Research On Integrated Navigation Technology Based On Federated Strong Tracking Kalman Filter

Navigation and positioning technology comes from the technological development.Although traditional navigation systems are basically able to realize the positioning goal alone,they still have drawbacks.For instance,positioning accuracy of GPS is heavily disturbed by external environments in spite of high precision during a short period.In addition,INS is an autonomous navigation system while its location error will accumulate with time.In order to give full play to the advantages of each system and offset their weakness,integrated navigation systems have emerged.A common integrated navigation is GPS+INS,which utilizes GPS to correct the error of INS to achieve high location precision.In this paper,we propose a new integrated navigation system including three sensors.This new integrated system adds a DVL to assist GPS+INS system and use the position information of GPS and the velocity information of DVL to eliminate the error of INS.The GPS+INS+DVL integrated navigation system can solve the problem of GPS+INS system that GPS can't correct the error of INS during the outage of GPS signal.As a result and has a wider applied range and higher precision.On the other hand,navigation estimation algorithm has also been a hot research focus in recent years.Traditional estimation algorithms include KF algorithm applied in linear model,EKF algorithm applied in nonlinear model,Sage-Husa adaptive KF algorithm with robustness and FKF algorithm applied in information fusion.To better parallel performance of different algorithms,this paper firstly proposes a new algorithm evaluation index---residual variance and demonstrates the reliability of this index through rigorous mathematical derivation based on the orthogonality principle.Then this paper deduces expression of KF's residual variance under three unsatisfactory conditions:1)inaccurate system model;2)erroneous initial value;3)abrupt change of states.We find that KF has poor estimation performance under the above three cases.Nevertheless,the residual of STF algorithm can satisfy the orthogonality principle no matter in which unsatisfactory cases because of the introduction of fading factor.Finally,this paper establishes a single GPS navigation simulation model to verify the performance of KF and STF.Through the simulation it is easily found that the performance of STF outperforms that of KF in unsatisfactory cases.Based on the new navigation model and the analysis of estimation algorithm,this paper proposes a new GPS+INS+DVL integrated navigation system based on RAFSTKF algorithm to achieve high precision in navigation and positioning.This proposed algorithm includes two parts:local filter and information fusion of main filter.It can take full advantages of observations of all sensors.Both of the two local filters are STF and the stable variable is the error of INS.Observation inputs of two filters are position differences between GPS and INS as well as velocity differences between DVL and INS respectively.The master filter firstly gets the direct fusion based on the least square filter and the direct fusion result is suboptimal estimation.Then the master filter utilizes the weighting adaptive least square filter on account of the suboptimal estimation to get the global optimal estimation.The weighting factor lies on the two norm of the difference between the local filter estimation as well as the one step prediction and the suboptimal estimation.Finally,the estimated INS errors will be fed back to INS to correct errors and realize the precise positioning.Simulations demonstrate that the GPS+INS+DVL system based on RAFSTKF has higher positioning precision and more stable performance.