"Extra information can be prior information, for example a given velocity or a given heading. Another way is to consider jointly other measurements such as frequency measurements; this supposes that the source emits pure single tones and makes necessary a dedicated processing . We can sometimes benefit from bearing measurements collected by another sensor, if the target is detected by both platforms and if the respective bearing tracks are properly associated . "
[Show abstract][Hide abstract] ABSTRACT: This paper addresses the problem of bearings-only target motion analysis) of a maneuvering source, whose trajectory is composed by two legs at constant velocity. Under very large assumptions, the target trajectory is proved observable from a non-maneuvering platform. Two cases are considered: first, the time of the target maneuver (t<sub>M</sub>) is perfectly known. Secondly, t<sub>M</sub> is unknown. In both cases, a batch estimator is proposed and its performance is compared with the classic cramer-rao lower bound (CRLB). Monte-Carlo simulations reveal the efficiency of the estimator.
Information Fusion, 2008 11th International Conference on; 01/2008
[Show abstract][Hide abstract] ABSTRACT: Estimating position and velocity of a moving source requires at
least an ownship maneuver when performing bearing-only tracking (BOT).
By concurrently processing bearing and Dopplerized radiated frequencies
a unique target motion analysis (TMA) solution is available without any
maneuver. A simple, fast, non-time-recursive estimator, working on
several unknown measured spectral lines, is presented. The statistical
efficiency of such a Doppler and bearing tracking (DBT) is proved by
computer simulation. Simulation results show that DBT can yield proper
estimates of source trajectory: range accuracy better than 10% within at
most 10 min for a source at 20 km and nine sinusoids
Acoustics, Speech, and Signal Processing, 1989. ICASSP-89., 1989 International Conference on; 06/1989
[Show abstract][Hide abstract] ABSTRACT: A Newton-type method is used to solve the target motion analysis
(TMA) problem with respect to bearing and frequency measurements from a
passive sonar system. In many long-range sonar situations the TMA
problem is ill conditioned and suffers from a small signal-to-noise
ratio. Although Kalman filters have been investigated extensively it is
known that maximum likelihood (ML) estimation is superior in these
cases. The main reason for the good performance of the ML method is that
the underlying numerical optimization problem deals with the ill
conditioning of the problem. This work illustrates how the conditioning
depends on the geometry of the tracks and the signal-to-noise ratio.
Monte Carlo simulations with respect to the measurement noise show the
influence on the ML estimation performance for three specific cases
concerning multileg situations and bottom bounce measurements
IEEE Transactions on Signal Processing 06/1992; 40(5-40):1216 - 1225. DOI:10.1109/78.134483 · 3.20 Impact Factor
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