Quantized innovations Kalman filter: stability and modification with scaling quantization

The stability of quantized innovations Kalman filtering (QIKF) is analyzed. In the analysis, the correlation between quantization errors and measurement noises is considered. By taking the quantization errors as a random perturbation in the observation system, the QIKF for the original system is equivalent to a Kalman-like filtering for the equivalent state-observation system. Thus, the estimate error covariance matrix of QIKF can be more exactly analyzed. The boundedness of the estimate error covariance matrix of QIKF is obtained under some weak conditions. The design of the number of quantized levels is discussed to guarantee the stability of QIKF. To overcome the instability and divergence of QIKF when the number of quantization levels is small, we propose a Kalman filter using scaling quantized innovations. Numerical simulations show the validity of the theorems and algorithms.

A novel soft reliability-based iterative majority-logic decoding algorithm with uniform quantization

In this paper, a novel soft reliability-based iterative majority-logic decoding algorithm with uniform quantization is proposed for regularly structured low density parity-check(LDPC) codes. A weighted measure is introduced for each check-sum of the parity-check matrix and a scaling factor is used to weaken the overestimation of extrinsic information. Furthermore, the updating process of the reliability measure takes advantage of turbo-like iterative decoding strategy. The main computational complexity of the proposed algorithm only includes logical and integer operations with the bit uniform quantization criterion. Simulation results show that the novel decoding algorithm can achieve excellent error-correction performance and a fast decoding convergence speed.