Proc. SPIE. 3809, Signal and Data Processing of Small Targets 1999
KEYWORDS: Target detection, Detection and tracking algorithms, Data storage, Image processing, Signal processing, Neodymium, Signal detection, Performance modeling, Atmospheric modeling, Systems modeling
Target detection and tracking is a subject of great value to the defence community. With the need to build better detection and tracking system comes the need to evaluate the performance of such systems. Performance evaluation not only aids the system user in selecting the appropriate system for the specific application, but can also aid the developer in building and improving systems. An earlier paper discussed using the Neyman-Pearson (NP) criterion for evaluating the performance of tracking systems. The NP criterion is especially appropriate for evaluating the performance of tracking systems where the culminating action is an event such as the queuing of another systems or the engagement of a target. In this paper, some of the issues that have arisen since the earlier paper are addressed. First, a review of the application of the NP criterion to tracker performance assessment is given including a statement on the statistical significance of the declaration threshold setting. Performance is evaluated by examining lists of track declarations generated by running the system under test on real scenes without targets and on real scenes with targets inserted at given target strengths. One of the difficulties with the NP method of tracker performance evaluation is the computation and data storage requirements for setting the track declaration threshold. The use of models for reducing these requirements is discussed.
In this paper, a method of testing combinations of image processing, track-before-detect, and track-after-detect algorithms is presented. It emphasizes false track confirmation rates and the time required to confirm true tracks whereas methods in the literature emphasize track purity and accuracy in estimating the target state. This method, which is an extension of the Neyman- Pearson criterion, yields a single performance measure, the expected time to confirm a true target track. The value of this method is in potential for component algorithm (spatial filter, track-before-detect, track-after-defect) tradeoff and track discriminant studies. Using it, one may quantitatively compare the effect of different spatial filters or different track discriminants, or the effect of using more computationally intensive track-after-detect algorithms and less computationally intensive track-before-detect algorithms.
Recently several authors have investigated the use of parametric families of linear filters for discrete frequency estimation. The proposed methods are similar in that they use iterative filtering procedures for estimating the frequencies of underlying periodic components embedded in noise. In this paper we combine parametric filtering with a contraction mapping principle to recursively estimate the frequencies of discrete spectral components. By incorporating the contraction mapping idea with parametric filtering a fundamental property is determined which when satisfied, guarantees the convergence of the iterative procedure. Several examples are provided which illustrate the method.
This paper presents a comparative performance evaluation of three signal processing architectures for the point-target detection problem in infrared surveillance systems. The comparison involves an adaptive linear spatial filter of the least-mean-square (LMS) type, with a median subtraction filter, and a morphological based processor. The median and morphological filters are nonlinear operators that are based on the order-statistics of the input samples. The three architectures are exercised, via computer simulation, against real deterministic data sets comprising a range of horizon backgrounds. Receiver operating characteristic (ROC) curves are presented and provide a quantitative measure for the performance comparison. Some advantages and disadvantages of each filter type are indicated, with recommendations for a hybrid architecture which incorporates the different filtering schemes.