Quantum mechanics

Algorithm

Gaussian

Wireless sensor network

Engineering

Observational error

Common emitter

Statistics

Node (physics)

Tracking (education)

Gaussian noise

Pedagogy

Telecommunications

Estimation theory

Computer science

Acoustics

Cramér–Rao bound

Computer network

Time of arrival

Physics

Psychology

Upper and lower bounds

Mathematics

Image (mathematics)

Multilateration

Electronic engineering

Mathematical analysis

Artificial intelligence

Monte Carlo method

Programming language

Wireless

Noise (video)

Constant (computer programming)

In this paper, passive emitter localization using Time Difference of Arrival (TDOA) measurements within a sensor network is investigated. Contrary to the case of a moving sensor pair the fact of correlated measurements in a single time step arises dependent on the choice of the measurement set. In tracking simulations, the Crame´r Rao Lower Bound (CRLB) and simulation results are often computed assuming constant standard deviations of the incoming measurements. Though, the measurement error is parameter dependent, it depends for example on the emitter-sensors-geometry. In Monte Carlo simulations, a comparison is performed between the localization in a static sensor network with three sensors and the localization using a moving sensor pair. Measurements are generated assuming additive white Gaussian noise with constant and parameter dependent variance. Results confirm the attainable localization accuracy and show the superior performance of an additional sensor compared to additional motion.

Accuracy analysis for TDOA localization in sensor networks