5D Parameter Estimation of Near-Field Sources Using Hybrid Evolutionary Computational Techniques

Hybrid evolutionary computational technique is developed to jointly estimate the amplitude, frequency, range, and 2D direction of arrival (elevation and azimuth angles) of near-field sources impinging on centrosymmetric cross array. Specifically, genetic algorithm is used as a global optimizer, whereas pattern search and interior point algorithms are employed as rapid local search optimizers. For this, a new multiobjective fitness function is constructed, which is the combination of mean square error and correlation between the normalized desired and estimated vectors. The performance of the proposed hybrid scheme is compared not only with the individual responses of genetic algorithm, interior point algorithm, and pattern search, but also with the existing traditional techniques. The proposed schemes produced fairly good results in terms of estimation accuracy, convergence rate, and robustness against noise. A large number of Monte-Carlo simulations are carried out to test out the validity and reliability of each scheme

Hybridization of Cognitive Radar and Phased Array Radar Having Low Probability of Intercept Transmit Beamforming

A novel design of a cognitive radar (CR) hybridized with a phased array radar (PAR) having a low probability of intercept (LPI) transmit beam forming is proposed. PAR directed high gain property reveals its position to interceptors. Hence, the PAR high gain scanned beam patterns, over the entire surveillance region, are spoiled to get the series of low gain basis patterns. For unaffected array detection performance, these basis patterns are linearly combined to synthesize the high gain beam pattern in the desired direction using the set of weight. Genetic algorithm (GA) based evolutionary computing technique finds these weights offline and stores to memory. The emerging CR technology, having distinct properties (i.e., information feedback, memory, and processing at receiver and transmitter), is hybridized with PAR having LPI property. The proposed radar receiver estimates the interceptor range and the direction of arrival (DOA), using the extended Kalman filter (EKF) and the GA, respectively, and sends as feedback to transmitter. Selector block in transmitter gets appropriate weights from memory to synthesize the high gain beam pattern in accordance with the interceptor range and the direction. Simulations and the results validate the ability of the proposed radar

Defense against Malicious Users in Cooperative Spectrum Sensing Using Genetic Algorithm

In cognitive radio network (CRN), secondary users (SUs) try to sense and utilize the vacant spectrum of the legitimate primary user (PU) in an efficient manner. The process of cooperation among SUs makes the sensing more authentic with minimum disturbance to the PU in achieving maximum utilization of the vacant spectrum. One problem in cooperative spectrum sensing (CSS) is the occurrence of malicious users (MUs) sending false data to the fusion center (FC). In this paper, the FC takes a global decision based on the hard binary decisions received from all SUs. Genetic algorithm (GA) using one-to-many neighbor distance along with z-score as a fitness function is used for the identification of accurate sensing information in the presence of MUs. The proposed scheme is able to avoid the effect of MUs in CSS without identification of MUs. Four types of abnormal SUs, opposite malicious user (OMU), random opposite malicious user (ROMU), always yes malicious user (AYMU), and always no malicious user (ANMU), are discussed in this paper. Simulation results show that the proposed hard fusion scheme has surpassed the existing hard fusion scheme, equal gain combination (EGC), and maximum gain combination (MGC) schemes by employing GA

Nature Inspired Computational Technique for the Numerical Solution of Nonlinear Singular Boundary Value Problems Arising in Physiology

We present a hybrid heuristic computing method for the numerical solution of nonlinear singular boundary value problems arising in physiology. The approximate solution is deduced as a linear combination of some log sigmoid basis functions. A fitness function representing the sum of the mean square error of the given nonlinear ordinary differential equation (ODE) and its boundary conditions is formulated. The optimization of the unknown adjustable parameters contained in the fitness function is performed by the hybrid heuristic computation algorithm based on genetic algorithm (GA), interior point algorithm (IPA), and active set algorithm (ASA). The efficiency and the viability of the proposed method are confirmed by solving three examples from physiology. The obtained approximate solutions are found in excellent agreement with the exact solutions as well as some conventional numerical solutions

An Application of Artificial Intelligence for the Joint Estimation of Amplitude and Two-Dimensional Direction of Arrival of Far Field Sources Using 2-L-Shape Array

An easy and efficient approach, based on artificial intelligence technique, is proposed to jointly estimate the amplitude, elevation, and azimuth angles of far field sources impinging on 2-L-shape array. In these proposed artificial intelligence techniques, the metaheuristics based on genetic algorithm and simulated annealing are used as global optimizers assisted with rapid local version of pattern search for optimization of the adaptive parameters. The performance metric is employed on a fitness evaluation function depending on mean square error which is optimum and requires single snapshot to converge. The proposed approaches are easy to understand, and simple to implement; the genetic algorithm specifically hybridized with pattern search generates fairly good results. The comparison of the given schemes is carried out with 1-L-shape array, as well as, with parallel-shape array and is found to be in good agreement in terms of accuracy, convergence rate, computational complexity, and mean square error. The effectiveness and efficiency of the given schemes are examined through Monte Carlo simulations and their inclusive statistical analysis

COMMUNITY BASED HOME ENERGY MANAGEMENT SYSTEM

In a Smart Grid (SG) scenario, domestic consumers can gain cost reduction benefit by scheduling their Appliance Activation Time (AAT) towards the slots of low charge. Minimization in cost is essential in Home Energy Management Systems (HEMS) to induce consumers acceptance for power scheduling to accommodate for a Demand Response (DR) at peak hours. Despite the fact that many algorithms address the power scheduling for HEMS, community based optimization has not been the focus. This paper presents an algorithm that targets the minimization of energy costs of whole community while keeping a low Peak to Average Ratio (PAR) and smooth Power Usage Pattern (PUP). Objective of cost reduction is accomplished by finding most favorable AAT by Particle Swarm Optimization (PSO) in conjunction with Inclined Block Rate (IBR) approach and Circular Price Shift (CPS). Simulated numerical results demonstrate the effectiveness of CPS to assist the merger of PSO & IBR to enhance the reduction/stability of PAR and cost reduction

M-PAM Signals Classification Using Modified Gabor Filter Network

A Modified Gabor Filter (MGF) network based approach is used for feature extraction and classification of M-ary Pulse Amplitude Modulated (M-PAM) signals by adaptively tuning the parameters of MGF network. Modulation classification of M-PAM signals is done under the influence of additive white Gaussian noise (AWGN) and channel effects such as Rayleigh flat fading and Rician flat fading. The MGF network uses the network structure of two layers. First layer which is input layer constitutes the adaptive feature extraction part and second layer constitutes the signal classification part. The Gabor atom parameters are tuned using Delta rule and updating of weights of MGF using Recursive Least Square (RLS) algorithm. The simulation results in the form confusion matrix show that proposed modified modulation classification algorithm has high classification accuracy at low signal to noise ratio (SNR). The performance comparison with state-of-the-art existing techniques shows the significant performance improvement of proposed MGF based classifier