The aim of this study is to analyse the accuracy of passive location systems by using the Cramér-Rao lower bound (CRLB). The study is focused on sensor networks derived from the well-known Multilateration (MLAT) systems, used for aircraft and vehicle surveillance. The measurement used in the standard MLAT configuration is the Time Difference Of Arrival, but it can be improved by using other types of measurement like angle of arrival, round trip delay or time integration. Here, the CRLB is used to model the system accuracy performance using different measurements and related to the target kinematics, to evaluate a possible system evolution. In this frame, the CRLB analysis is useful to define the basic limitations and the advantages of different system architectures as well as to optimise existing architectures by adding new measurement capabilities to some receiving stations. © 2012 The Institution of Engineering and Technology

We present a general scheme for analyzing the performance of a generic localization algorithm for multilateration (MLAT) systems (or for other distributed sensor, passive localization technology). MLAT systems are used for airport surface surveillance and are based on time difference of arrival measurements of Mode S signals (replies and 1,090 MHz extended squitter, or 1090ES). In the paper, we propose to consider a localization algorithm as composed of two components: a data model and a numerical method, both being properly defined and described. In this way, the performance of the localization algorithm can be related to the proper combination of statistical and numerical performances. We present and review a set of data models and numerical methods that can describe most localization algorithms. We also select a set of existing localization algorithms that can be considered as the most relevant, and we describe them under the proposed classification. We show that the performance of any localization algorithm has two components, i.e., a statistical one and a numerical one. The statistical performance is related to providing unbiased and minimum variance solutions, while the numerical one is related to ensuring the convergence of the solution. Furthermore, we show that a robust localization (i.e., statistically and numer- ically efficient) strategy, for airport surface surveillance, has to be composed of two specific kind of algorithms. Finally, an accuracy analysis, by using real data, is performed for the analyzed algorithms; some general guidelines are drawn and conclusions are provided.

Hyperbolic passive location of targets (e.g. used in the Multilateration systems for civil aviation) calls for the solution of highly non-linear systems. In this paper, we show how the effect of typical ill-conditioning of passive location, arising in many operational conditions, may be significantly mitigated by regularization techniques. The pertaining advantages with respect to the conventional (non-regularized) approach are shown in a realistic situatio

In this paper, the use of regularization methods to solve the location problem in multilateration systems, using Mode-S signals, is studied, evaluated, and developed. The Tikhonov method has been implemented as a first application to solve the classical system of hyperbolic equations in multilateration systems. Some simulations are obtained and the results are compared with those obtained by the well-established Taylor linearization and with the Cramér-Rao lower bound analysis. Significant improvements, for the accuracy, convergence, and the probability of location, are found for the application of the Tikhonov method.

A new version of an open form localisation algorithm, called regularised location estimator (RLE), for time difference of arrival (TDOA) multilateration systems is proposed. This algorithm is based on Tikhonov regularisation and on maximumlikelihood estimation. It is aimed to solve the localisation problem for both well-conditioned and ill-conditioned situations. It provides solutions that are statistically and numerically efficient, in contrast to the algorithms in the literature providing solutions that are only efficient in one sense and that do not solve the ill-conditioned situations, which can be found in some operational conditions. A recursive formulation for estimating the regularisation parameter is provided and some mathematical analyses about the proposed estimator are also presented. A comparison with a large number of open and closed form algorithms in the literature is performed with both simulated and real data. The results show that the novel TDOA-RLE provides superior performance even in the case of well-conditioned situations. © The Institution of Engineering and Technology 2014