# A Comparison of Faulty Antenna Detection Methodologies in Planar Array
## 1 Introduction  
The usage of a phased array antenna is a well-known method for generating signals to be directed at a chosen angle. The wide usage of phased array antennas in space systems is undeniable [1]. The introduction of 5G telecommunication and Massive-Input-Massive-Output (MIMO) created a necessity for a huge number of antennas to be present in a planar array at transmitter and receiver ends. An increased number of antennas increases the failure probability in an antenna system. Antenna failure(s) in an array affects the symmetrical structure, which will severely damage the radiation pattern and cause the sidelobe levels to be increased, weakening the null depth and null shift [1].  
The normal method is to obtain near-field measurements at the antenna to identify the faulty element in an array [2; 3; 4; 5]. However, in systems that are unreachable to humans, such as spacecraft, this conventional method is impractical [6]. Another technique to identify faulty elements is by using test couplers and calibration probes. These methods are difficult to operate and also expensive [7]. This demands extra investigation into a far-field radiation patterns to locate the array's defective element(s) [8].  
On the other hand, publications [9; 10; 11; 12] have proposed methods that can be used to correct the failed radiation pattern. However, to make the adjustments to the array, information on the location of the failed element is required.  
There is much research on linear array faulty element detection. The methods include the bacteria foraging [13], neural network [14; 15], fast Fourier transform [16], firefly algorithm [17] and compressed sensing methods [18; 19]. For a huge number of elements in an array, [PERSON] et al. recommend a parallel coordinate descent, implemented using a hybrid genetic algorithm [19] and a hybrid cultural algorithm with differential evolution [20]. The methods that work for linear arrays do not necessarily work for a planar array.  
For planar array, in [21], [PERSON] and [PERSON] worked on a systematic process of detection of failed components in arrays with cumulative sum (CUSUM) and radiation pattern correction, with least square estimation for both linear and planar arrays. They used hardware results for the detection and correction process. For planar arrays, they worked on 8 \(\times\) 8 array sizes with 30% element failures. In [22], [PERSON] and [PERSON] used particle swarm optimization in a space-borne planar array with a 2 \(\times\) 2 array size and one faulty element. [PERSON] and [PERSON] in [23] discussed detecting faulty elements in a planar array with cross-scanning along the x and y axis in 2D scanning. Ref. [24] uses a three-layer MLP neural network to train a network using failed radiation with the back-propagation learning mode. This work considers the mutual coupling effects from the failed element, with the simulated failed radiation pattern of a 6 x 6 array.  
This paper extends the search for a suitable optimization method using metaheuristic algorithms such as the Particle Swarm Optimization (PSO), Simulated Annealing (SA), and Pattern Search (PS) methods for faulty element detection for antennas in a planar array in the MATLAB environment. In this study, a failed radiation pattern is generated for a planar array with some failed antennas. Then, the optimization methods discussed above were developed to identify the faulty element location in the planar array. The results of the methods are discussed, and finally, a successful method is recommended to identify faulty elements in the planar array.