Multi-radar cooperative detection mutual interference calculation method based on GPU

Abstract

The invention discloses a multi-radar cooperative detection mutual interference calculation method based on a GPU, and the method comprises the steps: reading transmitted signals of different radars from a radar system and arrival signals received by each radar, dividing the number of thread blocks included in a two-dimensional grid for echo and mutual interference calculation according to the total number of radars, the number of transmitted pulse sequences, the number of targets and the number of mutual interference paths, and calculating the number of the thread blocks included in the two-dimensional grid; and determining the number of threads included in each thread block. A GPU kernel function is called at the host end to start calculation, and each thread in the grid calculates relevant parameters of echo signals or mutual interference signals. According to the method, the powerful parallel computing capacity of the GPU is fully utilized, traditional CPU serial processing is converted into large-scale parallel processing, the processing speed of mass data in multi-radar cooperative detection is remarkably improved, the problems that in a CPU computing method, computing time is long, real-time performance is poor, and hardware resources are not fully utilized are effectively solved, and the method is suitable for mass data processing. And efficient and real-time radar mutual interference analysis and signal processing are realized.

Description

Technical Field

[0001] This application belongs to the field of radar signal processing technology, specifically relating to a GPU-based method for calculating mutual interference in multi-radar cooperative detection. Background Technology

[0002] With the rapid increase in the number of modern radars, mutual interference between radars has become a significant problem. When multiple radars operate in similar frequency bands, the signal transmitted by one radar may be received by another, creating unwanted interference signals. In severe cases, this can lead to false alarms, missed detections, or target measurement errors. Furthermore, in real-world environments, radar signals may be reflected through buildings, terrain, and other pathways, resulting in multipath effects. This causes changes in signal amplitude, phase, and time delay, further complicating the radar's ability to distinguish real targets. To assess and predict this interference effect, simulations in a laboratory environment are necessary. The core of the simulation is to calculate the mutual interference signals from other radars to the main radar in real time, while also simulating the signal distortion and time delay spread caused by multipath effects and superimposing these onto the echo signal of the main radar.

[0003] Currently, the conventional implementation scheme is based on simulation methods using general-purpose CPUs. This scheme addresses the complex calculations of echoes and mutual interference by serially and cyclically calculating the echoes of different radars to different targets, while also calculating the mutual interference between radars and the effects of multipath effects. The CPU simulation method has the following shortcomings: CPU-based multi-radar cooperative detection and mutual interference calculation methods suffer from low processing efficiency when handling large amounts of real-time data, failing to achieve real-time processing. Furthermore, these methods utilize only the CPU's computing power, neglecting the powerful parallel computing capabilities of GPUs, resulting in a waste of hardware resources. Summary of the Invention

[0004] To address the aforementioned problems in the existing technology, this application provides a GPU-based method for calculating mutual interference in multi-radar cooperative detection. The technical problem to be solved by this application is achieved through the following technical solution: A GPU-based method for calculating mutual interference in multi-radar cooperative detection includes: S100, read the transmitted signals of different radars from the radar system, and the received arrival signals after each radar transmits according to the transmitted signal; wherein, the arrival signal consists of the echo signal fed back by the target and the mutual interference signal; S200, based on the total number of radars transmitting signals, the number of pulse sequences transmitted by each radar, the number of targets, and the number of mutual interference paths, divides the two-dimensional thread grid for echo and mutual interference calculation into two-dimensional thread blocks, and determines the number of two-dimensional parallel threads included in each two-dimensional thread block. S300, on the host side, the echo and / or inter-interference calculation is started by calling the corresponding GPU kernel function, so that each two-dimensional parallel thread in the two-dimensional thread grid corresponding to the echo calculation calculates the relevant parameters of the echo signal and / or each two-dimensional parallel thread in the two-dimensional thread grid corresponding to the inter-interference calculation calculates the relevant parameters of the inter-interference signal.

[0005] A GPU-based multi-radar cooperative detection and mutual interference computing platform includes: The configuration module is used to read the transmitted signals of different radars from the radar system, as well as the received signals after each radar transmits according to the transmitted signal; wherein, the received signals consist of the echo signals fed back by the target and the radar mutual interference signals; based on the total number of radars transmitting signals, the number of transmitted pulse sequences of each radar, the number of targets and the number of mutual interference paths, the module plans the number of two-dimensional thread blocks included in the two-dimensional thread grid for echo and mutual interference calculation, and determines the number of two-dimensional parallel threads included in each two-dimensional thread block; The execution module is used to initiate echo and / or inter-interference calculations on the host side by calling the corresponding GPU kernel function, so that each two-dimensional parallel thread in the two-dimensional thread grid corresponding to the echo calculation calculates the relevant parameters of the echo signal and / or each two-dimensional parallel thread in the two-dimensional thread grid corresponding to the inter-interference calculation calculates the relevant parameters of the inter-interference signal.

[0006] Beneficial effects 1. This application discloses a GPU-based multi-radar cooperative detection mutual interference calculation method, comprising: reading the transmitted signals of different radars and the received arrival signals of each radar from the radar system, wherein the arrival signals include target echoes and radar mutual interference signals; dividing the two-dimensional thread grid for echo and mutual interference calculation into two-dimensional thread blocks according to the total number of radars, the number of transmission sequences, the number of targets, and the number of mutual interference paths, and determining the number of two-dimensional parallel threads included in each two-dimensional thread block. On the host side, the GPU kernel function is called to start the calculation according to the above configuration, and each two-dimensional parallel thread in the grid calculates a specific task, such as calculating the relevant parameters of the echo signal or mutual interference signal, according to its global index. This method makes full use of the powerful parallel computing capabilities of the GPU, transforming the traditional CPU serial processing into large-scale parallel processing, significantly improving the processing speed of massive data in multi-radar cooperative detection, effectively solving the problems of long calculation time, poor real-time performance, and waste of hardware resources due to failure to utilize GPU computing power in the CPU calculation method, and realizing efficient and real-time radar mutual interference analysis and signal processing.

[0007] 2. When processing large amounts of data, this application leverages the powerful parallel processing capabilities of the GPU, with each two-dimensional parallel thread calculating one data point simultaneously, thus improving computational efficiency. It also makes the use of hardware resources more rational and efficient, resulting in an overall improvement in data processing speed and hardware resource utilization. Furthermore, this application is highly efficient in computation, therefore, there is no need to reduce the complexity of the algorithm when processing large amounts of data.

[0008] The present application will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0009] Figure 1 This is a flowchart illustrating the GPU-based multi-radar cooperative detection mutual interference calculation method provided in this application; Figure 2 This is a schematic diagram of multi-radar cooperative detection and mutual interference provided in this application; Figure 3 This is the echo calculation flowchart provided in this application; Figure 4 This is the flowchart of the mutual interference calculation provided in this application; Figure 5 This is a schematic diagram of the GPU two-dimensional thread grid provided in this application; Figure 6 These are schematic diagrams of the coordinate systems provided in this application. Detailed Implementation

[0010] The present application will be described in further detail below with reference to specific embodiments, but the implementation of the present application is not limited thereto.

[0011] like Figure 1 As shown, this application provides a GPU-based multi-radar cooperative detection mutual interference calculation method, including: S100, read the transmitted signals of different radars from the radar system, as well as the received arrival signals after each radar transmits according to the transmitted signal; wherein, the arrival signal consists of the echo signal fed back by the target and the radar mutual interference signal; refer to Figure 2 When N radars operate simultaneously, radar 1, while detecting target 2, transmits a signal and receives the echo signal from target 2. Target 2 is an extended target with eight scattering centers, each of which returns an echo signal. Simultaneously, radar 1 is also subject to interference from other radars, receiving signals transmitted by other radars and signals arriving at radar 1 after multipath effects. Therefore, the signal received by radar 1 consists of the effective echo from target 2, direct interference from other radars, and radar interference signals arriving after multipath effects.

[0012] Each radar transmits an independent signal sequence, and the calculation of target echo and inter-radar interference is also independent for each signal. When the number of radars and transmitted signals is large, this constitutes classic intensive and parallel data, which can be processed using CUDA for large-scale parallel computation. The GPU computing grid and the number of threads in each two-dimensional parallel thread block are rationally partitioned. The GPU kernel function is called on the host side to perform radar-to-target echo calculation and inter-radar interference calculation, and the calculation results are stored in the GPU device memory in an orderly manner.

[0013] S200: Based on the total number of radars transmitting signals, the number of transmitted pulse sequences per radar, the number of targets, and the number of interfering paths, plan the number of two-dimensional thread blocks included in the two-dimensional thread grid for echo and interfering calculations, and determine the number of two-dimensional parallel threads included in each two-dimensional thread block. The number of targets is determined based on the effective echo signals in the arriving signals. The two-dimensional thread grids for echo and mutual interference calculations contain the same number of two-dimensional thread blocks, which is the number of radars multiplied by the number of transmitted pulse sequences. The number of threads in each two-dimensional thread block of the two-dimensional thread grid corresponding to the echo calculation is the number of targets multiplied by the number of target extension points. The number of threads in each two-dimensional thread block of the two-dimensional thread grid corresponding to the mutual interference calculation is the number of radars multiplied by the number of mutual interference paths (direct arrival and multipath effects).

[0014] For example, taking 8 radars transmitting 50 pulses within a simulation step, for 16 extended targets, each target consists of 8 scattering centers, and there are 3 interfering multipath paths. It is necessary to calculate 8*50*16*8=51200 echo signals and 8*50*7*(3+1)=11200 interfering signals.

[0015] In this application, the GPU kernel functions of the two-dimensional thread grid corresponding to the echo calculation and the two-dimensional thread grid corresponding to the interference calculation are different. They are independent and parallel, running on the same host or different host.

[0016] S300, on the host side, the echo and / or inter-interference calculation is started by calling the corresponding GPU kernel function, so that each two-dimensional parallel thread in the two-dimensional thread grid corresponding to the echo calculation calculates the relevant parameters of the echo signal and / or each two-dimensional parallel thread in the two-dimensional thread grid corresponding to the inter-interference calculation calculates the relevant parameters of the inter-interference signal.

[0017] In one specific embodiment of this application, S300 includes: S310, Configure the two-dimensional thread grid for echo calculation on the host side and call the GPU kernel function to start the echo calculation, so that each two-dimensional parallel thread in the two-dimensional thread grid corresponding to the echo calculation reads data from the GPU device and calculates the relevant parameters of the echo signal received by the radar using the same radar as the transmitting and receiving source. S320: Configure a two-dimensional thread grid for mutual interference calculation on the host side and call the GPU kernel function to start mutual interference calculation, so that each two-dimensional parallel thread in the two-dimensional thread grid corresponding to the mutual interference calculation reads data from the GPU device, uses one radar as a receiving source and other radars as transmitting sources, and calculates the relevant parameters of the receiving source radar and the relevant parameters of the received mutual interference signal.

[0018] S310 and S320 are two independent and parallel steps that do not have a logical order.

[0019] In one specific embodiment of this application, combined with Figure 3 and Figure 5 In S310, using the same radar as both the transmitter and receiver, the relevant parameters of the echo signal received by this radar are calculated, including: a1, using the same radar that serves as both the transmitter and receiver as the execution radar, and establishing the enu coordinate system with the radar's body as the origin; a2, convert the target's ecef coordinates to the execution radar's enu coordinates, and calculate the first straight-line distance and the first radial relative velocity between the execution radar and the target; This step calculates the x and y direction indices based on the thread block ID (blockIdx.x, blockIdx.y) and thread ID (threadIdx.x, threadIdx.y). It then calculates the target position information parameters for the current moment, establishes an ENU coordinate system with the receiving radar's body as the origin, transforms the target's ECEF coordinates into the receiving radar's ENU, and calculates the first straight-line distance between the radar and the target. .

[0020]

[0021] In the formula, It is the value of the enu coordinates of the target or transmitting radar, established with the aircraft body executing the radar as the origin. In echo calculations, the target's ECEF coordinates are used; in mutual interference calculations, the transmitting radar's ECEF coordinates are used. These are the ECEF coordinates of the radar. It refers to the latitude and longitude of the radar.

[0022] Transform the target's ENU velocity into the receiving radar's ENU coordinate system and calculate the radial relative velocity. .

[0023]

[0024] In the formula, Let the velocity of the target be in its own enu coordinate system. The target's velocity coordinates in the radar ENU coordinate system will be converted into unit vectors. Calculate the radial relative velocity .

[0025] a3. Based on the first straight-line distance and the first radial relative velocity, calculate the propagation delay, phase change, Doppler shift, and received amplitude information of the echo signal.

[0026] Calculate the propagation delay of the echo signal. Calculate the phase change of the received signal , The frequency of the transmitted signal, Given the speed of light in a vacuum, the relative velocity of the receiving channel with respect to the target is calculated to determine the Doppler frequency shift. Calculate the received amplitude information of the received signal. ,in The output power of the radar transmitter. For the transmit antenna gain, To receive antenna gain, It is the wavelength of the radar signal. Radar cross section, To receive the straight-line distance between the radar and the target, This is due to system overhead. The data is saved, and the computation of this GPU thread ends.

[0027] In one specific embodiment of this application, combined with Figure 4 and Figure 5 In S320, one radar is used as the receiving source and other radars are used as transmitting sources. The relevant parameters for calculating the mutual interference signals received by this radar from other radars include: b1, take the radar that serves as the receiving source as the executing radar, and establish the enu coordinate system with the body of the executing radar as the origin. b2, transform the ecef coordinates of the transmitting radar to the enu coordinates of the executing radar, and calculate the second straight-line distance and the second radial relative velocity between the transmitting radar and the executing radar; b3, based on the second straight-line distance and the second radial relative velocity, calculate the propagation delay, phase change, Doppler shift and amplitude information to the receiver of the mutual interference signal; b4, for each virtual multipath point, calculates the propagation time delay, Doppler shift, and beam amplitude information of the interfering signal reaching the executing radar based on its reflection coefficient.

[0028] This application calculates the x and y direction indices based on the thread block ID (blockIdx.x, blockIdx.y) and thread ID (threadIdx.x, threadIdx.y). Since inter-radar interference occurs between different radars, when the receiving and transmitting radars are the same radar, interference does not exist, and the thread exits the calculation. When the frequency of the transmitted signal is outside the frequency range of the receiving radar, the interference signal is empty, and the thread exits the calculation. The current position information parameters of the transmitting radar are calculated, and an ENU coordinate system is established with the receiving radar's body as the origin. The ECEF coordinates of the transmitting radar are converted to the ENU coordinates of the receiving radar. The straight-line distance between the receiving and transmitting radars is calculated. Transform the transmitting radar's own ENU velocity into the receiving radar's ENU coordinate system, and calculate the radial relative velocity. .

[0029] Calculate the propagation delay of interfering signals The relative velocity of the receiving channel with respect to the transmitting radar is calculated to determine the Doppler frequency shift. Calculate the received amplitude information of the received signal. .

[0030] Insert virtual multipath points, determine the terrain type at the multipath points and obtain the reflection coefficient, and calculate the distance from the emitted radar to the multipath points. and the distance from the multipath point to the receiving radar The signal propagation distance is the sum of the two. The signal propagation time delay is... The Doppler frequency shift of the receiving radar is , To achieve the Doppler frequency shift of the transmitted radar to the multipath point, The Doppler frequency shift from the multipath point to the receiving radar is calculated. The beam amplitude information of the receiving radar is then calculated. The data is saved, and the computation of this GPU thread ends.

[0031] The relevant parameters of the echo signal, the relevant parameters of the receiving source radar, and the relevant parameters of the mutual interference signal mentioned in this application are all stored in the GPU device memory.

[0032] This application also provides a GPU-based multi-radar cooperative detection and mutual interference calculation platform, including: The configuration module is used to read the transmitted signals of different radars from the radar system, as well as the received signals after each radar transmits according to the transmitted signal; wherein, the received signals consist of the echo signals fed back by the target and the radar mutual interference signals; based on the total number of radars transmitting signals, the number of transmitted pulse sequences of each radar, the number of targets, and the number of mutual interference paths, the number of two-dimensional thread blocks included in the two-dimensional thread grid for echo and mutual interference calculation is divided, and the number of two-dimensional parallel threads included in each two-dimensional thread block is determined; The execution module is used to initiate echo and / or inter-interference calculations on the host side by calling the corresponding GPU kernel function, so that each two-dimensional parallel thread in the two-dimensional thread grid corresponding to the echo calculation calculates the relevant parameters of the echo signal and / or each two-dimensional parallel thread in the two-dimensional thread grid corresponding to the inter-interference calculation calculates the relevant parameters of the inter-interference signal.

[0033] To verify the effectiveness of this application, a simulation was performed. The simulation environment configuration is as follows: The CPU is an Intel i7-12700 with 16GB of RAM, the GPU is an NVIDIA GeForce RTX 5080, and the operating system is Windows 10 64-bit. This simulation experiment reads various radar information, including radar antenna data, azimuth information, carrier frequency, pulse width, and other parameters, and stores them in a global radiation source management list for later use. The radar azimuth information is established in the following coordinate system (see appendix). Figure 6 Based on the information of each radar at the current moment, calculate the signal sequence emitted by the radar within the simulation time and store it in the global signal memory pool. Allocate GPU memory and copy scene parameters, radar parameters, target parameters, and emitted signals from the CPU memory to the GPU device memory. Divide the grid according to the number of radars and the number of radar emitted pulses within the simulation step. Specifically, the number of thread blocks in the grid is the number of radars × the number of pulses within the radar simulation step. The number of threads in each two-dimensional parallel thread block in the echo module is the number of targets × the number of target extension points, and the number of threads in each two-dimensional parallel thread block in the interference module is the number of radars × the number of signal arrival paths (direct arrival and multipath effects). The host calls the GPU kernel function to start calculating the echo signal, using the same radar as the transmitter and receiver source, to calculate the target's echo signal. The host calls the GPU kernel function to start calculating the interference signal, using one radar as the receiver source and other radars as transmitter sources, to calculate the interference signals received by that radar from other radars. Save and copy the calculation results back to the host.

[0034] As shown in Table 1 below, the calculation results of the CPU and GPU under different parameters are as follows:

[0035] Table 1 shows the number of targets in this application: m×n, indicating that there are m extended targets, and each extended target has n scattering centers. Multipath Count: The total number of signal paths reaching the receiver after reflection, diffraction, and scattering through different paths. Pulse Repetition Interval: The time interval between two adjacent pulses. Simulation Step Size: The duration of the simulation. Number of Pulses: The number of pulses in the transmitted signal within the simulation step size.

[0036] Table 1 shows that, with the same parameters, the CPU computation time is much longer than the simulation step size. By using the technical solution of this application, the GPU parallel processing significantly improves the computation time, allowing the calculation to be completed within the simulation step size, thus meeting the real-time requirements.

[0037] It is worth noting that the terms "first" and "second" in this application are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this application, "multiple" means two or more, unless otherwise explicitly specified.

[0038] The above description, in conjunction with specific preferred embodiments, provides a further detailed explanation of this application and should not be construed as limiting the specific implementation of this application to these descriptions. For those skilled in the art, various simple deductions or substitutions can be made without departing from the concept of this application, and all such modifications or substitutions should be considered within the scope of protection of this application.

Claims

1. A GPU-based method for calculating mutual interference in multi-radar cooperative detection, characterized in that, include: S100, read the transmitted signals of different radars from the radar system, as well as the received arrival signals after each radar transmits according to the transmitted signal; wherein, the arrival signal consists of the echo signal fed back by the target and the mutual interference signal; S200, based on the total number of radars transmitting signals, the number of pulse sequences transmitted by each radar, the number of targets, and the number of mutual interference paths, divides the two-dimensional thread grid for echo and mutual interference calculation into two-dimensional thread blocks, and determines the number of two-dimensional parallel threads included in each two-dimensional thread block. S300, on the host side, the echo and / or inter-interference calculation is started by calling the corresponding GPU kernel function, so that each two-dimensional parallel thread in the two-dimensional thread grid corresponding to the echo calculation calculates the relevant parameters of the echo signal and / or each two-dimensional parallel thread in the two-dimensional thread grid corresponding to the inter-interference calculation calculates the relevant parameters of the inter-interference signal.

2. The GPU-based multi-radar cooperative detection mutual interference calculation method according to claim 1, characterized in that, The two-dimensional thread grids for echo and mutual interference calculations contain the same number of two-dimensional thread blocks, which is the number of radars multiplied by the number of transmitted pulse sequences; the number of threads in each two-dimensional thread block of the two-dimensional thread grid corresponding to the echo calculation is the number of targets multiplied by the number of target extension points; the number of threads in each two-dimensional thread block of the two-dimensional thread grid corresponding to the mutual interference calculation is the number of radars multiplied by the number of mutual interference paths.

3. The GPU-based multi-radar cooperative detection mutual interference calculation method according to claim 1, characterized in that, The GPU kernel functions of the two-dimensional thread grid corresponding to the start echo calculation and the two-dimensional thread grid corresponding to the interference calculation in S300 are different. They are independent and parallel, running on the same host or different host.

4. The GPU-based multi-radar cooperative detection mutual interference calculation method according to claim 1, characterized in that, The target quantity is determined based on the valid echo signal in the arrival signal.

5. The GPU-based multi-radar cooperative detection mutual interference calculation method according to claim 1, characterized in that, The S300 includes: S310, Configure the two-dimensional thread grid for echo calculation on the host side and call the GPU kernel function to start the echo calculation, so that each two-dimensional parallel thread in the two-dimensional thread grid corresponding to the echo calculation reads data from the GPU device and calculates the relevant parameters of the echo signal received by the radar using the same radar as the transmitting and receiving source. S320: Configure a two-dimensional thread grid for mutual interference calculation on the host side and call the GPU kernel function to start mutual interference calculation. This allows each two-dimensional parallel thread in the two-dimensional thread grid corresponding to the mutual interference calculation to read data from the GPU device. Using one radar as the receiving source and other radars as the transmitting source, calculate the relevant parameters of the receiving source radar and the relevant parameters of the received mutual interference signal.

6. The GPU-based multi-radar cooperative detection mutual interference calculation method according to claim 5, characterized in that, In S310, using the same radar as both the transmitter and receiver, the relevant parameters of the echo signal received by this radar are calculated, including: a1, using the same radar that serves as both the transmitter and receiver as the execution radar, and establishing the enu coordinate system with the radar's body as the origin; a2, convert the target's ecef coordinates to the execution radar's enu coordinates, and calculate the first straight-line distance and the first radial relative velocity between the execution radar and the target; a3. Based on the first straight-line distance and the first radial relative velocity, calculate the propagation delay, phase change, Doppler shift, and received amplitude information of the echo signal.

7. The GPU-based multi-radar cooperative detection mutual interference calculation method according to claim 5, characterized in that, In S320, one radar is used as the receiving source and other radars are used as transmitting sources. The relevant parameters for calculating the mutual interference signals received by this radar from other radars include: b1, take the radar that serves as the receiving source as the executing radar, and establish the enu coordinate system with the body of the executing radar as the origin. b2, transform the ecef coordinates of the transmitting radar to the enu coordinates of the executing radar, and calculate the second straight-line distance and the second radial relative velocity between the transmitting radar and the executing radar; b3, based on the second straight-line distance and the second radial relative velocity, calculate the propagation delay, phase change, Doppler shift and amplitude information to the receiver of the mutual interference signal; b4, for each virtual multipath point, calculates the propagation time delay of the interfering signal to the executing radar, the Doppler frequency shift of the executing radar, and the beam amplitude information based on its reflection coefficient.

8. The GPU-based multi-radar cooperative detection mutual interference calculation method according to claim 6 or 7, characterized in that, The target or transmitting radar, in the enu coordinate system with the radar's body as the origin, is represented as follows: In the formula, It is the value of the enu coordinates of the target or transmitting radar, established with the aircraft body executing the radar as the origin. In echo calculations, the target's ECEF coordinates are used; in mutual interference calculations, the transmitting radar's ECEF coordinates are used. These are the ECEF coordinates of the radar. It refers to the latitude and longitude of the radar.

9. The GPU-based multi-radar cooperative detection mutual interference calculation method according to claim 5, characterized in that, The relevant parameters of the echo signal, the relevant parameters of the receiving source radar, and the relevant parameters of the mutual interference signal are all stored in the GPU device memory.

10. A GPU-based multi-radar cooperative detection mutual interference computing platform, characterized in that, include: The configuration module is used to read the transmitted signals of different radars from the radar system, as well as the received signals after each radar transmits according to the transmitted signal; wherein, the received signals consist of the echo signals fed back by the target and the radar mutual interference signals; based on the total number of radars transmitting signals, the number of transmitted pulse sequences of each radar, the number of targets, and the number of mutual interference paths, the module plans the number of two-dimensional thread blocks included in the two-dimensional thread grid for echo and mutual interference calculation, and determines the number of two-dimensional parallel threads included in each two-dimensional thread block; The execution module is used to initiate echo and / or inter-interference calculations on the host side by calling the corresponding GPU kernel function, so that each two-dimensional parallel thread in the two-dimensional thread grid corresponding to the echo calculation calculates the relevant parameters of the echo signal and / or each two-dimensional parallel thread in the two-dimensional thread grid corresponding to the inter-interference calculation calculates the relevant parameters of the inter-interference signal.

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