This paper develops an algorithm that can be used to solve the data association problem faced by a surveillance aircraft using Direction of Arrival angle measurements to locate a stationary RF signal source. The algorithm is based on statistical clustering of measurements with clusters being formed using a Mahalanobis distance association criterion. This approach accounts for angle measurement error statistics and avoids the computational complexity of an exhaustive combinatorial assignment. The optimal cluster is the one that maximized the target position log-likelihood function. This cluster is used to compute a target position estimate then removed from the set of measurements. The process is repeated until no additional clusters can be formed. Simulation results are shown where 100 measurements are distributed randomly across 7 target signal sources.
This paper develops a batch processing algorithm that can be used to track a constant velocity surface target. The purpose of this algorithm is to facilitate passive tracking when sensor-target geometry is poor, which can prevent the convergence of a recursive estimator. The target's position is considered to be the output of an ordinary differential equation having unknown parameters to be estimated. This contrasts with the model used for the design of recursive estimators such as a Kalman filter where the target's position is the output of a dynamic system driven by white noise. Batch processing of all sensor measurements and Iterated Least-Squares (ILS) are used to estimate the target model parameters. Numerical integration is used to propagate the target's position and the Jacobian needed by ILS. Simulation results are shown for a maritime surveillance mission.
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