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Rural environmental problems have become a prominent problem in the current environmental field that needs to be solved, and livestock breeding waste is an important problem to the rural environmental pollution management. The government has been combating environmental pollution by strengthening environmental regulation policies, but the effect of environmental regulation implementation needs to be improved. Therefore, exploring the intrinsic mechanism of environmental regulation on farmers’ environmental management behavior is an important way to realize the construction of rural civilization. This paper analyzes the influence of environmental regulations on meat duck farmers’ environmentally friendly behavior from the perspective of the “cost effect” and “Porter effect.” In addition, the potential role of risk perception is explored by analyzing the heterogeneity of farmers’ environmentally friendly behavior at different breeding scales. The results show an inverted U-shaped relationship between the intensity of environmental regulations and meat duck farmers’ environmentally friendly behavior; different environmental regulations significantly affect the environmentally friendly behavior of farmers. In these regulations, the guiding regulation plays a significant positive role in the resulting environmentally friendly behavior of farmers in terms of constraint regulation and incentive regulation. There is heterogeneity in the impacts of environmental regulation on the environmentally friendly behaviors of farmers of different scales. Risk cognition has a partial mediating effect on the effects of environmental regulations on farmers’ environmentally friendly behavior. The research not only enriches the study of environmental governance, but also its relevant findings have important guidance and reference significance for optimizing environmental regulation policies, promoting farmers’ cognition of waste environmental pollution management and implementation of environmentally friendly behaviors, and realizing low-carbon and healthy breeding of meat ducks.

It is difficult for a receiver to intercept the signals from a radar system that can emit low probability of intercept (LPI) polyphase coded signals. The traditional Wigner Hough transform (WHT) algorithm requires a large amount of computation and takes a long time to estimate the parameters of the LPI radar polyphase coded signals. To address this problem, an iterative angle search (IAS) algorithm, which when used in combination with the WHT algorithm significantly reduces the computational cost is proposed. When the signal-to-noise ratio is in the range of −4 to 20 dB, the carrier frequency, number of subcodes, and number of cycles of the carrier frequency per subcode of five polyphase coded signals, namely, Frank, P1, P2, P3, and P4, are accurately estimated in simulation experiments. Based on the selected IAS algorithm parameters, the estimation accuracy of the proposed method is the same as that of the traditional WHT algorithm. However, the operation time is only 5.14% of that of the traditional method. The IAS algorithm has certain application prospects. Experiments indicate that the proposed algorithm provides excellent performance and can rapidly and accurately estimate the parameters of LPI polyphase codes.

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Low probability of intercept radar signal is widely used because it is difficult to be intercepted by non-cooperative receivers in electronic warfare. We need to binarize the time-frequency images when analyzing LPI radar signals based on time-frequency distribution. However, the existing binarization algorithms cannot distinguish noise from the signal frequency at low signal-to-noise ratios. In this paper, we propose to use K-means algorithm to binarize the gray time-frequency images of LPI radar signals. We use F1-score to comprehensively consider the effect of binarization. Based on linear frequency modulation signals, the simulation experiments show that the F1-score of the proposed algorithm exceeds the F1-score of Otsu’s method by 40% to 80% when the signal-to-noise ratio is from -6 dB to 2 dB. The binarization effect of K-means is excellent and has great application prospects.