Abstract
At a recent ASA meeting, Monte Carlo‐based noise performance analysis of several formulations and solution algorithms for the time‐of‐arrival (TOA) based impulsive source localization problem were presented. That analysis included two algorithms for parameter estimation using the (exact) linear formulation for the localization problem. Those algorithms were standard linear least‐squares (LLS) and linear total least‐squares (TLS). Using systematically derived, but in no way optimized, choice of weights, the TLS algorithm clearly demonstrated the ability to overcome the bias associated with LLS under conditions associated with low signal‐to‐noise ratio. Unfortunately, the bias reduction was obtained at a cost of high variance as compared to solutions associated with nonlinear formulations. This presentation presents results from investigations into performance of optimally chosen (variance reducing) weighting schemes for TLS and compares them to the performance of nonlinear formulations.
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