Rotation and Scaling Invariant Target Tracking Using Particle Filters in Infrared Image Sequences

A new rotation and scaling invariant target tracking algorithm is proposed using particle filters. Specifically, the target aspect is modelled by a continuous-valued affine model which is augmented to the target's kinematic parameters and whose dynamics are assumed to follow a first-order Markov model. Two specific particle filtering algorithms are implemented, i.e., Sequential Importance Re-sampling (SIR) and Auxiliary Particle Filter (APF). The Gaussian-Markov Random Field (GMRF) is used to characterize the spatial clutter of the background, and a target signature model is used to simulate the presence of a target. Simulation results show good tracking performance on targets with time varying rotation angles and scale factors even under low signal-to-noise ratios.School of Electrical & Computer Engineerin

Target tracking with composite linear filters on noisy scenes

A tracking system using a bank of adaptive linear filters is proposed. Tracking is carried out by means of multiple target detections. The linear filters are designed from multiple views of a target using synthetic discriminant functions. For each view an optimum filter is derived from noisy reference image and disjoint background model. An iterative algorithm is used to improve the performance of the synthesized filters. The number of filters in the bank can be controlled to guarantee a prescribed tracking accuracy. Computer simulation results show that the proposed algorithm is able to precisely track a target.This work was supported the Russian Science Foundation grant №15-19-10010

Robust Multi-Object Tracking: A Labeled Random Finite Set Approach

The labeled random finite set based generalized multi-Bernoulli filter is a tractable analytic solution for the multi-object tracking problem. The robustness of this filter is dependent on certain knowledge regarding the multi-object system being available to the filter. This dissertation presents techniques for robust tracking, constructed upon the labeled random finite set framework, where complete information regarding the system is unavailable

Improved nonlinear filtering for target tracking.

The objective of this research is to develop robust and accurate tracking algorithms for various tracking applications. These tracking problems can be formulated as nonlinear filtering problems. The tracking algorithms will be developed based on an emerging promising nonlinear filter technique, known as sequential importance sampling (nick-name: particle filtering). This technique was introduced to the engineering community in the early years of 2000, and it has recently drawn significant attention from engineers and researchers in a wide range of areas. Despite the encouraging results reported in the current literature, there are still many open questions to be answered. For the first time, the major research effort will be focusing on making improvement to the particle filter based tracking algorithm in the following three aspects: (I) refining the particle filtering process by designing better proposal distributions (II) refining the dynamic model by using multiple-model method, (i.e. using switching dynamics and jump Markov process) and (III) refining system measurements by incorporating a data fusion stage for multiple measurement cues

Active Control Strategies for Chemical Sensors and Sensor Arrays

Chemical sensors are generally used as one-dimensional devices, where one measures the sensor’s response at a fixed setting, e.g., infrared absorption at a specific wavelength, or conductivity of a solid-state sensor at a specific operating temperature. In many cases, additional information can be extracted by modulating some internal property (e.g., temperature, voltage) of the sensor. However, this additional information comes at a cost (e.g., sensing times, power consumption), so offline optimization techniques (such as feature-subset selection) are commonly used to identify a subset of the most informative sensor tunings. An alternative to offline techniques is active sensing, where the sensor tunings are adapted in real-time based on the information obtained from previous measurements. Prior work in domains such as vision, robotics, and target tracking has shown that active sensing can schedule agile sensors to manage their sensing resources more efficiently than passive sensing, and also balance between sensing costs and performance. Inspired from the history of active sensing, in this dissertation, we developed active sensing algorithms that address three different computational problems in chemical sensing. First, we consider the problem of classification with a single tunable chemical sensor. We formulate the classification problem as a partially observable Markov decision process, and solve it with a myopic algorithm. At each step, the algorithm estimates the utility of each sensing configuration as the difference between expected reduction in Bayesian risk and sensing cost, and selects the configuration with maximum utility. We evaluated this approach on simulated Fabry-Perot interferometers (FPI), and experimentally validated on metal-oxide (MOX) sensors. Our results show that the active sensing method obtains better classification performance than passive sensing methods, and also is more robust to additive Gaussian noise in sensor measurements. Second, we consider the problem of estimating concentrations of the constituents in a gas mixture using a tunable sensor. We formulate this multicomponent-analysis problem as that of probabilistic state estimation, where each state represents a different concentration profile. We maintain a belief distribution that assigns a probability to each profile, and update the distribution by incorporating the latest sensor measurements. To select the sensor’s next operating configuration, we use a myopic algorithm that chooses the operating configuration expected to best reduce the uncertainty in the future belief distribution. We validated this approach on both simulated and real MOX sensors. The results again demonstrate improved estimation performance and robustness to noise. Lastly, we present an algorithm that extends active sensing to sensor arrays. This algorithm borrows concepts from feature subset selection to enable an array of tunable sensors operate collaboratively for the classification of gas samples. The algorithm constructs an optimized action vector at each sensing step, which contains separate operating configurations for each sensor in the array. When dealing with sensor arrays, one needs to account for the correlation among sensors. To this end, we developed two objective functions: weighted Fisher scores, and dynamic mutual information, which can quantify the discriminatory information and redundancy of a given action vector with respect to the measurements already acquired. Once again, we validated the approach on simulated FPI arrays and experimentally tested it on an array of MOX sensors. The results show improved classification performance and robustness to additive noise

Investigation related to multispectral imaging systems

A summary of technical progress made during a five year research program directed toward the development of operational information systems based on multispectral sensing and the use of these systems in earth-resource survey applications is presented. Efforts were undertaken during this program to: (1) improve the basic understanding of the many facets of multispectral remote sensing, (2) develop methods for improving the accuracy of information generated by remote sensing systems, (3) improve the efficiency of data processing and information extraction techniques to enhance the cost-effectiveness of remote sensing systems, (4) investigate additional problems having potential remote sensing solutions, and (5) apply the existing and developing technology for specific users and document and transfer that technology to the remote sensing community

The 1991 Marshall Space Flight Center research and technology

A compilation of 194 articles addressing research and technology activities at the Marshall Space Flight Center (MSFC) is given. Activities are divided into three major areas: advanced studies addressing transportation systems, space systems, and space science activities conducted primarily in the Program Development Directorate; research tasks carried out in the Space Science Laboratory; and technology programs hosted by a wide array of organizations at the Center. The theme for this year's report is 'Building for the Future'

Sea Mines and Countermeasures: A Bibliography

This compilation was prepared for the Dudley Knox Library, Naval Postgraduate School, Monterey, CA