SENSOR ARRAYS FOR IMPROVED INSTRUMENTAL LANDING SYSTEMS G. G. M. Barbieri, E. D. Di Claudio, R. Parisi INFOCOM Dpt. University of Rome “La Sapienza” Via Eudossiana 18, I-00184 ROMA RM Italy
[email protected]
ABSTRACT The importance of air transportation in everyday’s life has increased dramatically in the last few years. Among the consequences of the growing demand for air-related services, the need of assuring an exact and timely flight arrival has become a crucial factor, both for safety and economical reasons. At the same time, broadcasting antennas, radio links and cellular telephone basis stations demand continuously increasing spaces around airports, representing a source of disturbing interferences for radio-navigation systems. In this paper, an improved Instrumental Landing System (ILS) receiver based on array processing concepts is proposed for safer automatic landing procedures. The new approach is based on Direction of Arrival (DOA) estimation performed by the UN-MUSIC algorithm. The superior performance of the proposed system with respect to currently employed techniques in the presence of unwanted interferences has been tested in extensive computer simulations.
1. INTRODUCTION Standard ILS operates in VHF/UHF bands with two ground transmitters [1]: the so-called localizer gives information about the angular offset of the aircraft from the center line of the runway, while the glide slope gives information about the elevation angle with respect to a fixed glide path (usually at an angle of 3° over the ground). The system uses proper training sinusoidal signals, whose relative amplitude contains the information about departures from the center line or the correct glide angle. The error signal should be proportional to angles within narrow route sectors that have an amplitude of ±2.5° in the horizontal plane around the center line and ±0.25° for the vertical plane, around the prefixed glide angle. The localizer transmitter is located 350 m after the end of the runway, along the center line. Instead, the glide slope transmitter is localized 350m from the beginning of the runway, on the left side. Carrier frequencies are between 108 and 112 MHz for the localizer and between 328.6 and 335.4 MHz for the glide slope. Both kinds of information are drawn from amplitude modulation indexes of
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two continuous tones at 90 and 150 Hz. The angular position is computed as a function of the Difference in Depth of Modulation (DDM) by the two tones, that varies in the range (-0.155, +0.155) through the horizontal route sector, and from zero to 0.175 for the vertical route sector. Both DDMs are zero along the correct landing trajectory [1][2][3][4]. The classical ILS suffers from some limitations. For a correct and stable signal propagation, a wide and flat area (Obstacle Clearance Limit) is required around the runway. ILS has also a limited operating flexibility, since the true angular displacement from the descent path is nearly proportional to the error signal amplitude only within a very narrow cone centered on the correct path. When the aircraft is outside the ILS route sector, the pilot cannot know the entity of the displacement, but receives only a generic out of range information. This leads to a lack of flexibility of the landing procedure, which may reduce the efficiency of the air traffic control, especially with modern jet aircrafts and crowded routes. The most critical issue of ILS is represented by the easy deteriorability of the assistance information in the presence of uncorrelated interferences in the bands assigned to the localizer and the glide slope transmitters. It is worth to point out that the working band of the ILS is close to UHF TV channels and often broadcasting transmitters generate spurious signals that can produce interference. Other sources of interference are represented by industrial machinery, such as electric welders and ovens, and electronic devices. Furthermore, the mainlobe of the radiation pattern of the localizer is pointed at about 20° degrees of elevation, while the landing aircrafts approach at a very low glide angle [1][2][4]. So, unpredictable reflections from other flying aircrafts may impinge on the ILS receiver of the landing plane. The power of these reflections may exceed the power of the useful signal. In this work, we propose a new onboard ILS receiver that is able to improve the system performance in a disturbed scenario, using currently available ground transmitters. The new system is based on an antenna array that receives the (known) ILS signal and
uses the estimated location of the transmitter to map the exact position of the aircraft. The localizing algorithm is based on an enhanced version of the UN-MUSIC procedure, described in [7][8]. UN-MUSIC is known to be largely insensitive to the presence of noise and interference uncorrelated with the signal of interest, even if characterized by an unknown spatio-temporal covariance. In the proposed application, we introduce a modified UN-MUSIC which uses a local replica of the ILS signal as a reference, instead of the outputs of an auxiliary uncalibrated array. The resulting algorithm is able to furnish consistent and unbiased estimates of the ILS transmitter location. In the following the main theoretical aspects of the proposed solution are briefly described. Then, a simulation of a landing approach is presented to demonstrate the performance improvement of the new algorithm with respect to the standard ILS.
Y = BS ,
(2.2)
where B is a steering matrix which is not specified. This choice of Y differs from the original UN-MUSIC algorithm, which uses an auxiliary array, whose noise is statistically uncorrelated with signals and noise contained in X. It is important to remark that the frequency of the tones contained in Y may be changed to compensate for Doppler shifts.
2.3 Square-root UN-MUSIC The proposed algorithm is a square-root version [6] of the original UN-MUSIC, which does not require covariance estimates and matrix inversion calculations, to improve the numerical stability and better cope with strong interferences. The reduced-size SVD [6] is applied to the rows of matrices X and Y, obtaining:
X = Q xΣ x U x
2. PROPOSED RECEIVER
(2.3)
and
2.1 Description The receiver array for the localization in azimuth is formed by 15 conformal microstrip antennas, interspaced by one-quarter wavelength, that can be mounted on the entry border of wings. Other 21 antennas, spaced by half wavelength, are assigned to the glide slope receiver and are placed on the entry border of the vertical stabilizer fin [5]. Sensor outputs are narrow-band filtered, demodulated to the baseband and digitized, generating a sequence of snapshot vectors. The sampling period is long enough, so that consecutive snapshots can be considered independent [7][8][9][10]. In the simulated array configuration, signals from the two arrays are processed separately.
2.2 Signal model
Y = Q yΣ yU y
(2.4)
This processing can be interpreted as a spatial whitening of array and reference signals. The SVD of the cross-correlation matrix UxUyH defines the signal subspace Es and its orthogonal complement En: U x U Hy = [ Es
0 H. Σ En ] 1 V 0 Σ 2
(2.5)
The largest singular values in Σ 1 indicate the number of partially incoherent impinging wavefronts and are very close to unity at reasonable signal to noise ratio (SNR). Ideally there should be an unique dominant singular value. It can be shown that the remaining singular values on the main diagonal of Σ 2 [6] are of −1
The (m × n) matrix X contains n consecutive snapshots of lenght m collected by the generic array. The signal model obeys the standard narrow-band equations described in [10]: X = A (θ )S + N ,
(2.1)
where the (m × q) steering matrix A(θ) [9] is formed by the steering vectors a(θi) that characterize the array response with respect to wavefronts containing the ILS useful signal S. The matrix N contains all interferences and noise uncorrelated with S, that are modelled by circular random processes with finite second- and fourth-order moments. According to the original formulation of the UN-MUSIC algorithm [7][8], no other constraint is posed on the spatial covariance structure of N. The matrix S contains samples of the two ILS tones at 90 and 150 Hz. A local replica of the same tones with fixed unitary amplitude is used to build the reference (h × n) matrix Y. This matrix Y is related to the signal matrix S by the following equation:
order n 2 . The signal subspace is defined by the left singular vectors, associated with the singular values in Σ 1. From eqs. (2.1) and (2.3) it is found that the original steering matrix A(θ) is changed into: Σ −x 1Q Hx A(θ ) = R −1A(θ ) .
(2.6)
2.4 DOA estimation The standard UN-MUSIC null-spectrum is defined by [7]:
P(θ ) =
η(θ )
2
.
(2.7)
R −1a(θ )
The direction of arrivals are found by the local minima of the function P(θ) with respect to angles θ. The computation of the vector η(θ) can be done stably through the Paige algorithm described in [6], which solves the Generalized Least Square matrix system:
RE s x(θ ) + Rη(θ ) = a(θ ) .
(2.8)
If the array geometry is linear and equispaced, or can be interpolated to a linear one [11], a computationally efficient ROOT-UN-MUSIC [12] algorithm can be derived from eqn. (2.8). In this case, angles are computed via polynomial rooting techniques. The location of ground transmitters is obtained from the angle(s) found by the UN-MUSIC and reported to the aircraft cockpit.
3. EXPERIMENTAL TRIALS An aircraft approaching procedure has been simulated on a computer to compare the new method to the standard ILS processing. For each array on the aircraft we collected 256 simulated snapshots of the baseband signal, sampled at 500 Hz. This acquisition is repeated at regular intervals to update the aircraft position. Spatially and temporally white Gaussian noise was added to the array signals. The SNR was chosen according to the propagation model, deduced from ILS specifications [1]. The noise factor of sensors, used during simulations, has been doubled with respect to the noise factor of commercial VHF/UHF devices, to reduce the costs of the arrays to a level comparable to a single ILS receiver. The interference was supposed originated by a single far-field source. Three different Interference-to-Signal ratios (ISRs) were considered (–10 dB, 0 dB, 10 dB). We hypothesized a 3000 m runway oriented from South to North, with a standard 3° descending path. A Gaussian signal source was placed in various positions with respect to the airplane. The variance of the interference was supposed to increase when approaching the runway. Figure 1 shows the results as the pilot would see them on the on board ILS instrument panel. Solid orthogonal lines indicate the attitude of the aircraft; the crossing point indicates the axis of the route sector cone. Three different cases are shown on each row, corresponding to different moments of the same landing procedure. Columns from left to right show the ideal instrument representation (in the absence of interferences), the standard ILS and the proposed UN-MUSIC ILS technique respectively. Labels used in the figure have the following meanings: • • • • • • •
Distance is the distance from the runway head. Teta is the displacement angle from the center line, also called course angle. Head is the heading of the longitudinal axis of the airplane with respect to North. Height is the height of the airplane with respect to the runway head. Glide is the vertical angular offset from the glide slope. Int is the ISR. Int Dir is the interference direction with respect to the longitudinal axis of the airplane.
Solid bars represent the average ILS measurement after 500 independent tests for each position, while the white area
indicates the corresponding standard deviation. It is clear that the ILS with UN-MUSIC has superior performance with respect to the standard one. The high bias and variance of the standard ILS makes the instrument practically unreadable by the pilot. Figures 2 and 3 show the aircraft position and some numerical data corresponding to the third case of figure 1. The black aircraft corresponds to the ILS with UN-MUSIC, while the white dot in fig. 2 indicates the exact location of the aircraft. The grey aircraft shows the position estimated by the standard ILS technique. In fig. 2 ILSc is the course angle from the localizer, while in fig. 3 ILSg is the actual glide angle. Mean and variance of the actual angle are shown together with the corresponding position errors and standard deviation in meters. For a more complete comparison, figures show also the numerical results obtained by a Minimum Variance (MV) Beamformer [13], based on the prior knowledge of the exact position of the transmitters.
4. CONCLUSION A new ILS receiver has been proposed and described. The new system was shown to be robust and reliable in all tested situations, also in the presence of strong interferences. Angular discrimination of ILS transmitters allows to create more flexible approaching procedures, making air traffic control tasks easier in proximity of the airport. The flexibility of the system allows to process at the same time information from different radio-navigation systems (such as another ILS or different kinds). Moreover, adoption of the new solution would make it possible to reduce the safeguard areas around transmitters, with savings in terms of space occupation and costs. In addition, the proposed system could also give information about the distance from the runway, which is today transmitted to the aircraft on a small bandwidth radio channel, so that only a limited number of users can be served simultaneously. Finally, the use of low cost receivers together with the possibility of working only on on-board devices are other appealing features of the proposed system.
Figure 1
Figure 3
5. REFERENCES
Figure 2
[1] ICAO, International Standard and Recommended Practices, Annex 10, Vol. I, Part I - Equipment and systems, 1999. [2] P.D. Boffa, Atterraggio Strumentale-ILS di Transizione ed MLS, ed. Siderea, Rome, Italy, 1983. [3] R.Trebbi, Teoria del volo, ed. Aviabooks, Italy, 1996. [4] D. Christiansen, Electronics Engineers’ Handbook, IEEE Press, fourth edition, McGraw Hill, 1997. [5] Alitalia, Manuale Operativo settore MD80, Italy, 1999. [6] G.H. Golub, C.F. Van Loan, Matrix Computations, The John Hopkins University Press, Baltimore, third edition, 1997. [7] Q. Wu, K.M. Wong, “UN-MUSIC and UN-CLE :An Application of Generalized Correlation of the Direction of Arrival of Signal in Unknown Correlated Noise,” IEEE Transactions on Signal Processing, Vol. 42, No. 9, September 1994. [8] Q. Wu, K.M. Wong, “ Estimation of DOA in Unknown Noise: Performance Analysis of UN-MUSIC and UN-CLE , and the Optimality of CCD,” IEEE Trans on Signal Processing , Vol. 43, No.2, February 1995. [9] G. Di Mario, E.D. Di Claudio, G. Orlandi, “Fast SVDBased Algorithm for Signal Selective DOA Estimation,” Proc. of the IEEE Int. Symposium on Circuit and Systems, Vol. 2 pp. 497-500, Atlanta, USA, May 11-15, 1996. [10] Ralph O. Schimdt, “Multiple Emitter Location and Signal Parameter Estimation,” IEEE Trans. on Antennas and Propagation, Vol. AP-34, No. 3, March 1986. [11] B. Friedlander and A.J. Weiss, “Direction finding for wideband signals using an interpolated array,” IEEE Trans.
Signal Processing, Vol. 41, No. 4, pp. 1618-1635, April 1993. [12] B. D. Rao, K.V.S. Hari, “Performance Analysis of RootMUSIC,” IEEE Trans. on Acoustic Speech and Signal Processing, Vol. 37, No. 12, pp. 1939-1949, December 1989. [13] Haykin, Litva, and Shepherd eds., Radar Array Processing, Springer-Verlag, Berlin, 1993.
