Non-intrusive soil carbon content quantification methods using machine learning algorithms:A comparison of microwave and millimeter wave radar sensors

Agricultural and forestry biomass can be converted to biochar through pyrolysis gasification,making it a significant carbon source for soil.Applying biochar to soil is a carbon-negative process that helps combat climate change,sustain soil biodiversity,and regulate water cycling.However,quantifying soil carbon content conventionally is time-consuming,labor-intensive,imprecise,and expensive,making it difficult to accurately measure in-field soil carbon’s effect on storage water and nutrients.To address this challenge,this paper for the first time,reports on extensive lab tests demonstrating non-intrusive methods for sensing soil carbon and related smart biochar applications,such as differentiating between biochar types from various biomass feedstock species,monitoring soil moisture,and biochar water retention capacity using portable microwave and millimeter wave sensors,and machine learning.These methods can be scaled up by deploying the sensor in-field on a mobility platform,either ground or aerial.The paper provides details on the materials,methods,machine learning workflow,and results of our investigations.The significance of this work lays the foundation for assessing carbon-negative technology applications,such as soil carbon content accounting.We validated our quantification method using supervised machine learning algorithms by collecting real soil mixed with known biochar contents in the field.The results show that the millimeter wave sensor achieves high sensing accuracy(up to 100%)with proper classifiers selected and outperforms the microwave sensor by approximately 10%–15%accuracy in sensing soil carbon content.

A Method for Improving Ranging Accuracy of Short-range Millimeter Wave Radar

The range-velocity ambiguity caused by moving target influences on the ranging accuracy of a short-range millimeter wave radar greatly.A new method was presented in this paper to reduce the range-velocity ambiguity and improve the ranging accuracy by estimating parameters of the echo signal with fractional Fourier transform and self-correlation.And,a new quick searching algorithm was given also to increase the calculation speed.Compared to the Chinese remainder theorem method,the proposed method is excellent for its simplicity and reducing the computation complexity.The simulation results show its validity.

Convolution neural network and 77 GHz millimeter wave radar based intelligent liquid classification system

An intelligent liquid classification system based on 77 GHz millimeter wave radar and convolution neural network are proposed in this paper.The data are collected by the AWR1843 radar platform and processed by the neural network on the host PC in real-time.The doppler heatmap generated by radar signal processing is tried for the first time as the input of the system.The information carried by the heatmap in 2 dimensions is analyzed and the reason why the doppler heatmap could be used for classification is explained.The feasible experiment proved that the proposed method can successfully classify 8 kinds of common liquid with high accuracy.The result of the experiment is explained and the limitations of the experiment are discussed.It can be drawn that the combination of FMCW millimeter wave radar and convolution neural network is a method with great potential for liquid classification.The advantages of real time,non-invasive and unilateral measurement can also be used for the detection of dangerous liquids.

Study of Aircraft Icing Warning Algorithm Based on Millimeter Wave Radar

In order to avoid accidents due to aircraft icing, an algorithm for identifying supercooled water was studied. Specifically, a threshold method based on millimeter wave radar, lidar, and radiosonde was used to retrieve the coverage area of supercooled water and a fuzzy logic algorithm was used to classify the observed meteorological targets. The macrophysical characteristics of supercooled water could be accurately identified by combing the threshold method with the fuzzy logic algorithm. In order to acquire microphysical characteristics of supercooled water in a mixed phase, the unimodal spectral distribution of supercooled water was extracted from a bimodal or trimodal spectral distribution of a mixed phase cloud, which was then used to retrieve the effective radius and liquid water content of supercooled water by using an empirical formula. These retrieved macro- and micro-physical characteristics of supercooled water can be used to guide aircrafts during takeoff, flight, and landing to avoid dangerous areas.

Compensation for the Influence of Radar-Target Relative Speed on Step Frequency MMW Target Identification

Aim To study the influence of radar-target relative speed on frequency MMW high-resolution ore-dimension distance profile and the compensation for it. Methods Based on the distance travelled by the electromagnetic wave, analyses were made for the compensation algorithm and the expression of the inverse FFT base distance was given.The relative importance of different compensation terms was studied in great detail. The concept of searching compensation was put forward. Results and Condclusion Dcm-△Dvimis the be distance of inverse FFT transformation, the effect caused by the distance △Dim on one-dimension profile is negligible, and the effect caused by the distance Dvim should not be neglected and must be compensated.

A Ku and Ka Dual Band Experimental Radar

Due to the good performance of tracking low elevation target as compared to microwave and the superiority in penetrating smoke, dust, fog, and dry snow as compared to infrared, a Ku and Ka dual band experimental radar was designed and developed. This Ku and Ka dual band experimental radar is an arnplitute-comparison monopulse tracking and guiding radar. The constitution and parameters of this radar is described in paragraph 2. Paragraph 3 deals with two experiments for testing the tracking performances against low elevation target, and gives the important results. Both Ku and Ka band have high tracking precision when they track high elevation targets, while Ka band has much better tracking performance than Ku band when they track low elevation targets. Ka band can track a helicopter, whose radar cross section is about 6 square meters, at 40m, 20m, 10m, and even 5m above sea. Ku band can only track the same helicopter at 160m and higher above sea.