Sonar operation in coastal waters is challenging due to high false alarm rates and strongly varying sonar conditions. Optimal choices for sonar design and pulse characteristics depend strongly on target location and velocity, as well as the present environment. Given a description of the target and environment, acoustical models may estimate sonar performance for different sonar parameters. Updating sonar parameters to best meet shifting sonar conditions impose an unnecessary workload on operators and must be automated for unmanned systems. We suggest an optimization approach that takes into account both a variable environment and a random target. An acoustic ray trace model is run in all directions for a large number of different environment, target, and sonar realisations. Target parameters such as Doppler and aspect are modelled, and optimal sonar parameters are determined. The method is demonstrated for a littoral test case, where both the sonar design and its pulse parameters are optimized. The design takes into account whether the sonar is towed or hull-mounted, and its frequency. The pulse parameters include pulse length and pulse repetition time. The method can easily be extended to other sonar parameters, but the main intent here is to demonstrate the approach.
Active sonar performance is highly dependent on the surrounding environment. Conventional detection algorithms apply thresholds to acoustic data passed through a normalizer in order to detect targets. These methods fail to fully exploit available knowledge of the environment, which leads to higher false alarm rates than necessary, particularly in littoral environments. Furthermore, they do not exploit negative information, e.g. the significance of the repeated lack of threshold crossings in a given area. Sonar performance models may estimate both the probability of detection and false alarm in a known environment. Proper exploitation of this data allows for reduced false alarm rates in cluttering and reverberating environments. Furthermore, areas lacking threshold crossings and with high probability of detection, may be classified as target free. Bayesian occupancy grids is a probabilistic approach that takes into account both sensor information and prior information of surrounding walls or topography for localization of robots. Here we apply the method on synthetic sonar data combined with a sonar performance model. The synthetic sonar data contains moving and stationary targets in a littoral environment. The method's performance is compared to conventional algorithms, and its robustness to errors in the sound speed profile used in the modelling is assessed.
Sonar performance modelling is an essential part of active sonar operations, as it is used both during planning of the operation as well as assessment of the expected coverage managed during the operation. Conventionally, the acoustic model has been input with a single realisation of the present environment in order to generate 2D coverage plots either as vertical cross sections in a selected direction from the sonar or as horizontal coverage plots encircling the sonar. The impact of environmental uncertainty on sonar performance is well known and documented. Errors in the input sound speed may give rise to large errors in the expected sonar performance. Monte Carlo methods are a well known method for capturing this uncertainty. This requires both a multitude of model runs and probability density functions representing the model input instead of single realisations. Here we propose using a fast raytrace model for estimating the sonar performance of a large number of different environmental and target realisations. The model results are then marginalized to collapse all but a few dimensions for efficient and concise presentation of the results for the sonar operator.
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