Least Squares Algorithms for Constant-Acceleration Target Tracking
SOUTH AUSTRALIA UNIV MAWSON LAKES (AUSTRALIA)
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A unified treatment of several least squares LS algorithms is presented for bearing-only tracking of a target moving at constant acceleration. The close link between the maximum likelihood ML estimator and other nonlinear and linearized LS algorithms is explored under the assumption of Gaussian bearing noise. In this context, a new asymptotically unbiased closed-form instrumental variables IV algorithm is derived. Reduced-bias total least squares TLS and constrained TLS CTLS algorithms are developed. The equivalence of the ML algorithm to the structured TLS STLS algorithm is established. Simulation examples are provided to demonstrate the improved performance of the IV and TLS estimators vis-a-vis the pseudolinear estimator.
- Theoretical Mathematics
- Statistics and Probability
- Target Direction, Range and Position Finding