This paper presents an efficient double-layer ant colony algorithm, called DL-ACO, for autonomous robot navigation. This DL-ACO consists of two ant colony algorithms which run independently and successively. First, a parallel elite ant colony optimization (PEACO) method is proposed to generate an initial collision-free path in a complex map, and then we apply a path improvement algorithm called turning point optimization algorithm (TPOA), in which the initial path is optimized in terms of length, smoothness and safety. Besides, a piecewise B-spline path smoother is presented for easier tracking control of the mobile robot. Our method is tested by simulations and compared with other path planning algorithms. The results show that our method can generate better collision-free path efficiently and consistently, which demonstrates the effectiveness of the proposed algorithm. Furthermore, its performance is validated by experiments in indoor and outdoor environments.

For classification of underwater acoustic signals, we propose a novel sparse anisotropic chirplet transform (ACT) to reveal fine time-frequency structures. The signal features in the form of a time-frequency map are fed into a deep convolutional neural network, referred to as a time-frequency feature network (TFFNet), which brings flexibility to signal classification. The TFFNet is based on a novel efficient feature pyramid enhancing feature (EFP) maps by aggregating the context information at different scales. To remove the gridding artifacts on enhanced feature maps, a form of aggregating transformation, a forward feature fusion, is utilized to merge the forward feature maps. Main contributions of this work are a novel sparse ACT, a TFFNet classifier, and an EFP with forward feature fusion. Experimental results demonstrate that the sparse ACT provides a high-resolution time-frequency representation of underwater signals and the TFFNet improves the classification performance compared to known networks and two machine learning methods (random forest and support vector machine with radial basis function kernel) on two real data sets, an underwater acoustic communication signal data set and whale sounds data set.

This paper studies the mean and mean-square behaviors of the M-estimate based normalized subband adaptive filter algorithm (M-NSAF) with robustness against impulsive noise. Based on the contaminated-Gaussian noise model, the stability condition, transient and steady-state results for the algorithm are formulated analytically. The analysis results help us to better understand the M-NSAF performance in impulsive noise. To further obtain fast convergence and low steady-state estimation error, we derive a variable step size (VSS) M-NSAF algorithm. This VSS scheme is also generalized to proportionate M-NSAF variants for sparse systems. Computer simulations on system identification in impulsive noise and acoustic echo cancellation with double-talk are performed to demonstrate our theoretical analysis and the effectiveness of the proposed algorithms.

In underwater acoustic (UWA) communications, Doppler estimation is one of the major stages in a receiver. Two Doppler estimation methods are often used: the cross-ambiguity function (CAF) method and the single-branch autocorrelation (SBA) method. The former results in accurate estimation but with a high complexity, whereas the latter is less complicated but also less accurate. In this paper, we propose and investigate a multibranch autocorrelation (MBA) Doppler estimation method. The proposed method can be used in communication systems with periodically transmitted pilot signals or repetitive data transmission. For comparison of the Doppler estimation methods, we investigate an orthogonal frequency-division multiplexing (OFDM) communication system in multiple dynamic scenarios using the Waymark simulator, allowing virtual UWA signal transmission between moving transmitter and receiver. For the comparison, we also use the OFDM signals recorded in a sea trial. The comparison shows that the receiver with the proposed MBA Doppler estimation method outperforms the receiver with the SBA method and its detection performance is close to that of the receiver with the CAF method, but with a significantly lower complexity.

Underwater acoustical communication channels are characterized by the spreading of received signals in space (direction of arrival) and in time (delay). The spread is often limited to a small number of space-time clusters. In this paper, the space-time clustering is exploited in a proposed receiver designed for guard-free orthogonal frequency-division multiplexing with superimposed data and pilot signals. For separation of space clusters, the receiver utilizes a vertical linear array (VLA) of hydrophones, whereas for combining delay-spread signals within a space cluster, a time-domain equalizer is used. We compare a number of space-time processing techniques, including a proposed reduced-complexity spatial filter, and show that techniques exploiting the space-time clustering demonstrate an improved detection performance. The comparison is done using signals transmitted by a moving transducer, and recorded on a 14-element nonuniform VLA in sea trials at distances of 46 and 105 km.