Abstract
In this paper, we propose a compact threshold-based resampling algorithm and architecture for efficient hardware implementation of particle filters (PFs). By using a simple threshold-based scheme, this resampling algorithm can reduce the complexity of hardware implementation and power consumption. Simulation results indicate that this algorithm has approximately equal performance with the traditional systematic resampling (SR) algorithm when the root-mean-square error (RMSE) and lost track are considered. Experimental comparison of the proposed hardware architecture with those based on the SR and the residual systematic resampling (RSR) algorithms was conducted on a Xilinx Virtex-II Pro field programmable gate array (FPGA) platform in the bearings-only tracking context, and the results establish the superiority of the proposed architecture in terms of high memory efficiency, low power consumption, and low latency.
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A. Athalye, M. Bolic, S. Hong, P.M. Djuric, Architectures and memory schemes for sampling and resampling in particle filters. Digit. Signal Process. Work. 1, 92–96 (2004)
A. Athalye, M. Bolic, S. Hong, P.M. Djuric, Generic hardware architectures for sampling and resampling in particle filters. EURASIP J. Appl. Signal Process. 2005(17), 2888–2902 (2005)
E.R. Beadle, P.M. Djuric, A fast weighted Bayesian bootstrap filter for nonlinear model state estimation. IEEE Trans. Aerosp. Electron. Syst. 33(1), 338–343 (1997)
M. Bolic, Architectures for efficient implementation of particle filters. Ph.D. Dissertation, Stony Brook University, NY (2004)
M. Bolic, P.M. Djuric, S. Hong, New resampling algorithms for particle filters. IEEE Int. Conf. Acoust. Speech Signal Process. (ICASSP’03) 2, 589–592 (2003)
M. Bolic, A. Athalye, P.M. Djuric, S. Hong, Algorithmic modification of particle filters for hardware implementation. Eur. Signal Process. Conf. (EUSIPCO’04) 1, 1641–1644 (2004)
M. Bolic, P.M. Djuric, S. Hong, Resampling algorithms for particle filters: a computational complexity perspective. EURASIP J. Appl. Signal Process. 2004(15), 2267–2277 (2004)
A. Doucet, X.-D. Wang, Monte Carlo methods for signal processing: a review in the statistical signal processing context. IEEE Signal Process. Mag. 22(6), 152–170 (2005)
A. Doucet, S. Godsill, C. Andrieu, On sequential Monte Carlo sampling methods for Bayesian filtering. Stat. Comput. 10(3), 197–208 (2000)
A. Doucet, N. de Freitas, N. Gordon (eds.), Sequential Monte Carlo Methods in Practice (Springer, New York, 2004)
S.-C. Du, Z.-G. Shi, W. Zang, K.-S. Chen, Using interacting multiple model particle filter to track airborne targets hidden in blind Doppler. J. Zhejiang Univ. Sci. A 8(8), 1277–1282 (2007)
N.J. Gordon, D.J. Salmond, A.F.M. Smith, A novel approach to nonlinear and non-Gaussian Bayesian state estimation. IEE Proc. Radar Sonar. Navig. 140(2), 107–113 (1993)
J.D. Hol, T.B. Schön, F. Gustafsson, On resampling algorithms for particle filters. Nonlinear Stat. Signal Process. Work. 1, 79–82 (2006)
S.-H. Hong, Z.-G. Shi, K.-S. Chen, Compact resampling algorithm and hardware architecture for particle filters. IEEE Int. Conf. Commun. Circ. Syst. (ICCCAS’08) 2, 886–890 (2008)
S.-H. Hong, Z.-G. Shi, K.-S. Chen, Novel roughening algorithm and hardware architecture for bearings-only tracking using particle filter. J. Electromagn. Wave 22, 411–422 (2008)
S.-H. Hong, Z.-G. Shi, K.-S. Chen, Simplified algorithm and hardware implementation for particle filter applied to bearings-only tracking. J. Electron. Info. Tech. 31(1), 91–100 (2009) (in Chinese)
K. Muhammad, Algorithmic and architectural techniques for low power signal processing. Ph.D. Dissertation, Purdue Univ., West Lafayette, IN (1999)
J.M. Rabaey, Digital Integrated Circuits: A Design Perspective (Prentice-Hall, Englewood Cliffs, 1996)
B. Ristic, S. Arulampalam, N. Gordon, Beyond the Kalman Filter: Particle Filters for Tracking Applications (Artech House, Norwood, 2004)
A.C. Sankaranarayanan, R. Chellappa, A. Srivastave, Algorithmic and architectural design methodology for particle filters in hardware. IEEE Int. Conf. Comput. Des. (ICCD’05) 1, 275–280 (2005)
Z.-G. Shi, S.-H. Hong, J.-M. Chen, K.-S. Chen, Y.-X. Sun, Particle filter-based synchronization of chaotic Colpitts circuits combating AWGN channel distortion. Circ. Syst. Signal Process. 27(6), 833–845 (2008)
Z.-G. Shi, S.-H. Hong, K.-S. Chen, Experimental study on tracking the state of analog Chua’s circuit with particle filter for chaos synchronization. Phys. Lett. A 372(34), 5575–5580 (2008)
Z.-G. Shi, S.-H. Hong, K.-S. Chen, Tracking airborne targets hidden in blind Doppler using current statistical model particle filter. Prog. Electromagn. Res. 82, 227–240 (2008)
W. Zang, Z.-G. Shi, S.-C. Du, K.-S. Chen, Novel roughening method for reentry vehicle tracking using particle filter. J. Electromagn. Wave 21(14), 1969–1981 (2007)
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Hong, SH., Shi, ZG., Chen, JM. et al. A Low-Power Memory-Efficient Resampling Architecture for Particle Filters. Circuits Syst Signal Process 29, 155–167 (2010). https://doi.org/10.1007/s00034-009-9117-4
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DOI: https://doi.org/10.1007/s00034-009-9117-4