The problem of ballistic missile tracking in the presence of clutter is investigated. Probabilistic data association
filter (PDAF) is utilized as the basic filtering algorithm. We propose to use sequential Monte Carlo methods,
i.e., particle filters, aided with amplitude information (AI) in order to improve the tracking performance of a
single target in clutter when severe nonlinearities exist in the system. We call this approach "Monte Carlo
probabilistic data association filter with amplitude information (MCPDAF-AI)." Furthermore, we formulate a
realistic problem in the sense that we use simulated radar cross section (RCS) data for a missile warhead and a
cylinder chaff using <i>Lucernhammer</i><sup>1</sup>, a state of the art electromagnetic signature prediction software, to model
target and clutter amplitude returns as additional amplitude features which help to improve data association and
tracking performance. A performance comparison is carried out between the extended Kalman filter (EKF) and
the particle filter under various scenarios using single and multiple sensors. The results show that, when only
one sensor is used, the MCPDAF performs significantly better than the EKF in terms of tracking accuracy under
severe nonlinear conditions for ballistic missile tracking applications. However, when the number of sensors is
increased, even under severe nonlinear conditions, the EKF performs as well as the MCPDAF.
Real-time fusion of data collected from a variety of radars that acquire information from multiple perspectives and/or
different frequencies, is being shown to provide a more accurate picture of the adversary threat cloud than any single
radar or group of radars operating independently. This paper describes a cooperative multi-sensor approach in which
multiple radars operate together in a non-interference limited manner, and where decision algorithms are applied to
optimize the acquisition, tracking, and discrimination of moving targets with low false alarm rate. The approach is twofold:
(i) measure and process radar returns in a shared manner for target feature extraction by exploiting frequency and
spatial diversity; and (ii) employ feature-aided track/fusion algorithms to detect, discriminate, and track real targets
from the adversary noise cloud. The results of computer simulations are provided that demonstrate the advantages of
Image registration is a technique for precisely aligning the content of two or more images. It is often used as a preprocessing stage for further analysis, such as automatic target recognition, change detection, and environmental remote sensing. However, there are many different registration algorithms available to the image analyst, and it's difficult to know which one is the best one to use for a particular pair of images. These various algorithms also have a multitude of settings and parameters that must be given proper values for best results. Consequently, it is often difficult to know which algorithm will perform the best in a given situation, under constraints of time or accuracy. We propose constructing an expert system, with rules based on experimental results, that will automatically select the appropriate registration algorithm and perform appropriate preprocessing steps to prepare the images for registration.
Intensity based image registration is one of the most popularly used methods for automatic image registration. In the recent past, various improvements have been suggested, ranging from variation in the similarity metrics (Correlation Ratio, Mutual Information, etc.) to improvement in the interpolation techniques. The performance of one method over the other is observed either from the final results of registration or visual presence of artifacts in the plots of the objective function (similarity metric) vs. the transformation parameters. None of these are standard representations of the quality of improvement. The final results are not indicative of the effect of the suggested improvement as it depends on various other components of the registration process. Also visual assessment of the presence of artifacts is feasible only when the number of parameters in the transformation involved are less than or equal to two. In this paper, we introduce a novel approach and a metric to quantify the presence of artifacts, which in turn determines the performance of the registration algorithm. This metric is based on the quality of objective-function landscape. Unlike, the already existing methods of comparison, this metric provides a quantitative measure that can be used to rank different algorithms. In this paper, we compare and rank different interpolation techniques based on this metric. Our experimental results show that the relative ordering provided by the metric is consistent with the observation made by traditional approaches like visual interpretation of the similarity metric plot. We also compare and compute the proposed metric for different variants of intensity-based registration methods.