Cost-effective constrained particle filter for indoor localization

Constrained particle filter is widely used in indoor localization applications. With environmental information, the cascaded hypothesis modifier can improve accuracy by rejecting particles those have invalid transitions. However, the memory requirement and computation complexity of constrained particle filter are both large, and the low spatial correlations between the sequentially accessed particles make the computation inefficient. This paper proposes two techniques to improve the efficiency: location constrained multi-prediction and dynamically cascaded hypothesis modifier. The experimental result shows that the proposed techniques can achieve higher accuracy at lower cost, both in storage and computation.