This paper presents a novel coverage-based cooperative target acquisition algorithm for hypersonic interceptions. Firstly, the difficulties in the hypersonic trajectory prediction are introduced which invalidate the c...This paper presents a novel coverage-based cooperative target acquisition algorithm for hypersonic interceptions. Firstly, the difficulties in the hypersonic trajectory prediction are introduced which invalidate the conventionally used predicted impact point based mid-course guidance and seeker acquisition. Secondly, in order to optimally estimate and predict the target trajectory information, the interacting multiple model(IMM) algorithm is used with the constant velocity(CV) model, the constant acceleration(CA) model and the Singer model serving as the model set. The target states are described with the probability density function(PDF) based on the IMM prediction. Thirdly, the interceptor seeker target acquisition model is established which considers the blur edge region of the field of view. The cooperative target acquisition algorithm is designed by maximizing the interceptor seekers cooperative coverage of the target high probability region(HPR). Finally, digital simulations prove the effectiveness of the proposed method and reveal that the real challenge in the hypersonic target acquisition is the poor trajectory prediction accuracy which may further result to the unsteadiness of the interceptor trajectories.展开更多
Aims The accurate estimation of aboveground biomass in vegetation is critical for global carbon accounting.Regression models provide an easy estimation of aboveground biomass at large spatial and temporal scales.Yet,o...Aims The accurate estimation of aboveground biomass in vegetation is critical for global carbon accounting.Regression models provide an easy estimation of aboveground biomass at large spatial and temporal scales.Yet,only few prediction models are available for aboveground biomass in rangelands,as compared with forests.In addition to the development of prediction models,we tested whether such prediction models vary with plant growth forms and life spans,and with the inclusion of site and/or quadrat-specific factors.Methods We collected dataset of aboveground biomass from destructive harvesting of 8088 individual plants belonging to 79 species in 735 quadrats across 35 sites in semi-steppe rangelands in Iran.A logarithmic transformation of the power-law model was used to develop simple prediction models for the easy estimation of above-ground biomass using plant coverage and vegetation density as predictors for the species-specific model,multispecies and plants of different growth forms and life spans.In addition,additive and multiplicative linear regression models were developed by using plant coverage and one categorical variable from the site and/or quadrat-specific factors.Important Findings The log-transformed power-law model based on plant coverage pre-cisely predicted aboveground biomass across the whole dataset for ei-ther most of the species-specific model,multispecies or plants of the same growth forms(shrubs,forbs or graminoids)and life spans(annuals,biennials or perennials).The addition of vegetation density as a single or in a compound predictor variable had relatively poor performance com-pared with the model having plant coverage only.Although generalizing at the levels of plant group forms and/or life spans did not substantially enhance the model-fit and validation of the plant coverage-based mul-tispecies model,the inclusion of plant growth forms or life spans as a categorical predictor variable had performed well.Generalized models in this study will greatly contribute to the accurate and easy pre展开更多
基金supported by the National Natural Science Foundation of China(Grant Nos.61573374,61503408,61703421,and 61773398)
文摘This paper presents a novel coverage-based cooperative target acquisition algorithm for hypersonic interceptions. Firstly, the difficulties in the hypersonic trajectory prediction are introduced which invalidate the conventionally used predicted impact point based mid-course guidance and seeker acquisition. Secondly, in order to optimally estimate and predict the target trajectory information, the interacting multiple model(IMM) algorithm is used with the constant velocity(CV) model, the constant acceleration(CA) model and the Singer model serving as the model set. The target states are described with the probability density function(PDF) based on the IMM prediction. Thirdly, the interceptor seeker target acquisition model is established which considers the blur edge region of the field of view. The cooperative target acquisition algorithm is designed by maximizing the interceptor seekers cooperative coverage of the target high probability region(HPR). Finally, digital simulations prove the effectiveness of the proposed method and reveal that the real challenge in the hypersonic target acquisition is the poor trajectory prediction accuracy which may further result to the unsteadiness of the interceptor trajectories.
基金This work was supported by the University of Tehran,Iran(grant No.3870306)We would like to thank Mr.Mohsen Hosseini,Drs.Esmaeil Alizadeh and Azad Rastegar for their contributions to this work.A.A.is financially supported by Guangdong Provincial Government(grant No.205588)for conducting ecological research at South China Normal University.
文摘Aims The accurate estimation of aboveground biomass in vegetation is critical for global carbon accounting.Regression models provide an easy estimation of aboveground biomass at large spatial and temporal scales.Yet,only few prediction models are available for aboveground biomass in rangelands,as compared with forests.In addition to the development of prediction models,we tested whether such prediction models vary with plant growth forms and life spans,and with the inclusion of site and/or quadrat-specific factors.Methods We collected dataset of aboveground biomass from destructive harvesting of 8088 individual plants belonging to 79 species in 735 quadrats across 35 sites in semi-steppe rangelands in Iran.A logarithmic transformation of the power-law model was used to develop simple prediction models for the easy estimation of above-ground biomass using plant coverage and vegetation density as predictors for the species-specific model,multispecies and plants of different growth forms and life spans.In addition,additive and multiplicative linear regression models were developed by using plant coverage and one categorical variable from the site and/or quadrat-specific factors.Important Findings The log-transformed power-law model based on plant coverage pre-cisely predicted aboveground biomass across the whole dataset for ei-ther most of the species-specific model,multispecies or plants of the same growth forms(shrubs,forbs or graminoids)and life spans(annuals,biennials or perennials).The addition of vegetation density as a single or in a compound predictor variable had relatively poor performance com-pared with the model having plant coverage only.Although generalizing at the levels of plant group forms and/or life spans did not substantially enhance the model-fit and validation of the plant coverage-based mul-tispecies model,the inclusion of plant growth forms or life spans as a categorical predictor variable had performed well.Generalized models in this study will greatly contribute to the accurate and easy pre
