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This paper addresses the problem of designing objective functions for autonomous surveillance-target search & tracking (S&T)-by unmanned aerial vehicles (UAVs). A typical S&T mission inherently includes multiple, most often conflicting, objectives such as detection, survival, and tracking. A common approach to cope with this issue is to optimize a convex combination (weighted sum) of the individual objectives. In practice, determining the weights of a multiobjective combination is, more or less, a guesswork whose success is highly dependant on the designer's assessment and intuition. An optimal (tradeoff) point in the performance space is hard to come up with by varying the weights of the individual objectives. In this paper the optimal weights design problem is treated more systematically, in a rigorous multiobjective optimization (MOO) framework. The approach is based on finding a set of optimal points (Pareto front) in the performance space and solving the inverse problem -determine the weights corresponding to a chosen optimal performance (trade-off) point. The implementation is done through the known normal boundary intersection (NBI) numerical method for computing the Pareto front. The use of the proposed methodology is illustrated by several case studies of typical S&T scenarios.

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The presence of a “modal noise” component leads to estimator instability when Capon's maximum likelihood (ML) method is applied to the processing of data from a vertical array in an acoustic waveguide. The physics of the waveguide forces signal vectors and noise vectors alike to be projected onto the span of the “mode” vectors, when the number of sensors (N) exceeds the number of propagating modes (M). The instability occurs whenever the (single snapshot) N × 1 data vectors have the form x = Us + Uγ + white noise, where the matrix U is N × M (sampling the normal modes at the hydrophone locations and independent of the actual acoustic disturbances present), and s and γ correspond to signal and ambient noise sources, respectively. This condition arises in normal-mode and local normal-mode propagation. The dominant eigenvectors of R−1 (where R is the cross-spectral matrix) are sensitive to slight inaccuracies in the calculation of R−1 in ways that affect the performance of the ML estimator. Following transformation of the N × N matrix R to the M × M modal space cross-spectral matrix T, Capon's method is applied to T to obtain the “reduced maximum likelihood” (RML) estimator. This procedure, which is a development of the sector focused stability technique of Steele and Byrne [Proceed. ISSPA 87, 24–28 August 1987, Brisbane, Australia, pp. 408–412], largely eliminates instabilities due to inaccurate inversion of R. Simulations are presented for a shallow-water environment to provide comparison between the ML and the RML estimators. These indicate that the degree of instability depends upon the level of noise (both correlated noise and white noise) and that a significant improvement in performance can be expected by use of the RML estimator in both cases.

The results of a shallow-water localization experiment, performed 19 miles south of Panama City, Florida in October 1985, are presented. The experiment involved a 450-Hz source placed 2.2 km from a vertical array of 16 hydrophones in ∼33 m of water. The experimental site was essentially range independent with a flat, hard, sandy bottom. Successful passive localization of the source was obtained using a maximum-likelihood matched-field processor. Studies were undertaken to determine the robustness of the localization to variation of the following parameters: water depth, sediment sound-speed profile, sediment density and attenuation, and array tilt. It was found that, in order to ensure localization accuracy and robustness, the environmental parameters important to know well are the water depth, sediment sound speed, and array tilt. However, the matched-field processor is much more tolerant to inaccuracies in estimates of the sediment density and attenuation. This corresponds clearly with the results of two simulation studies by DelBalzo et al. [J. Acoust. Soc. Am. 83, 2180–2185 (1988)] and Feuillade et al. [J. Acoust. Soc. Am. 85, 2354–2364 (1989)]. A further conclusion drawn from the experiment is that localization in a shallow-water waveguide using maximum-likelihood processing is complicated by the repetitive sidelobe structure of the acoustic field. This suggests that time-domain and other broadband localization techniques may achieve better localization performance because of their additional frequency-averaging capability.