STAP for GPS receiver synchronization

A space-time adaptive processing (STAP) algorithm for delay tracking and acquisition of the GPS signature sequence with interference rejection capability is developed. The interference can consist of both broadband and narrowband jammers, and is mitigated in two steps. The narrowband jammers are modelled as vector autoregressive (VAR) processes and rejected by temporal whitening. The spatial ing is implicitly achieved by estimating a sample covariance matrix and feeding its inverse into the extended Kalman filter (EKF). The EKF estimates of the code delay and the fading channel are used for a t-test for acquisition detection. Computer simulations demonstrate robust performance of the algorithm in severe jamming, and also show that the algorithm outperforms the conventional delay-locked loop (DLL).     

Decentralized linear estimation in correlated measurement noise

Some results and perspectives are provided on the problem of optimally combining estimates from different sensors when the measurement noise processes are correlated. The authors consider only the static estimation problem and limit the discussion to fusion with two sensors. A necessary and sufficient condition for optimality of the decentralized estimator in the presence of correlated measurement noise processes is presented. The result ties together previously reported work, and yields additional insights.<>     

State estimation using an approximate reduced statistics algorithm

The problem of state estimation using nonlinear additive Gaussian noise measurements is addressed. A geometric model for the posterior state density is assumed based on a multidimensional Haar basis representation. An approximate reduced statistics (ARS) algorithm, suggested by the parameter estimator of Kulhavy is then developed, using successive minimization of relative entropy between model densities and an approximate posterior density. The state estimator thus derived is applied to a bearings-only target tracking problem in a multiple sensor scenario     

Minimax detection for signals with unknown parameters

Minimax detectors are often used for the detection of signals with unknown parameters in additive white Gaussian noise. Several minimax strategies are discussed for both real and complex signals, including one based on the least favorable distribution of the unknown parameters and a second, distribution-free method based on the worst case parameter values. Also discussed are the relationships between these minimax detectors and equal correlation filters for real signals and equal deflection quadratic detectors for complex signals. Conditions are given under which the two different minimax strategies produce identical detectors     

Neural solution to the multitarget tracking data associationproblem

The problem of tracking multiple targets in the presence of clutter is addressed. The joint probabilistic data association (JPDA) algorithm has been previously reported to be suitable for this problem in that it makes few assumptions and can handle many targets as long as the clutter density is not very high. However, the complexity of this algorithm increases rapidly with the number of targets and returns. An approximation of the JPDA that uses an analog computational network to solve the data association problem is suggested. The problem is viewed as that of optimizing a suitably chosen energy function. Simple neural-network structures for the approximate minimization of such functions have been proposed by other researchers. The analog network used offers a significant degree of parallelism and thus can compute the association probabilities more rapidly. Computer simulations indicate the ability of the algorithm to track many targets simultaneously in the presence of moderately dense clutter