Real-Time In-Situ Laser Ranging via Back Propagation Neural Network on FPGA

To increase the measurement rate and reduce the ranging error of real-time in-situ laser ranging in full-waveform light detection and ranging (LiDAR), a back propagation (BP) neural network-based online ranging method (BPO) implemented on FPGA is proposed. In BPO, a three-layer BP neural network model is trained and implemented on a Virtex-6 FPGA to estimate the parameters of laser pulses for ranging in real time. The training dataset is generated using the multi-pulse coherent average method combined with the B-spline interpolation method. In the implementation of BPO, the parallel-pipeline architecture is employed to accelerate the computational process. Simulations proved that BPO decreased the time consumption of a single measurement to 2.95 μs. The measurement rate was increased to 339 kHz by BPO, which was approximately as 1.2 times as the measurement rate of Gauss-Newton-based online ranging method (GNO). Experiments were conducted by an FPGA-based in-situ ranging system to evaluate the ranging error of BPO. The results revealed that the mean and standard deviation of ranging errors of BPO were 0.8 mm and 1.4 cm for a laser pulse with an SNR of 36.4 dB, respectively. For a laser pulse with an SNR of 21.8 dB, the mean and standard deviation of ranging errors of BPO were 1.7 cm and 5.5 cm, which decreased 0.8 cm and 1.7 cm from those of GNO, implying that the proposed method is capable of mapping the trailing laser pulse and reducing the ranging error caused by the trailing distortion.