Terrain Aided Navigation(TAN)technology has become increasingly important due to its effectiveness in environments where Global Positioning System(GPS)is unavailable.In recent years,TAN systems have been extensively r...Terrain Aided Navigation(TAN)technology has become increasingly important due to its effectiveness in environments where Global Positioning System(GPS)is unavailable.In recent years,TAN systems have been extensively researched for both aerial and underwater navigation applications.However,many TAN systems that rely on recursive Unmanned Aerial Vehicle(UAV)position estimation methods,such as Extended Kalman Filters(EKF),often face challenges with divergence and instability,particularly in highly non-linear systems.To address these issues,this paper proposes and investigates a hybrid two-stage TAN positioning system for UAVs that utilizes Particle Filter.To enhance the system’s robustness against uncertainties caused by noise and to estimate additional system states,a Fuzzy Particle Filter(FPF)is employed in the first stage.This approach introduces a novel terrain composite feature that enables a fuzzy expert system to analyze terrain non-linearities and dynamically adjust the number of particles in real-time.This design allows the UAV to be efficiently localized in GPS-denied environments while also reducing the computational complexity of the particle filter in real-time applications.In the second stage,an Error State Kalman Filter(ESKF)is implemented to estimate the UAV’s altitude.The ESKF is chosen over the conventional EKF method because it is more suitable for non-linear systems.Simulation results demonstrate that the proposed fuzzy-based terrain composite method achieves high positional accuracy while reducing computational time and memory usage.展开更多
To improve the navigation accuracy of an autonomous underwater vehicle （AUV）, a novel terrain passive integrated navigation system （TPINS） is presented. According to the characteristics of the underwater environme...To improve the navigation accuracy of an autonomous underwater vehicle （AUV）, a novel terrain passive integrated navigation system （TPINS） is presented. According to the characteristics of the underwater environment and AUV navigation requirements of low cost and high accuracy, a novel TPINS is designed with a configuration of the strapdown inertial navigation system （SINS）, the terrain reference navigation system （TRNS）, the Doppler velocity sonar （DVS）, the magnetic compass and the navigation computer utilizing the unscented Kalman filter （UKF） to fuse the navigation information from various navigation sensors. Linear filter equations for the extended Kalman filter （EKF）, nonlinear filter equations for the UKF and measurement equations of navigation sensors are addressed. It is indicated from the comparable simulation experiments of the EKF and the UKF that AUV navigation precision is improved substantially with the proposed sensors and the UKF when compared to the EKF. The TPINS designed with the proposed sensors and the UKF is effective in reducing AUV navigation position errors and improving the stability and precision of the AUV underwater integrated navigation.展开更多
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 esti...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.展开更多
In view of the airborne application characteristics such as flexible flight, large error of altimeter, large initial error of inertial navigation system, etc., a new terrain matching system architecture which is suita...In view of the airborne application characteristics such as flexible flight, large error of altimeter, large initial error of inertial navigation system, etc., a new terrain matching system architecture which is suitable for airborne application is presented. The key techniques in terrain matching system realizing process including system workflow, terrain matching algorithm and selection criterion of matching region are expatiated. The experimental results prove the rationality and feasibility of the proposed solution.展开更多
In this paper, the marginal Rao-Blackwellized particle filter （MRBPF）, which fuses the Rao-Blackwellized particle filter （RBPF） algorithm and the marginal particle filter （MPF） algorithm, is presented. The state...In this paper, the marginal Rao-Blackwellized particle filter （MRBPF）, which fuses the Rao-Blackwellized particle filter （RBPF） algorithm and the marginal particle filter （MPF） algorithm, is presented. The state space is divided into linear and non-linear parts, which can be estimated separately by the MPF and the optional Kalman filter. Through simulation in the terrain aided navigation （TAN） domain, it is demonstrated that, compared with the RBPF, the root mean square errors （RMSE） and the error variance of the nonlinear state estimations by the proposed MRBPF are respectively reduced by 29% and 96%, while the unique particle count is increased by 80%. It is also found that the MRBPF has better convergence properties, and analysis has shown that the existing RBPF is nothing more than a special case of the MRBPF.展开更多
Small-scale soft robots with high morphological flexibility show significant potential for precise operation and sensing in confined environments.However,due to the coupled driving mechanism and the influence of envir...Small-scale soft robots with high morphological flexibility show significant potential for precise operation and sensing in confined environments.However,due to the coupled driving mechanism and the influence of environmental disturbances,the highly adaptable and stable navigation across diverse terrains through multimodal motion,which involves morphing shape and maintaining the reshaped configuration,still presents a major challenge for soft millirobots.展开更多
文摘Terrain Aided Navigation(TAN)technology has become increasingly important due to its effectiveness in environments where Global Positioning System(GPS)is unavailable.In recent years,TAN systems have been extensively researched for both aerial and underwater navigation applications.However,many TAN systems that rely on recursive Unmanned Aerial Vehicle(UAV)position estimation methods,such as Extended Kalman Filters(EKF),often face challenges with divergence and instability,particularly in highly non-linear systems.To address these issues,this paper proposes and investigates a hybrid two-stage TAN positioning system for UAVs that utilizes Particle Filter.To enhance the system’s robustness against uncertainties caused by noise and to estimate additional system states,a Fuzzy Particle Filter(FPF)is employed in the first stage.This approach introduces a novel terrain composite feature that enables a fuzzy expert system to analyze terrain non-linearities and dynamically adjust the number of particles in real-time.This design allows the UAV to be efficiently localized in GPS-denied environments while also reducing the computational complexity of the particle filter in real-time applications.In the second stage,an Error State Kalman Filter(ESKF)is implemented to estimate the UAV’s altitude.The ESKF is chosen over the conventional EKF method because it is more suitable for non-linear systems.Simulation results demonstrate that the proposed fuzzy-based terrain composite method achieves high positional accuracy while reducing computational time and memory usage.
基金Pre-Research Program of General Armament Department during the11th Five-Year Plan Period (No51309020503)the National Defense Basic Research Program of China (973Program)(No973-61334)+1 种基金the National Natural Science Foundation of China(No50575042)Specialized Research Fund for the Doctoral Program of Higher Education (No20050286026)
文摘To improve the navigation accuracy of an autonomous underwater vehicle （AUV）, a novel terrain passive integrated navigation system （TPINS） is presented. According to the characteristics of the underwater environment and AUV navigation requirements of low cost and high accuracy, a novel TPINS is designed with a configuration of the strapdown inertial navigation system （SINS）, the terrain reference navigation system （TRNS）, the Doppler velocity sonar （DVS）, the magnetic compass and the navigation computer utilizing the unscented Kalman filter （UKF） to fuse the navigation information from various navigation sensors. Linear filter equations for the extended Kalman filter （EKF）, nonlinear filter equations for the UKF and measurement equations of navigation sensors are addressed. It is indicated from the comparable simulation experiments of the EKF and the UKF that AUV navigation precision is improved substantially with the proposed sensors and the UKF when compared to the EKF. The TPINS designed with the proposed sensors and the UKF is effective in reducing AUV navigation position errors and improving the stability and precision of the AUV underwater integrated navigation.
文摘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.
基金This work was supported by the National Key Basic Research and Development （973） Program of China （Grant No. 2010CB731806） and Aeronautical Science Foundation of China （Grant No. 20100818018）.
文摘In view of the airborne application characteristics such as flexible flight, large error of altimeter, large initial error of inertial navigation system, etc., a new terrain matching system architecture which is suitable for airborne application is presented. The key techniques in terrain matching system realizing process including system workflow, terrain matching algorithm and selection criterion of matching region are expatiated. The experimental results prove the rationality and feasibility of the proposed solution.
基金National Natural Science Foundation of China （60572023）
文摘In this paper, the marginal Rao-Blackwellized particle filter （MRBPF）, which fuses the Rao-Blackwellized particle filter （RBPF） algorithm and the marginal particle filter （MPF） algorithm, is presented. The state space is divided into linear and non-linear parts, which can be estimated separately by the MPF and the optional Kalman filter. Through simulation in the terrain aided navigation （TAN） domain, it is demonstrated that, compared with the RBPF, the root mean square errors （RMSE） and the error variance of the nonlinear state estimations by the proposed MRBPF are respectively reduced by 29% and 96%, while the unique particle count is increased by 80%. It is also found that the MRBPF has better convergence properties, and analysis has shown that the existing RBPF is nothing more than a special case of the MRBPF.
基金supported by the National Natural Science Foundation of China under grant numbers 62222305,62088101,and U22A2064the Beijing Natural Science Foundation under grant L242023the Fundamental Research Funds for the Central Universities under grants 2025CX01003 and 2024CX06008.
文摘Small-scale soft robots with high morphological flexibility show significant potential for precise operation and sensing in confined environments.However,due to the coupled driving mechanism and the influence of environmental disturbances,the highly adaptable and stable navigation across diverse terrains through multimodal motion,which involves morphing shape and maintaining the reshaped configuration,still presents a major challenge for soft millirobots.
