ABSTRACT
This paper presents a linear Kalman filter for yaw estimation of land vehicles using magnetic angular rate and gravity (MARG) sensors. A gyroscope measurement update depending on the vehicle status and constraining yaw estimation is introduced. To determine the vehicle status, the correlations between outputs from different sensors are analyzed based on the vehicle kinematic model and Coriolis theorem, and a vehicle status marker is constructed. In addition, a two-step measurement update method is designed. The method treats the magnetometer measurement update separately after the other updates and eliminates its impact on attitude estimation. The performances of the proposed algorithm are tested in experiments and the results show that: the introduced measurement update is an effective supplement to the magnetometer measurement update in magnetically disturbed environments; the two-step measurement update method makes attitude estimation immune to errors induced by magnetometer measurement update, and the proposed algorithm provides more reliable yaw estimation for land vehicles than the conventional algorithm.
ABSTRACT
By the methods of sampling plot harvesting method and allometric dimension analysis, we measured the belowground and aboveground standing biomass and net primary productivity (NPP) of the secondary evergreen broadleaved forest in Huangmian Forest farm of Guangxi, southern China, with the location of 24 degrees 51'N and 109 degrees 51'E and an altitude of about 315 m. The total biomass was 99.96 t x hm(-2), aboveground and belowground biomasses accounted for 69.41% and 30.59%, respectively. The leaf area index of trees and undergrowth shrubs was 6.50, and the total annual NPP was 24.65 t x hm(-2) x yr(-1) by estimate, aboveground and belowground NPP accounted for 44.54% and 55.46%, respectively. The NPP of fine roots was 11.79 t x hm(-2) x yr(-1), being 86.24% of the belowground NPP.
