• Journal of Positioning, Navigation, and Timing
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• v.9 no.2
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• pp.99-115
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• 2020

Since GNSS is easily affected by jamming and/or spoofing, alternative navigation systems can be operated as backup system to prepare for outage of GNSS. Alternative navigation systems are being researched over the world, and a multi-radio integrated navigation system using alternative navigation systems such as KNSS, eLoran, Loran-C, DME, VOR has been researched in Korea. Least Square or Kalman filter can be used to estimate navigation parameters in the navigation system. A large number of measurements of the Kalman filter may lead to heavy computational load. The decentralized Kalman filter and the federated Kalman filter were proposed to handle this problem. In this paper, the decentralized Kalman filter and the federated Kalman filter are designed for the multi-radio integrated navigation system and the performance evaluation result are presented. The decentralized Kalman filter and the federated Kalman filter consists of local filters and a master filter. The navigation parameter is estimated by local filters and master filter compensates navigation parameter from the local filters. Characteristics of three Kalman filters for a linear system and nonlinear system are investigated, and the performance evaluation results of the three Kalman filters for multi-radio integrated navigation system are compared.

• International Journal of Control, Automation, and Systems
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• v.3 no.2
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• pp.173-182
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• 2005

In this paper, a target tracking algorithm for tracking maneuvering vehicles is presented. The overall algorithm belongs to the category of an interacting multiple-model (IMM) algorithm used to detect multiple targets using fused information from multiple sensors. First, two kinematic models are derived: a constant velocity model for linear motions, and a constant-speed turn model for curvilinear motions. Fpr the constant-speed turn model, a nonlinear information filter is used in place of the extended Kalman filter. Being equivalent to the Kalman filter (KF) algebraically, the information filter is extended to N-sensor distributed dynamic systems. The model-matched filter used in multi-sensor environments takes the form of a federated nonlinear information filter. In multi-sensor environments, the information-based filter is easier to decentralize, initialize, and fuse than a KF-based filter. In this paper, the structural features and information sharing principle of the federated information filter are discussed. The performance of the suggested algorithm using a Monte Carlo simulation under the two patterns is evaluated.

• Journal of Institute of Control, Robotics and Systems
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• v.5 no.6
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• pp.764-776
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• 1999

In this paper, a tracking algorithm for a maneuvering single target in the presence of multiple data from multiple sensors is investigated. Allowing individual sensors to function by themselves, the estimates from individual sensors on the same target are fused for the purpose of improving the state estimate. The filtering method adopted in the local sensors is the variable dimensional filter with input estimatio technique, which consists of a constant velocity model and a constant acceleration model. A posteriori probability for the maneuvering hypothesis is newly derived. It is shown that the relation function of the a posteriori probability is a function of only the covariance of the fused estimates. Simulation results are provided.

• International Journal of Aeronautical and Space Sciences
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• v.14 no.1
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• pp.91-98
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• 2013

The rigorous requirements of modern spacecraft missions necessitate a precise attitude determination strategy. This paper mainly researches that, based on three space-borne attitude sensors: 3-axis rate gyros, 3-antenna GPS receiver and star-sensor. To obtain global attitude estimation after an information fusion process, a feedback-involved Federated Kalman Filter (FKF), consisting of two subsystem Kalman filters (Gyros/GPS and Gyros/Star-sensor), is established. In these filters, the state equation is implemented according to the spacecraft's kinematic attitude model, while the residual error models of GPS and star-sensor observed attitude are utilized, to establish two observation equations, respectively. Taking the sensors' different update rates into account, these two subsystem filters are conducted under a variable step size state prediction method. To improve the fault tolerant capacity of the attitude determination system, this paper designs malfunction warning factors, based on the principle of ${\chi}^2$ residual verification. Mathematical simulation indicates that the information fusion strategy overwhelms the disadvantages of each sensor, acquiring global attitude estimation with precision at a 2-arcsecs level. Although a subsystem encounters malfunction, FKF still reaches precise and stable accuracy. In this process, malfunction warning factors advice malfunctions correctly and effectively.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.1348-1351
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• 1997

The federated Kaman filter(FKF) is being widely used in many multisensor navigatiion systems. It is know that the FKF has advantages of simplicity and fault-tolerance over other decentralized filter techniques. In this paper, optimal and suboptimal FKF configuratiions are mentioned and a covariance analysis technique for the suboptimal FKF is newly presented. The suboptimal FKF configuration, known as No-reset(NR) mode, has better fault tolerance capability than the optimal FKF coniguratioin. In the suggested technique, a suboptimal fusion process of FKF is considered a swell as suboptimal gains of local filters. An upper boun of error covariance for suboptimal FKF is derived. Also, it is mathematically shown that this bound is smaller thanexisting bound in the literatrue. A vehicle-navigaion system is designed using the FKF. In thissystem, a map constraing equation is introduced and used as a measurement equatioin of Kalman filter. Performance analysis is done by the suggested covariance analysis techniques.

• International Journal of Control, Automation, and Systems
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• v.1 no.4
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• pp.444-452
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• 2003

An integration system with multi-measurement sets can be realized via combined application of a centralized and federated Kalman filter. It is difficult for the centralized Kalman filter to remove a failed sensor in comparison with the federated Kalman filter. All varieties of Kalman filters monitor innovation sequence (residual) for detection and isolation of a failed sensor. The innovation sequence, which is selected as an indicator of real time estimation error plays an important role in adaptive mechanism design. In this study, the centralized Kalman filter with adaptive measurement fusion is introduced by means of innovation sequence. The objectives of adaptive measurement fusion are automatic isolation and recovery of some sensor failures as well as inherent monitoring capability. The proposed adaptive filter is applied to the GPS/SDINS integration system with an additional sensor. Simulation studies attest that the proposed adaptive scheme is effective for isolation and recovery of immediate sensor failures.

• Journal of the Korea Institute of Military Science and Technology
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• v.22 no.2
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• pp.197-206
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• 2019

Safe aircraft requires highly reliable navigation information. The traditionally used inertial navigation system (INS) often displays faulty location information due to its innate errors. To overcome this, the INS/GNSS or INS/TRN integrated navigation can be used. However, GNSS is vulnerable to jamming and spoofing, while TRN can be degraded in the flat and repetitive terrains. In this paper, to improve the performance and ensure the high reliability of the navigation system, the INS/GNSS/TRN integrated navigation based on federated filter is designed. Master filter of the integrated navigation uses the estimates and covariances of two local filters - INS/GNSS and INS/TRN integrated filters. The local filters are designed with the EKF that is feedforward type and composed of the 17st state variables. And the INS/GNSS integrated navigation includes the barometer error compensation method. Finally, the proposed INS/GNSS/TRN integrated navigation is verified by vehicle and captive flight tests.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.38 no.10
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• pp.1012-1019
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• 2010

In this paper, The flight data processing system was designed for multiple target flight test. For flight data processing, multiple target grouping, data fusion processing, and data slaving processing were performed and, as a data fusion filter, centralized, and federated Kalman filters were designed. A centralized kalman filter was modified in order to improve the vehicle's low altitude measurement using radar's SNR and estimation process. From the testing of multiple target missile, it confirmed flight trajectory measurement was improved in low altitude area and the beginning stage of vehicle.