Shafqat Ullah Khan

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.

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.

Cognitive frequency offset calculation for frequency diverse array radar

Frequency diverse array (FDA) radars have the ability to generate a beam pattern that is a function of range angle and time. The wavy beam pattern can be controlled by using a suitable frequency increment applied at the input of the array elements. Instead of using fixed frequency increment for FDA, we propose a cognitive frequency offset calculation method that supports to beam steer the target position (direction and range), adaptively. The receiver estimates the position of the target and tunnels the predicted position to the transmitter. Based on the feedback from the receiver, the proposed transmitter calculates the new frequency increment at each cycle. Monte-Carlo based simulation results validate the performance of the proposed method.

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.

Detection of Defective Sensors in Phased Array Using Compressed Sensing and Hybrid Genetic Algorithm

A compressed sensing based array diagnosis technique has been presented. This technique starts from collecting the measurements of the far-field pattern. The system linking the difference between the field measured using the healthy reference array and the field radiated by the array under test is solved using a genetic algorithm (GA), parallel coordinate descent (PCD) algorithm, and then a hybridized GA with PCD algorithm. These algorithms are applied for fully and partially defective antenna arrays. The simulation results indicate that the proposed hybrid algorithm outperforms in terms of localization of element failure with a small number of measurements. In the proposed algorithm, the slow and early convergence of GA has been avoided by combining it with PCD algorithm. It has been shown that the hybrid GA-PCD algorithm provides an accurate diagnosis of fully and partially defective sensors as compared to GA or PCD alone. Different simulations have been provided to validate the performance of the designed algorithms in diversified scenarios.

An application of hybrid differential evolution to 3-D near field source localization

3-D near field source localization is one of the hot areas of research which has found direct applications in Radar, Sonar and digital communication. In this work, we propose hybrid meta-heuristic based algorithm to estimate jointly and efficiently the range, elevation angle and amplitude of the near field sources impinging on uniform linear array. For this, first the Differential evolution (DE) and Interior Point Algorithm (IPA) are employed independently and then both of them are hybridized with each other to improve the accuracy and convergence rate further. In this hybridization, DE is used as a global optimization method while the IPA is acted as rapid local search optimizer. Mean Square Error is used as an objective evaluation function which requires single snapshot to converge and avoids any ambiguity among the angles that are supplement to each other. The proposed hybrid scheme (DE-IPA) produced better results as compare to DE and IPA alone. Moreover, the DE-IPA is also compared with the other hybrid meta-heuristic technique based on Genetic Algorithm hybridized with Interior point Algorithm (GA-IPA). The comparison is made on the basis of estimation accuracy, robustness against noise, convergence rate and root mean square error. The validity and effectiveness of the proposed hybrid scheme is exploited on the basis of large number of Monte-Carlo simulations.

Diagnosis of Faulty Sensors in Phased Array Radar Using Compressed Sensing and Hybrid IRLS---SSF Algorithm

In this work, a compressed sensing technique for diagnosis of faulty sensor in an array antenna is proposed. This technique starts from collecting the measurement of the far field pattern. The system relating the difference among the field measured using the healthy reference array and the field radiated by the array under test is analyzed using a parallel coordinate descent (PCD) algorithm, separable surrogate functionals (SSF) algorithm, iterative-reweighted-least-squares (IRLS) algorithm and hybrid IRLS–SSF. These algorithms are applied for complete and partial defective sensors in an array antenna. The simulation results indicate that the proposed hybrid algorithm outperforms in terms of localization of failure sensor with a less number of measurements. It has been shown that the hybrid IRLS–SSF algorithm provides an accurate diagnosis of complete and partial defective sensors as compared to PCD, SSF or IRLS alone. Variety of simulations has been provided to validate the performance of the designed algorithms in diversified scenarios.

Recovery of Failed Element Signal with a Digitally Beamforming Using Linear Symmetrical Array Antenna.

An antenna array element failure problem is one of the practical and challenging issues in the field of adaptive beamforming. The complete radiation pattern of the array is distorted when any one of the elements fails. Sidelobes level increases, nulls are shifted and null depth is also decreased tremendously. In order to mitigate the problem, we have given a new and easy approach coined as conjugate symmetry approach of the array. In this, the failed element is given a conjugate of the output from its symmetrical counterpart element in the array. The Classical Dolph-Chebyshev and Taylor pattern are taken as the test antenna. The simulation results of both patterns show the validity and presentation of the proposed approach.

Correction of faulty pattern using cuckoo search algorithm and symmetrical element failure technique along with distance adjustment between the antenna array

In this work, we propose a cuckoo search algorithm (CSA) based on symmetrical element failure technique along with distance adjustment between the array element for the correction of faulty beams. Our assumption is that, the faulty element location is known. Due to element fails, the whole radiation pattern is disturbed in terms of raise in sidelobe levels and dislocation of nulls from their required positions. In this approach, the CSA is used to find the locations dn, of the active elements with minimum sidelobes level and null position in failed array antenna. The meta-heuristic cuckoo search algorithm (CSA) is used for the suppression of sidelobes level and steering of nulls at their required positions. The CSA is based on the normal oblige offspring bloodsucking behavior of the cuckoo species in combination with the Levy flight behavior of some birds. The fitness function is used to minimize the error between the desired and estimated pattern along with null constraints. Simulation results for a Chebyshev pattern of 21 elements have been given to display the validity and performance of the proposed technique.

Detecting faulty sensors in an array using symmetrical structure and cultural algorithm hybridized with differential evolution

The detection of fully and partially defective sensors in a linear array composed of N sensors is addressed. First, the symmetrical structure of a linear array is proposed. Second, a hybrid technique based on the cultural algorithm with differential evolution is developed. The symmetrical structure has two advantages: (1) Instead of finding all damaged patterns, only (N–1)/2 patterns are needed; (2) We are required to scan the region from 0° to 90° instead of from 0° to 180°. Obviously, the computational complexity can be reduced. Monte Carlo simulations were carried out to validate the performance of the proposed scheme, compared with existing methods in terms of computational time and mean square error.