DocumentCode :
1684863
Title :
Analytically-selected multi-hypothesis incremental MAP estimation
Author :
Huang, Guo ; Kaess, Michael ; Leonard, John J. ; Roumeliotis, Stergios I.
Author_Institution :
Comput. Sci. & Artificial Intell. Lab., Massachusetts Inst. of Technol., Cambridge, MA, USA
fYear :
2013
Firstpage :
6481
Lastpage :
6485
Abstract :
In this paper, we introduce an efficient maximum a posteriori (MAP) estimation algorithm, which effectively tracks multiple most probable hypotheses. In particular, due to multimodal distributions arising in most nonlinear problems, we employ a bank of MAP to track these modes (hypotheses). The key idea is that we analytically determine all the posterior modes for the current state at each time step, which are used to generate highly probable hypotheses for the entire trajectory. Moreover, since it is expensive to solve the MAP problem sequentially over time by an iterative method such as Gauss-Newton, in order to speed up its solution, we reuse the previous computations and incrementally update the square-root informationmatrix at every time step, while batch relinearization is performed only periodically or as needed.
Keywords :
Gaussian processes; iterative methods; matrix decomposition; maximum likelihood estimation; target tracking; Gauss Newton; analytically selected multihypothesis incremental MAP estimation; batch relinearization; highly probable hypotheses; iterative method; maximum a posteriori estimation algorithm; multimodal distributions; Current measurement; Estimation; Jacobian matrices; Noise; Radar tracking; Target tracking; Trajectory; Maximum a posteriori (MAP); QR factorization; analytical solution; multi-hypothesis tracking;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Acoustics, Speech and Signal Processing (ICASSP), 2013 IEEE International Conference on
Conference_Location :
Vancouver, BC
ISSN :
1520-6149
Type :
conf
DOI :
10.1109/ICASSP.2013.6638914
Filename :
6638914
Link To Document :
بازگشت