Parameterized design framework for hardware implementation of particle filters

Particle filtering methods provide powerful techniques for solving non-linear state-estimation problems, and are applied to a variety of application areas in signal processing. Because of their vast computational complexity, real-time hardware implementation of particle-filter-based systems is a challenging task. However, many particle filter applications share common characteristics, and the same system design can be reused with appropriate streamlining. To achieve this, a parameterized design framework for particle filters is proposed in this paper. In this framework, parameterization of system features that vary over specific implementations enables reuse of a generic design for a wide range of applications with minimal re-design effort. Using this framework, we explore different design options for implementing two different particle filtering applications on field-programmable gate arrays (FPGAs), and we present associated results on trade-offs between area (FPGA resource requirements) and execution speed.