ITC/USA 2007 Conference Proceedings / The Forty-Third Annual International Telemetering Conference and Technical Exhibition / October 22-25, 2007 / Riviera Hotel & Convention Center, Las Vegas, Nevada / The Viterbi algorithm has uses for both the decoding of convolutional codes and the detection of signals distorted by intersymbol interference (ISI). The operation of these processes is characterized by a trellis. An ARTM Tier-1 space-time coded telemetry receiver required the use of an irregular Viterbi trellis decoder to solve the dual antenna problem. The nature of the solution requires the trellis to deviate from conventional trellis structure and become time-varying. This paper explores the architectural challenges of such a trellis and presents a solution using a modified systolic array allowing the trellis to be realized in hardware.

No description available.

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.

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.

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. / Science, Faculty of / Computer Science, Department of / Graduate

In this work, a new method of approximating the Maximum-likelihood estimate has been presented. The method consists of first using the Viterbi algorithm to estimate the log likelihood of the state, and then approximating that log likelihood to keep the computational complexity down. Various methods for approximating the log likelihood are introduced, most of these using linear regression and feature vectors. The methods were compared to a Kalman filter or Extended Kalman filter (depending on wether the system was linear or nonlinear) as well as a Particle filter modified to return a maximum likelihood estimate. Two systems were used for testing, one very simple linear system as well as a complex nonlinear system. Both of these were 1-dimensional. When applied to the simple system, the presented method outperformed both the Kalman filter and the Particle filter. While many approximation methods gave a good results the best one was using a cubic spline. For the more complex system, the method presented here could not outperform the particle filter. The most promising approximation method for this system was a Chebyshev approximation.

No description available.

No description available.

International Telemetering Conference Proceedings / October 25-28, 1999 / Riviera Hotel and Convention Center, Las Vegas, Nevada / Gaussian Minimum Shift Keying (GMSK) is a form of Continuous Phase Modulation (CPM) whose spectral occupancy can be easily tailored to the available channel bandwidth by a suitable choice of signal parameters. The constant envelope of the GMSK signal enables it to corporate with saturated power amplifier without the spectral re-growth problem. This paper provides a quantitative synopsis of binary GMSK signals in terms of their bandwidth occupancy and coherent demodulation performance. A detailed account of how to demodulate such signals using the Viterbi Algorithm (VA) is given, along with analytical power spectral density (PSD) and computer simulated bit-error-rate (BER) results for various signal BT products. The effect of adjacent channel interference (ACI) is also quantified. Ideal synchronization for both symbol time and carrier phase is assumed.