Analysis of queuing behavior of automatic ESM systems

Radar electronic support measures (ESM) systems detect active emitters in a given area and determine their identities and bearings. The high arrival rate of radar pulses in dense emitter environments demands fast automatic processing of arriving pulses so that the ESM system can fulfill its functions properly in real time. Yet, the performance analysis of automatic ESM system in real life Is difficult since both pulse arrivals and widths can be specified only probabilistically. The success of queuing theory in many applications such as computer communication networks and flow-control has encouraged designers to utilize queuing theory in qualifying and judging the performance of automatic ESM systems in dense emitter environments. The queuing behavior of these systems is analytically evaluated under different service disciplines and elaborate computer simulations validate the results. The analysis involves statistical modeling of arrival and departure processes as well as distribution of service times. It permits estimating the blocking probability due to high arrival rates of intercepted radar pulses or due to limited speed of the deinterleaver processor. Queuing analysis is shown to be quite useful to quantitatively assess tradeoffs in ESM systems design     

Nonstochastic adaptive decision fusion in distributed-detection systems

Implementing the optimal Neyman-Pearson (NP) fusion rule in distributed detection systems requires the sensor error probabilities to be a priori known and constant during the system operation. Such a requirement is practically impossible to fulfil for every resolution cell in a multiflying target multisensor environment. The true performance of the fusion center is often worse than expected due to fluctuations of the observed environment and instabilities of sensor thresholds. This work considers a nonparametric data fusion situation in which the fusion center knows only the number of the sensors, but ignores their error probabilities and cannot control their thresholds. A data adaptive approach to the problem is adopted, and combining P reports from Q independent distributed sensors through a least squares (LS) formulation to make a global decision is investigated. Such a fusion scheme does not entail strict stationarity of the noise environment nor strict invariance of the sensor error probabilities as is required in the NP formulation. The LS fusion scheme is analyzed in detail to simplify its form and determine its asymptotic behavior. Conditions of performance improvement as P increases and of quickness of such improvement are found. These conditions are usually valid in netted radar surveillance systems.