Estimation-decoding on LDPC-based 2D-barcodes

In this paper we propose an extension of the Estimation-Decoding algorithm for the decoding of our Data Matrix Code (DMC), which is based on Low-Density-Parity-Check (LDPC) codes and is designed for use in industrial environment. To include possible damages in the channel-model, a Markov-modulated Gaussian channel (MMGC) was chosen to represent everything in between the embossing of a LDPC-based DMC and the camera-based acquisition. The MMGC is based on a Hidden-Markov-Model (HMM) that turns into a two-dimensional model when used in the context of DMCs. The proposed ED2D-algorithm (Estimation-Decoding in two dimensions) is implemented to operate on a 2D-LDPC-Markov factor graph that comprises of a LDPC code's Tanner-graph and a 2D-HMM. For a subsequent comparison between different barcodes in industrial environment, a simulation of typical damages has been implemented. Tests showed a superior decoding behavior of our LDPC-based DMC decoded with the ED2D-decoder over the standard Reed-Solomon-based DMC.