Hyper Beamforming Research Articles

Hyper beamforming with single-sideband time-modulated phased arrays for automotive radar

AbstractIn this study, a single-sideband time-modulated phased array (STMPA)-based hyper beamforming (HBF) system for automobile radar is suggested. The left beam and the right beam are generated once the improved STMPA is split into two subarrays. The HBF method is then used to produce the hyper beam. The switching sequence may be adjusted to generate the hyper beam in the desired direction. This study's benefits may be summed up as follows: (1) The hyper beam's sidelobe level is lower and its beamwidth is narrower than the conventional beam, which can help with estimating the direction of arrival. (2) The HBF can be achieved over a very wide scanning range. (3) With time as an additional controllable variable, the system's control mode is flexible and only two channels are needed, which lowers the system's cost and complexity. The effectiveness of the algorithm is tested through simulation, and the results highlight the system's potential when used with automobile radar.

Space‐time adaptive processing algorithm based on hyper beamforming for ionospheric clutter suppression in small‐array high‐frequency surface wave radar

AbstractSmall‐array high‐frequency surface wave radar (HFSWR) is widely used to monitor maritime targets as it can be used to save on‐land resources. In small‐array HFSWR systems, the main lobe of the receiving angle spectrum is significantly broadened. In complex clutter backgrounds, an extremely wide beam severely influences clutter suppression performance; consequently, targets with a low signal‐to‐clutter ratio (SCR) may be eliminated, or the angle may be barely estimated. This study proposes a space‐time adaptive processing (STAP) algorithm based on hyper beamforming (HBF) to improve the clutter suppression performance of small‐array HFSWR. In addition, HBF can obtain more independent identical distributed training samples than the conventional beamforming; thus, the STAP algorithm can extract the clutter information with high accuracy in the covariance matrix estimation. Moreover, this study combines an efficient STAP algorithm with a joint domain localised (JDL) algorithm to improve clutter suppression. Based on the experimental results, the proposed HBF‐JDL algorithm performs satisfactorily and significantly improves the SCR. Moreover, HBF‐JDL is still applicable at lower SCRs of the target compared with JDL.

Feasibility study of antenna synthesis using hyper beamforming

Wireless communication requires an effective antenna synthesis that characterizes adequate infrastructures to provide the broader bandwidth and reduced interference. Antenna design with minimal signal degradation, optimal gain directive main beam to sustain minimal loss has been a hot issue among many communication engineers for several years. In this paper, the effects of eccentricity of the antenna, element-spacing, number of elliptical rings and number of elements are evaluated. For efficient antenna synthesis, deep Side Lobe Level (SLL) reduction and superior directivity are critical. We have also studied the significance of hyper beamforming in Elliptical Cylindrical Antenna Array (ECAA) in comparison to the geometric configuration of the antenna parameters (eccentricity of the antenna, element-spacing, number of elliptical rings and number of elements). The hyper beam exponent has resulted in flexible pattern synthesis while simultaneously reducing the side lobe of the proposed antenna array, thus decreasing the SLL and increasing directivity that are vital for wideband applications.

Dynamic social behavior algorithm for real-parameter optimization problems and optimization of hyper beamforming of linear antenna arrays

The ever evolving complexity of real-world problems had become an impetus for the development of many new and efficient optimization algorithms. Meta-heuristics based on evolutionary computation and swarm intelligence are successful examples of nature-inspired optimization techniques. In this work, a new Dynamic Social Behavior (DSB) algorithm is proposed to solve global optimization problems. The DSB algorithm is based on the simulation of cooperative behavior of animal groups. In the proposed algorithm, individuals emulate the interaction of individuals based on biological laws of cooperative colony. This algorithm partially adopts the foraging strategy of animal groups and utilizes recruitment signal as a means of information transfer among individuals. In order to illustrate the proficiency and robustness of the proposed algorithm, it is compared with other well-known evolutionary algorithms. The comparison examines several series of widely used benchmark functions and an engineering problem on hyper beamforming optimization. The results testifies the superior performance of DSB compared with other state-of-the-art meta-heuristics.

CRPSOWM for Linear Antenna Arrays with Improved SLL and Directivity

ABSTRACTIn this paper, swarm-based algorithms craziness particle swarm optimization with wavelet mutation (CRPSOWM) have been applied for the optimal design of hyper beamforming of linear antenna array. The conventional gradient based optimization techniques are not efficient and get trapped on local optima for such multimodal highly non-linear constrained hyper beamforming optimization problem. Thus, the above evolutionary technique has been adopted. CRPSOWM incorporates a new definition of swarm updating with the help of wavelet theory. Wavelet mutation enhances the CRPSO to explore the solution space more effectively compared to the other optimization methods as reported in firefly algorithm (FFA). Thus CRPSOWM is apparently free from getting trapped at local optima and premature convergence. The simulation results show CRPSOWM outperforms FFA by achieving much greater reduction in side lobe level, improved directivity and much more improved first null beam width keeping the same value of hyper beam exponent. The optimized hyper beam is achieved by optimization of current excitation weights and uniform inter-element spacing. The approach is illustrated through 10-, 14-, and 20-element linear antenna arrays.

Optimized hyper beamforming of receiving linear antenna arrays using Firefly algorithm

In this paper, an optimized hyper beamforming method is presented based on a hyper beam exponent parameter for receiving linear antenna arrays using a new meta-heuristic search method based on the Firefly algorithm (FFA). A hyper beam is derived from the sum and difference beam patterns of the array, each raised to the power of a hyper beam exponent parameter. As compared to the conventional hyper beamforming of the linear antenna array, FFA applied to the hyper beam of the same array can achieve much more reduction in sidelobe level (SLL) and improved first null beam width (FNBW), keeping the same value of the hyper beam exponent. As compared to the uniformly excited linear antenna array with inter-element spacing of λ/2, conventional non-optimized hyper beamforming and optimal hyper beamforming of the same obtained by real-coded genetic algorithm, particle swarm optimization and Differential evolution, FFA applied to the hyper beam of the same array can achieve much greater reduction in SLL and same or less FNBW, keeping the same value of the hyper beam exponent parameter. The whole experiment has been performed for 10-, 14-, and 20-element linear antenna arrays.

Optimized Hyper Beamforming of Linear Antenna Arrays Using Collective Animal Behaviour

A novel optimization technique which is developed on mimicking the collective animal behaviour (CAB) is applied for the optimal design of hyper beamforming of linear antenna arrays. Hyper beamforming is based on sum and difference beam patterns of the array, each raised to the power of a hyperbeam exponent parameter. The optimized hyperbeam is achieved by optimization of current excitation weights and uniform interelement spacing. As compared to conventional hyper beamforming of linear antenna array, real coded genetic algorithm (RGA), particle swarm optimization (PSO), and differential evolution (DE) applied to the hyper beam of the same array can achieve reduction in sidelobe level (SLL) and same or less first null beam width (FNBW), keeping the same value of hyperbeam exponent. Again, further reductions of sidelobe level (SLL) and first null beam width (FNBW) have been achieved by the proposed collective animal behaviour (CAB) algorithm. CAB finds near global optimal solution unlike RGA, PSO, and DE in the present problem. The above comparative optimization is illustrated through 10-, 14-, and 20-element linear antenna arrays to establish the optimization efficacy of CAB.

Improved Particle Swarm Optimization Based Hyper Beamforming of Linear Antenna Arrays

Improved Particle Swarm Optimization Based Hyper Beamforming of Linear Antenna Arrays