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.

The novel time-frequency transform, called synchro-compensating chirplet transform, is proposed to represent optimally the overlapping signals in a time-frequency domain. By the control of the self-tuning demodulated operator, an instantaneous rotating operator is introduced in the proposed algorithm to blur the noise and compensate the energy of each component simultaneously. The effectiveness of the method for micro-Doppler signals with a high noise is validated through examples.

The squared sine adaptive (SSA) algorithm is presented for identification scenarios, such as acoustic-echo cancellation (AEC) applications, in non-Gaussian environments. To devise the SSA algorithm, a novel cost function is constructed by exerting a sliding window-type squared sine function on the estimation error vector, which provides robustness in impulsive-noise environments and speeds up convergence when the input is colored. Theoretical results are presented for predicting the mean-weight, convergence, transient excess-mean-square-error (EMSE), and tracking behaviour. Moreover, the minimum EMSE and the optimum step size for tracking are presented. The computational complexity of the SSA algorithm has also been investigated. Numerical experiments demonstrate that results of the theoretical analysis match the simulated results very well and the proposed SSA algorithm outperforms known algorithms in AEC applications.

Multiple features can be extracted from time–frequency representation of signals for the purpose of acoustic event detection. However, many underwater acoustic signals are formed by multiple events (impulsive and tonal), which generates difficulty on the high-resolution TFR for each component. For the characterization of such different events, we propose an anisotropic chirplet transform to achieve the TFR with high energy concentration. Such transform applies a time–frequency varying Gaussian window to compensate the energy of each component while suppressing unwanted noise. Using a set of directional chirplet ridges from the obtained TFR, a structure-split-merge algorithm is designed to reconstruct a multimodal sparse representation, which provides instantaneous frequency and time features. Specifically, a pulsed-to-tonal ratio, based on these features, is computed to distinguish two acoustic signals. The presented method is validated using shallow water experimental underwater acoustic communication signals and large sequences of harmonics and pulsed bursts from common whales.