Iterative Joint Detection of Kalman Filter and Channel Decoder for Sensor-to-Controller Link in Wireless Networked Control Systems

In this letter, we propose an iterative joint detection algorithm of Kalman filter (KF) and channel decoder for the sensor-to-controller link of wireless networked control systems, which utilizes the prior information of control system to improve control and communication performance. In this algorithm, we first use the KF to estimate the probability density of the control system outputs and calculate the prior probability of received signals to assist decoder. Then, the possible outputs of the control system are traversed to update the prior probability in order to implement iterative detection. The simulation results show that the prior information and the iterative structure can reduce the block error rate performance of communications while improving the root mean square error performance of controls.

💡 Research Summary

The paper addresses the problem of jointly improving communication reliability and control performance in wireless networked control systems (WNCS) where sensor measurements are transmitted over a noisy wireless link to a controller. Conventional designs treat the communication and control layers separately: channel codes are designed assuming uniformly distributed bits, while the controller uses a Kalman filter (KF) that assumes perfect or independently erased measurements. This separation ignores the valuable prior information that the control system can provide about the statistics of the transmitted bits.

To bridge this gap, the authors propose an iterative joint detection algorithm that tightly couples a Kalman filter with a channel decoder (e.g., an LDPC belief‑propagation decoder). The key idea is to convert the KF’s prediction of the plant state into prior log‑likelihood ratios (LLRs) for the quantized sensor bits, feed these priors to the decoder, obtain updated posterior LLRs, and then feed the posterior probabilities back to the KF. This exchange is repeated for a limited number of iterations.

System model: The plant follows a discrete‑time linear time‑invariant model
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