Time-Division-Multiplexed Online Gauss-Newton-Based Multi-Echo Decomposition Method for Real-Time In-Situ Laser Ranging

Abstract

In forestry and agriculture investigations, the laser spot of FW-LiDAR may simultaneously illuminate multiple targets, which reflect multiple echoes. To realize the real-time in-situ laser ranging of multiple targets, a time-division-multiplexed online Gauss-Newton-based multi-echo decomposition method (GMOT) is proposed for decomposing the multi-echo in an online manner. In waveform preprocessing, a local-region linear analysis method was employed in GMOT to recognize the echo components of multi-target. Gauss-Newton method was then used to decompose the multi-echo by fitting the waveform. In the waveform fitting, a sparse Jacobi matrix was employed to improve the target extraction ratio. Due to the ability of the parallel computation, a Kintex-7 FPGA was employed as the hardware platform. GMOT was implemented on the embedded FPGA with the parallel-pipeline architecture and time-division multiplexing method to improve the measurement rate for decomposing the multi-echo in real-time. Simulations proved that GMOT achieves a measurement rate of 238.1 kHz, approximately 3.5 times as fast as online Gauss-Newton-based multi-echo decomposition method. An FPGA-based in-situ ranging system was built to evaluate the ranging performance of GMOT by experiments. The results revealed that GMOT increases the target extraction ratio by 11.4%, compared with the Gauss-Newton-based multi-echo post-processing decomposition method (GMP). The ranging error of GMOT is on the same scale of GMP. The proposed method will be used for the real-time multi-target ranging in the forestry survey and intelligent driving.