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The noise suppression of high-resolution range profile (HRRP) is a prerequisite for Geiger-mode of avalanche photodiodes (GM-APD) lidar to achieve precise sensing. However, it is difficult to balance the suppression effect and the integrity of detailed information. Considering this problem, we propose a Bayesian network-based improved loop filter (BNILF) for abnormal noise suppression. Based on the ILF, the Bayesian non-average local filtering model is established to calculate a distance of Pearson distance, which gives the criterion of noise judgment. Furthermore, the block preselection is used to accelerate identify abnormal noise and complete range profile noise suppression. To evaluate the performance of this algorithm, simulation and physical system experiments are carried out. The results show that the proposed algorithm has a better noise suppression effect and a higher detailed information protection ability in comparison with the existing typical approaches.

A plot experiment including four treatments, CK (N 105 kg ha−1 as urea, including a basal N application of 35 kg ha−1 and a topdressing N 70 kg ha−1 at turned green stage) and optimized N management (OPT1, OPT2 and OPT3, applied two-thirds, one-third and two-fifths N at jointing stage, respectively, total N 60 kg ha−1), was conducted to evaluate the effects of nitrogen management on growth and N uptake of winter wheat (Triticum aestivum), Dongnong 1, which is the first highly cold tolerant winter wheat in China. Index of population quality, N uptake and yield were determined. The ear-bearing tiller rate was increased by above 12%, and the leaf area index, biomass and N uptake were significantly decreased (P