 Research Article
 Open Access
A Propagation Environment Modeling in Foliage
 Jing Liang^{1}Email author,
 Qilian Liang^{1} and
 Sherwood W. Samn^{2}
https://doi.org/10.1155/2010/873070
© Jing Liang et al. 2010
 Received: 16 September 2009
 Accepted: 22 January 2010
 Published: 21 March 2010
Abstract
Foliage clutter, which can be very large and mask targets in backscattered signals, is a crucial factor that degrades the performance of target detection, tracking, and recognition. Previous literature has intensively investigated land clutter and sea clutter, whereas foliage clutter is still an openresearch area. In this paper, we propose that foliage clutter should be more accurately described by a loglogistic model. On a basis of pragmatic data collected by ultrawideband (UWB) radars, we analyze two different datasets by means of maximum likelihood (ML) parameter estimation as well as the root mean square error (RMSE) performance. We not only investigate loglogistic model, but also compare it with other popular clutter models, namely, lognormal, Weibull, and Nakagami. It shows that the loglogistic model achieves the smallest standard deviation (STD) error in parameter estimation, as well as the best goodnessoffit and smallest RMSE for both poor and good foliage clutter signals.
Keywords
 Radar
 Root Mean Square Error
 False Alarm
 Probability Density Function
 Nakagami Distribution
1. Introduction and Motivation
Detection and identification of military equipment in a strong clutter background, such as foliage, soil cover, or building has been a longstanding subject of intensive study. It is believed that solving the target detection through foliage environment will significantly benefit sensethroughwall and many other subsurface sensing problems. However, to this date, the detection of foliagecovered targets with satisfied performances is still a challenging issue. Recent investigations in environment behavior of tree canopies have shown that both signal backscattering and attenuation are significantly influenced by tree architecture [1]. Therefore using the return signal from foliage to establish the clutter model that accounts for environment effects is of great importance for the sensingthroughfoliage radar detection.
Clutter is a term used to define all unwanted echoes from natural environment [2]. The nature of clutter may necessarily vary on a basis of different applications and radar parameters. Most previous studies have investigated land clutter or sea clutter, and some conclusions have been reached. For example, lognormal, Weibull, and Kdistributions have been proven to be better suited for the clutter description other than Rayleigh and Rician models in highresolution radar systems. Fred did statistical comparisons and found that sea clutter at lowgrazing angles and high range resolution is spiky based on the data measured from various sites in Kauai and Hawaii [3]. David generalized radar clutter models using noncentral chisquare density by allowing the noncentrality parameter to fluctuate according to the gamma distribution [4]. Furthermore, Leunget al. used a NeuralNetworkbased approach to predict the sea clutter model [5, 6].
However, as far as clutter modeling in forest is concerned, it is still of great interest and will be likely to take some time to reach any agreement. A team of researchers from MIT [7] and U. S. Army Research Laboratory (ARL) [8, 9] have measured ultrawideband (UWB) backscatter signals in foliage for different polarizations and frequency ranges. The measurements show that the foliage clutter is impulsively corrupted with multipath fading, which leads to inaccuracy of the Kdistributions description [10]. The US Air Force Office of Scientific Research (AFOSR) has conducted field measurement experiment concerning foliage penetration radar since 2004 and noted that metallic targets may be more easily identified with wideband than with narrowband signals.
In this investigation, we will apply ultrawideband (UWB) radar to model the foliage clutter. UWB radar emissions are at a relatively low frequency typically between 100 MHz and 3 GHz. Additionally, the fractional bandwidth of the signal is very large (greater than 0.2). Such a radar sensor has exceptional range resolution, as well as the ability to penetrate many common materials (e.g., walls). Law enforcement personnel have used UWB groundpenetrating radars (GPRs) for at least a decade. Like the GPR, sensingthroughfoliage radar takes advantage of UWB's very fine resolution (time gating) and the low frequency of operation.
In our work, we investigate the loglogistic distribution (LLD) to model foliage clutter and illustrate the goodnessoffit to real UWB clutter data. Additionally, we compare the goodnessoffit of LLD with existing popular models, namely, lognormal, Weibull, and Nakagami by means of maximum likelihood estimation (MLE) and the root mean square error (RMSE). The result shows that loglogistic model provides the best fit to the foliage clutter. Our contribution is not only the new proposal on the foliage clutter model with detailed parameter estimation, but also providing the criteria and approaches based on which the statistical analysis is obtained. Further, based on LLD the theoretical study about the probability of detection as well as the probability of false alarm are discussed.
The rest of this paper is organized as follows. Section 2 provides a review on statistical models of loglogistic, lognormal, Weibull, and Nakagami, and discusses their applicability for foliage clutter modeling. Section 3 summarizes the measurement and the two sets of clutter data that are used in this paper. Section 4 discusses estimation on parameters and the goodnessoffit for loglogistic, lognormal, Weibull and Nakagami models, respectively. Section 5 analyzes the performance of radar detection in the presence of foliage clutter. Finally, Section 6 concludes this paper and describes some future research topics.
2. Clutter Models
Many radar clutter models have been proposed in terms of distinct statistical distributions; most of which describe the characteristics of clutter amplitude or power. Before detailed analysis on our measurement, we would like to discuss the properties and applicability of loglogistic, lognormal, Weibull, and Nakagami statistic distributions, which are designated as "curvefit" models in Section 4, since they are more likely to provide good fit to our collections of pragmatic clutter data. Detailed explanations would be given in the following subsections.
2.1. LogLogistic Model
Recently Loglogistic model has been applied in hydrological analysis. This distribution is a special case of Burr's typeXII distribution [11] as well as a special case of the kappa distribution proposed by Mielke andJohnson [12]. Lee et al. employed the loglogistic distribution (LLD) for frequency analysis of multiyear drought durations [13], whereas Shoukri et al. employed LLD to analyze extensive Canadian precipitation data [14], and Narda and Malik used LLD to develop a model of root growth and water uptake in wheat [15]. In spite of its intensive application in precipitation and streamflow data, the loglogistic distribution (LLD) [16] statistical model, to the best of our knowledge, has never been applied to radar foliage clutter. The motivation for considering loglogistic model is based on its higher kurtosis and longer tails, as well as its probability density function (PDF) similarity to lognormal and Weibull distributions. It is intended to be employed to estimate how well the model matches our collected foliage clutter statistics.
2.2. LogNormal Model
The lognormal distribution is most frequently used when the radar sees land clutter [17] or sea clutter [18] at lowgrazing angles ( 5 degrees) since lognormal has a long tail. However, it has been reported that the lognormal model tends to overestimate the dynamic range of the real clutter distribution [19]. Furthermore, whether it is applicable to model foliage clutter still requires detailed analysis.
2.3. Weibull Model
The Weibull distribution, which is named after Waloddi Weibull, can be made to fit clutter measurements that lie between the Rayleigh and lognormal distribution [21]. It has been applied to land clutter [22, 23], sea clutter [24, 25] and weather clutter [26]. However, in very spiky sea and foliage clutter, the description of the clutter statistics provided by Weibull distributions may not always be sufficiently accurate [27].
2.4. Nakagami Model
In the foliage penetration setting, the target returns suffer from multipath effects corrupted with fading. As Nakagami distribution is used to model scattered fading signals that reach a receiver by multiple paths, it is natural to investigate how well it fits the foliage clutter statistics.
3. Experiment Setup and Data Collection
The principle pieces of equipment are
 (i)
Dual antenna mounting stand,
 (ii)
Two antennas,
 (iii)
A trihedral reflector target,
 (iv)
Barth pulse source (Barth Electronics, Inc. model 732 GL) for UWB,
 (v)
Tektronix model 7704 B oscilloscope,
 (vi)
Rack system,
 (vii)
HP signal Generator,
 (viii)
IBM laptop,
 (ix)
Custom RF switch and power supply,
 (x)
Weather shield (small hut).
4. Statistical Analysis of the Foliage Clutter
4.1. Maximum Likelihood Estimation
Using the collected clutter data mentioned above, we apply Maximum Likelihood Estimation (MLE) approach to estimate the parameters of the loglogistic, lognormal, Weibull, and Nakagami models. MLE is often used when the sample data are known and parameters of the underlying probability distribution are to be estimated [29, 30]. It is generalized as follows.
The maximum likelihood estimate of , , , is the set of values , , , that maximize the likelihood function .
Estimated parameters for dataset I.
LogLogistic  Lognormal  Weibull  Nakagami  

Data 1 







 



 



 
Data 2 







 



 



 
Data 3 







 



 



 
Data 4 







 



 



 
Data 5 







 



 



 
Data 6 







 



 



 
Data 7 







 



 



 
Data 8 







 



 



 
Data 9 







 



 



 
Data 10 







 



 




Averaged estimated parameters for dataset I.
LogLogistic  Lognormal  Weibull  Nakagami  

Average 







 



 




From Tables 1 and 2, it can be easily seen that STD errors for loglogistic and lognormal parameters are less than 0.02 and the estimated parameters for these two models vary little from data to data compared to parameters of Weibull and Nakagami. It is obvious that loglogistic model provides the smallest STD error for all the 10 collections compared to lognormal. Although accurate shape parameter estimation can be achieved by both Weibull and Nakagami models, their scale parameters are not acceptable.
Estimated and averaged parameters for dataset ii.
LogLogistic  Lognormal  Weibull  Nakagami  

Data 1 







 



 



 
Data 2 







 



 



 
Average 







 



 




In view of smaller error in parameter estimation, loglogistic model fits the collected data best compared to lognormal, Weibull, and Nakagami. Lognormal model is also acceptable.
4.2. GoodnessofFit in Curve and RMSE
Here we apply n 100 for each model and k is the number of data collections for each set.
Similarly, in Figure 11 histogram bars denote the PDF of the absolute amplitude of one collection of clutter data from set II. Compared to Figure 10, the loglogistic and the lognormal provide similar extent of goodnessoffit. Weibull is worse since it cannot fit well in either kurtosis or tail, while Nakagami is the worst and unacceptable. Also, we obtain , , , . This illustrates that for clutter backscattering dataset II, the loglogistic model still fits the best.
5. Target Detection Performance
One of the primary goals for a radar is target detection; therefore based on clutter models that have been investigated in the previous sections, we apply a special case of the Bayesian criterion named NeymanParson criterion to analyze the target detection performance in the foliage environment.
where and represent the random variable of clutter and noise, respectively. follows loglogistic model with both parameters and , and is Gaussian noise with zero mean and variance . is the target signal, which assumes to be a constant for simplicity.
Therefore and mean that
(i) PDF of given that a target was not present,
(ii) PDF of given that a target was present.
6. Conclusion
On a basis of two sets of foliage clutter data collected by a UWB radar, we show that it is more accurate to describe the amplitude of foliage clutter using loglogistic statistic model ratherthanlognormal, Weibull, or Nakagami. Lognormal is also acceptable. The goodnessoffit for Weibull is worse whereas that of Nakagami is the worst. Our contribution is not only the new proposal on the foliage clutter model with detailed parameters, but also providing the criteria and approaches based on which the statistical analysis is obtained. Further, the theoretical study on the probability of detection and the probability of false alarm in the presence of loglogistic foliage clutter are discussed. Future research will investigate the characteristics of targets and the design of radar receivers for the loglogistic clutter so as to improve the performance of target detection, tracking and identification in foliage.
Declarations
Acknowledgments
This work was supported in part by the U.S. Office of Naval Research under Grant N000140710395 and Grant 000140711024 and in part by the National Science Foundation under Grant CNS0721515, Grant CNS0831902, Grant CCF0956438, and Grant CNS0964713.
Authors’ Affiliations
References
 Imhoff ML: A theoretical analysis of the effect of forest structure on synthetic aperture radar backscatter and the remote sensing of biomass. IEEE Transactions on Geoscience and Remote Sensing 1995, 33(2):341352. 10.1109/36.377934View ArticleGoogle Scholar
 Skolnik MI: Introduction to Radar Systems. 3rd edition. McGrawHill, New York, NY, USA; 2001.Google Scholar
 Posner FL: Spiky sea clutter at high range resolutions and very low grazing angles. IEEE Transactions on Aerospace and Electronic Systems 2002, 38(1):5873. 10.1109/7.993229MathSciNetView ArticleGoogle Scholar
 Shnidman DA: Generalized radar clutter model. IEEE Transactions on Aerospace and Electronic Systems 1999, 35(3):857865. 10.1109/7.784056View ArticleGoogle Scholar
 Hennessey G, Leung H, Drosopoulos A, Yip PC: Seaclutter modeling using a radialbasisfunction neural network. IEEE Journal of Oceanic Engineering 2001, 26(3):358372. 10.1109/48.946510View ArticleGoogle Scholar
 Xie N, Leung H, Chan H: A multiplemodel prediction approach for sea clutter modeling. IEEE Transactions on Geoscience and Remote Sensing 2003, 41(6):14911502. 10.1109/TGRS.2003.811690View ArticleGoogle Scholar
 Fleischman JG, Ayasli S, Adams EM, Gosselin DR: Foliage attenuation and backscatter analysis of SAR imagery. IEEE Transactions on Aerospace and Electronic Systems 1996, 32(1, part 3):135144.View ArticleGoogle Scholar
 Mccorkle JW: Early results from the Army Research Laboratory ultrawidebandwidth foliage penetration SAR. Underground and Obscured Object Imaging and Detection, April 1993, Orlando, Fla, USA, Proceedings of SPIE 1942:View ArticleGoogle Scholar
 Sheen DR, Malinas NP, Kletzli DW, Lewis TB, Roman JF: Foliage transmission measurements using a groundbased ultrawide band (300–1300 MHz) SAR system. IEEE Transactions on Geoscience and Remote Sensing 1994, 32(1):118130. 10.1109/36.285195View ArticleGoogle Scholar
 Watts S: Radar detection prediction in Kdistributed sea clutter and thermal noise. IEEE Transactions on Aerospace and Electronic Systems 1987, 23(1):4045.View ArticleGoogle Scholar
 Burr IW: Cumulative frequency functions. Annals of Mathematical Statistics 1942, 13: 215232. 10.1214/aoms/1177731607MathSciNetView ArticleMATHGoogle Scholar
 Mielke PW, Johnson ES: Threeparameter kappa distribution maximum likelihood estimates and likelihood ratio tests. Monthly Weather Review 1973, 101: 701709. 10.1175/15200493(1973)101<0701:TKDMLE>2.3.CO;2View ArticleGoogle Scholar
 Lee KS, Sadeghipour J, Dracup JA: An approach for frequency analysis of multiyear dought duration. Water Resources Research 1986, 22(5):655662. 10.1029/WR022i005p00655View ArticleGoogle Scholar
 Shoukri MM, Mian IUH, Tracy DS: Sampling properties of estimators of the loglogistic distribution with application to Canadian precipitation data. The Canadian Journal of Statistics 1988, 16(3):223236. 10.2307/3314729MathSciNetView ArticleMATHGoogle Scholar
 Narda NK, Malik RK: Dynamic model of root growth and water uptake in wheat. Journal of Agricultural Engineering 1993, 3(34):147155.Google Scholar
 Gupta RC, Akman O, Lvin S: A study of loglogistic model in survival analysis. Biometrical Journal 1999, 41(4):431443. 10.1002/(SICI)15214036(199907)41:4<431::AIDBIMJ431>3.0.CO;2UView ArticleMATHGoogle Scholar
 Warden M: An experimental study of some clutter characteristics. Proceedings of AGARD Conference on Advanced Radar Systems, May 1970Google Scholar
 Trunk GV, George SF: Detection of targets in nonGaussian sea clutter. IEEE Transactions on Aerospace and Electronic Systems 1970, 6(5):620628.View ArticleGoogle Scholar
 Schleher DC: Radar detection in Weibull clutter. IEEE Transactions on Aerospace and Electronic Systems 1976, 12(6):735743.Google Scholar
 Limpert E, Stahel WA, Abbt M: Lognormal distributions across the sciences: keys and clues. BioScience 2001, 51(5):341352. 10.1641/00063568(2001)051[0341:LNDATS]2.0.CO;2View ArticleGoogle Scholar
 Weibull W: A statistical distribution function of wide applicability. Journal of Applied Mechanics 1951, 18(3):293297.MATHGoogle Scholar
 Boothe RR: The Weibull distribution applied to the ground clutter backscatter coefficient. U.S. Army Missile Command; 1969.Google Scholar
 Sekine M, Ohtani S, Musha T, et al.: Weibulldistributed ground clutter. IEEE Transactions on Aerospace and Electronic Systems 1981, 17(4):596598.View ArticleGoogle Scholar
 Fay FA, Clarke J, Peters RS: Weibull distributed applied to sea clutter. Proceedings of the International Radar Conference (Radar '77), October 1977 155: 101104.Google Scholar
 Sekine M, Musha T, Tomita Y, Hagisawa T, Irabu T, Kiuchi E: Weibulldistributed sea clutter. IEE Proceedings F 1983, 130(5):476.Google Scholar
 Sekine M, Musha T, Tomita Y, Hagisawa T, Irabu T, Kiuchi E: On Weibulldistributed weather clutter. IEEE Transactions on Aerospace and Electronic Systems 1979, 15(6):824830.View ArticleGoogle Scholar
 Tsihrintzis GA, Nikias CL: Evaluation of fractional, lowerorder statisticsbased detection algorithms on real radar seaclutter data. IEE Proceedings: Radar, Sonar and Navigation 1997, 144(1):2937. 10.1049/iprsn:19970933Google Scholar
 Henning JA: Design and performance of an ultrawideband foliage penetrating noise radar, M.S. thesis. University of Nebraska; May 2001.Google Scholar
 Devore JL: Probability and Statistics for Engineering and the Sciences. BrooksiCole, Monterey, Calif, USA; 1982.MATHGoogle Scholar
 Barkat M: Signal Deteciton and Estimation. 2nd edition. Artech House, London, UK; 2005.Google Scholar
 Richards MA: Fundamentals of Radar Signal Processing. McGrawHill, New York, NY, USA; 2005.Google Scholar
Copyright
This article is published under license to BioMed Central Ltd. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.