Evaluation of soft output decoding for turbo codes is presented. Coding theory related to this research is studied, including convolutional encoding and Viterbi decoding. Recursive systematic convolutional (RSC) codes and nonuniform interleavers commonly used in turbo code encoder design are analyzed. Fundamentals such as reliability estimation, log-likelihood algebra, and soft channel outputs for soft output Viterbi algorithm (SOVA) turbo code decoding are examined. The modified Viterbi metric that incorporates a-priori information used for SOVA decoding is derived. A low memory implementation of the SOVA decoder is shown. The iterative SOVA turbo code decoding algorithm is described with illustrative examples. The performance of turbo codes are evaluated through computer simulation. It has been found that the SOVA turbo code decoding algorithm, as described in the literature, did not perform as well as the published results. Modifications to the decoding algorithm are suggested. The simulated turbo code performance results shown after these modifications more closely match with current published research work. / Master of Science

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International Telemetering Conference Proceedings / October 25-28, 1993 / Riviera Hotel and Convention Center, Las Vegas, Nevada / This paper describes algorithms implemented by the Magellan High Rate Processor to recover radar data corrupted by the failure of an onboard tape recorder that dropped bits. For data with error correction coding, an algorithm was developed that decodes data in the presence of bit errors and missing bits. For the SAR data, the algorithm takes advantage of properties in SAR data to locate corrupted bits and reduce there effects on downstream processing. The algorithms rely on communication approaches, including an efficient tree search and the Viterbi algorithm to maintain the required throughput rate.

<p>A forward error correction technique known as convolutional coding with Viterbi decoding was explored in this final thesis. This Viterbi project is part of the baseband Error control project at electrical engineering department, Linköping University.</p><p>In this project, the basic Viterbi decoder behavior model was built and simulated. The convolutional encoder, puncturing, 3 bit soft decision, BPSK and AWGN channel were implemented in MATLAB code. The BER was tested to evaluate the decoding performance.</p><p>The main issue of this thesis is to implement the RTL level model of Viterbi decoder. With the testing results of behavior model, with minimizing the data path, register size and butterflies in the design, we try to achieve a low silicon cost design. The RTL Viterbi decoder model includes the Branch Metric block, the Add-Compare-Select block, the trace-back block, the decoding block and next state block. With all done, we further understand about the Viterbi decoding algorithm and the DSP implementation methods.</p>

Our main objective in this thesis is to study the effect of narrow band interference on OFDM systems operating in the 2.4 Ghz ISM band and identify ways to improve upon existing techniques to deal with them. We first consider how narrow band signals interfere with OFDM systems. Various noise variance estimation and signal to noise ratio estimation techniques for OFDM systems are then discussed. We also study the conventional Viterbi Algorithm that is used in OFDM wireless systems and the proposed modifications to it in the literature. Our main contribution is a detailed experimental analysis of a modified Viterbi Algorithm that outperforms the conventional one in the presence of narrow band interference. Interference samples captured using a wireless hardware platform were used in simulation to test this modified algorithm. From our analysis we realize that in the presence of narrow band frequency selective interference (such as Bluetooth), the conventional Viterbi Algorithm can be modified to improve the performance of OFDM systems.

Our main objective in this thesis is to study the effect of narrow band interference on OFDM systems operating in the 2.4 Ghz ISM band and identify ways to improve upon existing techniques to deal with them. We first consider how narrow band signals interfere with OFDM systems. Various noise variance estimation and signal to noise ratio estimation techniques for OFDM systems are then discussed. We also study the conventional Viterbi Algorithm that is used in OFDM wireless systems and the proposed modifications to it in the literature. Our main contribution is a detailed experimental analysis of a modified Viterbi Algorithm that outperforms the conventional one in the presence of narrow band interference. Interference samples captured using a wireless hardware platform were used in simulation to test this modified algorithm. From our analysis we realize that in the presence of narrow band frequency selective interference (such as Bluetooth), the conventional Viterbi Algorithm can be modified to improve the performance of OFDM systems.

Signal model and observation model are commonly used to describe a dynamic system model in system identification or estimation such as Kalman filtering. The signal model is usually described by a linear dynamical equation driven by generating noise. The observation model is composed of a linear transformed signal and an additive white Gaussian noise. In this thesis, we set the generating noise to be a white binary sequence. This discrete generating noise makes the generating signal to be discrete. In contrast, the conventional generating signal is continuous. Discrete signal is simpler than the continuous signal. However, there still are too many states for this discrete signal. Therefore, defining the states and reducing the number of states are important in our work. In this thesis, we apply the tree structure to define the states. The number of states is reduced by focusing on the most probable working states. Afterwards, we apply two methods to recover the white sequence using the observation data. One is the Viterbi method; the other is Extended Kalman filter. Both methods are based upon the concept of signal states. Finally, we compare the error rates with the signal generated by continues phase modulation method.

This thesis addresses the design and implementation of a dual-mode channel decoder for two advanced wireless communication systems. One of the targetsystems is the digital video broadcasting for hand-held terminals (DVB-H) , and the other one is Worldwide Interoperability for Microwave Access (WiMAX) system based on the recently approved IEEE 802.16e. Both standards promise to deliver high data bandwidth within very broad regions. The error control coding schemes of both standards are all built on the similar concatenated code, with the exception of the way of data interleaving. Therefore, the decoders for both standards can be highly integrated. To achieve the low-cost and low-power decoder, this thesis proposes several novel design ideas. First, a fast dynamic multiple path convergence mechanism is proposed for the design of Viterbi decoder module, which can determine the survivor path at earlier stage. Furthermore, a new modified forward path prediction method is also presented which can efficiently predict the possiblesurvivor path such that the number of memory operations during the trace-back canbe significantly reduced. The proposed methodology can reduce up to 50% to 80%of memory operations compared with the best prediction scheme in the literature at high signal-to-noise ration. Secondly, for the block deinterleaver adopted by IEEE 802.16. a new multi-bank architecture is proposed by properly splitting and allocating the input data to suitable bank. The proposed block deinterleaver can be highly integrated with the byte-level convolutional deinterleaver adopted by the DVB-H standard by realizing the multiply First-In-First-Out (FIFO) data branches as the circular buffer. The other salient feature of the proposed dual-mode decoder is that all the major data storage units can all be realized by single-port memory such that the overall cost can be highly reduced.

We address the problem of multi-view multi-target tracking using multiple stationary cameras in the application of hockey tracking and test the approach with data from two cameras. The system is based on the previous work by Okuma et al. [50]. We replace AdaBoost detection with blob detection in both image coordinate systems after background subtraction. The sets of blob-detection results are then mapped to the rink coordinate system using a homography transformation. These observations are further merged into the final detection result which will be incorporated into the particle filter. In addition, we extend the particle filter to use multiple observation models, each corresponding to a view. An observation likelihood and a reference color model are also maintained for each player in each view and are updated only when the player is not occluded in that view. As a result of the expanded coverage range and multiple perspectives in the multi-view tracking, even when the target is occluded in one view, it still can be tracked as long as it is visible from another view. The multi-view tracking data are further processed by trajectory smoothing using the Maximum a posteriori smoother. Finally, automatic camera selection is performed using the Hidden Markov Model to create personalized video programs.

We address the problem of multi-view multi-target tracking using multiple stationary cameras in the application of hockey tracking and test the approach with data from two cameras. The system is based on the previous work by Okuma et al. [50]. We replace AdaBoost detection with blob detection in both image coordinate systems after background subtraction. The sets of blob-detection results are then mapped to the rink coordinate system using a homography transformation. These observations are further merged into the final detection result which will be incorporated into the particle filter. In addition, we extend the particle filter to use multiple observation models, each corresponding to a view. An observation likelihood and a reference color model are also maintained for each player in each view and are updated only when the player is not occluded in that view. As a result of the expanded coverage range and multiple perspectives in the multi-view tracking, even when the target is occluded in one view, it still can be tracked as long as it is visible from another view. The multi-view tracking data are further processed by trajectory smoothing using the Maximum a posteriori smoother. Finally, automatic camera selection is performed using the Hidden Markov Model to create personalized video programs.