*4.2.1 Sensor failures in ULAs*

In an array of identical elements, the overall pattern depends on parameters such as the array geometry, inter-element spacing, amplitudes and phases of the individual elements and the inherent pattern of each element [16]. In most cases, the array geometry and the type of array elements is fixed. For example, assume a linear array with patch antennas. In such cases, only the spacing of elements, their relative amplitudes and phases can be altered to modify the array pattern.

Perturbations in any of these parameters can distort the array's response. Worst of all is the partial or complete failure of one or more sensors in the array. Element failures in sensor arrays can cause distortions in the main beam, side lobe levels and null placements, thereby disrupting the normal functioning of the array. Fault diagnosis and fault compensation are needed to ensure smooth operation of arrays. Several methods have been reported in literature that can (i) identify the location of the faulty element(s) and (ii) compensate or restore the array response through suitable weighting of the remaining healthy antennas in the array. Consider a 10-element ULA with uniform feeding. Imagine that the seventh sensor fails. In this case, the algorithm should be able to identify the position of the failed element and also determine suitable weights to be applied to the remaining nine sensors so that the compensated pattern closely resembles the response of the healthy array. As the weights are no more uniform, the use of digital beamforming is called for. Many bio-inspired algorithms or compressed sensing techniques have been used in the past either to detect sensor failures or to compensate the pattern of a faulty array or for both [73–80]. An extreme case and new perspective is presented in [81], where a sparse array is said to be formed when one or more elements of an ULA fail at random.
