Aircraft Localization Using a Passive Acoustic Method. Experimental Test 78 13 79 A,∗ B a a 14Q2 Sara R
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Martín , Meritxell Genescà , Jordi Romeu , Arnau Clot 80 15 81 a Laboratory of Acoustics and Mechanical Engineering (LEAM), Universitat Politècnica de Catalunya, C/Colom 11, 08222 Terrassa, Spain 16 82 b Acoustics Research Centre, Department of Electronics and Telecommunications, Norwegian University of Science and Technology, Trondheim, Norway 17 83 18 84 19 85 a r t i c l e i n f o a b s t r a c t 20 86 21 Article history: A passive acoustic method for aircraft localization is experimentally tested in this paper. The method 87 22 Received 23 January 2015 relies on the Doppler effect influencing the signals received by a mesh of microphones distributed over 88 Received in revised form 17 August 2015 23 the acoustic area of interest. The relative Doppler stretch factors between the microphone signals are 89 Accepted 13 November 2015 24 estimated using a one-dimensional version of the Ambiguity function. Then, a Genetic Algorithm is used 90 Available online xxxx 25 to solve the non-linear system of equations that relates the aircraft’s position and velocity to this relative 91 26 Keywords: stretch factors. This method is used in this study to locate a radio controlled airplane equipped with 92 27 Aircraft a Global Positioning System (GPS). Seven microphones are distributed in the airfield area. Although the 93 Localization localization errors are influenced by the uncertainty in the microphones position, the acoustic system 28 94 Wideband cross ambiguity function succeeds at locating the airplane. 29 95 Acoustical Doppler effect © 2015 Published by Elsevier Masson SAS. 30 96 31 97 32 98 33 99 34 100 35 101 36 1. Introduction array. Under these circumstances, a possible approach is to use a 102 37 distributed network of nodes – a node being in this context a sin- 103 38 The interest in passive acoustic aircraft localization systems gle acoustic sensor or a set of several acoustic sensors – where 104 39 arises when the performance of RADAR systems is reduced such every node provides an estimate of the aircraft bearing. The 3D 105 40 as non-line-of-sight tracking or when electromagnetic radiation is position of the aircraft can be calculated afterwards by triangula- 106 41 present. Acoustic aircraft localization systems can also be a cheaper tion of the bearing estimates from, at least, two nodes. Such an 107 42 alternative in small airports and, in addition, the data from the approach has the benefit that only the bearing data has to be 108 43 acoustic sensors can also be processed for source classification or transmitted to a central processing unit, but the disadvantage is 109 44 noise monitoring purposes. that the nodes are complex systems such as microphone arrays 110 45 Several acoustic methods have been developed to determine the [10–13] or, more recently, acoustic vector sensors [14]. 111 46 motion parameters such as height and speed of both jet and pro- An alternative approach to passive acoustic 3D localization that 112 47 peller driven aircraft flying in a straight line at constant flight level uses a network of single microphones was initially described in 113 48 and speed. The methods developed for propeller driven aircraft [15,16]. The methods were based on the estimation of time delay 114 49 take advantage of the Doppler effect and require one single micro- between the signals at the different microphones, and therefore 115 50 phone [1–3] or a distributed array of microphones [4]. The meth- they only obtain the bearing of the aircraft. 116 51Q5 ods developed for jet aircraft use the time differences between a In contrast, the method used in the present paper relies on both 117 52 microphone array [3,5], the interference between the direct and the time delay (retardation effect) and the time stretch (Doppler 118 53 ground reflected sounds [6,7] or both [8]. effect) [17,18] to obtain the source position and velocity. Seven mi- 119 54 Other techniques are used for the 3D localization of maneuver- crophones are distributed on the ground within the acoustic area 120 55 ing aircraft without limitations on the trajectory. For a low altitude of influence of the moving sound source. This acoustic method for 121 56 aircraft, the sound wavefronts can be considered spherical and the aircraft localization is experimentally tested in this paper. 122 57 bearing and distance of the aircraft can be estimated with a planar The rest of the paper is organized as follows. Section 2 de- 123 58 microphone array [9]. At larger distances, the front waves become scribes the acoustic localization method, Section 3 presents the 124 59 planar and only the bearing can be obtained with a microphone experimental setup, Section 4 describes the parameters used in the 125 60 implementation of the algorithm for the test, Section 5 shows the 126 61 experimental results, Section 6 discusses different sources of er- 127 62 * Corresponding author. rors, and finally Section ?? summarizes the main findings of this Q4 128 63 E-mail address: [email protected] (S.R. Martín). paper. 129 64 130 http://dx.doi.org/10.1016/j.ast.2015.11.023 65 131 1270-9638/© 2015 Published by Elsevier Masson SAS. 66 132 JID:AESCTE AID:3491 /FLA [m5G; v1.168; Prn:23/11/2015; 11:22] P.2(1-8) 2 S.R. Martín et al. / Aerospace Science and Technology ••• (••••) •••–••• 1 be easily shown that the signals received at two different micro- 67 2 phones are time shifted and time stretched to each other disre- 68 3 garding the amplitude. 69 4 The underlying idea of this localization method is that, since 70 5 the Doppler stretch term is related to the speed and position of 71 6 the aircraft, if the value of the Doppler stretch could be estimated 72 7 by comparing the signals of the different microphones, the aircraft 73 8 could be localized. 74 ∗ 9 The method is iterative, and for each iteration k ∈ N the initial 75 o o 10 position p(t ) of the aircraft at a time t needs to be known from 76 11 the previous iteration. To initialize the method it is necessary that 77 12 the real position of the aircraft is known at an arbitrary time. Let 78 initial initial 13 p at t = 0sbe a known value such that 79 14 80 t 15 to = (k − 1) 81 16 2 82 17 initial = 83 o = p if k 1, 18 p(t ) − (3) 84 pk 1 if k = 1. 19 85 20 86 where t is the time interval between two successive position es- 21 k−1 87 timates, and p is the position estimate obtained in the previous Fig.