KEYWORDS: Infrared search and track, Detection and tracking algorithms, Target detection, Algorithm development, Missiles, Signal to noise ratio, Computer simulations
This paper describes the algorithms that Arete with support from the Office of Naval Research (ONR) is developing for shipboard infrared search and track (SIRST) detection of low-observable targets, such as subsonic, sea-skimming cruise missiles. Early detection of low signal-to-noise (SNR) targets (6 - 10 dB) is provided by Arete's Bayesian field tracker (BFT), which is a track- before-detect algorithm. Candidate detections from the BFT are used to initialize the position, velocity and likelihood of candidate tracks in a peak likelihood track-after-detect tracker. False alarm mitigation is accomplished in part by requiring the temporal evolution of a candidate track to be consistent with that of an incoming sea-skimming cruise missile. The overview of the algorithms involved in these trackers and results from both real targets and simulated targets injected into real images are presented.
This paper describes the algorithms Arete is developing for shipboard infrared search and track (SIRST) detection of low-observable targets, such as subsonic, sea-skimming cruise missiles. The key algorithm is Arete's Bayesian (probability) field tracker, which is a track-before-detect algorithm. The basic concept of this tracker is to update in successive time steps the probability of all possible target positions and velocities before thresholding. Sample results are presented for simulated low (approximately 6 dB) signal-to-noise ratio (SNR) targets injected into both simulated and real ocean horizon scenes. More conventional detection algorithms require greater SNR for each temporal update. Since the cruise missile signature decreases with increasing range between the sensor and the cruise missile, our Bayesian tracker can detect subsonic, low-observable cruise missiles at greater ranges. To mitigate false alarms the measurement likelihood is modified to account for non-Gaussian noise/clutter statistics (large intensity outliers). False alarm mitigation is demonstrated for injected signatures into real data.
IRTool is an IRST X Windows analysis tool, which is being developed by Arete Associates and NSWC/WO under the sponsorship of the Office of Naval Research in support of the Infrared Analysis Modeling and Measurements Program (IRAMMP). The tool consists of an integrated set of physics based modules to support IRST multispectral and space-time analyses. The primary modules are for (1) modeling atmospheric effects, (2) simulating ocean and cloud scenes without and with sensor effects, (3) modeling and injecting target signatures into real and simulated data, and (4) analytic calculation of the expected signal-to-noise ratio (ESNR) for an airborne target on a specified trajectory. Additional modules support data processing and analysis for clutter characterization and model validation. These modules have undergone extensive verification and comparison with data. IRTool has an interactive X Windows driver, which launches stand alone modules to run in the UNIX background. The user can interactively display and plot module outputs using IDL programs written for IRTool. IRTool is available from the IRAMMP program manger (Douglas Crowder).
Ocean clutter in infrared images at low grazing angles is of interest for both airborne and shipborne detection of low flying targets over the ocean. In this paper a 2-D simulation model for infrared cutter at low grazing angle and data analysis results of three IRAMMP ocean tests are presented. The data analyzed correspond to IRAMMP calibrated dual band (mid-wave and long-wave) IR images. The observed ocean clutter is characterized by vertical profiles of the mean and variance, single channel power spectra, and the dual channel coherence as a function of environmental conditions and sensor geometry. The measured variance in radiance typically has a rapid increase with increasing look down angle below the horizon. Preliminary simulation predictions are consistent with data analysis results.
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