Fawad Zaman

AMPLITUDE AND DIRECTIONAL OF ARRIVAL ESTIMATION: COMPARISON BETWEEN DIFFERENT TECHNIQUES

In this work, we propose a method based on Genetic algorithm hybridized with Pattern Search for joint estimation of Amplitude and Direction of Arrival, azimuth as well as elevation angles using L-type array. Four other schemes, i.e., the Genetic algorithm, Pattern Search, Simulated Annealing and Simulated Annealing hybridized with Pattern Search are also discussed and compared with Genetic algorithm hybridized with Pattern Search. Multiple sources are taken in the far fleld of sensors array and Mean Square Error is taken as a fltness function. This fltness function is optimal in nature and requires only a single snapshot. It avoids any ambiguity or required permutation as in some other methods to link it with angles found in the previous snapshot. The reliability and efiectiveness of the proposed scheme is tested on the basis of Monte- Carlo simulations and its statistical analysis.

JOINT ESTIMATION OF AMPLITUDE, DIRECTION OF ARRIVAL AND RANGE OF NEAR FIELD SOURCES USING MEMETIC COMPUTING

In this paper, we propose a method based on evolutionary computations for joint estimation of amplitude, Direction of Arrival and range of near fleld sources. We use memetic computing in which the problem starts with a global optimizer and ends up with a local optimizer for flne tuning. For this, we use Genetic algorithm and Simulated annealing as a global optimizer while Interior Point Algorithm as a rapid local optimizer. We set up Mean Square Error as a fltness evaluation function which deflnes an error between actual and estimated signal. This fltness function is optimum and is derived from Maximum likelihood principle. It requires only single snapshot to converge and does not require any permutations to link it with the angles found in the previous snapshot as in some other methods. The e-ciency and reliability of the proposed scheme is tested on the basis of Monte-Carlo simulations and its inclusive statistical analysis.

Independent null steering by decoupling complex weights

In this paper, we propose a structure for independent null steering by decoupling complex weights. It has the advantage of independent steering of(N-1) nulls for a linear array having N elements and the complex weights are decoupled such that each weight corresponds to a particular null. For this purpose, the position of each zero of the array factor on the unit circle in the complex plane is controlled by the corresponding weight in the structure. It means that if a single jammer changes its position, only a single corresponding complex weight has to be recalculated, which reduces computational complexity.

NULL PLACEMENT AND SIDELOBE SUPPRESSION IN FAILED ARRAY USING SYMMETRICAL ELEMENT FAILURE TECHNIQUE AND HYBRID HEURISTIC COMPUTATION

In this paper, we have addressed three major problems of uniform linear array in case of a sensor failure at any position. We assume that sensor position is known. The problems include increase in sidelobe levels, displacement of nulls and diminishing of null depth. The desired null depth is achieved by making the weight of symmetrical counterpart element passive. Genetic algorithm (GA) along with pattern search (PS) is used for reduction of sidelobe levels, and adjustment of nulls. Fitness function minimizing the error between the desired and estimated beam pattern along with null constraints is used. Simulation results for diversifled scenarios have been given to demonstrate the validity and performance of the proposed algorithm.

Design of Bio-inspired Heuristic Techniques Hybridized with Sequential Quadratic Programming for Joint Parameters Estimation of Electromagnetic Plane Waves

In this study, intelligent hybrid computing techniques are developed using variants of genetic algorithms (GAs) to estimate jointly direction of arrival and amplitude of electromagnetic plane waves. Fitness evaluation function is formulated for parameter estimation model by exploiting the approximation theory in mean square sense based on error between the desired and estimated responses. Optimization of design variables of the model is carried out with hybrid schemes through variant of GAs integrated with sequential quadratic programming for rapid refinement. Proposed schemes are applied to number of electromagnetic plane waves impinging on uniform linear array from different directions with different amplitudes. Comparison of the results is done with true parameters of the system in order to evaluate the performance of the algorithms. Monte-Carlo simulations for the design approaches are carried out to analyze their strength in terms of estimation accuracy, robustness against noise, convergence and proximity effects.Graphical Abstract

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.

An application of hybrid computing to estimate jointly the amplitude and Direction of Arrival with single snapshot

In this paper, utilization of hybrid computational approach is evaluated for the joint estimation of amplitude and Direction of Arrival of far field sources impinging on a uniform linear array. In this hybrid approach, swarm intelligence based on Particle swarm optimization is exploited as a global optimizer assisted with pattern search technique as a rapid local search technique. The optimization of adaptive parameters depending upon the amplitudes and direction of arrival is performed using the fitness function based on Mean Square Error that defines an error between desired response and estimated response. The interest in this function is due to its ease in implementation, efficiency and simplicity of concept. It is derived from Maximum Likelihood and requires only single snapshot to converge. The proposed algorithm is robust enough to produce fairly good results even in the presence of low signal-to-Noise Ratio and requires relatively less number of antenna elements in the array. The results of hybrid technique are much better as compared to Particle Swarm Optimization and pattern search alone. A number of test cases are discussed on the basis of different number of sources impinging on the array with different number of sensors in the array. The accuracy and reliability of the proposed scheme is tested on the basis of Monte-Carlo simulations and its superior statistical analysis.

Correction of faulty sensors in phased array radars using symmetrical sensor failure technique and cultural algorithm with differential evolution.

Three issues regarding sensor failure at any position in the antenna array are discussed. We assume that sensor position is known. The issues include raise in sidelobe levels, displacement of nulls from their original positions, and diminishing of null depth. The required null depth is achieved by making the weight of symmetrical complement sensor passive. A hybrid method based on memetic computing algorithm is proposed. The hybrid method combines the cultural algorithm with differential evolution (CADE) which is used for the reduction of sidelobe levels and placement of nulls at their original positions. Fitness function is used to minimize the error between the desired and estimated beam patterns along with null constraints. Simulation results for various scenarios have been given to exhibit the validity and performance of the proposed algorithm.

Four-dimensional parameter estimation of plane waves using swarming intelligence

This paper proposes an efficient approach for four-dimensional (4D) parameter estimation of plane waves impinging on a 2-L shape array. The 4D parameters include amplitude, frequency and the two-dimensional (2D) direction of arrival, namely, azimuth and elevation angles. The proposed approach is based on memetic computation, in which the global optimizer, particle swarm optimization is hybridized with a rapid local search technique, pattern search. For this purpose, a new multi-objective fitness function is used. This fitness function is the combination of mean square error and the correlation between the normalized desired and estimated vectors. The proposed hybrid scheme is not only compared with individual performances of particle swarm optimization and pattern search, but also with the performance of the hybrid genetic algorithm and that of the traditional approach. A large number of Monte—Carlo simulations are carried out to validate the performance of the proposed scheme. It gives promising results in terms of estimation accuracy, convergence rate, proximity effect and robustness against noise.

Amplitude and direction of arrival estimation using differential evolution

In this paper, we proposed an evolutionary algorithm for joint estimation of amplitude and DOA of signals impinging from far field on a uniform linear array. We used Differential Evolution algorithm with Mean Square Error as a fitness evaluation function. This fitness function defines an error between actual and estimated signal and is derived from Maximum Likelihood Principle. It does not require any permutations to link it with the angles estimated in the previous snapshot. It requires only single snapshot to converge and produce fairly good results even in the presence of low Signal to Noise Ratio. The usefulness and competence of proposed algorithm is tested on the basis of large number of Monte-Carlo simulations and its statistical analysis.