We evaluate the use of a probability hypothesis density (PHD) filter in a bearings-only tracking application. The main feature of a PHD filter is that it propagates the first-order statistical moment of a multisource posterior distribution. Multisource estimation using a PHD filter has been shown to reliably track multiple simulated targets in the bearings-only case. In this paper we evaluate the utility of the sequential Monte-Carlo PHD filter for tracking surface ships using bearings-only data acquired from a Bluefin-21 unmanned underwater vehicle in Boston Harbor. The unmanned underwater vehicle was equipped with a rigidly mounted planar hydrophone array that measures the bearing angle to sources of acoustic noise, of which shipping traffic is the dominant source. We further evaluate several target maneuvering models, including clockwise and counter-clockwise coordinated turns. The combination of the coordinated turn models with a constant velocity model is used in a multiple model PHD filter. The results of the multiple model PHD filter are compared to the results of a PHD filter using only a constant velocity model.
Typically, the detection of an object of interest improves as we view the object from multiple angles. For cases where viewing angle matters, object detection can be improved further by optimally selecting the relative angles of multiple views. This motivates the search for viewing angles that maximize the expected probability of detection. Although our work is motivated by applications in subsea sensing, our fundamental analysis is easily adapted for other classes of applications. The specific challenge that motivates our work is the selection of optimal viewing angles for subsea sensing in which sonar is used for bathymetric imaging.
The constitutive behavior of magnetostrictive materials exhibits many nonlinearities. One of the dominate nonlinearities is the quadratic dependence between the drive current and the transducer displacement. While the transducer can be operated at low drive levels with little distortion, at high drive levels the square law distortion is evident. In this paper we propose a nonlinear feedback loop for the drive amplifier such that the amplifier provides a compensation for this transducer nonlinearity. Thus the combination of the amplifier and the magnetostrictive transducer presents a linear input output relationship to the user. The effectiveness of this nonlinear control is demonstrated in simulation.