Iterative channel estimation-based soft successive interference cancellation for multiuser underwater acoustic communications

Time domain turbo equalization based on vector approximate message passing for multiple-input multiple-output underwater acoustic communications

This paper proposes a high-performance receiver for underwater acoustic communications based on time reversal processing for multiple-input multiple-output (MIMO) systems. The receiver employs the vector approximate message passing (VAMP) algorithm as a soft equalizer in turbo equalization. By performing self-iteration between the inner soft slicer and the inner soft equalizer, the VAMP algorithm achieves near-optimal performance. Furthermore, an iterative channel-estimation-based soft successive interference cancellation method is incorporated to suppress co-channel interference in the MIMO system. Additionally, the introduction of passive time reversal technology can combine multiple channels into a single channel, which greatly reduces the computational complexity of the MIMO system, especially for large MIMO systems. The effectiveness of the proposed receiver is verified using experimental data collected in Songhua Lake, China in 2019. The results demonstrate that the proposed receiver significantly reduces the complexity of the traditional parallel-VAMP receiver without sacrificing performance and outperforms other receivers of the same type. Moreover, our experimental results also verify that the VAMP-turbo outperforms the generalized approximate message passing (GAMP)-turbo in terms of bit error rate and convergence performance.

Underwater Source Counting with Local-Confidence-Level-Enhanced Density Clustering

Source counting is the key procedure of autonomous detection for underwater unmanned platforms. A source counting method with local-confidence-level-enhanced density clustering using a single acoustic vector sensor (AVS) is proposed in this paper. The short-time Fourier transforms (STFT) of the sound pressure and vibration velocity measured by the AVS are first calculated, and a data set is established with the direction of arrivals (DOAs) estimated from all of the time-frequency points. Then, the density clustering algorithm is used to classify the DOAs in the data set, with which the number of the clusters and the cluster centers are obtained as the source number and the DOA estimations, respectively. In particular, the local confidence level is adopted to weigh the density of each DOA data point to highlight samples with the dominant sources and downplay those without, so that the differences in densities for the cluster centers and sidelobes are increased. Therefore, the performance of the density clustering algorithm is improved, leading to an improved source counting accuracy. Experimental results reveal that the enhanced source counting method achieves a better source counting performance than that of basic density clustering.

Recognition of Underwater Materials of Bionic and Natural Fishes Based on Blue-Green Light Reflection

Thanks to the advantages of low disturbance, good concealment and high mobility, bionic fishes have been developed by many countries as equipment for underwater observation and data collection. However, differentiating between true and bionic fishes has become a challenging task. Commonly used acoustic and optical technologies have difficulty in differentiating bionic fishes from real ones due to their high similarity in shape, size, and camouflage ability. To solve this problem, this paper proposes a novel idea for bionic fish recognition based on blue-green light reflection, which is a powerful observation technique for underwater object detection. Blue-green light has good penetration under water and thus can be used as a signal carrier to recognize bionic fishes of different surface materials. Three types of surface materials representing bionic fishes, namely titanium alloy, carbon fiber, and nylon, are investigated in this paper. We collected 1620 groups of blue-green light reflection data of these three kinds of materials and for two real fishes. Following this, three machine learning algorithms were utilized for recognition among them. The recognition accuracy can reach up to about 92.22%, which demonstrates the satisfactory performance of our method. To the best of our knowledge, this is the first work to investigate bionic fish recognition from the perspective of surface material difference using blue-green light reflection.

Wideband signal detection in multipath environment affected by impulsive noise

Detecting the presence or absence of a known signal is an important aspect of underwater acoustic (UWA) communications as it is a vital first step to process the received data. The influence of impulsive noise and multipath propagation on detection are both considered in this paper. Many robust detectors have been designed to cope with impulsive noise, but they tend to ignore the impact of multipath propagation, which may decrease the detection probability. To emphasize the role of multipath channel, we propose two-stage detection methods. In the first stage, the channel is estimated based on robust orthogonal matching pursuit, while in the second stage, detectors that use the channel estimation from the first stage, namely augmented log-likelihood ratio detector and augmented pseudo-correlation detector, are developed. The former detector is parametric and the latter one is non-parametric. The improved performance is demonstrated in simulated multipath channel and actual UWA channel with both simulated noise and recorded snapping shrimp noise, indicating that the proposed detectors are resistant to impulsive noise. It is also shown that the use of multipath information helps to improve the detection performance.