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• W1971291960 abstract "An important component of tracking fusion systems is the ability to fuse various sensors into a coherent picture of thescene. When multiple sensor systems are being used in an operational setting, the types of data vary. A significant butoften overlooked concern of multiple sensors is the incorporation of measurements that are unobservable. Anunobservable measurement is one that may provide information about the state, but cannot recreate a full target state. Aline of bearing measurement, for example, cannot provide complete position information. Often, such measurementscome from passive sensors such as a passive sonar array or an electronic surveillance measure (ESM) system.Unobservable measurements will, over time, result in the measurement uncertainty to grow without bound. While sometracking implementations have triggers to protect against the detrimental effects, many maneuver tracking algorithmsavoid discussing this implementation issue.One maneuver tracking technique is the neural extended Kalman filter (NEKF). The NEKF is an adaptive estimationalgorithm that estimates the target track as it trains a neural network on line to reduce the error between the a priori targetmotion model and the actual target dynamics. The weights of neural network are trained in a similar method to the stateestimation/parameter estimation Kalman filter techniques. The NEKF has been shown to improve target trackingaccuracy through maneuvers and has been use to predict target behavior using the new model that consists of the a priorimodel and the neural network.The key to the on-line adaptation of the NEKF is the fact that the neural network is trained using the same residuals asthe Kalman filter for the tracker. The neural network weights are treated as augmented states to the target track.Through the state-coupling function, the weights are coupled to the target states. Thus, if the measurements cause thestates of the target track to be unobservable, then the weights of the neural network have unobservable modes as well. Inrecent analysis, the NEKF was shown to have a significantly larger growth in the eigenvalues of the error covariancematrix than the standard EKF tracker when the measurements were purely bearings-only. This caused detrimentaleffects to the ability of the NEKF to model the target dynamics. In this work, the analysis is expanded to determine thedetrimental effects of bearings-only measurements of various uncertainties on the performance of the NEKF when theseunobservable measurements are interlaced with completely observable measurements. This analysis provides the abilityto put implementation limitations on the NEKF when bearings-only sensors are present." @default.
• W1971291960 created "2016-06-24" @default.
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• W1971291960 date "2009-05-01" @default.
• W1971291960 modified "2023-09-23" @default.
• W1971291960 title "Effects of measurement unobservability on neural extended Kalman filter tracking" @default.
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• W1971291960 doi "https://doi.org/10.1117/12.818384" @default.
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