Mapping extended Kalman filters onto linear arrays

• Published in: IEEE transactions on automatic control 1990-12, Vol.35 (12), p.1310-1319
• Main Authors: Baheti, R.S., O'Hallaron, D.R., Itzkowitz, H.R.
• Format: Article
• Language: English
• Subjects: Algorithm design and analysis; Applied sciences; Circuit properties; Electric, optical and optoelectronic circuits; Electronic circuits; Electronics; Exact sciences and technology; Filters; Frequency filters; Measurement standards; Parallel algorithms; Parallel machines; Target tracking; Velocity measurement; Very large scale integration; Workstations
• ISSN: 0018-9286; 1558-2523
• DOI: 10.1109/9.61007

Techniques for mapping extended Kalman filters onto linear arrays of programmable cells designed for real-time applications are described. First, a general method for mapping a standard (nonsquare root) Kalman filter, where the columns of the covariance matrix are updated in parallel, is introduced. Next, a general method for mapping a factorized (square root) filter, where fast Givens rotations are used to triangularize the prematrix and where rotations of the rows of the prematrix are performed in parallel, is introduced. These mappings are used to implement an extended Kalman filter commonly used in target tracking applications on the Warp computer. The Warp is a commercially available linear array of 10 or more programmable cells connected to an MC68020-based workstation. The Warp implementation of the standard Kalman filter running on 8 Warp cells achieves a measured speedup of 7 over the same filter running on a single cell. The Warp implementation of the factorized filter running on 10 Warp cells achieves a measured speedup of 2.< >