Proc. SPIE. 5913, Signal and Data Processing of Small Targets 2005
KEYWORDS: Target detection, Infrared search and track, Signal to noise ratio, Optical filters, Sensors, Digital filtering, Interference (communication), Signal processing, Signal detection, Filtering (signal processing)
Track features help reduce the number of false associations during tracking. It is expected that the spectral signature vector from a target would tend to be consistent over short periods of time. Therefore, the spectral signature vector direction is a potential track feature candidate. The target's spectral signature and covariance are measured from the data at the output of the spatial-spectral match filter. The spectral vector feature is not independent from the local signal to clutter plus noise ratio (S(C+N)R), at the output of the anomaly detector which is often also used as a track feature. A correction term is introduced in this paper to account for the correlation between these two features. Results from field data collections using a multi-spectral Infra-Red Search and Track System (IRST) are summarized with ROC curves showing the performance improvements achieved by using the unit spectral vector as a feature for both moving and stationary targets.
Proc. SPIE. 4728, Signal and Data Processing of Small Targets 2002
KEYWORDS: Target detection, Infrared search and track, Signal to noise ratio, Spatial frequencies, Sensors, Interference (communication), Monte Carlo methods, Solids, Statistical modeling, Systems modeling
This paper presents a Fourier transform model of the aliasing effects caused by two-dimensional sensor undersampling. The SNR probability distribution at the sensor output is numerically generated and compared with simulation results. This distribution is needed for estimating the track score gain when SNR is used as a track feature. Modeling of aliasing makes it possible to calculate the sensor mean signal to noise ratio (SNR), the sensor radiometric measurement precision (RMP) and the sensor position measurement precision (PMP) without the use of Monte Carlo simulations. An example of a sensor design trade is presented in which the detector size is maximized with respect to ROC performance.
Proc. SPIE. 4473, Signal and Data Processing of Small Targets 2001
KEYWORDS: Target detection, Infrared search and track, Signal to noise ratio, Detection and tracking algorithms, Computing systems, Computer simulations, Electroluminescence, Data processing, Signal processing, Palladium
Hypothesis formation is a major computational burden for any multiple hypotheses tracking (MHT) method. In particular, a track-oriented MHT method defines compatible tracks to be tracks not sharing common observations and then re-forms hypotheses from compatible tracks after each new scan of data is received. The Cheap Joint Probabilistic Data Association (CJPDA) method provides an efficient means for computing approximate hypothesis probabilities. This paper presents a method of extending CJPDA calculations in order to eliminate low probability track branches in a track-oriented MHT method. The method is tested using IRST data. This approach reduces the number of tracks in a cluster and the resultant computations required for hypothesis formation. It is also suggested that the use of CJPDA methods can reduce assignment matrix sizes and resultant computations for the hypothesis-oriented (Reid’s algorithm) MHT implementation.
Typically, a tracker receives the position coordinates of the threshold exceedances from the detection process. The threshold nonlinearity serves to prevent superfluous data from entering the tracker; it also prevents other information about a detection from being used by the tracker. Track features were developed to provide a shunt for useful information around the detection threshold. Track features such as the measured C(C+N)R of a detection or local measures of clutter severity have been shown to significantly reduce track confirmation times and the probability of confirming a false track. This paper considers the development of track features for multispectral data. The multispectral track features are used in conjunction with available spatial and temporal track features. Ideally, multispectral track features would provide the tracker with information about how target-like the spectral signature of a detection is. Unfortunately, the spectral signature of the target is unknown a priori because of its dependence upon unmeasured environmental variables, uncertainties in factors effecting the emissivity and reflectivity of the target's surface and unknown operating history. This prevents the general development of a multispectral track features that provide target- likeness. The alternative, which is developed in this paper, is to use the consistency of the spectral signatures of the detections that form a track as a track feature. This multispectral track feature helps suppress the formation of tracks from random detections. It also inhibits a true track from branching to a false detection. Finally, it reduces the true track confirmation time.
Proc. SPIE. 3373, Signal and Data Processing of Small Targets 1998
KEYWORDS: Target detection, Infrared search and track, Signal to noise ratio, Infrared sensors, Data modeling, Sensors, Signal processing, Electronic filtering, Atmospheric modeling, Filtering (signal processing)
This paper describes an analytic model which generates a synthetic list of detection observations from an IRST. The observation list contains both false detects and target detections. The false detects are generated from a statistical model of the clutter and noise. The user is able to select from a menu of clutter types. This selection determines the values of the statistical parameters. The target type and trajectory are user specified. The target type is selected from a menu and determines the signature of the target. Both the target signature and clutter are propagated through the atmosphere and the sensor. The sensor is modeled as the cascade of transfer functions. The sensor model includes optics, detectors, electronics and noise sources. The signal processing which is part of the sensor model assumes a matched filter is used to increase the S(C + N)R prior to detection. The detection threshold is set to provide the user specified probability of false alarm. Each entry in the observation list includes the observation list includes the observation time, the angular position of the observation, the estimated S(C + N)R of the observation and the number of degrees of freedom which is a measure of clutter severity in the region of the observation. The model is intended to be used as part of a larger simulation for example in a sensor fusion study or to provide tracker test sequences for performance comparison and evaluation.
Proc. SPIE. 2759, Signal and Data Processing of Small Targets 1996
KEYWORDS: Target detection, Infrared search and track, Signal to noise ratio, Point spread functions, Sensors, Interference (communication), Signal processing, Performance modeling, Systems modeling, Filtering (signal processing)
Common module and staring focal plane arrays used in IR search and track applications exhibit inherent under sampling in either one or both spatial dimensions producing signal and clutter aliasing is modeled as a stochastic noise process with a uniformly distributed sample phasing. This paper transcends previous attempts at modeling aliasing by deriving the joint density function of the matched filtered SNR normalized by the density function of the local nose estimate. The resulting probability of detection distribution has been compared with experimental results through simulation. Finally, the use of this probability density function is discussed to further enhance the performance of a multiple hypothesis tracker.