Sparse FIR filter design using binary particle swarm optimization

Recently, design of sparse finite impulse response (FIR) digital filters has attracted much attention due to its ability to reduce the implementation cost. However, finding a filter with the fewest number of nonzero coefficients subject to prescribed frequency domain constraints is a rather difficult problem because of its non-convexity. In this paper, an algorithm based on binary particle swarm optimization (BPSO) is proposed, which successively thins the filter coefficients until no sparser solution can be obtained. The proposed algorithm is evaluated on a set of examples, and better results can be achieved than other existing algorithms.