# GPU-Accelerated Signal Processing for Passive Bistatic Radar

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## Abstract

**:**

## 1. Introduction

## 2. Radar Signal Processing

#### 2.1. Radar System Analysis

#### 2.2. Clutter Suppression

#### 2.3. Range Doppler Processing

#### 2.4. CFAR Processing

## 3. GPU Acceleration Realization

#### 3.1. GPU Parallel Algorithm for Clutter Suppression

Algorithm 1: Kernel Function SigDivision |

input: Reference signal ${S}_{ref}$ and segment number i.output: Segmented signal matrix ${X}_{i}$ 1 $\mathrm{idx}=\mathrm{threadIdx}.\mathrm{x}+\mathrm{blockIdx}.\mathrm{x}*\mathrm{blockDim}{.\mathrm{x}+\mathrm{i}*\mathrm{N}}_{\mathrm{B}}$;2 $\mathrm{idy}=\mathrm{threadIdx}.\mathrm{y}+\mathrm{blockIdx}.\mathrm{y}*\mathrm{blockDim}.\mathrm{y}$;3 $\mathrm{R}=\mathrm{K}-1$; 4 $\mathbf{i}\mathbf{f}idyK\wedge idx(i+1)\ast {N}_{B}\wedge i\ast {N}_{B}\le idx\mathbf{t}\mathbf{h}\mathbf{e}\mathbf{n}$5 $b\leftarrow idy+(idx\mathrm{mod}{N}_{B})\ast K$;6 $a\leftarrow R-idy+idx$;7 ${X}_{i}\left[b\right].x\leftarrow ref\left[a\right].x$;8 ${X}_{i}\left[b\right].y\leftarrow ref\left[a\right].y$;9 $\mathbf{e}\mathbf{n}\mathbf{d}$ |

#### 3.2. GPU Parallel Algorithm for Range Doppler Processing

- (1)
- Using the cufftPlan2d function, zero-padding and FFT computations are performed on the clutter-suppressed echo signal, ${S}_{echo}$, and the reference signal;
- (2)
- Complex multiplication on the transformed signals using a kernel function is performed. After that, the range–domain correlation operation can be completed by performing IFFT computations;
- (3)
- gpuFilter() is used to achieve downsampling. To avoid aliasing issues stemming from downsampling, it is imperative to apply anti-aliasing filtering concurrently during the downsampling procedure;
- (4)
- FFT is performed on the correlated data along the Doppler dimension.

#### 3.3. GPU Parallel Algorithm for CFAR Processing

- (1)
- Malloc() and cudaMalloc() are used to allocate space for CPU variables and GPU variables;
- (2)
- The cudaMemcpy() function is used with the cudaMemcpyHostToDevice parameter to copy CPU variables to GPU;
- (3)
- The thread grid and block sizes are allocated and the square law detection kernel function on the GPU, which can be called from official CUDA libraries, is executed;
- (4)
- Threshold calculation and the decision making kernel function on the detection results are called;
- (5)
- The cudaMemcpy() function is used with the cudaMemcpyDeviceToHost parameter to copy the results from the GPU back to the CPU;
- (6)
- Free() and cudaFree() are called to free the memory resources consumed on the CPU and GPU.

## 4. Experimental Results

#### 4.1. Experimental Settings

#### 4.2. GPU Parallel Algorithm Correctness Verification

#### 4.3. GPU Parallel Algorithm Acceleration Performance Verification

## 5. Conclusions

## Author Contributions

## Funding

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

## References

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**Figure 9.**(

**a**–

**d**) Target detection results after signal processing by serial CPU and parallel GPU algorithm.

**Figure 10.**Average execution time of serial and parallel algorithms for passive bistatic radar signal processing in various stages.

Device Type | Model | Key Parameters |
---|---|---|

CPU | Intel i9-10980XE | Base clock: 3.0 GHz Boost clock: 4.6 GHz |

Cores: 18 Threads: 36 | ||

L3 cache: 24.75 MB | ||

GPU | NVIDIA RTX3090 | CUDA cores: 10496 |

GPU frequency: 19.5 GHz | ||

GPU memory: 24 GB(GDDR6) | ||

GPU computing power: 8.6 |

Parameters | Value |
---|---|

Signal type | FM radio broadcast |

Center frequency | 101.9 MHz |

Band width | 200 kHz |

Sampling rate | 2.4 MHz |

Maximum bistatic range | 200.11 km |

Range resolution | 1.14 Km |

Maximum Doppler frequency shift | 256.04 Hz |

Doppler resolution | 0.5 Hz |

Signal Processing | CPU (s) | GPU (s) | Speedup |
---|---|---|---|

ECA-B | 0.122 | 0.009 | 14.37 |

RD-Processing | 12.490 | 0.344 | 36.31 |

2D-CA-CFAR | 6.440 | 0.026 | 247.69 |

Whole Algorithm | 19.052 | 0.379 | 50.34 |

Data Volume | ECA-B | RD-Processing | 2D-CA-CFAR | Whole Algorithm |
---|---|---|---|---|

10 frames (80 MB) | 8.21 | 24.77 | 61.24 | 29.29 |

20 frames (160 MB) | 10.52 | 23.28 | 214.25 | 31.99 |

50 frames (400 MB) | 14.37 | 36.31 | 247.69 | 50.34 |

100 frames (800 MB) | 17.87 | 55.91 | 243.49 | 74.72 |

150 frames (1200 MB) | 23.61 | 92.11 | 227.39 | 113.13 |

200 frames (1600 MB) | 25.95 | 90.54 | 271.02 | 112.95 |

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**MDPI and ACS Style**

Zhao, X.; Liu, P.; Wang, B.; Jin, Y.
GPU-Accelerated Signal Processing for Passive Bistatic Radar. *Remote Sens.* **2023**, *15*, 5421.
https://doi.org/10.3390/rs15225421

**AMA Style**

Zhao X, Liu P, Wang B, Jin Y.
GPU-Accelerated Signal Processing for Passive Bistatic Radar. *Remote Sensing*. 2023; 15(22):5421.
https://doi.org/10.3390/rs15225421

**Chicago/Turabian Style**

Zhao, Xinyu, Peng Liu, Bingnan Wang, and Yaqiu Jin.
2023. "GPU-Accelerated Signal Processing for Passive Bistatic Radar" *Remote Sensing* 15, no. 22: 5421.
https://doi.org/10.3390/rs15225421