Performance analysis tools for enterprise distributed real-time and embedded (DRE) systems require instrumenting heterogeneous sources (such as application- and system-level hardware and software resources). Traditional techniques for software instrumentation of such systems, however, are tightly coupled to system design and metrics of interest. It is therefore hard for system testers to increase their knowledge base and analytical capabilities for enterprise DRE system performance using existing instrumentation techniques when metrics of interest are not known during initial system design. This paper provides two contributions to research on software instrumentation for enterprise DRE systems. First, it presents OASIS, which is service-oriented middleware for instrumenting enterprise DRE systems to collect and extract metrics without design time knowledge of which metrics are collected. Second, this paper empirically evaluates OASIS in the context of a representative enterprise DRE system from the domain of shipboard computing. Results from applying OASIS to a representative enterprise DRE system show that its flexibility enables DRE system testers to precisely control the overhead incurred via instrumentation.

Multifunction radars (MFRs) are sophisticated sensors with complex dynamical modes that are widely used in surveillance and tracking. This paper demonstrates that stochastic context-free grammars (SCFGs) are adequate models for capturing the essential features of the MFR dynamics. Specifically, MFRs are modeled as systems that “speak” a language that is characterized by an SCFG. The paper shows that such a grammar is modulated by a Markov chain representing radar's policy of operation. The paper also demonstrates how some well-known statistical signal processing techniques can be applied to MFR signal processing using these stochstic syntactic models. We derive two statistical estimation approaches for MFR signal processing—a maximum likelihood sequence estimator to estimate radar's policies of operation, and a maximum likelihood parameter estimator to infer the radar parameter values. Two layers of signal processing are introduced in this paper. The first layer is concerned with the estimation of MFR's policies of operation. It involves signal processing in the CFG domain. The second layer is concerned with identification of tasks the radar is engaged in. It involves signal processing in the finite-state domain. Both of these signal processing techniques are important elements of a bigger radar signal processing problem that is often encountered in electronic warfare applications—the problem of the estimation of the level of threat that a radar poses to each individual target at any point in time.

We present a new approach to radar pulse train analysis in electronic warfare. We consider an alternative to the classical time-of-arrival (TOA) histogram technique commonly used for extraction of complex pulse patterns. We derive a hidden Markov model for the radar word templates, and develop a modified version of the Viterbi algorithm to extract radar words from noisy and corrupted pulse sequences. We argue the advantages of this approach compared to the standard TOA histogram technique, and illustrate operation of the algorithm with computer simulation results.