*Sparse Linear Antenna Arrays: A Review DOI: http://dx.doi.org/10.5772/intechopen.99444*

optimization problems (objective functions). Going by the above fact, it might so turn out that there might exist a few algorithms which could be more suitable over others for synthesizing MRAs and MHAs. As many powerful nature inspired optimization algorithms (grey wolf optimizer, moth flame optimizer, whale optimization, sparrow search algorithm and other hybrid meta-heuristic approaches etc. [95–98]) have been introduced in the recent past, it would be a worthwhile effort to try synthesizing MRAs and MHAs using such algorithms. One such attempt to determine large MRAs using parallel processing has been recently reported [99]. In recent times, deep learning methods are being employed to synthesize sparse arrays for joint requirements such as hole-free coarrays, low peak side lobes, and optimum far-field performance [100, 101].

In the future, research could be done to determine large RMRAs such that tabulated entries on the optimum RMRA configurations for a given number of sensors could be widely made available to the scientific community. Similarly, efforts could be made to find robust nested arrays with closed-form expressions for sensor positions as an alternative to RMRAs [33, 85].

In sparse arrays, beamforming is usually performed in the co-array domain [102–104]. The weights of the virtual sensors in the coarray are adjusted to obtain the desired beam pattern. It would be interesting to see how the failure of one or more sensors in the physical array affects these beamforming weights. Detection of failed sensors in the physical array and subsequent compensation of the beam pattern in the coarray are a few open research challenges for sparse array analysis.
