Innovations-based state estimation with wireless sensor networks

We study a state estimation architecture for sensor networks, where several sensors transmit quantized innovations to a central estimator. Transmission is via a wireless channel, which is prone to fading leading to random packet loss. State estimation is carried out at the gateway via a time-varying Kalman filter which accounts for packet loss and quantization effects. To form the innovations at the sensors, the estimator transmits information regarding its current state estimate to the sensors. This information could be dedicated to each sensor or broadcast to all sensors. In addition, the gateway also decides upon power levels and quantization step-sizes to be used by each sensor node. Here, we adopt elements of predictive control to trade off estimation performance versus energy use.