This thesis examines the performance of some dim target detection algorithms in low-SNR imaging scenarios. In the past research, there have been numerous attempts for detection and tracking barely visible targets for military surveillance applications with infrared sensors. In this work, two of these algorithms are analyzed via extensive simulations. In one of these approaches, dynamic programming is exploited to coherently integrate the visible energy of dim targets over possible relative directions, whereas the other method is a Bayesian formulation for which the target likelihood is updated along time to be able to detect a target moving in any direction. Extensive experiments are conducted for these methods by using synthetic image sequences, as well as some real test data. The simulation results indicate that it is possible to detect dim targets in quite low-SNR conditions. Moreover, the performance might further increase, in case of incorporating any a priori information about the target trajectory.

The analysis of performance gains arising from cueing in cooperative search systems with autonomous vehicles has been studied using Continuous Time Markov Chains; where the search time distributions are assumed to follow the exponential distributions. This work proposes the use of Petri Nets to model and analyze these systems. The Petri Net model considers two types of autonomous vehicles: a search-only vehicle and n search-engage vehicles. Specific performance metrics are defined to measure system’s performance. Through simulation, it is shown that the search time with stationary targets and cues that provide exact target location follows a triangular distribution. A methodology for approximating general distributions and incorporating them into the Petri Net model for performance analysis is presented.

The active surveillance of public and private sites is increasingly becoming a very important and critical issue. It is therefore, imperative to develop mobile surveillance systems to protect these sites. Modern surveillance systems encompass spatially distributed mobile and static sensors in order to provide effective monitoring of persistent and transient objects and events in a given Area Of Interest (AOI). The realization of the potential of mobile surveillance requires the solution of different challenging problems such as task allocation, mobile sensor deployment, multisensor management, cooperative object detection and tracking, decentralized data fusion, and interoperability and accessibility of system nodes. This thesis proposes a market-based framework that can be used to handle different problems of mobile surveillance systems. Task allocation and cooperative target-tracking are studied using the proposed framework as two challenging problems of mobile surveillance systems. These challenges are addressed individually and collectively.

The small target detection in High Frequency Surface Wave Radar is limited by the presence of various clutter and interference. Several novel signal processing techniques are developed to improve the system detection performance. As an external interference due to local lightning, impulsive noise increases the broadband noise level and then precludes the targets from detection. A new excision approach is proposed with modified linear predictions as the reconstruction solution. The system performance is further improved by de-noising the estimated covariance matrix through signal property mapping method. The existence of non-stationary sea clutter and ionospheric clutter can result in excessive false alarm rate through the high sidelobe level in adaptive beamforming. The optimum threshold discrete quadratic inequality constraints method is proposed to guarantee the sidelobe-controlling problem consistently feasible and optimal. This constrained optimization problem can be formulated into a second order cone problem with efficient mathematical solution. Both simulation and experimental results validate the improved performance and feasibility of our method. Based on the special noise characteristics of High Frequency radar, an adaptive switching Constant False Alarm Rate detector is proposed for targets detection in the beamformed range-Doppler map. The switching rule and adaptive footprint are applied to provide the optimum background noise estimation. For this new method about 14% probability of detection improvement has been verified by experimental data, and meanwhile the false alarm rate is reduced significantly compared to the original CFAR. The conventional Doppler processing has difficulty to recognize a target if its frequency is close to a Bragg line. One detector is proposed to solve this co-located co-channel resolvability problem under the assumption that target/clutter have different phase modulation. Moreover with the pre-whitening processing, the Reversible Jump Markov Chain Monte Carlo method can provide target number and Direction-of-Arrival estimation with lower detection threshold compared to beamforming and subspace methods. RJMCMC is able to convergent to the optimal resolution for a data set that is small compared with information theoretic criteria.

Unmanned aerial systems (UAS) are an influential technology which can enhance life in multiple ways. However, they must be able to sense and operate safely with manned aircraft. Radar is an attractive sensor for UAS because of its all-weather performance. It is challenging, though, to meet the size, weight, and power (SWaP) limitations of UAS and especially small-UAS while still maintaining the needed sensing capability. A working FMCW radar prototype has been created which meets the SWaP requirement of small-UAS. A simulation model for the radar was developed to test the processing algorithms of the radar and proved to be advantageous in that purpose. An automatic target detection algorithm was also successfully developed to allow the radar to identify targets of interest in a cluttered and dynamic environment. Fixed-wing airborne tests have been performed with the radar which show that the radar meets the SWaP requirements of small-UAS. They also show the prototype requires a higher sensitivity to detect other small-UAS. A successful redesign of the radar's receivers was done to make the radar more sensitive.

This dissertation develops new techniques employing the Low-rank and Sparse Representation approaches to improve the performance of state-of-the-art algorithms in hyperspectral image analysis. The contributions of this dissertation are outlined as follows. 1) Low-rank and sparse representation approaches, i.e., low-rank representation (LRR) and low-rank subspace representation (LRSR), are proposed for hyperspectral image analysis, including target and anomaly detection, estimation of the number of signal subspaces, supervised and unsupervised classification. 2) In supervised target and unsupervised anomaly detection, the performance can be improved by using the LRR sparse matrix. To further increase detection accuracy, data is partitioned into several highly-correlated groups. Target detection is performed in each group, and the final result is generated from the fusion of the output of each detector. 3) In the estimation of the number of signal subspaces, the LRSR low-rank matrix is used in conjunction with direct rank calculation and soft-thresholding. Compared to the state-of-the-art algorithms, the LRSR approach delivers the most accurate and consistent results across different datasets. 4) In supervised and unsupervised classification, the use of LRR and LRSR low-rank matrices can improve classification accuracy where the improvement of the latter is more significant. The investigation on state-of-the-art classifiers demonstrate that, as a pre-preprocessing step, the LRR and LRSR produce low-rank matrices with fewer outliers or trivial spectral variations, thereby enhancing class separability.

Explosive hazards, above and below ground, are a serious threat to civilians and soldiers. In an attempt to mitigate these threats, different forms of explosive hazard detection (EHD) exist; e.g, multi-sensor hand-held platforms, downward looking and forward looking vehicle mounted platforms, etc. Robust detection of these threats resides in the processing and fusion of different data from multiple sensing modalities, e.g., radar, infrared, electromagnetic induction (EMI), etc. The focus of this thesis is on the implementation of two new algorithms to form a new energy-based prescreener in hand-held ground penetrating radar (GPR). First, B-scan signal data is curvelet filtered using either Reverse- Reconstruction followed by Enhancement (RRE) or selectivity with respect to wedge information in the Curvelet transform, Wedge Selection (WS). Next, the result of a bank of matched filter are aggregated and run a size contrast filter with Bhattacharyya distance. Alarms are then combined using weighted mean shift clustering. Results are demonstrated in the context of receiver operating characteristics (ROC) curve performance on data from a U.S. Army test site that contains multiple target and clutter types, burial depths, and times of the day.

Rapid and high-sensitivity detections of biological analytes are critically important to ensure timely diagnosis of disease and effective monitoring of public health. Although various new biosensing platforms have been established as alternatives to conventional laboratory methods, most of these biosensing platforms suffer from insufficient sensitivities that severely limit their wide applications. To improve the detection sensitivities of these biosensors, surface modifications based on poly(amidoamine) (PAMAM) dendrimers and rolling circle amplification (RCA) have been proven to be effective methods. In this thesis, surface modification strategies based on PAMAM dendrimers and RCA have been applied on three biosensing platforms, including enzyme-linked immunosorbent assay (ELISA), localized surface plasmon resonance (LSPR) sensor chip, and affinity membrane, to improve their detection sensitivities. For the ELISA platform, glass-bottom and poly(styrene) 96-well plates are surface modified by dendrimer-aptamer conjugates to improve detection performances of human platelet-derived growth factor-BB using ELISA. The results show that the ELISA performed using the modified 96-well plates presents a much broader linear detection range and a significantly lower limit of detection (LOD) than conventional ELISA plates. For the LSPR platform, the dendrimer and aptamer modification strategy is employed to surface modify LSPR sensor chips for sensitive detection of the SARS-CoV-2 virus, and an RCA-AuNPs complex is developed to amplify the detection signals. The results show that the modified chip can sensitively detect the SARS-CoV-2 virus with a LOD of 148 vp/mL, suggesting that the modified LSPR chip and signal amplification method can be used for early diagnosis of Covid-19. For the affinity membrane platform, nylon membranes with dendrimer and dual-RCA surface modifications are developed to detect Escherichia coli O157:H7 in food samples. The surface-modified membranes significantly reduce the detection time of the target bacteria to two hours instead of several days using traditional bacterial detection methods. In addition, the new membranes achieve higher sample throughputs (around 4-5 mL/s) with a lower LOD (10 cells/ 250 mL) in processing real-world food samples compared to other similar detection platforms. The excellent properties of our surface modification approaches may provide further advantages when employed in other platforms, such as target separation and enrichment, antifouling and antibacterial, and drug delivery applications.

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