A method for detecting a low observable target track using an acceleration-based overall motion model is proposed. Unlike the existing track-before-detect methods that are based on sequential state updates, this method computes integrated echo energy for the entire hypothesized motion. The detection and the estimation of the track are made simultaneously using the batch processing approach. A comparison of track detection probability shows higher performance against low observable targets. Using a motion similarity metric and motion model homogeneity, a performance prediction model is derived and compared with the simulation results.INDEX TERMS Tracking, track before detect, detection, batch processing, hidden Markov model, motion model, weak target, low observable target, marine vehicle.
Using a detailed waveform-based simulation framework, a study on the feasibility of LPI sonar is performed for covert range estimation. A frequency selective channel filter is applied for realistic ocean simulation. The platform uses matched filtering and the target uses energy detection for processing the received signal. The objective of the platform is to estimate the range to the target while ensuring that the target still fails to detect the platform's pinging LPI waveform. A platform-target encounter scenario was designed for Monte Carlo simulation. Evaluation of system parameters was performed to assess for the feasible conditions for covert range estimation.
Synthetic aperture sonar imagery is typically generated using data collected with unmanned underwater vehicles. The prohibitive cost of collecting underwater data and the need for well-controlled factors such as collection geometry and object configuration has provided the motivation for devising a benchtop in-air circular acoustic data collection framework. This set-up makes it practically feasible to explore a multitude of parameters that are not as feasible with underwater measurement scenarios, including waveform type, object shapes and material. It is also practically feasible to explore various representations of the collected acoustic data that help better emphasize different aspects of the information embedded in the acoustic signal, which various machine learning algorithms can utilize. Signal processing and feature organization are critical to improving performance of machine learning algorithms. For example, geometric scattering response of objects is well-represented in spatial imagery with sharp contrast of pixel intensity between the object and surrounding environment, while spatial spectrum of the complex SAS image better represents the aspect-dependent spectral response of the object that help discriminate objects of the same shape, but with different material. We will discuss the relationship between the choice of representation and discriminatory information with illustrative classification problems.
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