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.

<p> This thesis addresses the design of fractionally-spaced equalizers for a digital communication system which is susceptible to Adjacent Channel Interference (ACI). ACI can render an otherwise well designed system prone to excess bit errors. Algorithms for a trained adaptive FIR linear fractionally-spaced equalizer (FSE) with explicit sidelobe control are developed in order to provide robustness to ACI. The explicit sidelobe control is achieved by imposing a quadratic inequality constraint on the frequency response of the equalizer at a discrete set of frequency points in the sidelobe region.</p> <p> Algorithms are developed for both block adaptive and symbol-by-symbol adaptive modes. These algorithms use interior point optimization techniques to find the optimal equalizer coefficients. In the block adaptive mode, the problem is reformulated as a Second Order Cone Program (SOCP). In the symbol-by-symbol adaptive mode, the philosophy of the barrier approach to interior point methods is adopted. The concept of a central path and the Method of Analytic Centers (MAC) are used to develop two practically implementable algorithms, namely IPM2 and SBM, for performing symbol-by-symbol adaptive, fractionally-spaced equalization, with multiple quadratic inequality constraints.</p> <p> The performance of the proposed algorithms is compared to that of the Wiener filter, and the standard RLS algorithm with explicit diagonal loading. In the computer simulations, the proposed algorithms perform better in the sense that they provide the desired robustness when the communication model is prone to intermittent interferers in the sidelobe region of the frequency response of the FSE. Although the proposed algorithms have a moderately higher computational cost, their insensitivity to the deleterious effects of ACI make them an attractive choice in certain applications.</p> / Thesis / Master of Applied Science (MASc)

Collaborative Beamforming (CB) has been introduced in Wireless Sensor Networks (WSNs) context as a long-distance and power-efficient communication scheme. One challenge for CB is the randomness of sensor node locations where different network realizations result in different CB beampatterns. First, we study the effect of sensor node spatial distribution on the CB beampattern. The characteristics of the CB beampattern are derived for circular Gaussian distributed sensor nodes and compared with the case of uniform distributed sensor nodes. It is shown that the mainlobe behavior of the CB beampattern is essentially deterministic. This suggests that the average beampattern characteristics are suitable for describing the mainlobe of a sample beampattern. However, the CB beampattern sidelobes are random and highly depends on the particular sensor node locations. Second, we introduce the multi-link CB and address the problem of random sidelobes where high level sidelobes can cause unacceptable interference to unintended Base Stations or Access Points (BSs/APs). Centralized sidelobe control techniques are impractical for distributed sensor nodes because of the associated communication overhead for each sensor node. Therefore, we propose a node selection scheme as an alternative to the centralized sidelobe control which aims at minimizing the interference at unintended BSs/APs. Our algorithm is based on the use of the inherent randomness of the channels and a low feedback that approves/rejects tested random node combinations. The performance of the proposed algorithm is analyzed in terms of the average number of trials and the achievable interference suppression and transmission rate. Finally, we study CB with power control aiming at prolonging the lifetime of a cluster of sensor nodes in the WSN. The energy available at different sensor nodes may not be the same since different sensor nodes may perform different tasks and not equally frequently. CB with power control can be used to balance the individual sensor nodes' lifetimes. Thus, we propose a distributed algorithm for CB with power control that is based on the Residual Energy Information (REI) at each sensor node while achieving the required average SNR at the BS/AP. The effectiveness of the proposed CB with power control is illustrated by simulations. / Communications

No description available.