E. Dalberg

Underwater target tracking by means of acoustic and electromagnetic data fusion

An interesting possibility for improved surveillance capabilities in littoral waters is the integration of multisensor systems by means of data fusion. Here, we describe how data fusion can be used for localisation and tracking of targets by means of passive underwater acoustic and electric field sensors. The data was fused using a Kalman filter. The filter was applied on bearing estimates from the acoustic data and estimates of the target position from the electrode array. The results show the positions of the targets are gained by fusion between the acoustic bearing and the electrode sensors, within the limits of their sensitivity

Fused Classification of Surface Ships Based on Hydroacoustic and Electromagnetic Signatures

This paper reports a classification study using data-fusion on real-world, underwater signatures from surface ships. In this study we design and evaluate classifiers optimized to discriminate between small and big ships based on features extracted from extremely low frequency electric (ELFE) and hydroacoustic signatures. Classification is performed separately on each signature, and finally the individual decisions are fused into a common decision. The data set analysed here was recorded at a sea trial conducted in the Baltic Sea off the east coast of Sweden. The data set contains 23 passages of surface ships of various size, divided into small (up to 21484 tons) and big (above 21484 tons)ships based on their displacement.

Analysis of underwater acoustic and electric noise in the port of Gothenburg

In this paper we study some of the underwater background noise properties in the port of Gothenburg. The analysis includes both electric and acoustic noise. We investigate the dynamic range of the absolute noise power as well as its temporal variations. Furthermore, we study the spectral variations of the low-frequency noise power. Stationarity and normality tests are applied in order to quantify the amount of transients and temporal variations in the data. We find that the properties of the acoustic and electric fields are very different. The temporal variability is larger for the acoustic data. The local shipping noise is the most important acoustic source, while the wind is less important. Shipping also give rise to the highest recorded levels in the electric field noise, but due to the short detection ranges, compared with acoustics, only ships passing nearby the sensors will contribute to the background.

Fused passive acoustic and electric detection of divers

Underwater surveillance against small and low signature targets is challenging, especially in disturbed and shallow water areas such as harbors. Today the most frequently used system for surveillance in harbors is active sonar. However, the detection range of an active system can rapidly degrade due to changes in the sound propagation conditions and in the ambient noise. We focus our research on how passive underwater sensors can be used as a complement to active acoustic systems. We have previously reported on diver detectors for passive acoustic and electric field data, with promising results. The use of the two sensor systems together is mainly motivated by the fact that the acoustic and electric background noise often are uncorrelated. In this paper we test different data fusion methods in order to combine the decisions from the two detectors into one unified decision. The performance of the detectors and the data fusion methods is evaluated using data from a sea trial conducted in the port of Gothenburg in 2009. We present an approach to fusion that will result in a robust system solution for harbor security.

Improved Classification Ability in Littoral Environments by Decision Fusion

In this paper we investigate if the classification ability in littoral environments can be improved by decision fusion. We design and evaluate classifiers for discrimination of two ship types. The data was recorded with an underwater multisensor system at a sea trial conducted in the Baltic Sea off the east coast of Sweden. The sensor system consisted of both electrode sensors and hydrophones. The electrode sensors recorded extremely low frequency electric (ELFE) signatures and the hydrophones recorded underwater acoustic signatures of passing surface ships. Classification is performed separately on each signature and finally the separate decisions are fused into one unified decision. In addition, the possible benefits of temporal decision fusion are investigated.

Performance evaluation of underwater target tracking using data fusion on acoustic and electromagnetic data

Underwater passive acoustic target tracking is challenging in littoral environments. One way to mitigate the difficulties is to add non-acoustic sensors and use data fusion. The topic of this paper is how to evaluate, in an objective way, the performance of data fusion in this application. Different performance measures are discussed. The performance measures are applied on data from a trial where one acoustic and one electric source were towed by a ship, simulating an underwater target. The trial was performed in a very shallow water littoral environment. Data were recorded using a passive acoustic horizontal line-array and an array of underwater electric sensors. The results show that there is a risk of degraded performance after fusion, if the data from the sensors have very different quality. In order to reduce this risk, it is crucial to reliably evaluate the quality of each estimate at all times.

Surface Ship Classification in a Littoral Environment using Fusion of Hydroacoustic and Electromagnetic Data

In this paper we investigate if underwater electrode arrays can complement hydrophones in passive surveillance situations with occasionally poor hydroacoustic conditions. The application is classification of passing surface ships as either light or heavy. Different signal processing and fusion techniques are compared in conjunction with a support vector machine classifier. It is shown that in some situations, the fusion of electrode and hydroacoustic data actually improves the performance of the passive surveillance system. At best, our system correctly classifies 22 ship passages out of 23 possible