The novelty of this letter is that it capitalizes on noise waveform to construct measurement operator at the transmitter and presents a new method of how the analogue to digital converter (ADC) sampling rate in the monostatic multiple-input multiple-output (MIMO) noise radar can be reduced-without reduction in waveform bandwidth-through the use of compressive sensing (CS). The proposed method equivalently converts the measurement operator problems into radar waveform design problems. The architecture is particularly apropos for signals that are sparse in the target scene. In this letter, Estimates of both target directions and target amplitudes using CS for monostatic MIMO noise radar are presented. Sparse bases are constructed using array steering vectors. Orthogonal least squares (OLS) algorithm for reconstruction of both target directions and target amplitudes is implemented. Finally, the conclusions are all demonstrated by simulation experiments.

Abstract-Compressed sensing (CS) has attracted significant attention in the radar community. The better understanding of CS theory has led to substantial improvements over existing methods in CS radar. But there are also some challenges that should be resolved in order to benefit the most from CS radar, such as radar signal with low signal to noise ratio (Low-SNR). In this paper, we will focuses on monostatic chaotic multiple-inputmultiple-output (MIMO) radar systems and analyze theoretically and numerically the performance of sparsityexploiting algorithms for the parameter estimation of targets at Low-SNR. The novelty of this paper is that it capitalizes on chaotic coded waveform to construct measurement operator incoherent with noise and singular value decomposition (SVD) to suppress noise. In order to improve the robustness of azimuth estimation interpolation method is applied to construction of sparse bases. The gradient pursuit (GP) algorithm for reconstruction is implemented at Low-SNR. Finally, the conclusions are all demonstrated by simulation experiments.

Carbon labeling describes carbon dioxide emissions across food lifecycles, contributing to enhancing consumers’ low-carbon awareness and promoting low-carbon consumption behaviors. In a departure from the existing literature on carbon labeling that heavily relies on interviews or questionnaire surveys, this study forms a hybrid of an auction experiment and a consumption experiment to observe university students’ purchase intention and willingness to pay for a carbon-labeled food product. In this study, students from a university in a city (Chengdu) of China, the largest carbon emitter, are taken as the experimental group, and cow’s milk is selected as the experimental food product. The main findings of this study are summarized as follows: (1) the purchase of carbon-labeled milk products is primarily influenced by price; (2) the willingness to pay for carbon-labeled milk products primarily depends on the premium; and (3) the students are willing to accept a maximum price premium of 3.2%. This study further offers suggestions to promote the formation of China’s carbon product-labeling system and the marketization of carbon-labeled products and consequently facilitate low-carbon consumption in China.

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