## 1 Introduction  
For reliable communications, covertness in military security has recently received much attention in the field of air and underwater environments [1, 2]. Nowadays, Unnamed Air Vehicles (UAVs) are playing an important role in global society due to their ability to carry a payload [3]. In respect to underwater acoustic communications, a covert underwater communication system designed for the purpose of transmission signals is not intercepted by other unintended receivers. Typically, covert communication systems use a spread spectrum technique called the direct sequence spread spectrum (DSSS) [4]. DSSS involves increasing the system's symbol rate by a factor of chips. The larger bandwidth helps mitigate performance degradation due to the narrowband interference. The problem of the DSSS technique in an underwater acoustic channel involves extreme inter-symbol interferences (ISI) caused by multi-path, propagation loss, and Doppler effects [5, 6]. These are the main factors that degrade performance in underwater channel with a low SNR. Therefore, multi-band technology with forward error correction (FEC) are essential for reliable underwater communications. For the purpose of achieving high reliability, a turbo-coded frequency shift keying (FSK) method was employed in the paper [7]. Multi-band technology, where a larger carrier spacing for the same data helps to improve the robustness of frequency variation was also used [8]. To apply the multi-band turbo-coded FSK technology, the available bandwidth needs to be divided into specific bands, and the turbo-coded FSK symbols allocate the different specific bands. It overcomes frequency-selective fading due to a large delay spread and sparse channel impulse responses and noise. However, since the performance loss in a particular band affects the entire band, the multi-band structure may have worse performance than the single-band one. This problem can be solved by using a receiving end that analyzes the receiver power of each band and allocates the lower weights to inferior bands. Therefore, it is very important to find which band is inferior one. There are two conventional methods of deciding inferior band [9; 10]. The first is the Effective Signal to Noise Ratio (ESNR) estimation. To estimate a receiving SNR, this method uses a pilot signal or preamble data, which are already known between the receiving and transmitting ends. The relation between the estimated SNR, and the coded error rate of a channel decoding algorithm is utilized to set a weighting value that satisfies the Quasi-Error-Free (QEF) condition of the channel-coding algorithm. The second is the Post-Equalization SNR (PES) estimation. The PES method measures an error element, which is the difference in output between an equalizer and a received signal, at the equalizer that applies Least Mean Square (LMS) or Recursive Least Square (RLS) types to the receiving end [11; 12]. Due to various multi-paths, the measured values using the ESNR and the PES methods in receiving end of each band are not much different between bands. We cannot distinguish which band is inferior by using these conventional methods [13]. Therefore, this paper proposed an estimated coded bit error rates (EC-BER) algorithm, which analyzes the performance of each band and allocates lower weighting values to inferior bands. Since the demodulation error rate and the decoded error rate have a close relationship, the demodulation error rate must be low to satisfy the QEF condition after channel decoding. By using this point, the EC-BER algorithm is a method for measuring the reliability of the received data by checking how many differences there are between the demodulation and the decoded data. Because EC-BER curve falls sharply when the decoded BER is perfectly corrected, we decide optimal weighting values in the range of the lowest error rate of the EC-BER. Finally, the turbo-coded equalization algorithm, which improves the performance by receiver-based iteration, is also used [14].