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Minimax optimization is a widely-used formulation for robust design in multiple operating or environmental scenarios, where the worst-case performance among multiple scenarios is the optimization objective requiring a large number of quality assessments. Consequently, minimax optimization using evolutionary algorithms becomes prohibitive when each quality assessment involves computationally expensive numerical simulations or costly physical experiments. This work employs evolutionary multitasking optimization and surrogate techniques to address the challenges of the high-dimensional search space and high computation cost of minimax optimization. To this end, finding the worst-case scenario for different candidate solutions is considered as the optimization of multiple problems that can be solved simultaneously using the evolutionary multitasking optimization approach. In order to further speed up the proposed algorithm, a surrogate model in the joint space of the decision and scenario spaces is built to replace part of the expensive function evaluations. A generation-based model management strategy using a statistical hypothesis test is designed to manage the surrogate model. Experimental results on both benchmark problems and an airfoil design application indicate that the proposed algorithm can find satisfactory solutions with a very limited computational budget. [ABSTRACT FROM AUTHOR]

Steganographic transmission waveforms are desirable for many applications, such as underwater acoustic communications, surveillance, detection, and tracking. In the context of active sonar tracking, an overall approach is presented that covers several topics including background sound modeling, steganographic security assessment of transmission waveforms, target motion modeling, and batch track detection. In shallow water, environment background sound dominated by snapping shrimp, the presented approach shows feasibility of steganographic sonar for target track detection. A background sound modeling approach is presented that utilizes symbolic time-series analysis with phase space representation and clustering of time-series segments. The time-evolving characteristics are captured with a hidden Markov model. Steganographic security of the transmission waveform can be assessed by measuring the Kullback–Leibler divergence between the cover model and the stego model. Conventional tracking approaches fail to perform reliably and existing track-before-detect approaches also fail due to overwhelming search computation when they use simplistic short timescale motion models. A long timescale target motion model with considerations for plausibility is presented and utilized in a batch track detection approach. A Monte-Carlo-based simulation is performed to find the scenarios that demonstrate the feasibility of steganographic sonar in a shallow water environment with snapping shrimp sound. [ABSTRACT FROM AUTHOR]

In solving many real-world optimization problems, neither mathematical functions nor numerical simulations are available for evaluating the quality of candidate solutions. Instead, surrogate models must be built based on historical data to approximate the objective functions and no new data will be available during the optimization process. Such problems are known as offline data-driven optimization problems. Since the surrogate models solely depend on the given historical data, the optimization algorithm is able to search only in a very limited decision space during offline data-driven optimization. This paper proposes a new offline data-driven evolutionary algorithm to make the full use of the offline data to guide the search. To this end, a surrogate management strategy based on ensemble learning techniques developed in machine learning is adopted, which builds a large number of surrogate models before optimization and adaptively selects a small yet diverse subset of them during the optimization to achieve the best local approximation accuracy and reduce the computational complexity. Our experimental results on the benchmark problems and a transonic airfoil design example show that the proposed algorithm is able to handle offline data-driven optimization problems with up to 100 decision variables. [ABSTRACT FROM AUTHOR]

Many real-world optimization problems involve computationally intensive numerical simulations to accurately evaluate the quality of solutions. Usually, the fidelity of the simulations can be controlled using certain parameters and there is a tradeoff between simulation fidelity and computational cost, i.e., the higher the fidelity, the more complex the simulation will be. To reduce the computational time in simulation-driven optimization, it is a common practice to use multiple fidelity levels in search for the optimal solution. So far, not much work has been done in evolutionary optimization that considers multiple fidelity levels in fitness evaluations. In this paper, we aim to develop test suites that are able to capture some important characteristics in real-world multifidelity optimization, thereby offering a useful benchmark for developing evolutionary algorithms for multifidelity optimization. To demonstrate the usefulness of the proposed test suite, three strategies for adapting the fidelity level of the test problems during optimization are suggested and embedded in a particle swarm optimization (PSO) algorithm. Our simulation results indicate that the use of changing fidelity is able to enhance the performance and reduce the computational cost of the PSO, which is desired in solving expensive optimization problems. [ABSTRACT FROM AUTHOR]

Function evaluations (FEs) of many real-world optimization problems are time or resource consuming, posing a serious challenge to the application of evolutionary algorithms (EAs) to solve these problems. To address this challenge, the research on surrogate-assisted EAs has attracted increasing attention from both academia and industry over the past decades. However, most existing surrogate-assisted EAs (SAEAs) either still require thousands of expensive FEs to obtain acceptable solutions, or are only applied to very low-dimensional problems. In this paper, a novel surrogate-assisted particle swarm optimization (PSO) inspired from committee-based active learning (CAL) is proposed. In the proposed algorithm, a global model management strategy inspired from CAL is developed, which searches for the best and most uncertain solutions according to a surrogate ensemble using a PSO algorithm and evaluates these solutions using the expensive objective function. In addition, a local surrogate model is built around the best solution obtained so far. Then, a PSO algorithm searches on the local surrogate to find its optimum and evaluates it. The evolutionary search using the global model management strategy switches to the local search once no further improvement can be observed, and vice versa. This iterative search process continues until the computational budget is exhausted. Experimental results comparing the proposed algorithm with a few state-of-the-art SAEAs on both benchmark problems up to 30 decision variables as well as an airfoil design problem demonstrate that the proposed algorithm is able to achieve better or competitive solutions with a limited budget of hundreds of exact FEs. [ABSTRACT FROM PUBLISHER]

We present a radio frequency (RF) front-end nonlinearity estimator that is based on the knowledge of a training sequence to perform specific emitter identification (SEI), which discerns radio emitters of interest. Design and fabrication variations provide unique signal signatures for each emitter, and we extract those characteristics through the estimation of transmitter nonlinearity coefficients. The algorithm provides robust identification by first using alternative degrees of nonlinearities associated with symbol amplitudes for initial estimation, and then iteratively estimating the channel coefficients and distorted transmit symbols to overcome the inter-symbol interference (ISI) effect. The convergence and unbiasedness of the iterative estimator are demonstrated semi-analytically. Based on this analysis, we also trade error performance for complexity reduction using the regularity of the estimation process. The algorithm is applicable to a wide range of multi-amplitude modulation schemes, and we present an SEI system designed for an orthogonal-frequency-division multiplexing (OFDM) system over an empirical indoor channel model with associated numerical results. [ABSTRACT FROM PUBLISHER]

Modulation recognition finds its application in today's cognitive systems ranging from civilian to military installations. Existing modulation classification algorithms include classic likelihood approaches and feature-based approaches. In this study, approximate entropy, a nonlinear method to analyze a time series, is proposed as a unique characteristic of a modulation scheme. It is projected as a robust feature to identify signal parameters such as number of symbol levels, pulse lengths, and modulation indices of a continuous phase modulated (CPM) signal. The method is then extended to classify CPM signals with differing pulse shapes, which include raised cosine and Gaussian pulses with varying roll-off factors and bandwidth-time products, respectively. This approximate entropy feature-based approach results in high classification accuracies for a variety of signals and performs robustly even in the presence of synchronization errors and carrier phase offsets. Results are presented in the form of extensive simulations. [ABSTRACT FROM PUBLISHER]

A signal overlay technique employing the empirical mode decomposition procedure is presented here. A weak narrowband signal is added to the primary signal that shares the same frequency band. Careful signal design reduces interference caused to primary users while ensuring successful recovery of the added signal. At the receiver a stationary filtering approach is ineffective in separating the signals because a fixed filter designed to isolate one of the signals will also capture significant portion of the other signal energy due to overlapping spectrums. However, the empirical mode decomposition technique, that isolates signal components based on their instantaneous frequencies, is ideally suited to separate these time-varying signals with overlapping frequency components. The choice of overlay signal transmission frequencies relative to that of the primary signal is made in such a way that leads to greater resemblance of one of the extracted components to the original overlay signal. An application to commercial frequency modulation overlay is presented with associated analysis and empirical performance results. [ABSTRACT FROM PUBLISHER]

The Hilbert-Huang transform (HHT) is a relatively new time-frequency analysis tool. We present a new signal feature extraction technique based on HHT. This technique is used to extract diurnal and semidiurnal tides from atmospheric pressure data obtained from a microbarograph stationed at the Arecibo Observatory. Observation of seasonal variations of semidiurnal tides is possible due to the high precision offered by this technique. Furthermore, we apply the signal extraction procedure to isolate and remove high-amplitude disturbance signals from the time-series signal. This is demonstrated by extracting a hurricane event from the pressure data. The superior capabilities of the HHT-based technique to analyze time-varying signals compared to traditional linear techniques such as the wavelet transform and the fast Fourier transform are demonstrated. [ABSTRACT FROM AUTHOR]

The nonlinear least square fitting techniques that the authors have employed until recently for measuring the Doppler velocities of head-echo meteor returns have proven to be limited in their effectiveness under certain circumstances where significant incoherent scattering from the ionosphere is present. Two alternative methods developed to independently but jointly determine the Doppler speeds are presented. The first method uses the Moore-Penrose matrix inverse to obtain the relevant meteor parameters. The second method is the steepest descent searching technique. These techniques have been applied to simulated meteor returns as well as meteor data set from February 2001. The Doppler velocities obtained using the new algorithms are presented. The implications of the improved Doppler resolution as well as robustness of the new approach are also discussed. [ABSTRACT FROM AUTHOR]

In this paper, we present signal processing techniques to detect meteor returns from Arecibo Observatory 430-MHz UHF radar data. We exploit the characteristics of the transmit waveform in the frequency domain as well as in the time domain. Two detection methods are investigated. First, when a meteor is present in the radar return over several interpulse periods, there will be a periodic structure in frequency spectrum. By detecting this structure, we detect the presence of a meteor. Second, we construct a matched filter bank to detect the energy of different Doppler frequency components. When the energy exceeds a certain threshold, we declare meteor detection. We also find the altitude of a meteor by finding the peak of the matched filter output. Analytical and experimental results show that we can efficiently and reliably detect meteors using these methods. [ABSTRACT FROM AUTHOR]

Presents a study which examined digital watermarking, a copyright system, and the possible effectivity of using a wavelet-based watermarking algorithm for ownership verification of digital images. Growth of unauthorized copying and distribution of multimedia data through the Internet; Requirements for practical watermarking systems; Experimental results and analysis of the proposed algorithm.

Presents a blind adaptive interference suppression algorithms for DS/CDMA communication systems. Description of the system; Generalized projection algorithms for filter estimation; Analysis of the proposed algorithms.

Presents a novel acoustic echo canceler for teleconference and speakerphone systems, based on input orthogonalization and spread spectrum communication technique. What the echo canceler represents; Study of the introduction and performance of a decorrelation based adaptive filtering algorithm; Details on the impact of the implementation of the echo canceler on a TMS320C30 DSP processor.

Studies an improved direct-sequence spread spectrum interference rejection filter using the fast transversal filter (FTF) adaptive algorithm. Reduction of self-noise effects; Implementation of the constrained filter using the stabilized version of the FTF; Linearly constrained interference rejection.

Presents a new method of rejecting narrowband interference in direct-sequence spread spectrum communications. Projection into convex sets; Application to the interference rejection problem; Simulation results.

Presents a method based on projection onto convex sets (POCS) for enhancing the narrow-band interference rejection capability of direct-sequence spread-spectrum systems employing an adaptive notch filter. Application of POCS to direct-sequence signal reconstruction; Simulation results of convex sets used in signal reconstruction; Conclusions.