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

TL;DR

The paper tackles the sensor-to-controller link in wireless networked control systems by exploiting prior control information to enhance both communication reliability and control accuracy. It introduces an iterative joint detection algorithm that exchanges prior information between a Kalman filter and a channel decoder (CRC+LDPC), converting KF predictions into LLR priors and refining them through decoder outputs. The method updates priors by exploring possible outputs and iteratively feeding information back to KF, with a fallback decoding path when iterations saturate. Simulations on a rotary inverted pendulum show notable improvements in block error rate and RMSE, demonstrating the value of integrating control priors into communications for real-time, parallelizable implementations.

Abstract

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.

Figures (5)

• Figure 1: The system structure of WNCSs with control layer, communication layer and iterative joint detection.
• Figure 2: An example of $P\left( \left. b_{i_2}^{\beta \left( y_{i_1}\left[k\right] \right)} =0\right|{{y}_{i_1}\left[k\right]} \right)$ with $n = 4$.
• Figure 3: The BLER performance of the iterative joint detection with $I = 1$ under different system disturbances.
• Figure 4: The RMSE performance of the iterative joint detection with $I = 1$ under different system disturbances.
• Figure 5: The BLER performance of the iterative joint detection with different $I$ under $\sigma_W^2 = \sigma_V^2 = 10^{-8}$.