###### Abstract  
For the purpose of attaining a high degree of freedom (DOF) for the direction of arrival (DOA) estimations in radar technology, coprime sensor arrays (CSAs) are evaluated in this paper. In addition, the global and local minima of extremely non-linear functions are investigated, aiming to improve DOF. The optimization features of the cuckoo search (CS) algorithm are utilized for DOA estimation of far-field sources in a low signal-to-noise ratio (SNR) environment. The analytical approach of the proposed CSAs, CS and global and local minima in terms of cumulative distribution function (CDF), fitness function and SNR for DOA accuracy are presented. The parameters like root mean square error (RMSE) for frequency distribution, RMSE variability analysis, estimation accuracy, RMSE for CDF, robustness against snapshots and noise and RMSE for Monte Carlo simulation runs are explored for proposed model performance estimation. In conclusion, the proposed DOA estimation in radar technology through CS and CSA achievements are contrasted with existing tools such as particle swarm optimization (PSO). cuckoo search; root mean square error; direction of arrival; root mean square error; coprime sensor arrays +
Footnote †: journal: _electronics_  
_Article_  
Far-Field DOA Estimation of Uncorrelated RADAR Signals through Coprime Arrays in Low SNR Regime by Implementing Cuckoo Search Algorithm  
[PERSON]  
1 Department of Electrical Engineering, International Islamic University, Islamabad 44000, Pakistan;  
[EMAIL_ADDRESS] (K.H. ); [EMAIL_ADDRESS] (W.K. ); [EMAIL_ADDRESS] (M.S.Q.) [PERSON]  
3 Department of Physics, College of Science, Princess Nourah Bint Abdulrahman University, P.O. Box 84428,  
Riyadh 11671, Saudi Arabia; [EMAIL_ADDRESS]  
[PERSON]  
7 Department of Electrical Engineering, Qurttuba University of Science and IT, Dera Ismail Khan 29050, Pakistan  
[PERSON]  
6 Department of Electrical Engineering, Qurttuba University of Science and IT, Dera Ismail Khan 29050, Pakistan  
[PERSON]  
6 Department of Electrical Engineering, Qurttuba University of Science and IT, Dera Ismail Khan 29050, Pakistan  
[PERSON]  
6 Department of Electrical Engineering and Computer Science, KTH Royal Institute of Technology,  
28911 Madrid, Spain; [EMAIL_ADDRESS]. DOA estimation [5; 6; 7]. One of them is uniform linear arrays [8; 9; 10] and the second one is sparse arrays [11; 12]. A lot of research has been done on uniform linear arrays (ULA). ULA is one of the simplest forms of antenna array structures, but it can estimate only \((N-1)\) targets by using \(N\) antenna elements through subspace-based methods. In radar antenna array signal processing, we want to enhance freedom without increasing hardware costs. Nevertheless, sparse arrays can resolve more targets than several antenna elements. Therefore, sparse arrays have become a scorching area of research since the last decade in order to estimate DOA with an enhanced degree of freedom (DOF) precisely. In the perspective of radar technology, DOF is defined as the number of sources that can be resolved with a given number of antenna elements. Minimum redundancy array (MRA) [13; 14], nested arrays (NA) [15] and coprime sensor arrays (CSA) [16] are the most prominent types of non-uniform linear arrays that provide higher degree of freedom. [PERSON] introduced MRA in 1968 [17] to improve the DOF, but this array structure has no closed-form expression to identify the location of antenna elements. Later on, in [18], nested array structure was proposed by [PERSON] and [PERSON] in 2010 and this array structure is the combination of dense and sparse uniform linear sub-arrays. On the one hand, hole-free difference co-array and \(O(N^{2})\) DOF are the features of this array structure, but on the other hand, severe mutual coupling occurs in this array structure due to the part of dense sub-array. This mutual coupling is the primary cause of performance degradation of nested arrays in DOA estimation.