Information Theory, Inference & Learning Algorithms
Information Theory, Inference & Learning Algorithms
Bayesian Logical Data Analysis for the Physical Sciences
Bayesian Logical Data Analysis for the Physical Sciences
Applications of simulated annealing for the design of specialdigital filters
IEEE Transactions on Signal Processing
A polynomial-time algorithm for designing FIR filters withpower-of-two coefficients
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
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The design approach presented in this paper applies Bayesian inference to the design of finite impulse response (FIR) filters with signed power-of-two (SPoT) coefficients. Given a desired frequency magnitude response specified by upper and lower bounds in decibels, Bayesian parameter estimation and model selection are adapted to produce a distribution of potential designs, all of which perform at or close to the specified standard. In the process, having incorporated prior information such as the maximum acceptable number of SPoT terms and filter length, and the practical design requirement to use the fewest bits possible, the total number of bits, filter taps and SPoT terms, and the filter length required in a design are automatically determined. The produced design candidates have design complexity appropriate to the design specifications and requirements, as designs with higher design complexity than required are rendered less probable by the embedded Ockham's razor. This innate ability is a prominent advantage that the newly developed framework possesses over many optimization based techniques as it leads to designs that require fewer SPoT terms and filter taps. Most importantly, it avoids the intricacy, arduousness and rigorousness involved in devising an appropriate scheme for balancing design performance against design complexity.