any restrictions on it.

[0065] Furthermore, for point B, if all n = 1, 2, 3, ..., N, |b n -a n If all values ​​do not exceed m, then B is a point of A within its m-neighborhood, where m is an integer. In this application, the preset neighborhood of a point on the heatmap refers to all points within that point's m-neighborhood. Typical preset values ​​for m are 1, 2, and 3. Larger values ​​for m help retain more data in the heatmap, but increase data transmission volume. The range of the neighborhood needs to be preset according to the actual transmission bandwidth and requirements, but this invention does not impose any restrictions on it.

[0066] In another preferred embodiment, step S200, when filtering one-dimensional or two-dimensional heatmap data, also includes the operation of identifying inflection points for each data point in the heatmap. The selected data points also include inflection points in the heatmap data whose energy values ​​are greater than a first threshold M1, and several data points within a preset neighborhood of the inflection point. For example, for one-dimensional heatmap data, the data points corresponding to the inflection points on the heatmap that are greater than the first threshold M1 and the Q points extending to the left and right of the inflection point's coordinate position are selected, where typical values ​​for Q are 1, 2, and 3; for two-dimensional heatmap data, the data points within the rectangular area defined by the inflection points on the heatmap that are greater than the first threshold M1 and the P data points extending bidirectionally along the two coordinate axes with the inflection point's coordinate position as the center are selected, where typical values ​​for P are 1, 2, and 3.

[0067] In another preferred embodiment, step S200, for heatmap data of two dimensions or higher, includes a saddle point identification operation for each data point in the heatmap during the data filtering process. The selected data points also include saddle points in the heatmap data with energy values ​​greater than a first threshold M1, and several data points within a preset neighborhood of the saddle point. For example, in three-dimensional heatmap data, saddle points on the heatmap with energy values ​​greater than the first threshold are selected, along with all data points within a cubic region defined by K data points extending bidirectionally along the three coordinate axes centered on the saddle point's coordinate position. Typical values ​​for K are 1, 2, and 3.

[0068] In the two preferred embodiments described above, the inflection point and the saddle point follow the definitions in the published documents, and the present invention will not elaborate on them or impose any restrictions on them.

[0069] The radar data transmission method described above filters the data, retaining the coordinates and energy intensity values ​​of selected data points in the heatmap as the data to be transmitted, thus constructing a dataset for transmission; points not selected in the heatmap are discarded. This significantly reduces the amount of data transmitted to the host while retaining the necessary key information, thereby significantly reducing the data throughput pressure on the radar system.

[0070] Furthermore, in step S300 of the radar data transmission method described above, when forming a transmission dataset based on the selected data points, the method also includes compressing the coordinates and energy values ​​of the selected data points in the heat map data, which can further reduce the amount of data.

[0071] Specifically, each maximum point (or inflection point, saddle point) and its neighborhood data are compressed as a group. The compression scheme can be to fit each group with a P-order polynomial model, calculate the least squares fitting coefficients, obtain the residual between the fitted data and the real data, and encode and compress the residual data (such as Huffman coding, quantization into low-bit-width data, or differential coding). The fitting coefficients and the encoded and compressed residuals are used as the corresponding transmission content.

[0072] Understandably, data within a neighborhood is correlated, and the fitted value will be close to the true value, resulting in a relatively small residual. Encoding a small residual requires far fewer bits than directly encoding the original data. Optionally, based on the correlation of the data, a simpler differential compression scheme can be used. This involves grouping each maximum point (or inflection point, saddle point) and its neighborhood data into a group, selecting the maximum point (or inflection point, saddle point) as a reference point, calculating the difference between the neighborhood points and the reference point, and encoding and compressing the difference (e.g., Huffman coding, quantization into low-bit-width data, or differential coding). The reference point and the compressed residual serve as the corresponding transmission content. It should be noted that regardless of whether the data is compressed or filtered, the transmitted content must include the coordinates of each transmitted data point so that the host can reconstruct the heatmap. To further reduce the amount of data transmitted, the coordinates of the data points to be transmitted can be compressed, such as transmitting only the coordinates of the reference point (i.e., the maximum point, inflection point, or saddle point) in its entirety, while the coordinates of the points in the neighborhood of the center point are not transmitted. Instead, the coordinates of the points in the neighborhood are implicitly represented by agreeing on the data transmission order with the host in advance. That is, the radar and the host agree on the coordinate offset of each transmitted data point relative to the reference point within a group. The host can deduce the coordinates of each transmitted data point based on the data reception order within the group and the coordinates of the center point.

[0073] Finally, in step S400 of the radar data transmission method described above, reconstructing the heatmap makes it continuous, facilitating subsequent signal processing and spectrum visualization. In step S400, the host's operation of reconstructing the heatmap data includes the following optional methods: filling the locations of discarded data points with fixed constant values; and or, filling the locations of discarded data points with random values ​​that follow a preset statistical distribution, with the radar noise floor as a reference.

[0074] Feasible methods for filling the locations of discarded data points with fixed constant values ​​include: for heatmap data expressed in linear amplitude units, padding with zeros at the missing locations; for heatmap data expressed in logarithmic power units, filling the missing locations with values ​​that are 3 to 5 dB lower than the minimum signal power that the radar system can detect.

[0075] A more preferred approach is to fill the locations of discarded data points with random values ​​that follow a preset statistical distribution. Feasible methods include:

[0076] ① Determine random values ​​using a Gaussian distribution with radar noise floor energy as the variance; the specific process is as follows: Let the radar noise floor energy be... This produces a mean of 0 and a variance of . The Gaussian white noise signal is used to fill the positions of the discarded data with the modulus or modulus square of the generated signal. Among them, filling with random values ​​based on a Gaussian distribution can bring pseudo-random fluctuations compared to filling with constant values, simulating the data jitter caused by noise in real heat maps. This results in better visual effects when visualized on a host computer. It is particularly suitable for scenarios where thermal noise is dominant (determined by the characteristics of radar circuits), since thermal noise usually follows a Gaussian distribution, and random values ​​based on a Gaussian distribution are closer to reality.

[0077] ② Determine random values ​​based on a uniform distribution within a preset range; the specific process is as follows: generate uniformly distributed random values ​​in the interval [A, B], and fill the positions of discarded data with the generated values, where B is less than the preset first threshold value M1, and A is less than B. Filling with uniformly distributed random values ​​introduces pseudo-random fluctuations compared to filling with constant values, simulating data jitter caused by noise in a real heatmap. This results in better visual effects when visualized on a host computer. Generating uniformly distributed random numbers is relatively simple and has low complexity.

[0078] ③ Determine random values ​​based on the energy spectrum characteristics of flicker noise; where the essence of flicker noise is that its power spectral density satisfies The specific process is as follows: First, a time-domain Gaussian white noise signal is generated. Then, an FFT is performed on this time-domain signal to obtain a frequency-domain Gaussian white noise signal. The amplitude spectrum characteristics are designed to satisfy... A frequency domain filter is used to filter the frequency domain Gaussian white noise signal. After IFFT processing to return it to the time domain, the real part is taken and the gain is adjusted to obtain a random signal that satisfies the energy spectrum characteristics of flicker noise. The modulus or square of the generated signal is then filled into the positions of discarded data. Filling with random values ​​determined according to the energy spectrum characteristics of flicker noise, compared to filling with constant values, can introduce pseudo-random fluctuations, simulating data jitter caused by noise in real heat maps. This results in better visual effects when visualized on a host computer. It is suitable for scenarios where flicker noise is the dominant noise component (determined by the characteristics of radar circuitry), and more closely reflects the corresponding actual situation.

[0079] ④ Random values ​​are determined by filtering Gaussian white noise using a filter. The specific process is as follows: First, a time-domain Gaussian white noise signal with a mean of 0 and a variance of 1 is generated. Then, a target filter (high-pass / low-pass / band-pass) is designed according to requirements. Finally, the Gaussian white noise signal and the target filter are convolved in the time domain to achieve filtering, resulting in a random signal after filtering the Gaussian white noise. The modulus or square of the generated signal is then filled into the positions of the discarded data. Similar to the above, filling random values ​​determined by filtering Gaussian white noise introduces pseudo-random fluctuations, simulating data jitter caused by noise in real heatmaps. This provides a better visual effect when visualized on a host computer. It is suitable for scenarios dominated by other types of noise. By using Gaussian white noise in conjunction with frequency domain shaping of the filter, the characteristics of a specific type of noise can be simulated, making the filled pseudo-random values ​​more realistic and closely matching the actual noise jitter characteristics.

[0080] It should be noted that the random values ​​generated by all the above methods in the preset random distribution should be less than the preset first threshold value M1.

[0081] See Figure 2 As shown in the figure, a specific embodiment of the present invention also provides a radar system, including a radar and a host computer connected to the radar in communication; the radar and the host computer cooperate to perform the radar data transmission method described above.

[0082] Specifically, the radar includes:

[0083] The heat map data acquisition module is used to detect the environment and acquire heat map data;

[0084] The data filtering module filters the heat map data, including calculating the energy value and curvature value of each data point in the heat map. The selected data points include data points in the heat map data whose curvature value is greater than the second threshold M2 and whose energy value is greater than the third threshold M3.

[0085] The dataset generation module assembles a transmission dataset based on selected data points, the transmission dataset containing the coordinates and energy values ​​of the selected data points in the heatmap data;

[0086] The data sending module is used to send the data set to the host.

[0087] Specifically, the host includes:

[0088] A data receiving module is used to receive the transmitted dataset;

[0089] The heatmap reconstruction module, based on the received transmitted dataset, performs pseudo-random reconstruction of the heatmap data by filling the locations of discarded data with preset values ​​referenced to radar noise floor.

[0090] The above description discloses only preferred embodiments of the present invention and should not be construed as limiting the scope of the present invention. Therefore, equivalent variations made in accordance with the claims of the present invention are still within the scope of the present invention.

Claims

1. A radar data transmission method, characterized by, Comprise: S100, control radar to detect the environment, obtain heat map data; S200, control radar to the heat map data data point screening: including calculating the energy value and curvature value of each data point in the heat map operation, the selected data points include the data points in the heat map data whose curvature value is greater than the second threshold M2 and energy value is greater than the third threshold M3; S300, based on the selected data points to form the transmission data set, the transmission data set contains the coordinates and energy values of the selected data points in the heat map data; S400, control radar to host sends the transmission data set, host based on the data received by the transmission data set, by filling in the position of the discarded data with the preset value referred to radar noise, the heat map data is pseudo-randomly reconstructed.

2. The radar data transmission method of claim 1, wherein, The heat map data includes: radar obtained distance dimension, doppler dimension, pitch angle dimension, azimuth angle dimension or any combination of four dimensions to form two-dimensional, three-dimensional or four-dimensional heat map data.

3. The radar data transmission method of claim 1, wherein, In step S200, when the heat map data is screened, the selected data points also include the maximum value points in the heat map data whose energy value is greater than the first threshold M1 and a plurality of points in the preset neighborhood, M3≥M1.

4. The radar data transmission method of claim 3, wherein, In step S200, for one-dimensional or two-dimensional heat map data, when the heat map data is screened, it also includes the operation of inflection point identification of each data point in the heat map, and the selected data points also include the inflection points in the heat map data whose energy value is greater than the first threshold M1 and a plurality of data points in the preset neighborhood of the inflection points.

5. The radar data transmission method of claim 3, wherein, In step S200, for heat map data above two dimensions, when the heat map data is screened, it also includes the operation of identifying the saddle point of each data point in the heat map, and the selected data points also include the saddle points in the heat map data whose energy value is greater than the first threshold M1 and a plurality of data points in the preset neighborhood of the saddle points.

6. The radar data transmission method of claim 1, wherein, In step S300, based on the selected data points to form the transmission data set, it also includes the operation of compressing the coordinates and energy value data of the selected data points in the heat map data.

7. The radar data transmission method of claim 3, wherein, In step S400, the operation mode of pseudo-randomly reconstructing the heat map data is: Fill in the position of the discarded data point with a random value subject to a preset statistical distribution.

8. The radar data transmission method of claim 7, wherein, In step S400, the determination method of the random value of the preset statistical distribution includes at least one of the following: ① determine the random value with the radar noise energy as the variance of the Gaussian distribution; ② determine the random value with the uniform distribution in the preset range; ③ determine the random value according to the flicker noise energy spectrum characteristics; ④ determine the random value by filtering the Gaussian white noise through a filter; The random value is less than the preset first threshold M1.

9. A radar system comprising a radar and a host machine in communication connection with the radar; the radar and the host machine cooperate to perform the radar data transmission method of any one of claims 1-8.

10. The radar system of claim 9, wherein, The radar comprises: A heat map data acquisition module for detecting the environment and obtaining heat map data; A data screening module for screening data points of the heat map data, including calculating the energy value and curvature value of each data point in the heat map operation, the selected data points include the data points in the heat map data whose curvature value is greater than the second threshold M2 and energy value is greater than the third threshold M3; The data set generation module generates a transmission data set based on the selected data points, and the transmission data set contains coordinates and energy values of the selected data points in the heat map data. The data sending module is configured to send the transmission data set to the host computer. The host computer comprises: The data receiving module is configured to receive the transmission data set. The heat map reconstruction module performs pseudo-random reconstruction on the heat map data by filling a preset value with reference to a radar floor noise at a position of the discarded data based on the received data of the transmission data set.

Images