Catálogo de publicaciones - libros

On 7 March 2014 at 16:42, Malaysian Airlines flight MH370 departed from Kuala Lumpur (KL) International Airport bound for Beijing. There was a total of 239 persons on board (227 passengers and 12 crew).

The detailed chronological factual statement of known information about flight MH370 is given at [35]. A brief summary is given here sufficient to put the analysis in the rest of the book in context.

Bayesian inference methods [9] provide a well-studied toolkit for calculating a distribution of a quantity of interest given observed evidence (measurements). As such, they are well-suited for calculating a probability distribution of the final location of the aircraft given the data available from the Inmarsat satellite communication system.

The Bayesian approach described in the previous chapter is a recursive method that calculates the posterior state distribution at each measurement time from a distribution at the previous measurement time.

The Bayesian filter discussed in Chap.  relies on knowledge of three probability density functions: the state prior distribution, the state stochastic model, and the measurement conditional probability density.

The final piece of the Bayesian filter is the dynamics model. In object tracking, it is common to model either the velocity or the acceleration of the object as a random walk in two or three dimensions (e.g., [7]).

As discussed in the previous chapter, the dynamics model used for this analysis consists of a sequence of deliberate manoeuvres interspersed with periods of cruise, in which the speed and control angle are almost constant.

Solution of the Bayesian estimation method described in Chap.  requires one to recursively integrate the aircraft dynamics pdf () and multiply it by the likelihood ().

The variable rate model developed for MH370 was validated by analysing data from a collection of flights where the true aircraft location was known; we refer to these as validation flights. A total of six validation flights were used for testing.

The previous chapters have constructed a Bayesian method for estimating commercial aircraft trajectories using models of the information contained in satellite communications messages and of the aircraft dynamics. This chapter applies the estimator to the accident flight.