- Research Article
- Open Access
Biologically Inspired Target Recognition in Radar Sensor Networks
© Qilian Liang. 2010
- Received: 10 September 2009
- Accepted: 9 November 2009
- Published: 21 December 2009
One of the great mysteries of the brain is cognitive control. How can the interactions between millions of neurons result in behavior that is coordinated and appears willful and voluntary? There is consensus that it depends on the prefrontal cortex (PFC). Many PFC areas receive converging inputs from at least two sensory modalities. Inspired by human's innate ability to process and integrate information from disparate, network-based sources, we apply human-inspired information integration mechanisms to target detection in cognitive radar sensor network. Humans' information integration mechanisms have been modelled using maximum-likelihood estimation (MLE) or soft-max approaches. In this paper, we apply these two algorithms to cognitive radar sensor networks target detection. Discrete-cosine-transform (DCT) is used to process the integrated data from MLE or soft-max. We apply fuzzy logic system (FLS) to automatic target detection based on the AC power values from DCT. Simulation results show that our MLE-DCT-FLS and soft-max-DCT-FLS approaches perform very well in the radar sensor network target detection, whereas the existing 2D construction algorithm does not work in this study.
- Target Detection
- Fuzzy Logic System
- Synthetic Aperture Radar Imaging
- Radar Sensor
Humans display a remarkable capability to perform visual and auditory information integration despite noisy sensory signals and conflicting inputs. Humans are adept at network visualization, and at understanding subtle implications among the network connections. To date, however, human's innate ability to process and integrate information from disparate, network-based sources has not translated well to automated systems. Motivated by the above challenges, we apply human information integration mechnisms to cognitive radar sensor networks. A cognitive network is one that is aware of changes in user needs and its environment, adapts its behavior to those changes, learns from its adaptations, and exploits knowledge to improve its future behavior. A cognitive radar sensor network consists of multiple networked radar sensors and radar sensors sense and communicates with each other collaboratively to complete a mission. In real world, cognitive radar sensor network information integration is necessary in different applications. For example, in an emergency natural disaster scenario, such as China Wenchuan earthquake in May 2008, Utah Mine Collapse in August 2007, or West Virginia Sago mine disaster in January 2006, cognitive radar sensor network-based information integration for first responders is critical for search and rescue. Danger may appear anywhere at any time; therefore, first responders must monitor a large area continuously in order to identify potential danger and take actions. Due to the dynamic and complex nature of natural disaster, some buried/foleage victims may not be found with image/video sensors, and UWB radar sensors are needed for penetrating the ground or sense-through-wall. Unfortunately, the radar data acquired are often limited and noisy. Unlike medical imaging or synthetic aperture radar imaging where abundance of data is generally available through multiple looks and where processing time may not be crucial, practical cognitive radar sensor networks are typically the opposite: availability of data is limited and required processing time is short. This need is also motivated by the fact that humans display a remarkable capability to quickly perform target recognition despite noisy sensory signals and conflicting inputs. Humans are adept at network visualization and at understanding subtle implications among the network connections. To date, however, human's innate ability to process and integrate information from disparate, network-based sources for situational understanding has not translated well to automated systems. In this paper, we apply human information integration mechanisms to information fusion in cognitive radar sensor network.
The rest of this paper is organized as follows. In Section 2, we introduce the human information integration mechanisms and their mathematical modeling. In Section 3, we introduce the radar sensor network data collection. In Section 4, we apply the human information integration mechanisms to cognitive radar sensor network. In Section 5, we apply fuzzy logic system for target detection as a postprocessing for Section 4. In Section 6, we conclude this paper.
Recently, a maximum-likelihood estimation (MLE) approach was proposed for multisensory data fusion in human . In the MLE approach , sensory estimates of an environmental property can be represented by where is the physical property being estimated, is the operation the nervous system performs to derive the estimate, and is the perceptual estimate. Sensory estimates are subject to two types of error: random measurement error and bias. Thus, estimates of the same object property from different cues usually differ. To reconcile the discrepancy, the nervous system must either combine estimates or choose one, thereby ignoring the other cues. Assuming that each single-cue estimate is unbiased but corrupted by independent Gaussian noise, the statistically optimal strategy for cue combination is a weighted average :
where and is the weight given to the th single-cue estimate, is that estimates variance, and is the total number of cues. Combining estimates by this MLE rule yields the least variable estimate of and thus more precise estimates of object properties.
Besides, some other summation rules have been proposed in perception and cognition such as soft-max rule:  where denotes the input from an input source , and is the total number of sources. In this paper, we will apply MLE and soft-max human brain information integration mechanisms to cognitive radar sensor network information integration.
conserving radio resources such as bandwidth;
promoting spatial code reuse and frequency reuse;
simplifying the topology, for example, when a mobile radar changes its location, it is sufficient for only the nodes in attended clusters to update their topology information;
reducing the generation and propagation of routing information;
concealing the details of global network topology from individual nodes.
We made the above observations. However, in real world application, automatic target detection is necessary to ensure that our algorithms could be performed in real time. In Section 5, we apply fuzzy logic systems to automatic target detection based on the power of AC values (obtained via MLE-DCT or soft-max-DCT).
5.1. Overview of Fuzzy Logic Systems
where and both indicate the chosen -norm. There are many kinds of defuzzifiers. In this paper, we focus, for illustrative purposes, on the center-of-sets defuzzifier . It computes a crisp output for the FLS by first computing the centroid, , of every consequent set , and then computing a weighted average of these centroids. The weight corresponding to the th rule consequent centroid is the degree of firing associated with the th rule, , so that
5.2. FLS for Automatic Target Detection
Observe that, in Figures 7 and 8, the power of AC values are quite fluctuating and have lots of uncertainties. FLS is well known to handle the uncertainties. For convenience in describing the FLS design for Automatic Target Detection (ATD), we first give the definition of footprint of uncertainty of AC power values and region of interest in the footprint of uncertainty.
Definition 1 (Footprint of Uncertainty).
Uncertainty in the AC power values and time index consists of a bounded region, that we call the footprint of uncertainty of AC power values. It is the union of all AC power values.
Definition 2 (Region of Interest (RoI)).
An RoI in the footprint of uncertainty is a contour consisting of a large number (greater than 50) of AC power values where AC power values increase and then decrease.
Definition 3 (Fluctuating Point in RoI).
Our FLS for automatic target detection will classify each ROI (with target or no target) based on two antecedents: the centroid of the ROI and the number of fluctuating points in the ROI. The linguistic variables used to represent these two antecedents were divided into three levels: low, moderate, and high. The consequent—the possibility that there is a target at this RoI—was divided into 5 levels, Very Strong, Strong, Medium, Weak, andVery Weak. We used trapezoidal membership functions (MFs) to represent low, high, very strong, and very weak and triangle MFs to represent moderate, strong, medium, and weak. All inputs to the antecedents are normalized to 0–10.
Based on the fact the AC power value of target is nonfluctuating (somehow monotonically increase then decrease), and the AC power value of clutter behaves like random noise because generally the clutter has Gaussian distribution in the frequency domain, we design a fuzzy logic system using rules such as
The rules for target detection. Antecedent 1 is centroid of a RoI, Antecedent 2 is the number of fluctuating points in the ROI, and Consequent is the possibility that there is a target at this RoI.
We ran simulations to 1000 collections in the real world sense-through-foliage experiment and found that our FLS performs very well in the automatic target detection based on the AC power values obtained from MLE-DCT or soft-max-DCT and achieve probability of detection and false alarm rate .
Inspired by human's innate ability to process and integrate information from disparate, network-based sources, we applied human-inspired information integration mechanisms to target detection in cognitive radar sensor network. Humans' information integration mechanisms have been modelled using maximum-likelihood estimation (MLE) or soft-max approaches. In this paper, we applied these two algorithms to cognitive radar sensor networks target detection. Discrete-cosine-transform (DCT) was used to process the integrated data from MLE or soft-max. We applied fuzzy logic system (FLS) to automatic target detection based on the AC power values from DCT. Simulation results showed that our MLE-DCT-FLS and soft-max-DCT-FLS approaches performed very well in the radar sensor network target detection, whereas the existing 2-D construction algorithm could not work in this study.
The author would like to thank Dr. Sherwood W. Samn in AFRL/RHX for providing the radar data. This work was supported in part by the U.S. Office of Naval Research (ONR) under Grants N00014-07-1-0395, N00014-07-1-1024, and N00014-03-1-0466 and the National Science Foundation (NSF) under Grants CNS-0721515, CNS-0831902, and CCF-0956438. Some material in this paper has been presented at International Conference on Wireless Algorithms, Systems, and Applications, August 2009, Boston, MA.
- Miller EK, Cohen JD: An integrative theory of prefrontal cortex function. Annual Review of Neuroscience 2001, 24: 167-202. 10.1146/annurev.neuro.24.1.167View ArticleGoogle Scholar
- O'Reilly RC: Biologically based computational models of high-level cognition. Science 2006, 314(5796):91-94. 10.1126/science.1127242MathSciNetView ArticleMATHGoogle Scholar
- Chavis DA, Pandya DN: Further observations on cortico-frontal connections in the rhesus monkey. Brain Research 1976, 117: 369-386. 10.1016/0006-8993(76)90089-5View ArticleGoogle Scholar
- Jones EG, Powell TP: An anatomical study of converging sensory pathways within the cerebral cortex of the monkey. Brain 1970, 93(4):793-820. 10.1093/brain/93.4.793View ArticleGoogle Scholar
- Bruce C, Desimone R, Gross CG: Visual properties of neurons in a polysensory area in superior temporal sulcus of the macaque. Journal of Neurophysiology 1981, 46(2):369-384.Google Scholar
- Pandya DN, Barnes C: Architecture and connections of the frontal lobe. In The Frontal Lobes Revisited. Edited by: Perecman E. IRBN Press, New York, NY, USA; 1987:41-72.Google Scholar
- Hillis JM, Ernst MO, Banks MS, Landy MS: Combining sensory information: mandatory fusion within, but not between, senses. Science 2002, 298(5598):1627-1630. 10.1126/science.1075396View ArticleGoogle Scholar
- Pelli DG: Uncertainty explains many aspects of visual contrast detection and discrimination. Journal of the Optical Society of America A 1985, 2(9):1508-1532. 10.1364/JOSAA.2.001508View ArticleGoogle Scholar
- Dosher BA, Sperling G, Wurst SA: Tradeoffs between stereopsis and proximity luminance covariance as determinants of perceived 3D structure. Vision Research 1986, 26(6):973-990. 10.1016/0042-6989(86)90154-9View ArticleGoogle Scholar
- Graham NVS: Visual Pattern Analyzers. Oxford University Press, New York, NY, USA; 1989.View ArticleGoogle Scholar
- Lin CR, Gerla M: Adaptive clustering for mobile wireless networks. IEEE Journal on Selected Areas in Communications 1997, 15(7):1265-1275. 10.1109/49.622910View ArticleGoogle Scholar
- Iwata A, Chiang C-C, Pei G, Gerla M, Chen T-W: Scalable routing strategies for ad hoc wireless networks. IEEE Journal on Selected Areas in Communications 1999, 17(8):1369-1379. 10.1109/49.779920View ArticleGoogle Scholar
- Hou T-C, Tsai T-J: An access-based clustering protocol for multihop wireless ad hoc networks. IEEE Journal on Selected Areas in Communications 2001, 19(7):1201-1210. 10.1109/49.932689View ArticleGoogle Scholar
- Perkins CE: Cluster-based networks. In Ad Hoc Networking. Edited by: Perkins CE. Addison-Wesley, Reading, Mass, USA; 2001:75-138.Google Scholar
- NET-SENTRIC surveillance ONR BAA 07-017, http://www.onr.navy.mil/02/baa
- Withington P, Fluhler H, Nag S: Enhancing homeland security with advanced UWB sensors. IEEE Microwave Magazine 2003, 4(3):51-58. 10.1109/MMW.2003.1237477View ArticleGoogle Scholar
- Mendel JM: Uncertain Rule-Based Fuzzy Logic Systems. Prentice-Hall, Upper Saddle River, NJ, USA; 2001.MATHGoogle Scholar
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.