-There is an increasing interest on the various applications of the SSR Mode S ( Secondary surveillance radar -mode Selective) for air traffic and airport traffic management. This paper discusses the applications of ICAO standard Mode S signals, in particular in the down link channel around 1090 MHz, to locate and identify cooperating targets in the airport using Multilateration (MLAT) and in the terminal area/en route using Wide Area Multilateration (WAM). Performance analyses and some comparison with the more traditional SSR system (ATC radar beacon system : ATCBRS) are carried out.

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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.

In the system-level design for both conventional radars and noise radars, a fundamental element is the use of waveforms suited to the particular application. In the military arena, low probability of intercept (LPI) and of exploitation (LPE) by the enemy are required, while in the civil context, the spectrum occupancy is a more and more important requirement, because of the growing request by non-radar applications; hence, a plurality of nearby radars may be obliged to transmit in the same band. All these requirements are satisfied by noise radar technology. After an overview of the main noise radar features and design problems, this paper summarizes recent developments in “tailoring” pseudo-random sequences plus a novel tailoring method aiming for an increase of detection performance whilst enabling to produce a (virtually) unlimited number of noise-like waveforms usable in different applications.