# Ray tracing/correlation approach to estimation of surface-based duct parameters from radar clutter

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2 two boundaries of the trapping layer. Base height, thickness and M-deficit determine the height of the duct in the troposphere and the number of rays that would be trapped in the duct. In addition, they also affect the curvature of rays in the trapping layer. [4] If the value of base height reduces to zero, the trilinear profile will end up with a bilinear profile, (see Fig. 1(b)), which means that the bottom of the duct touches the ground. Fig. 1. Surface-based duct refractivity profiles obtained from the tri-linear refractivity model (a) and the bilinear refractivity model (b). The tri-linear refractivity profile created consists of four height-and-m-unit pairs, which are computed by using the following equation: [12] M(z i ) = M i = M i 1 + dm dz (z i z i 1 ), i i = 1, 2, 3, (1) where z 0 = 0 is the height at mean sea level, the corresponding M-unit value is M 0 = 339 M- units, dm/dz 1 = 0.13 M-units/m, and dm/dz 3 = M-units/m. The height above the duct is chosen arbitrarily. For simulation cases, 1000 m is selected. 3. Ray tracing and rank correlation 3.1. Ray tracing approach The ray tracing model is based on a small angle approximation to Snell s law. For a given vertical refractivity profile defined by a series of heights and corresponding M-unit values, the ray trajectory is determined by successive traces through each of the height/m-unit levels until the desired receiver range or height is reached. As described in Ref. [13], a single step of the ray tracing method could be summarized below. The elevation angle at the end of the step, α 1, is given by, α 1 = α (M i+1 M i ), (2) where α 0 is the elevation angle at the beginning of the step, and M i and M i+1 represent M-unit values at the beginning and end of the step respectively. Increment X and spreading increment S over the step are given as X = (α 1 α 0 )/g i, S = (α/α 1 α/α 0 )/g i, g i = 10 6 (M i+1 M i )/(z i+1 z i ), (3) where α is the elevation angle at the transmitter, and z i and z i+1 are the heights at the beginning and end of the step respectively. While there is no magnitude or phase associated with the geometrical ray trace used here, the density of rays striking the ground within any particular range interval is a good indicator of the proportionate field strength illuminating the ground and being backscattered. Two tri-linear refractivity profiles consist of different parameters and are indicated in Fig. 2. The solid profile represents a 350-m SBD created from values of base height, trapping layer thickness, and M- deficit of 300 m, 50 m and 50 M-units, respectively; and the corresponding values of the 100-m SBD (dashdot line) are 50 m, 50 m and 10 M-units. In order to illustrate the relationship between field strength and ray density, a parabolic equation model as described in Ref. [14] is used to produce synthetic clutter. Figure 3(a1) is the propagation loss coverage diagram computed by the APM software package with the 350-m SBD profile, and the corresponding values at the surface are shown in Fig. 3(a2). The radar is operating at a frequency of 5.6 GHz with an antenna height of 27 m above mean sea level. Five hundred rays were traced in launch angles ranging from a minimum of 0.5 to a maximum of 0.5 (with a launch angle increment of ) to encompass rays that lie within the critical angle determined by the duct. The critical angle Φ c is defined as Φ c = ± (M 0 M min ), (4) where M 0 is the value of M-unit at the antenna height, and M min is the minimum value of M-unit at all heights. The idea is, of course, that wave fronts radiated at ray angles outside the critical angle will not contribute to surface clutter seen in extended ranges. Figure 3(b1) shows ray tracing paths with the 350-m SBD, and Fig. 3(c1) with the 100-m SBD. Once ray

3 tracing has been performed the ray density is computed by simply counting the number of ray hits on the surface per range bin, which here is chosen to be approximately 1000 m. The ray density corresponding to Fig. 3(b1) is shown in Figs. 3(b2), 3(c1) and 3(c2). From similarities in the locations of peaks and lows between Figs. 3(a2) and 3(b2), it follows evidently that there is a strong relationship between regions of high return clutter power and regions of high ray density as well as low clutter and low ray density. A comparison between Fig. 3(a2) and Fig. 3(c2) shows that the relation between clutter power and ray density is poor, because of the different SBD parameters. The rank correlation coefficients will be given in the following section. Fig. 2. Tri-linear profiles of 350-m SBD and 100-m SBD. Fig. 3. (a1) Propagation loss coverage diagram based on a 350-m SBD; (a2) The corresponding values at the surface shown in (a1); (b1) Ray tracing trajectories in the presence of a 350-m SBD; (b2) Ray density of (b1) over 1000 m range bin; (c1) Ray trace trajectories in the presence of a 100-m SDB; (c2) Ray density of (c1) over 1000-m range bin. It should be pointed out that here a small angle approximation is applied to Snell s law in order to reduce the computation time. A ray tracing approach without approximation was developed by Yan et al. [1] and the integrator was contained in the shaomf software package. Adopting the shaomf software package to realize ray tracing will be the primary work in our future work Spearman rank-order correlation Once the ray density is computed for a given refractivity profile, the rank correlation coefficient between ray density and observed clutter signal is determined. The concept behind using rank correlation is that one can rank two data sets that are different in physical meaning, resulting in two uniformly distributed data sets where their correlation can then be obtained. The rank correlation is preferred to linear, or parametric, correlation for several reasons. Unlike the linear correlation, if a high coefficient is determined by using the rank correlation, then it truly exists and any relationship between data samples can be correctly interpreted. The rank correlation is also more robust in the sense that it is resistant to un

4 planned defects in the data. [12] The Spearman rankorder correlation coefficient was used for the estimation method presented here. Let P = {p 1,..., p m } and Q = {q 1,..., q m } denote the ranks of {X 1,..., X m } and {Y 1,..., Y m }, respectively. The expression of the Spearman rankorder correlation coefficient R could be expressed as [15] R = 1 6 (p i q i ) 2 m(m 2, i = 1,..., m. (5) 1) The rank correlation coefficient between Fig. 3(a2) and Fig. 3(b2) is 0.69, and that between Fig. 3(a2) and Fig. 3(c2) is 0.16, which shows a strong relationship between clutter returns and ray density based on the same refractivity profile. A flow chart of the ray tracing/correlation (RTC) approach for the estimation of surface-based duct parameters from radar clutter is shown in Fig. 4, where R max is the maximum rank-order correlation coefficient between the observed clutter power and ray density at the surface, P is the rank of the power of radar clutter and Q is the rank of the ray density at the surface, R i is the Spearman rank-order correlation coefficient between the observed clutter power and the ray density at the surface which is computed by the parameters of surface-based duct M i. Fig. 4. Flow chart of RTC for the estimation of surface-based duct parameters from radar clutter. 4. Numerical experiment 4.1. Simulation results For this simulation we begin with a tri-linear 350- m SBD profile described in Subsection 3.1. The refractivity profile will be used to produce our synthetic clutter as shown in Fig. 3(a2), using a parabolic equation model. The tri-linear profile will be the target refractivity profile that we will estimate. The interested space considered here is 200 km in range and 1000 m in height. The range increment is 1000 m and the height increment is 0.3 m. Since only the values near the surface are used, there will be 200 points in the range being calculated. Let {X 1,..., X 200 } denote the synthetic clutter values at the surface and, P = {p 1,..., p 200 } are the ranks of {X 1,..., X 200 }. Let {Y 1,..., Y 200 } denote the number of ray hits on the surface per range bin, and Q = {q 1,..., q 200 } are the ranks of {Y 1,..., Y 200 }. In

5 practice, based on the interested space and computation accuracy, we could change the number of the separated points. Over 6000 refractivity profiles are created based on the information provided in Table 1 which gives the search bounds for SBD tri-linear profile parameters. [12] Table 1. Search bounds for tri-linear profile parameters. parameter lower bound upper bound increment base height/m M-deficit/M-units thickness/m We can now consider rank correlation between ray density and synthetic clutter. The highest rankorder correlation coefficient is 0.71, and the corresponding parameters are base height 275 m, trapping layer thickness 30 m, and M-deficit 40 M-units. Here we should pay attention to an oddity that the highest rank correlation coefficient is even higher than the perfect matching profile (0.69), which shows that this method may not be used for accurate estimation. Table 2 gives the inversion results and computation time retrieved by RTC and the modified genetic algorithm for parabolic wave equation (PWE/MGA). [16] Our computation source is equipped with a Pentium CPU and 1 GB EMS memory, and operates in the Windows XP system. From Table 2 it is clearly seen that with the same computation time (1205 seconds), though the results from RTC are not accurate enough, they could be used to approximately describe real atmospheric conditions. Yet the quality of PWE/MGA is poor. However, with a longer inversion time, the results from PWE/MGA could be evolved perfectly. Table 2. Comparison between RTC and PWE/MGA. base height/m M-deficit/M-units thickness/m time/s true value RTC PWE/MGA PWE/MGA Analysis of different height increments It is assumed that the modified index of refraction is constant between two fixed layers, which will inevitably cause some errors in computation. As equations (2) and (3) show, the smaller the height bin is selected to be, the smaller the error in computation will be, and at the same time, the longer the computation time consumed will be. In order to find the optimal height bin, i.e. more accurate and less computing, we will divide the vertical height into 10 different types. The height bin is changed from 0.1 m to 1 m. The above 350-m tri-linear profile is taken as an example. For the sake of saving computation time and more accurate estimation, the search bounds and increments for SBD tri-linear profile parameters are changed. The lower bound, the upper bound and the increment for base height are 250 m, 350 m and 5 m, respectively. The corresponding values for M-deficit are 20 M-units, 80 M-units and 5 M-units, and those for thickness are 20 m, 80 m and 5 m, respectively. Table 3 gives the inversion results by RTC for different height bins, from which we can see that with the increase of the atmosphere layers, the accuracy of the retrieved values is indicated to have a little improvement. The computation time for 1.0 m is 194 s and for 0.1 m is 1939 s. For SBD, the base height and M-deficit are Table 3. Inversion results obtained by RTC for different height bins. height bin/m base height/m thickness/m M-deficit/M-units coefficient time/s

6 the most important parameters for the magnitude of the ducted field, [4] so we select 0.3 m as an optimal value for the height bin.%vs2mm 4.3. Real data results To better gauge the performance of RTC by using actual clutter measurements, we will now use clutter data taken from the Space Range Radar from Wallops Island, Virginia. Reference data are acquired from range-dependent refractivity profiles obtained with a helicopter. The clutter map presented here (Fig. 5(a)) is obtained from the surface-based ducting event that occurred on 2 April The radar is m above mean sea level and operates at a frequency of 2.84 GHz. The clutter return values along an azimuthal angle of 150 and km in range extracted from Fig. 5(a) are shown in Fig. 5(b). The helicopter measurements at different ranges are shown in Fig. 6 with thin solid lines, from which we can see that the refractivity varies a little in the horizontal direction, so the assumption that the environment is horizontally homogeneous is reasonable. The mean helicopter profile in range is denoted with a thick solid line. The inversion profiles retrieved from RTC and PWE/MGA are shown with a dash-dot line and dash line respectively. The height bin is selected to be 0.3 m. The highest rank correlation coefficient of RTC is 0.80 and the computation time is 737 s, while the time of PWE/MGA is 5430 s. For further information on the radar and the helicopter data, refer to the work by Gerstoft et al. [2003]. Fig. 5. (a) Clutter map from the Space Range Radar at Wallops Island, VA (Gerstoft et al. 2003), and (b) the clutter return as observed by the radar data Discussion of numerical experiments Fig. 6. Refractivity profiles, observed profiles measured from helicopter over ranges (thin solid thin lines), mean helicopter profile (thick solid line), RTC inversion profile (dash-dot line), and PWE/MGA inversion profile (dash line). PWE/MGA for a parabolic equation is an inverse problem for detailed values of clutter returns, while the RTC scheme only concerns the rank-order correlation between observed clutter signal and ray density. Since there is little influence of the noise ingredients on the rank-order of radar clutter arrays observed in calm sea or smooth land environments, the observed clutter could be directly used for inversion without further processing, which makes RTC intuitively simple and straightforward. Both simulation and real data cases have shown the quality of RTC for SBD parameter estimation

7 The results of RTC show that little accuracy is lost compared with PWE/MGA. However, the computational time of PWE/MGA is very long, whereas that of RTC is in real time. Different height bins could bring about different computation accuracies and efficiencies. Through analysis, we could see that 0.3 m is an optimal value for the selection of the height bin. Note that the aim of RFC is not to give an exact refractivity profile, but to propose a generic model able to render an accurate approximation of the real atmospheric conditions. From this point of view, RTC is a feasible method of estimating SBD parameters. 5. Conclusion A novel approach to estimating SBD parameters from surface clutter is presented. Although further studies are necessary to refine this method, the groundwork presented here shows the feasibility of this technique. Though the ray tracing/rank correlation scheme is not accurate enough to perfectly retrieve the refractivity profile, the advantage of this method is the real time working, which makes RTC more attractive for operational use. In this paper, only smooth terrain cases and horizontally homogeneous atmospheric refractivity are discussed; more complex environments will be further considered in future work. Appendix: ray tracing equation Manipulation of the On the assumption that the refractive index structure is horizontally homogeneous, the path of a radiowave ray will obey Snell s law in polar coordinates, which is expressed as [17] nr cos θ = const., (A1) where n is the refractive index of the atmosphere, r is the radial distance from the centre of the Earth to the point under consideration, and θ is the elevation angle made by the ray at the point under consideration. It is clear that r = a e + z, where a e is the radius of the Earth and z is the vertical distance from the surface of the Earth to the point under consideration. Inserting r into Eq. (A1) leads to Noting that then, n(1 + z/a e ) cos θ = const. (A2) m = n(1 + z/a e ), m cos θ = const. (A3) For the purposes of numerical calculation, the modified index of refractivity m = n + z/a e is introduced. In the troposphere, n is approximate to 1 and z a, m = n + z/a n + nz/a = n(1 + z/a) = m. (A4) So Snell s law in the m space could be obtained to be m cos θ = const. Acknowledgment (A5) The authors would like to thank Professor Peter Gerstoft at Marine Physical Laboratory, University of California, San Diego, La Jolla, California, USA, for providing the Wallops 98 radar clutter data and corresponding helicopter refractivity measurements. The authors would also thank Professor Yan H J for his insight into different layers in discussion. References [1] Yan H J, Fu Y and Hong Z J 2006 Introduction to Modern Atmospheric Refraction (Shanghai: Science and Educational Press) (in Chinese) [2] Rowland J R and Babin S M 1987 Johns Hopkins APL Tech. Dig [3] Gersoft P, Rogers L T, Krolik J K and Hodgkiss W S 2003 Radio Sci [4] Vasudevan S, Anderson R H, Kraut S, Gerstoft P, Rogers L T and Krolik J L 2007 Radio Sci [5] Yardim C, Gerstoft P and Hodgkiss W S 2008 IEEE Antennas Propag

8 [6] Douvenot R, Fabbro V, Gerstoft P, Bourlier C and Saillard J 2008 Radio Sci [7] Sheng Z, Huang S X and Zheng G D 2009 Acta Phys. Sin (in Chinese) [8] Liang G and Bertoni H L 1998 IEEE Antennas Propag [9] Erricolo D, Crovella U G and Uslenghi P 2002 IEEE Antennas Propag [10] Sevgi L 2004 IEEE Antennas and Propaga. Mag [11] Rogers L T 1996 IEEE Antennas Propag [12] Barrios A E 2004 Radio Sci [13] Barrios A E 2003 Considerations in the Development of the Advanced Propagation Model (APM) For U.S. Navy Applications AD-A [14] Kuttler J R and Dockery G D 1991 Radio Sci [15] Xu W C, Chang C Q, Hung Y S and Fung C W 2008 IEEE Transactions on Signal Processing [16] Huang S X, Zhao X F and Sheng Z 2009 Chin. Phys. B [17] Choi J 1996 Parametric Error Analysis of Tropospheric Propagation Effects AD-A

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