Due to the limited battery energy of underwater wireless sensor nodes and the difficulty in replacing or recharging the battery underwater, it is of great significance to improve the energy efficiency of underwater wireless sensor networks (UWSNs). We propose a novel energy-efficient clustering routing protocol based on data fusion and genetic algorithms (GAs) for UWSNs. In the clustering routing protocol, the cluster head node (CHN) gathers the data from cluster member nodes (CMNs), aggregates the data through an improved back propagation neural network (BPNN), and transmits the aggregated data to a sink node (SN) through a multi-hop scheme. The effective multi-hop transmission path between the CHN and the SN is determined through the enhanced GA, thereby improving transmission efficiency and reducing energy consumption. This paper presents the GA based on a specific encoding scheme, a particular crossover operation, and an enhanced mutation operation. Additionally, the BPNN employed for data fusion is improved by adopting an optimized momentum method, which can reduce energy consumption through the elimination of data redundancy and the decrease of the amount of transferred data. Moreover, we introduce an optimized CHN selecting scheme considering residual energy and positions of nodes. The experiments demonstrate that our proposed protocol outperforms its competitors in terms of the energy expenditure, the network lifespan, and the packet loss rate.

Because of the complicated underwater environment, the efficiency of data transmission from underwater sensor nodes to a sink node (SN) is faced with great challenges. Aiming at the problem of energy consumption in underwater wireless sensor networks (UWSNs), this paper proposes an energy-efficient clustering routing algorithm based on an improved ant colony optimization (ACO) algorithm. In clustering routing algorithms, the network is divided into many clusters, and each cluster consists of one cluster head node (CHN) and several cluster member nodes (CMNs). This paper optimizes the CHN selection based on the residual energy of nodes and the distance factor. The selected CHN gathers data sent by the CMNs and transmits them to the sink node by multiple hops. Optimal multi-hop paths from the CHNs to the SN are found by an improved ACO algorithm. This paper presents the ACO algorithm through the improvement of the heuristic information, the evaporation parameter for the pheromone update mechanism, and the ant searching scope. Simulation results indicate the high effectiveness and efficiency of the proposed algorithm in reducing the energy consumption, prolonging the network lifetime, and decreasing the packet loss ratio.

In order to detect weak underwater tonals, adaptive line enhancers (ALEs) have been widely applied in passive sonars. Unfortunately, conventional ALEs cannot perform well amid impulse noise generated by ice cracking, snapping shrimp or other factors. This kind of noise has a different noise model compared to Gaussian noise and leads to noise model mismatch problems in conventional ALEs. To mitigate the performance degradation of conventional ALEs in under-ice impulse noise, in this study, a modified ALE is proposed for passive sonars. The proposed ALE is based on the least mean p-power (LMP) error criterion and the prior information of the frequency domain sparsity to improve the enhancement performance under impulse noise. The signal-to-noise ratio (SNR) gain is chosen as the metric for evaluating the proposed ALE. The simulation results show that the output SNR gain of the proposed ALE was, respectively, 9.3 and 2.6 dB higher than that of the sparsity-based ALE (SALE) and the least mean p-power ALE (PALE) when the input GSNR was −12 dB. The results of processing the under-ice noise data also demonstrate that the proposed ALE is distinguished among the four ALEs.

The detection of tonal signals with unknown frequencies is an important area of study in underwater signal processing. A common approach to address this issue is to use the Discrete Fourier Transform (DFT) for observations. When a tone does not lie precisely at the discrete DFT frequency point, its energy will leak to adjacent frequency point. This phenomenon is known as scalloping loss or Picket Fence Effect (PFE). PFE leads to the degradation of detection performance based on DFT. This paper studies the problem of robust detection in the case of PFE. A coherently-averaged power processor utilizing the information of adjacent frequency bins is designed. The results of simulations and experiments show that the proposed method is robust against PFE, and is highly suitable for tone detection in practical circumstances.

Motion estimation is required to obtain high imaging quality in synthetic aperture sonars (SASs). Displaced phase center antenna (DPCA) micronavigation is an important technique of motion estimation in SASs. A key step in DPCA micronavigation is accurately determining the time delay between echoes received by the approximate “phase center” array between adjacent pings. Unfortunately, the accuracy of the existing method for estimating this time delay is often deteriorated by the ambiguity of the time delay estimates in the presence of noise or interference. This article proposes an accurate method of estimating the time delay based on the Robust Chinese Remainder Theorem (RCRT). The experimental results show that the proposed method decreases the ambiguous rate of time delay estimates by one order of magnitude, compared to the conventional approach, which means the estimation accuracy is improved significantly. The SAS imaging results demonstrate that the RCRT-based motion estimation helps to obtain higher-quality images.