Relative sensor registration with two‐step method for state estimation. Issue 2 (1st July 2019)

• Record Type: Journal Article
• Authors: Ge, Quanbo; Chen, Tianxiang; Duan, Zhansheng; Liu, Mingxin; Niu, Zhuyun
• Is Part Of: Cognitive computation and systems. Volume 1:Issue 2(2019)
• Page Start: 45
• Page End: 54
• Publication Date: 2019-07-01
• Subjects: nonlinear filters -- state estimation -- Kalman filters -- sensor fusion -- matrix algebra -- linearisation techniques; relative sensor registration problem -- global sensor -- local sensor -- attitude bias -- two‐step method -- location bias -- multiplatform multisensor observation system -- systematic bias -- measurement bias estimation -- state estimation -- rotation matrix -- unit quaternion method -- attitude bias -- linear least square algorithm; Cognitive science -- Periodicals; Artificial intelligence -- Periodicals; Neurosciences -- Periodicals; Computer science -- Periodicals; Neurosciences; Computer science; Cognitive science; Artificial intelligence; Periodicals; Electronic journals; 006.3
• DOI: 10.1049/ccs.2018.0006
• Languages: English
• ISSNs: 2517-7567

Abstract

Abstract : State estimation suffers from some new challenging problems with a multi‐platform multi‐sensor observation system. An important problem for multisensor integration is that the data from the local sensors needs to be transformed into a common reference frame free of systematic bias or registration. In this study, the relative sensor registration problem is discussed. It aligns measurement from the global sensor with the local sensor under the assumptions that the global sensor is bias free and all biases reside with the local sensor. The traditional methods failed in the condition when attitude bias becomes large because the error caused by linearisation of rotation matrix increases with growing attitude bias. Motivated by this, a two‐step method is established. By estimating the measurement bias through augmented extended Kalman filter in local sensor coordinate independent of attitude and location bias, and by introducing the unit quaternion method compute the attitude and location bias, the proposed method can avoid the problem the traditional methods faced. Simulation examples are provided to verify the proposed method by comparing with the existing linear least square algorithm.