An algorithm for the in situ adaptation of the survey route of an autonomous underwater vehicle (AUV) equipped with side-looking sonars is proposed. The algorithm immediately exploits the through-the-sensor data that is collected during the mission in order to ensure that quality data is collected everywhere in the area of interest. By introducing flexibility into the survey of the AUV, various limitations of pre-planned surveys are overcome. Experimental results demonstrate the benefit of the proposed approach in terms of higher area coverage in shorter mission times. The signal processing required by the algorithm is fast and computationally efficient such that real-time implementation is feasible. As proof, the proposed adaptive survey approach was implemented on an AUV and executed during a recent live scientific experiment at sea using real, in situ measured data. Results from this experiment are also shown.

A common approach to pattern classification problems is to train a bank of layered perceptrons or other classifiers by clustering the input training data and training each classifier with just the data from a specific cluster. There is no provision in such an approach, however, to assure the component layered perceptron is well suited to learn the training data cluster it is assigned. An alternate method of training, herein proposed, lets a layered perceptron in a classifier bank choose the cluster of inputs it processes on the basis of the perceptron's ability to successfully classify those inputs. During training, data is therefore processed only by the classifier in the bank that best classifies the data or, equivalently, to which the data is most receptive. This allows each classifier to learn a localized subset of data dictated by the classifier's own classification ability. Once each classifier in the bank is trained, a separate independent cluster pointer is trained to recognize to which cluster an input test pattern belongs. The cluster pointer is used in the test mode to identify which classifier in the bank will best classify the problem. The approach, also applicable to regression type problems, is illustrated through application on a simulated Gaussian data set and an active sonar test estimation problem. In both cases, the maximally receptive classifer/regression bank significantly outperforms a single layered perceptron trained on the same data.

We address several inter-related aspects of underwater network design within the context of a cross-layer approach. We first highlight the impact of key characteristics of the acoustic propagation medium on the choice of link layer parameters; in turn, the consequences of these choices on design of a suitable MAC protocol and its performance are investigated. Specifically, the paper makes contributions on the following fronts: a) Based on accepted acoustic channel models, the pointto- point (link) capacity is numerically calculated, quantifying sensitivities to factors such as the sound speed profile, power spectral density of the (colored) additive background noise and the impact of boundary (surface) conditions for the acoustic channel; b) It provides an analysis of the Micromodem-like linklayer based on FH-FSK modulation; and finally c) it undertakes performance evaluation of a simple MAC protocol based on ALOHA with Random Backoff, that is shown to be particularly suitable for small underwater networks.

We address the problem of determining the optimal number and placement of multistatic sonar sensors to achieve maximal coverage while minimizing the number of required sensors. The use of a computationally expensive environmentally-dependent acoustic model for sonar performance prediction precludes the direct application of population-based techniques. Instead, we present computationally-frugal methods based on particle swarm optimization (PSO). Comparison of the algorithms using a surrogate model for sonar performance prediction based on Cassini ovals against a reference set generated through brute force evaluation of all configurations is presented.

We study the problem of determining optimal pinging strategies in multistatic sonar systems with multiple sources. We are specifically investigating algorithms that determine optimal pinging strategies both for generalized search scenarios, and for holding confirmed target tracks with constraints related to maintaining search performance in the rest of the area. An important part of this work is the development of metrics to be used in the optimization procedures. For maintaining search coverage, we used a "probability of target presence" metric formulation. This formulation utilizes sonar performance prediction and a Bayesian update equation to incorporate negative information (i.e., searching an area but finding no targets). We also discuss strategies that can be used to increase the performance of a multistatic field, such as the use of bandwidth diversity

Sequential particle swarm optimization (S-PSO) is a modification of PSO suitable for high-dimensional optimization problems. S-PSO iteratively optimizes the objective function over randomly selected subspaces of the parameter search space instead of the entire parameter space at once as in standard PSO. This approach is advantageous since fewer particles are needed to solve the lower-dimension subproblems. S-PSO is applied to the distributed sonar sensor placement problem, where not only the dimensionality issue arises, but also the computational complexity of the objective function increases with the problem size. Simulations show that S-PSO outperforms standard PSO both in terms of convergence and computational efficiency.

An unsupervised learning system, implemented as an autonomous agent is presented. A simulation of a challenging path planning problem is used to illustrate the agent design and demonstrate its problem solving ability. The agent, dubbed the ORG, employs fuzzy logic and clustering techniques to efficiently represent and retrieve knowledge and uses innovative sensor modeling and attention focus to process a large number of stimuli. Simple initial fuzzy rules (instincts) are used to influence behavior and communicate intent to the agent. Self-reflection is utilized so the agent can learn from its environmental constraints and modify its own state. Speculation is utilized in the simulated environment, to produce new rules and fine-tune performance and internal parameters. The ORG is released in a simulated shallow water environment where its mission is to dynamically and continuously plan a path to effectively cover a specified region in minimal time while simultaneously learning from its environment. Several paths of the agent design are shown, and desirable emergent behavior properties of the agent design are discussed.

Preprocessing of data to be learned by a neural network is typically done to improve neural network performance. Output processing is especially important since it directly affects the influence of error in the hidden layers on the error of the neural network output. Principal component analysis is a commonly used preprocessing method that can improve the network performance by reducing the output dimensionality and reducing the number of parameters in a neural network model. Transforming the principal components of the outputs with an orthonormal matrix prior to scaling can further improve network performance.

Given a complicated and computationally intensive underwater acoustic model in which some acoustic measurement is a function of sonar system and environmental parameters, it is computationally beneficial to train a neural network to emulate the properties of that model. Given this neural network model, we now have a convenient means of performing geoacoustic inversion without the computational intensity required when attempting to do so with the actual model. This paper proposes an efficient and reliable method of performing the inversion of a neural network underwater acoustic model to obtain parameters pertaining to the characteristics of the ocean floor, using two different modified version of particle swarm optimization (PSO): two-step (gradient approximation) PSO and hierarchical cluster-based PSO.