Active Queue Management (AQM) methods attempt to predict and control network router queue levels and provide feedback regarding network congestion to data sources through packet marking/dropping. AQM methods have not employed statistical signal processing principles largely due to the requirement of low complexity. In this paper, we apply optimal filtering and target tracking methods to the design of AQM. In particular, we develop Kalman Filter based AQM which results in router queues with reduced queue level variance. To account for networks with more bursty traffic, we use Interacting Multiple Models (IMM) which similarly result in reduced queue variance in simulations with both long-term and bursty short-term traffic. In comparisons with other AQM methods, these low complexity target tracking-based AQM methods give a more constant queue length without any loss in source throughput.