Terrain-Aided Navigation Considering Map Error

Terrain referenced navigation estimates an aircraft navigation status by utilizing a radar altimeter measuring a distance between the aircraft and terrain elevation. Accurate digital elevation map is essential to estimate the aircraft states correctly. However, the elevation map cannot represent the real terrain perfectly and there exists map error between the estimated and the true maps. In this paper, an influence of the map error on measurement equation is analyzed and a technique to incorporate the error in the filter is proposed. The map error is divided into two sources, accuracy error and resolution error. The effectiveness of the suggested technique is verified by simulation results. The method modifies a sensor noise covariance only so there is no additional computational burden from the conventional filter.

Anomaly detection method based on kinematics model and nonholonomic constraint of vehicle

A method used to detect anomaly and estimate the state of vehicle in driving was proposed.The kinematics model of the vehicle was constructed and nonholonomic constraint conditions were added,which refer to that once the vehicle encounters the faults that could not be controlled,the constraint conditions are violated.Estimation equations of the velocity errors of the vehicle were given out to estimate the velocity errors of side and forward.So the stability of the whole vehicle could be judged by the velocity errors of the vehicle.Conclusions were validated through the vehicle experiment.This method is based on GPS/INS integrated navigation system,and can provide foundation for fault detections in unmanned autonomous vehicles.

Using a simulation analysis to evaluate the impact of crop mapping error on crop area estimation from stratified sampling

To analyze the efficiency of area estimations(i.e.estimation accuracy and variation of estimation)impacted by crop mapping error,we simulated error at eight levels for thematic maps using a stratified sampling estimation methodology.The results show that the estimation efficiency is influenced by the combination of the sample size and the error level.Evaluating the trade-offs between sample size and error level showed that reducing the crop mapping error level provides the most benefit(i.e.higher estimation efficiency).Further,sampling performance differed based on the heterogeneity of the crop area.The results demonstrated that the influence of increasing the error level on estimation efficiency is more detrimental in heterogeneous areas than in homogeneous ones.Therefore,to obtain higher estimation efficiency,a larger sample size and lower error level or both are needed,especially in heterogeneous areas.We suggest that existing land-cover maps should first be used to determine the heterogeneity of the area.The appropriate sample size for these areas then can be determined according to all three factors:heterogeneity,expected estimation efficiency,and sampling budget.Overall,extending our understanding of the impacts of crop mapping error is necessary for decision making to improve our ability to effectively estimate crop area.

An Adaptive Non-Linear Map and Its Application

A novel adaptive non-linear mapping （ANLM）, integrating an adaptive mapping error （AME） with a chaosgenetic algorithm （CGA） including chaotic variable, was proposed to overcome the deficiencies of non-linear mapping （NLM）. The value of AME weight factor is determined according to the relative deviation square of distance between the two mapping points and the corresponding original objects distance. The larger the relative deviation square between two distances is, the larger the value of the corresponding weight factor is. Due to chaotic mapping operator, the evolutional process of CGA makes the individuals of subgenerations distributed ergodieally in the defined space and circumvents the premature of the individuals of subgenerations. The comparison results demonstrated that the whole performance of CGA is better than that of traditional genetic algorithm. Furthermore, a typical example of mapping eight-dimenslonal olive oil samples onto two-dimensional plane was employed to verify the effectiveness of ANLM. The results showed that the topology-preserving map obtained by ANLM can well represent the classification of original objects and is much better than that obtained by NLM.

Seven possible states of geospatial data with respect to map projection and definition: a novel pedagogical device for GIS education

Conceptually,the theory and implementation of“map projection”in geographic information system(GIS)technology is difficult to comprehend for most introductory students and novice users.Compounding this difficulty is the concept of a“map projection file”that defines map projection parameters of geo-spatial data.The problem of the“missing projection file”appears ubiquitous for all users,especially in practice where data is widely shared.Another common problem is inadvertent misapplication of the“Define Projection”tool that can result in a GIS dataset with an incorrectly defined map projection file.GIS education should provide more guidance in differentiating the concepts of map projection versus projection files by increasing understanding and minimizing common errors.A novel pedagogical device is introduced in this paper:the seven possible states of GIS data with respect to map projection and definition.The seven possible states are:(1)a projected coordinate system(PCS)that is correctly defined,(2)a PCS that is incorrectly defined,(3)a PCS that is undefined,(4)a geographic coordinate system(GCS)that is correctly defined,(5)a GCS that is incorrectly defined,(6)a GCS that is undefined,and(7)a non-GCS.Recently created automated troubleshooting tools to determine a missing map projection file are discussed.