In this thesis, multiple ground target tracking algorithms are studied. From different aspects of the ground target tracking, three different types of tracking algorithms are proposed according to the specialties of the ground target motion and sensors employed. Firstly, the dependent target tracking for ground targets is studied. State dependency is a common assumption in traditional target tracking algorithms, while this may not be the true in ground target tracking as the motion of targets are constraint to certain path. To enhance the tracking algorithm for ground targets, starting with the dependency assumption, Markov Random Field (MRF) based Probabilistic Data Association (PDA) approach is derived to associate motion dependent targets. The driving behavior model is introduced to describe motion relationship among targets. The Posterior Cramer-Rao Lower Bound (PCRLB) is derived for this new motion model. Experiments and simulations show that the proposed algorithm can reduce the false associations and improve the predictions. Eventually, the proposed approach alleviates issues like the track impurity and coalescence problem and achieves better performance comparing to standard trackers assuming state independence. Ground target tracking using cameras is then studied. To build an efficient multi- target visual tracking algorithm, fast single target visual tracking is an important component. A novel visual tracking algorithm that has high speed and better or comparable performance to state-of-the-art trackers is proposed. The proposed approach solves the tracking task by using a mixed-motion proposal based particle filter with Ridge Regression observation likelihood calculation. This approach largely reduces the exhaustive searching in common state-of-art trackers while maintains efficient representation of the target appearance change. Experiments on 100 public benchmark videos, as well as a high frame rate benchmark, are carried out to compare the performance with the state-of-art published algorithms. The results of the experiment show the proposed tracker achieves good performance while beats other algorithms in speed with a large margin. The proposed visual target tracker is integrated into a new multiple ground tar- get tracking algorithm using a single camera. The multi-target tracker addresses the issues in the target detection, data association and track management aside from the single target tracker. A perspective aware detection algorithm utilizing the re- cent advanced Convolutional Neural Networks (CNN) based detector is proposed to detect multiple ground targets and alleviate the weakness of CNN detectors in detecting small objects. A hierarchical class tree based multi-class data association is presented to solve the multi-class association problem with potential misclassified detections. Track management is also improved utilizing the high efficiency detectors and a Support Vector Machine (SVM) based track deletion is proposed to correctly remove the dead tracks. Benchmarking is presented in experiments and results are analyzed. A case study of applying the proposed algorithm is provided demonstrating the usefulness in real applications. / Thesis / Doctor of Philosophy (PhD)

An algorithm for tracking single visual targets is developed in this study. Feature detection is the necessary and appropriate image processing technique for this algorithm. The main point of this approach is to use the data supplied by the feature detection as the observation from a group of targets having similar motion dynamics. Therefore a single visual target is regarded as a group of multiple targets. Accurate data association and state estimation under clutter are desired for this application similar to other multi-target tracking applications. The group tracking approach is used with the well-known probabilistic data association technique to cope with data association and estimation problems. The applicability of this method particularly for visual tracking and for other cases is also discussed.

International Telemetering Conference Proceedings / October 25-28, 1993 / Riviera Hotel and Convention Center, Las Vegas, Nevada / Rapid setup and verification of 4 automatic tracking antennas, 2 radio frequency switch matrixes, 32 telemetry receivers with 16 diversity combiners, an intermediate frequency switch matrix and the signal distribution equipment interface to the analog and digital fiber optic relay systems was required. This paper provides sample displays of the station status window, telemetry receiver and test parameter dialog boxes, mission event log window and test result windows for bit error rate, noise power ratio, solar calibration and antenna servo tests. Use of the software is apparent from sample displays so the text concentrates on lessons learned from site surveys, verification of configuration against mission files, accommodation of change of plug-in modules (e.g., IF filters in the telemetry receiver), tolerance of equipment removed from the system for maintenance, built-in test of serial and parallel communications and modular software design for replacement of equipment.

International Telemetering Conference Proceedings / October 25-28, 1999 / Riviera Hotel and Convention Center, Las Vegas, Nevada / In this paper, a new coherent correlation-loop architecture for tracking direct-sequence spread-spectrum signals is proposed. In the proposed correlation loop model, the mean-square tracking error is minimized by varying the cross-correlation function between the received signal and the locally generated signal. The locally generated signal is produced by passing a replica of the transmitted signal through a linear time-invariant filter, which is termed the VCC filter. The issue of bandwidth of a correlation loop is addressed and a bandwidth definition for comparative purposes is introduced. The filter characteristics to minimize the tracking errors are determined using numerical optimization algorithms. This work demonstrates that the amplitude response of the VCC filter is a function of the input signal-to-noise ratio (SNR). In particular, the optimum filter does not replicate a differentiator at finite signal-to-noise ratio as is sometimes assumed. The optimal filter characteristics and the knowledge of the input SNR can be combined to produce a device that has very low probability of loosing lock.

International Telemetering Conference Proceedings / October 17-20, 1994 / Town & Country Hotel and Conference Center, San Diego, California / This paper describes a method for determining high resolution time, space, and position information for a test range flight vehicle using four tracking receivers. Equipped with GPS time systems, each receiver records the exact time at which a time marker embedded in the transmitted TM data stream is received. With this information, the time difference of arrival for the time markers at three of the receivers can be calculated referenced to the fourth. Using this time difference, the position of the transmitter can be determined. The accuracy with which the received signal time delay can be calculated depends on the accuracy of the GPS time system at each receiver. The effect of time accuracy on positional resolution is evaluated.

An on-line 3D visual object tracking framework for monocular cameras by incorporating spatial knowledge and uncertainty from semantic mapping along with high frequency measurements from visual odometry is presented. Using a combination of vision and odometry that are tightly integrated we can increase the overall performance of object based tracking for semantic mapping. We present a framework for integration of the two data-sources into a coherent framework through uncertainty based fusion/arbitration.

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