Tracking and data association
Computer-controlled systems (3rd ed.)
Computer-controlled systems (3rd ed.)
Estimation with Applications to Tracking and Navigation
Estimation with Applications to Tracking and Navigation
Achieving relative time synchronization in wireless sensor networks
Journal of Control Science and Engineering
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Tracking filter design is discussed. It is argued that the basis of the present stochastic paradigm is questionable. White process noise is not adequate as a model for target manoeuvring, stochastic least-square optimality is not relevant or required in practice, the fact that requirements are necessary for design is ignored, and root mean square (RMS) errors are insufficient as performance measure. It is argued that there is no process noise and that the covariance of the assumed process noise contains the design parameters. Focus is on the basic tracking filter, the Kalman filter, which is convenient for clarity and simplicity, but the arguments and conclusions are relevant in general. For design the possibility of an observer transfer function approach is pointed out. The issues can also be considered as a consequence of the fact that there is a difference between estimation and design. The α-β filter is used for illustration.