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
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;
Conference_Titel :
Acoustics, Speech and Signal Processing (ICASSP), 2013 IEEE International Conference on
Conference_Location :
Vancouver, BC
DOI :
10.1109/ICASSP.2013.6638914