2 kAZ − Yk
2
F + λkZk 2,0 , (5)
where λ > 0 balances between data-fidelity and sparsity. This problem is nonconvex, noncontinuous, and NP hard due to its combinatorial nature. Yet, the single measurement vector (SMV) case (i.e., L = 1) has been widely studied, driven by the compressed sensing paradigm. Naturally, many **of** these approaches have been extended to the MMV setting, such as those mentioned in the introduction. Such extensions are essential as the resolution **of** MMV problems leads to an improvement in the size **of** the recoverable support [25].

‡ Toulouse University, F-31400 Toulouse, France § DGA, F-75509 Paris, France
Abstract—In this communication, **direction** finding perfor- mances **of** a reconfigurable wideband vector antenna are pre- dicted. The accurate **estimation** **of** the **direction** **of** arrivals across the 3D half-space **of** incoming electromagnetic fields is obtained over a 1.7:1 frequency range from only two colocated and orthogonal circular arrays **of** Vivaldi antennas.

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SW med (black dots), individual wavelengths SW (grey circles).
Non-square array and irregularly spaced sensors. We illustrate the performance **of** the proposed method for sensor arrangements on non-square domains and with unequal spac- ing (using 129 sensors). The domains considered are depicted in Fig. 2 (left) for equidistant (top) and and irregular (bottom) spacing, respectively. Results obtained for 100 independent noise realizations and SNR values **of** −10dB and −5dB are plotted in Fig. 2 (center and right column, respectively; same color code as in Fig. 1). Comparison with Fig. 1 yields the following conclusions. First and foremost, the proposed method is clearly effective for general sensor arrangements on non-square domains as well as for the case **of** irregularly spaced sensors, with performance comparable to those for the square regularly spaced sensor array. Second, the proposed joint wavelength based procedure DOA joint dramatically out-

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Researchers attempt to reduce the noise impact by estimat- ing the power **of** the noise [8], [9], [10] or applying signal enhancement techniques [11], [12]. Stoica et al. propose an eigenvalue-based method [8] to estimate the noise power, which requires the EVD on the data covariance matrix. The noise power is estimated by calculating the average **of** the eigenvalues belonging to the noise subspace. The eigenvalue method is proven to be statistically efficient [8]. However, it is not the focus **of** this paper since the major merit **of** the propagator-based methods is to avoid any EVD or SVD operation. In addition, Stoica et al. [8], Marcos and Sanchez- Araujo [6] also propose two different linear methods. In their methods, the noise power can be extracted from the structure **of** the data covariance matrix without EVD or SVD operation. These two linear methods are more computationally efficient, yet less statistically efficient, compared with the eigenvalue based methods [8].

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V. C ONCLUSIONS
This paper proposed to exploit a little known asymptotic formulation **of** the UML criterion together with an asymptotic approximation for small separation between two frequencies. As a result, a 1D-minimization procedure only is needed to estimate the two frequencies, which yields a computationally simple algorithm. The latter exhibits near efficient perfor- mance up to half the main lobe width frequency difference and an even better resolution than UML in case **of** very close sources. Moreover, we also get equivalent UML performance in case **of** highly correlated or coherent sources where the proposed method outperforms the CML algorithm as well as common high resolution techniques.

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SW med (black dots), individual wavelengths SW (grey circles).
Non-square array and irregularly spaced sensors. We illustrate the performance **of** the proposed method for sensor arrangements on non-square domains and with unequal spac- ing (using 129 sensors). The domains considered are depicted in Fig. 2 (left) for equidistant (top) and and irregular (bottom) spacing, respectively. Results obtained for 100 independent noise realizations and SNR values **of** −10dB and −5dB are plotted in Fig. 2 (center and right column, respectively; same color code as in Fig. 1). Comparison with Fig. 1 yields the following conclusions. First and foremost, the proposed method is clearly effective for general sensor arrangements on non-square domains as well as for the case **of** irregularly spaced sensors, with performance comparable to those for the square regularly spaced sensor array. Second, the proposed joint wavelength based procedure DOA joint dramatically out-

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a b s t r a c t
We address the problem **of** estimating the directions-**of**-**arrival** (DoAs) **of** multiple signals received in the presence **of** a combination **of** a strong compound-Gaussian external noise and weak internal white Gaussian noise. Since the exact distribution **of** the mixture is not known, we get an insight into optimum procedure via a related model where we consider the texture **of** the compound-Gaussian component as an unknown and deterministic quantity to be estimated together with DoAs or a basis **of** the signal subspace. Alternate maximization **of** the likelihood function is conducted and it is shown that it operates a separation between the snapshots with small/large texture values with respect to the additive noise power. The modiﬁed Cramér–Rao bound is derived and a prediction **of** the actual mean-square error is presented, based on separation between external/internal-noise dominated samples. Numerical simu- lations indicate that the suggested iterative DoA **estimation** technique comes close to the introduced bound and outperform a number **of** existing routines.

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Email:(jingjing.pan1@etu.univ-nantes.fr)
Abstract
In propagator based methods for **Direction** **of** **Arrival** (DoA) finding, conventional approaches for eliminating the noise impact are based on signal enhancement or noise power **estimation**. In this paper, a new method is introduced from another point **of** view. When the noise is an additive spatially and temporally white Gaus- sian noise, theoretically, it only impacts the diagonal elements **of** the data covariance matrix. Firstly, the proposed method utilizes the principle **of** Linear Prediction (LP) to rebuild the denoised diagonal elements **of** the data covariance matrix. Then, the Orthogonal Propagator Method (OPM) is directly applied for DoA **estimation**. Compared with the conventional OPM-based methods, the proposed method is more robust to the noise, especially in low Signal to Noise Ratio (SNR) scenarios. Simula- tion examples are provided to demonstrate the performance **of** the proposed method. Keywords: **Direction** **of** **Arrival** (DoA) **estimation**, Linear Prediction (LP),

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Jingjing Pan, Meng Sun, Yide Wang, Xiaofei Zhang
Abstract—Subspace-based methods suffer from the rank loss **of** the noise free data covariance matrix in the context **of** **direction** **of** **arrival** (DOA) **estimation** **of** coherent sources. The well-known spatial smoothing techniques are then widely employed to create a rank restored data covariance matrix. However, conventional spatial smoothing techniques, such as the spatial smoothing pre-processing (SSP), modified spatial smoothing pre-processing (MSSP), and improved spatial smoothing (ISS), do not make full use **of** the available information in the data covariance matrix. In this paper, an enhanced spatial smoothing (ESS) technique is proposed to exploit both the covariance matrices **of** indi- vidual subarrays and the cross-covariance matrices **of** different subarrays. Besides, the proposed method can work directly on the signal subspace (ESS-SS), since the signal subspace contains all the information **of** the DOAs **of** incoming signals. After de- correlation, the subspace method ESPRIT is adopted to estimate the DOAs. Compared with conventional approaches, the proposed method is more powerful to de-correlate the correlation between signals, and also more robust to the noise impact. The proposed method is tested on numerical data in coherent scenarios, and compared with conventional approaches. Simulation results show that the proposed method has an enhanced resolving capability and a lower signal-to-noise ratio threshold.

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V. C ONCLUSIONS
We have proposed a novel high-resolution method for **direction** **of** **arrival** **estimation** based on multiple frequency or wideband measurements. The method makes use **of** the Kronecker theorem for implicitly imposing the model equa- tions for each considered frequency through rank penalization **of** the Hankel matrices generated by the corresponding ap- proximating sequences. The resulting optimization problem is then resolved using ADMM. Although the problem is non- convex (and hence inherits the problem **of** local minima), numerical simulations demonstrate that the method yields excellent practical performance. In particular, it significantly outperforms ESPRIT based **estimation**, both at low and high SNR levels, and enables to obtain practically usable estimates even at very low SNR. Furthermore, the proposed method yields an algorithm that is simple and easy to implement.

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V. C ONCLUSIONS
We have proposed a novel high-resolution method for **direction** **of** **arrival** **estimation** based on multiple frequency or wideband measurements. The method makes use **of** the Kronecker theorem for implicitly imposing the model equa- tions for each considered frequency through rank penalization **of** the Hankel matrices generated by the corresponding ap- proximating sequences. The resulting optimization problem is then resolved using ADMM. Although the problem is non- convex (and hence inherits the problem **of** local minima), numerical simulations demonstrate that the method yields excellent practical performance. In particular, it significantly outperforms ESPRIT based **estimation**, both at low and high SNR levels, and enables to obtain practically usable estimates even at very low SNR. Furthermore, the proposed method yields an algorithm that is simple and easy to implement.

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Performance **of** two DOA **estimation** algorithms, MVB and ML **estimation** are studied. These methods were first adapted for our signal model and their performance were investigated through Monte Carlo simulations and compared against each other in terms **of** root mean square error. Simulation results showed that in low SNR situations, the RMSE **of** DOA **estimation** is large and the MVB method cannot separate two targets. In addition, it was shown that sampling from sidelobes **of** the antenna pattern decreases the performance **of** the MVB method. In other cases, by selecting correct parameters, both methods can separate targets with angular separations smaller than the antenna pattern beamwidth. We have also presented a calibration scheme which worked efficiently when it was applied to different antenna pattern shapes at each frequency and nonuniform scanning angles. In the next step, the proposed methods will be applied to the experimental data captured from a radar experiment using real targets.

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eric.chaumette@onera.fr
Abstract—Most high resolution **direction** **of** **arrival** (DoA) **estimation** algorithms exploit an eigen decomposition **of** the sample covariance matrix (SCM). However, their performance dramatically degrade in case **of** correlated sources or low number **of** snapshots. In contrast, the maximum likelihood (ML) DoA estimator is more robust to these drawbacks but suffers from a too expensive computational cost which can prevent its use in practice. In this paper, we propose an asymptotic simplification **of** the ML criterion in the case **of** two closely spaced sources. This approximated ML estimator can be implemented using only 1-D Fourier transforms. We show that this solution is as accurate as the exact ML one and outperforms all high-resolution techniques in case **of** correlated sources. This solution can also be used in the single snapshot case where very few algorithms are known to be effective.

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Unfortunately, travelers do not notice Sakurajima from the existing old Kagoshima Airport Terminal because of the uniform circulation which has no relation with its own site c[r]

The increasingly widespread use **of** sensor and actuator networks and in gen- eral **of** the Internet **of** Things (IoT) in several areas **of** precision, imposes upon localization systems that can often equip them with a robust and more precise localization. It is in this sense that UWB technology has proved to be one **of** the most powerful communication technologies for these localization systems; thanks, in particular to the bandwidth occupied instantaneously by the signal allowing a very fine temporal resolution. Constructors have set up localization kits based on various technologies. These kits facilitate in a way the work **of** localization **of** users. In this paper, we present results on the per- formance study **of** the Decawave PDoA Kit. This Kit uses the PDoA (Phase Difference **of** **Arrival**) to determine the Angle **of** **Arrival** (AoA) parameter with UWB technology. This study is in context **of** localization by AoA for an application to protect agricultural crops against grain-eating birds. The re- sults **of** the study show overall AoA measurement errors around 10 degrees in an ideal environment.

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The increasingly widespread use **of** sensor and actuator networks and in gen- eral **of** the Internet **of** Things (IoT) in several areas **of** precision, imposes upon localization systems that can often equip them with a robust and more precise localization. It is in this sense that UWB technology has proved to be one **of** the most powerful communication technologies for these localization systems; thanks, in particular to the bandwidth occupied instantaneously by the signal allowing a very fine temporal resolution. Constructors have set up localization kits based on various technologies. These kits facilitate in a way the work **of** localization **of** users. In this paper, we present results on the per- formance study **of** the Decawave PDoA Kit. This Kit uses the PDoA (Phase Difference **of** **Arrival**) to determine the Angle **of** **Arrival** (AoA) parameter with UWB technology. This study is in context **of** localization by AoA for an application to protect agricultural crops against grain-eating birds. The re- sults **of** the study show overall AoA measurement errors around 10 degrees in an ideal environment.

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mainly related to the distance between agent's eyes and target tiles, whatever the conditions and independently **of** adjustment **of** the eyelid positions: largest errors were made
to report the estimated tiles on a Distance separating the human eyes and the ground was set to 115 cm. To control the possible impact **of** the head orientation on gaze reading, the experimenter Figure 6). The tiles chosen randomly by the system were communicated to the experimenter by earphones. Just as in the HRI setup, the tone and the trial's number were heard by the participant who has Participants were informed about the random order **of** the conditions (i.e., they will perform the task four times with all **of** our three different robot's caps and with eyes' experimenter in random order) and the fact that the targeted tiles are randomly chosen (i.e., 64 tiles among the 100 are randomly chosen by the program). They were not informed that peripheral tiles are never looked at. In addition, they position adjustments that were again randomly activated. Lightings in the room were control their possible impact on gaze

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network.
We have simulated, using the model developed in [1], and summarized above, a spreading phenomenon on a subnetwork **of** the WAN, composed **of** the 2, 400 nodes for which the populations are larger than 10, 000 inhabitants and which corresponds to 98% **of** the total traffic [24]. The **arrival** times are computed by solving numerically the equations **of** the Rvachev-Longini model with dis- cretized random travel events, and averaging over 1, 000 realizations **of** the spreading with the same seed (one infectious individual in a given city). Figure 4 shows the obtained values **of** λhti versus χ for various initial seeds. We observe that the average **arrival** time is indeed determined by the value **of** χ in a given city: various cities with the same χ are reached at the same time by the disease propagation. While χ quantitatively overesti- mates the **arrival** time, the two quantities are correlated strongly enough, in order to obtain with a good confi- dence the order **of** **arrival** **of** the disease in different cities. More precisely, if we denote ∆χ(i, j) =| χ(j) − χ(i) |, we show in Fig. 5 the probability f c (∆χ) that the ar-

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Abstract—Airport is both the starting and ending point **of** air
traffic. The sharp increase in air traffic flow causes directly traffic congestion in Terminal Maneuvering Area (TMA) which affects the normal operation **of** the flights. Optimizing departure and **arrival** procedures is therefore crucial to regulate air traffic flow. This research focuses on generating 3D Standard Instrument Departure routes and Standard Terminal **Arrival** Routes in TMA at a strategic level. We propose an optimization approach to generate 3D routes avoiding obstacles and assuring a minimum separation between routes. The method combines Fast Marching Method and Simulated Annealing.

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