Array processing of SSR signals in the multilateration ... - IEEE Xplore

Proceedings of ESAV'08 - September 3 - 5 - Capri, Italy

Array processing of SSR signals in the multilateration context, a decade survey 1

N. Petrochilos1,G. Galati2 , E. Piracci2 CReSTIC, University of Reims, Moulins de la Housse, BP 1039, 51687 Reims Cedex 2, France 2 DISP and V. Volterra Center, Tor Vergata Uni., Via del Politecnico, 1 - 00133 Roma, Italy email: [email protected], [email protected]

Abstract—Secondary Surveillance Radar (SSR) was originated in the 40’s as an Identification Friend or Foe (IFF) system and, starting from the ’50 and ’60, provided the Air Traffic Control (ATC) with identity and altitude data for the aircraft under coverage, implementing a rough form of cooperative independent surveillance, complementing the non-cooperative surveillance of the primary radar and very often working in a joint manner with it (co-location, co-rotation of the antennae). In the ’70 and ’80 a deep system-level update of the SSR was adopted at international level, namely the selective mode, or Mode S, compatible with previous Modes A and C and providing a data link between the aircraft and the ground-station, i.e. the secondary radar. Following this update and its new signal formats, two main research directions were investigated from half ’90: distributed ground systems enabling multilateration, and array source separation enabling operation in high traffic on the downlink RF channel. This paper provides a survey of this last decade, as seen from the perspective of its authors.

I. I NTRODUCTION Secondary Surveillance Radar (SSR) is essential for Air Traffic Control (ATC), and was originally denominated ”Identification Friend or Foe” (IFF) during the Second World War. It is not only a surveillance means but, unlike the primary radar, it is also a communication link (using an airborne transponder) that informs the ATC about the identity and the altitude of the aircraft in the line of sight by standard signaling. A ground station, i.e. the radar, sends a sequence of pulses at 1030 MHz to interrogate an aircraft, which answers via its transponder a SSR reply signal: a pulse-position modulated finite-length signal modulated at a nominal carrier frequency of 1090 MHz [1], [2], [3]. The system (now referred to as SSR mode A and C) was designed in the 1940s, but due the significant growth of the air traffic is currently limited by the fact that all replies use nominally the same carrier frequency, and may overlap in time with the well-known “garbling” effect. A new signal format (mode S, for Selective) addresses the aircraft selectively and permits short data communications between the station and the aircraft [4]. The International Civil Aviation Organization (ICAO) recommended that at the end of 1999, all aircraft in the controlled airspace must update theirs transponders to be Mode S compliant [5]. This new standard is intended to reduce the reply rate, and will ultimately replace the mode A/C. It also includes a parity code for error correction [6]. It provides a data link between the

aircraft and the ground-station. This data link is part of the Aeronautical Telecommunication Network (ATN), which links aircraft, ground-stations and ATC-centres. This new mode with an interrogation function added on board - also permits the Traffic Advisory and Collision Avoidance System (TCAS) providing a ”safety net” against collisions between the aircraft. Note that also the new mode also allows spontaneous replies, called ”squitters” [7] and broadcast transmission of aircraft data (identity, position, intention) to other aircraft or to suitable ground systems with omni-directional antennae, i.e. the recent and very successful Automatic Dependent Surveillance Broadcast (ADS-B). From this starting point, a decade ago, research organizations and companies began to investigate the impact of distributed networks and array signal processing. In the next section we present a synthesis of the the advancements on the distributed systems ( i.e. multilateration) , while in the following section, we synthesize the advancements on the second item, i.e. the separation of overlapped signals by array processing. II. DISTRIBUTED NETWORK Multilateration systems (both ”short range” for airport coverage and ”wide area” or WAM for approach and terminal maneuvering area) are basically distributed surveillance and identification systems (Figure 1). They are made up by a network of some (e.g. ten to fifteen) receiving stations or ”sensor” stations (a part of them, e.g. one third, also has interrogation capabilities), one or a few reference transponders in geo-referenced positions to synchronize and calibrate the whole sensors system, a communication medium (typically, a Local Area Network) and a Central Processor where location (multilateration) algorithms run for the multi sensor fusion [11] - [13]. These algorithms rely on the estimation of the Time of Arrival (ToA) at the stations where the target is visible to perform intersection of many hyperbolic surfaces, as obtained by Time Difference of Arrival (TDoA) techniques. Therefore Time synchronization between all the stations is really important. It is interesting to know that this idea served in the past to reduce the track jitter, which method is known as MUlti Radar Trajectory REConstruction [9]. Note also that this is a similar concept to the Global Positioning System (GPS), where there is only one receiver, but several emitters. Hence, the concept of Dilution of Precision (DOP), an intrinsic


Airborne (Vehicle) transponder

Squitter (1090 MHz)

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Central Processing Facility Identity and position of cooperating targets

Fig. 1.

The distributed SSR system.

limitation due to the geometry of the receivers compared to the emitter, applies to both cases. The main two cases have different problems on their own: Surface Movement and Ground Control Systems (SMGCS), and their advanced version, suffer form shadowing (i.e. loss of line-of-sight for one or more receivers), and multipath (due to reflections on airport buildings or other surface). Whereas the Wide area concept, by dealing with in flight aircraft, while less concerned by these problem, suffers from larger and larger values of DOP as soon as an airplane is more and more out of the poly-gram defined by the receivers. In this area, we have identified some basic reference documents (papers/patents/reports/products). The main two companies that firstly proposed a SSR multi-lateration system were: Era, (now, ”ERA - beyond radar”, Rannoch) which claims to have over a hundred systems installed world-wide and Sensis, which claims to have over fifty. Note that other companies did enter this market segment as well, as Thales, Kinetic-avionic, Roke Manor Research, Selex S.I. etc. Note that often a joint effort between an academic research group and a private company has lead to products •

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Starting with the oldest ones produced in the Czech Republic, a pioneering joint effort between the Pardubice University, and the private company ERA produce several papers [11], [12], [13], [14], and a line of products: c (which was first a wide area system demonVERA strated in Ostrava, CZ). The work was mainly done in the Difference Time-Of-Arrival, and improving tracking algorithms. The company implemented a vertical array at each base station to offer vertical separation. The Rannoch Corporation was also working on multilateration, and synchronization [15], [16], and offered c . Last year, as they merged with a product: AirScene ERA, they became the main multi-lateration SSR producer. At University of Braunschweig, P. Form and al. had developed an experimental system with 4 receivers and 1 emitter station linked with a 5.7 Ghz telemetry link. The processing was done at the emitter site without combining, and the system was evaluated at the Cologne

airport [17], [18]. By the use of Kalman filtering after the multilateration, the achieved precision was 7.4 m. The former Thomson-CFC Airsys (now Thales) company delivered the Mode S Airport Ground Sensor System (MAGS) [19]. • At the Technical University of Delft IRCTR, van Genderen and al. assessed the improvement done by a distributed groundsystem [20], [21], and have shown the improvement in robustness of a distributed groundsystem over a conventional radar. • At the University of Roma Tor Vergata, Radarlab, G. Galati and al. had worked in different areas: – Space based constellation: Assessing the possibility to create a similar concept for the wide range and global case in which the receiving stations would be near-orbit satellites [22], [23], [24]. – Frequency agility for MLAT: Lately, the group is issuing the concept of frequency agility and demonstrate how it can improve airport safety [25], [26], [27], [28], [29]. Automatic Dependent Surveillance-Broadcast (ADS-B): As MLAT, ADS-B use the SSR mode S link, but differently from MLAT: a ADS-B station uses the GPS position obtained by the on-board receiver which is later transmitted in SSR mode S squitter. The two systems hence are complimentary, and it is foreseen that they will be merged. Indeed, several tests lead by P. Martone [30], [31] and al. for the Volpe center, MA, made with helicopter and planes in the gulf of Mexico have assessed the cooperation between ADS-B and Wide area Multi-lateration. III. A RRAY SOURCE SEPARATION Distributed surveillance systems and their sensors with omni-directional antennae may face with a dramatic increase of received replies per unit time, causing overlapping between replies in time domain. When replies overlap, very often the message transmitted by the aircraft is corrupted and cannot be recovered by conventional receivers/decoders within the present-day SSR Mode S stations. Hence, in such conditions the aircraft cannot be located nor identified. Figure 2 presents a typical case of mixed replies, where actually two mode S are present (boxes). 0.06 0.05

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A TYPICAL MULTILATERATION SYSTEM USING SSR MODE S SIGNALS

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Fig. 2.

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A record of overlapped replies (case S15).

Antenna arrays are a group of several antennas, each of them with an independent receiver that allows to combine their output digitally (sometimes called digital array). They have the power to perform several tasks as estimation of the


number of sources, adaptive beamforming or blind source separation. The advantage of blind source separation over beamforming 1 is 1) no need of antenna calibration, and 2) it works without the knowledge of the direction of arrivals i.e. blind. We consider the reception of d independent source signals on an m-element antenna array (of arbitrary form). The baseband antenna signals are sampled at a frequency greater than the signal bandwidth and stacked in vectors x[n] (size m). After collecting N samples, the observation model is X= M·S+N

(1)

where X = [x[1], · · · , x[N ]] is the m × N received signal matrix. S = [s[1], · · · , s[N ]] is the d×N source matrix, where s[n] = [s1 [n] , · · · , sd [n]]T is a stacking of the d source signals (superscript T denotes transposed). N is the m × N noise matrix. M is the m × d mixing matrix that contains the array signatures and the complex gains of the sources. The aim of Blind Source Separation is to determine a matrix W such that: ˆ ≈ WH X S where the coefficients of W are found such as the estimated sources ˆs have some desired properties that the real sources s have. For instance: retrieving independent sources [33], [34] either by whitening the Kurtosis tensor, or by forcing the output joint probability to be the exact product of the marginal probability of each source. Another way is to force the output data to take its values on a determined set of values: deterministic algorithms [35]. The sources may have non-white and with different spectrum, i.e. color property [36], or circular properties [37]... But only a few research groups specifically devised algorithms for SSR replies. Chronologically, we can trace, to the best of our knowledge, the various works performed: • 1993, P. Comon worked on an adaptation of his ICA [33] algorithm for the SSR problem [38]. At that time, only the sum and difference channels of a rotating antenna were used. Comon and al. have simulated their work on a 5-elements antenna and concluded to the feasibility of such a system. • 1997, In a preliminary work, J. Tol suggested to adapt several communication algorithms [39]. • 1997, A. van der Veen and al. first invented two deterministic algorithms [40]: AZCMA and AFZA. The former is based on the fact that the data is of constant modulus, so either 0 or 1. The later algorithm relies on that the phase difference between two consecutive samples is constant due to imperfect transponder and is different for each aircraft. • 1999, N. Petrochilos and al. adapted in [41] the ESPRIT algorithm [42] into a full architecture for an array antenna to detect, and separate the incoming replies, then to decode it. • 2002, N. Petrochilos and al. proposed several deterministic algorithms [43], [44], [45]: MS-ZCMA and MDA, and an adaptation of [36]: ESPRIT-SOBI. The MS-ZCMA 1 In

[32] an extensive survey of beamforming is presented.

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is in the continuity of [40] but with more temporal shifts, thus it is more robust and have better results. The MDA is based in the Manchester encoding of the data, and use the fact that the product of three consecutive samples is always equal to zero. For the later algorithm, as in SOBI the time cross-correlation matrices are jointly diagonalized to separate the sources, here the separation is done instead by using ESPRIT on these matrices. 2004, G. Galati and al. proposed to use the remaining frequency after down-conversion [46], [47], [48]. Indeed, the remaining frequency is transponder-dependent and can be used to discriminate the pulses one from another. 2005, E. Piracci and al. used cleverly the different time of arrival of the sources to propose various algorithm based on projection techniques [49], [50], [51], [52], [53]. Due to their packet format, in most of the cases the replies are not completely overlapping. Therefore, a mixture of two replies will present a time range at the beginning and the end of the mixture where only one source will be present. Using this range, it is therefore possible to estimate the array signature vector for each source, and to generate projectors that separate them, i.e. the Projection Algorithm (PA). In [53], the Extended PA (EPA), a deflation-based generalization of the PA to any number of sources, detects, projects out and removes a source iteratively until the last one. 2006, N. Petrochilos and al. presents a different technique to solve the last step of the MS-ZCMA [45] that involve joint diagonalization of a collection of symmetrical thirdorder tensors [54], instead of reducing the order then solving a joint diagonalization of matrices. 2007, E. Piracci and al. adapted the projection algorithms to the case of a single antenna [55], [56], by reshaping the time data into a data matrix. Indeed, it has been observed that in such a case, two array signature vectors are necessary to describe a single source over 14 virtual elements. Applying the same principle than in [49], it is then possible to create projections that separate the sources. 2008, N. Petrochilos and al. proposed to expand the concept of sparsity [57]. Sources are sparse if they have most of their energies in a short period, allowing to find a sufficient number of samples where only one source is present. Note that this is the extension of the concept behind [53].

Note that Cramer-Rao bounds specifically derived for the SSR mode S replies have been presented in [58]. Identifiability bounds have been first studied in [58] and refined bounds have been shown in [59]. A study of the high-Order Statistic for SSR signals in [60] have demonstrated that SSR mode S replies are pseudo-Gaussian up to the order 5, rendering therefore any kurtosis-based algorithm unreliable and nonrobust. To conclude this section, the separation problem for instantaneous mixture of incoming replies over an array antenna has been extensively studied and it appears that the most efficient solution is the Projection Algorithm (PA) with its extensions


(EPA, PASA), except in the very improbable case where a mixture of mode S replies have approximately the same time of arrival and are of the same length (for which the MDA is specially designed then). IV. C ONCLUSION AND P ERSPECTIVES We tried to be as extensive as the length of the paper and our knowledge of the field allowed us. However, some may have evaded our attention. As multi-lateration and array signal processing have a lot of interest, they represents an enormous bibliography. We intentionally reduce the focus to SSR only systems, although we also found an extensive literature to fuse primary and secondary radar, or to locate moving targets with other types of emitters on board (e.g. Distance Measuring Equipment - DME, airborne radar, radio communications), possibly with the help of sensor/data fusion with active (e.g. Surface Movement Radar) or passive (e.g. Infrared) systems. New focus area for algorithmic separation should deal with correcting multipath for SMGCS, Studying frequency agility for SGMCS, and overcoming shadowing at the system level. The transfer of the raw data received (for each element of the antenna) at each receiver site to the main processing unit allows us to design new space-time algorithm that would create unseen beams, and hence reduce the localization measurement error of the aircraft. R EFERENCES [1] M.C. Stevens, Secondary Surveillance Radar, Artech house, Norwood, MA, 1988. [2] S. Kingsley and S. Quegan, Understanding Radar Systems, Mc Graw-Hill, London, UK, 1992. [3] J. Shaw, Radar data processing with new generation monopulse SSR radar. [4] R.M. Trim, “Mode S: an introduction and overview,” Electronics & Communication Engineering Journal, vol. 2, pp. 53–59, Apr. 1990. [5] International Civil Aviation Organisation, International standards and recommended practices, aeronautical telecommunications: ICAO Doc. 7300/8.I, Annex 10. [6] J. L. Gertz, “Fundamentals of mode s parity coding,” Tech. Rep. DOT/FAA/PM-83/6 ATC-177, Lincoln Laboratory, MIT, Lexington, MA, Apr. 1984. [7] J.L. Gertz and V.A. Orlando, “SSR Improvements and collision avoidance systems panel SSR mode S system working group-1. Improved squitter reception update,” SICASP/WG-1 WP/1/585 2, June 1997. [8] Victor S. Chernyak, Fundamentals of Multisite Radar Systems: Multistatic Radars and Multiradar systems, Gordon and Breach Science Publisher, 1998, ISBN: 90-5699-165-5. [9] J. J. Renes and M. R. Best, “Theoretical background to the reconstruction facility MURATREC,” Rep. NLR TR 84008 L, National Aerospace Laboratory, Amsterdam, 1984. [10] John Erik Hershey, Ralph Thomas Hoctor, and all, “Digital Receiving System for Dense Environment of Aircraft,” 14 Sep. 2004, USA Patent 6,792,058 B1, by Lockheed Martin Corporation. [11] V. Kubecek and P. Sterba, “Passive surveillance system for air traffic control,” in 26th European Microwave Conference, Oct 1996, vol. 1, pp. 357–362. [12] P. Bezousek, “A passive radar surveillance system VERA for ATC,” in IRS’98, Munich, Germany, 1998. [13] V. Kubecek and P. Svoboda, “Passive surveillance system for air traffic control,” in 28th European Microwave Conference, Oct 1998, vol. 1, pp. 546–551.

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[52] N. Petrochilos, E. Piracci, and G. Galati, “Separation of multiple secondary surveillance radar sources in a real environment for the near-far case,” in Proc. of APS 2007, Honolulu, Hawai’i, 10-15 June 2007. [53] N. Petrochilos, Gaspare Galati, and Emilio Piracci, “Application of array processing to receiving stations of multilateration systems based on ssr signals,” IEEE Transactions on Areospace, 2008, Accepted. [54] Nicolas Petrochilos and Pierre Comon, “Link between the joint diagonalisation of symmetrical cubes and parafac: an application to secondary surveillance radar,” in Proc. of IEEE SAM 2006, Waltham (MA), 12-14 July 2006. [55] E. Piracci, N. Petrochilos, and G. Galati, “Super-imposed mode s signals: Single-antenna projection algorithm and processing architecture,” in Proc. of ISSPIT 2007, Cairo, Egypt, 15-18 December 2007, Submitted. [56] E. Piracci, N. Petrochilos, and G. Galati, “Single-antenna projection algorithm to discriminate super-imposed secondary surveillance radar mode s signals,” in Proc. of EuRAD 2007, Munich, Germany, 8-12 October 2007, Accepted. [57] N. Petrochilos, G. Galati, and E. Piracci, “Secondary surveillance radar: sparsity-based sources separation in a real environment,” in Proc. of ESAV 2008, 2008, Submitted. [58] N. Petrochilos and P. Comon, “Ml estimation of SSR signals, identifiability, and cramer-rao bounds,” in Proc. of EUSIPCO 2000, Tampere, Finlande, 5-8 Sept. 2000. [59] N. Petrochilos, Amir Leshem, and A.J. van der Veen, “Finite sample identifiability of multiple constant modulus sources,” in Proc. Of GRETSI 2001, Toulouse, France, 10-13 September 2001. [60] N. Petrochilos and P. Comon, “A zero-cumulant random variable and its applications,” Signal Processing Magazine, vol. 86, no. 11, pp. 3334–3338, November 2006.