A new system identification technique for harmonic decomposition

An extended and unifying system identification technique is presented for a class of systems that include all main signal models that arise in the harmonic decomposition problem. This technique unifies and extends the previously developed system identification techniques which are improvements on the Pisarenko harmonic decomposition (or, its spatial dual, MUSIC) as they arise in arrays of sensors. The advantages of the technique and some of its specializations include having no assumptions of stationarity on the stochastic processes involved. Another contribution of this technique is that it can also be used without any resort to probability theoretic concepts, thus bypassing the approximation of autocorrelations via time averages, yielding the system parameters exactly. This technique can be utilized to determine the dominant modes of vibrations of flexible structures as well. An analogy is established between arrays of sensors for target signal returns and those that can be used for vibrations in flexible structures. This enables the results developed for each one of these problems to be applied to the other     

Extended solution to multiple maneuvering target tracking

An improved algorithm for tracking multiple maneuvering targets is presented. This approach is implemented with an approximate adaptive filter consisting of the one-step conditional maximum-likelihood technique together with the extended Kalman filter and an adaptive maneuvering compensator. In order to avoid the extra computational burden of considering events with negligible probability, a validation matrix is defined in the tracking structure. With this approach, data-association and target maneuvering problems can be solved simultaneously. Detailed Monte Carlo simulations of the algorithm for many tracking situations are described. Computer simulation results indicate that this approach successfully tracks multiple maneuvering targets over a wide range of conditions