High-precision detection of microwave field information is important in the fields of space wireless communication, space microwave remote sensing, and satellite navigation. In this paper, the high-precision detection of broadband microwave is realized. High-precision detection of microwave fields has been realized for the first time based on the spin-mixing model of nitrogen-vacancy color centers and the continuous wave optically detected magnetic resonance (ODMR) process. By changing the power ratio between the signal and reference microwave fields, the validity of high-precision detection of microwaves is verified, and the microwave magnetic field detection resolution is less than 100 nW and the Pearson correlation coefficient of the system’s response to microwave intensity is 0.9974. Then, by optimizing the data acquisition time, the megahertz-level frequency resolution of the signal microwave is achieved. In addition, the gigahertz bandwidth and megahertz resolution were also verified by tuning the resonance frequency of the spin energy level to an external static magnetic field. These results provide an important technological basis for solid-state microwave receivers based on nitrogen-vacancy color centers, high-precision spectral resolution detection, and microwave sensing.

In recent years, the bionic polarized light compass has been widely studied for the unmanned aerial vehicle navigation. However, it is found from the obtained investigation results that a polarized light compass with a sensitive and high dynamic range polarimeter still provides inferior output precision of the heading angle due to the presence of the noise generating from the compass. The noise is existed not only in the angle of the polarization image acquired by polarimeters but also in the output heading data, which leads to a sharp reduction in the accuracy of a polarized light compass. Herein, we present noise analysis and a novel multiscale transform denoising method of a polarized light compass used for the unmanned aerial vehicle navigation. Specifically, a multiscale principle component analysis utilizing one-dimensional image entropy as classification criterion is directly implemented to suppress the noise in the acquired polarization image. Subsequently, a multiscale time–frequency peak filtering method using the sample entropy as classification criterion is applied for the output heading data so as to further increase the heading measurement accuracy from the denoised image above. These two approaches are combined to significantly reduce the heading error affected by different types of noises. Our experimental results indicate the proposed multiscale transform denoising method exhibits high performance in suppressing the noise of a polarized light compass used for the unmanned aerial vehicle navigation compared to existing prior arts.