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Computational Advances in Multi-Sensor Adaptive Processing, 2005 1st IEEE International Workshop on

Date 13-15 Dec. 2005

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Displaying Results 1 - 25 of 63
  • Author index

    Page(s): 0_1 - 0_3
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    Freely Available from IEEE
  • 1st IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (IEEE Cat. No.05EX1140C)

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  • Cognitive radar networks

    Page(s): 1 - 3
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    In my previous publication, I described, for the first time, the novel idea of cognitive radar, its attributes and potential applications. This article expands on one of the applications described therein - namely, cognitive radar networks. After briefly describing the constitution of this new radar system, we focus on the specific application of homeland security, for which a cognitive radar network is rather well suited. View full abstract»

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  • Multi-dimensional aperture design and analysis for SAR using the Cramer-Rao theorem

    Page(s): 4 - 7
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    We are applying the Cramer-Rao theorem to synthetic aperture radar (SAR) processing in order to establish flight paths that permit height estimation and minimize errors in reflectivity measurements. The Cramer-Rao bound (CRB) establishes a lower bound on the error variance of unbiased estimates. Error bounds are developed for multi-dimensional synthetic apertures that improve the overall performance and efficiency of monostatic, single-pass SAR missions. A computationally efficient means for the design and analysis of SAR waveforms is proposed using simulated scattering models that are limited in size. A comparison made with the error bounds for standard SAR show that estimates of scatterer range and cross-range positions are sufficiently accurate for multi-dimensional aperture SAR, even with the additional estimator for height. View full abstract»

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  • Structured covariance estimation and radar imaging with sparse linear models

    Page(s): 8 - 11
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    The problem of the computational complexity of the structure covariance EM algorithm is considered. Ordinarily this algorithm requires O(N3) floating point operations, per iteration, for the estimation of an N-point power spectrum. However, if the linear model relating the observations to the underlying variables is sparse, the computational burden can be reduced to O(N) operations. This sparsity can be achieved approximately by a data preprocessing step that causes the effect of each underlying variable to be seen in only one component of the preprocessed observation vectors. An illustrative example involving a rotating linear array as the sensor and a Chebyshev filter bank as the preprocessor is given. View full abstract»

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  • Two-set expected-likelihood GLRT technique for adaptive detection

    Page(s): 12 - 15
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    We introduce a new generalized likelihood-ratio test (GLRT) framework for adaptive detection that differs from Kelly's standard method (E.J. Kelly, 1986) in two main aspects. First, the separate functions of the primary and secondary data are respected, with a single set of interference estimates for both hypotheses being searched to optimize the detection performance. Second, instead of the traditional maximum likelihood (ML) principle, we propose to search for a set of estimates that generates statistically the same likelihood as the unknown true parameters. We present results for a typical example scenario that demonstrates considerable detection performance improvement. View full abstract»

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  • Autonomous intelligent radar system (AIRS) for multi-sensor radars

    Page(s): 16 - 19
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    An autonomous intelligent radar system (AIRS) deployed on a surveillance aircraft is briefly described. A net-centric compliant approach for integrating AIRS is presented. An overview of unmanned autonomous air vehicle research is provided along with a discussion of some of the issues with integrating AIRS aboard these vehicles. View full abstract»

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  • Target height estimation in an emulated bistatic radar via interferometric processing

    Page(s): 20 - 23
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    This paper investigates the application of emulated bistatic radar geometries to interferometric processing techniques. This is carried out in order to allow an estimation of a target's height without the complexity associated with standard interferometric processing systems. In this paper the interferometric technique is reformulated to incorporate an emulated bistatic radar configuration. Simulation results with error analysis are also provided. View full abstract»

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  • A beamforming approach to stepped-frequency synthetic aperture through-the-wall radar imaging

    Page(s): 24 - 27
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    A data-adaptive stepped-frequency synthetic aperture radar system based on quadratically constrained Capon beamforming is presented for through-the-wall wideband microwave imaging applications. Various effects of the presence of the wall, such as refraction, change in speed, and attenuation, are incorporated into the beamformer design. Proof of concept is provided using real data collected in a laboratory environment. The results show that the proposed Capon beamformer outperforms the non-adaptive through-wall delay-and-sum beamformer. View full abstract»

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  • A method for combining focused monostatic and bistatic GPR to reduce multipath effects

    Page(s): 28 - 31
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    Imaging of buried objects using subsurface microwave technology can result in images with numerous undesirable artifacts due in part to noise and multipath scattering. In order to alleviate the problem of multipath scattering, the authors propose the combined use of monostatic and bistatic systems. Focusing both images and compensating the bistatic system enables us to place the direct path scatterers at the same position as in the monostatic case. A multiplication of the final images will attenuate the scatterers that are formed by multiple reflections and will therefore reduce artifacts. Results are shown using simulations in which the signatures of several point scatterers overlap for the direct reflections and where the multipath signatures do not; thus allowing the multiplication to enhance the final image. View full abstract»

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  • Correlative interferometric imaging of extended objects for near field arrays

    Page(s): 32 - 35
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    High resolution imaging of objects in the Terahertz region of the electromagnetic spectrum requires operating in the near field region. Correlative interferometric approaches based on mathematical expressions for near field measurements are developed for reconstructing images of 1-D extended objects. The approach relies on providing constrained least squares fit between computed autocorrelation from sensor measurements and the expression for the near field autocorrelation. View full abstract»

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  • Ultra narrow band adaptive tomographic radar

    Page(s): 36 - 39
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    This paper addresses the issue of spatial diversity in radar applications. Typically, information concerning ground and air targets is obtained via monostatic radar. Increased information is often equated with increased bandwidth in these radar systems. However, geometric diversity obtained through multistatic radar operations also affords the user the opportunity to obtain additional information concerning threat targets. With the appropriate signal processing, this translates directly into increased probability of detection and reduced probability of false alarm. In the extreme case, only discrete ultra narrow band (UNB) frequencies of operation may be available for both commercial and military applications. With limited spectrum, UNB in the limiting case, the need for geometric diversity becomes imperative. This occurs because the electromagnetic spectrum available for commercial and military radar applications is continuously being eroded while the need for increased information via radio frequency (RF) detection of threat targets is increasing. In addition, geometric diversity improves target position accuracy and image resolution, which would otherwise remain unavailable with monostatic radar. View full abstract»

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  • Evolution of the radar target tracking algorithms: a move towards knowledge based multi-sensor adaptive processing

    Page(s): 40 - 43
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    Though there are a no. of methods for target tracking described in literature like Kalman filtering, extended Kalman filtering, Bayesian approach, IMM-PDA, ML-PDA, particle filters, random set theory, covariance intersection, neuro-fuzzy methods, tracking through genetic algorithms and so on, the goal has always been to bring adaptivity to tackle the changing situations. Since, no one sensor can perform well in all the conditions, Multi-sensor adaptive processing has been the inherent focus. This paper presents a brief account of the target tracking algorithms developed till date and to be developed in future and brings out the main development trends. As a novel way of presentation, a Boston Consulting Group (BCG) matrix analysis has been performed and the algorithms have been classified in four classes i.e. Question marks, stars, cash cows and dogs. It has been applied to the radar target tracking algorithms. The evolution and further discussion about future trends clearly show a shift towards knowledge based adaptivity and sensor fusion. Though a number of papers have come out bringing complete account of target tracking algorithms but their presentation format does not provide a way of their practical utilization in the system development. The mathematical formulations are complex and mixing is too much for a non-expert or even a system manager to take decisions. Thus a need was felt to provide a suitable format to the decision makers and provide the non-expert a balanced simple account of the algorithms. Further, a knowledge based perspective has been brought out well in this paper. Knowledge based theme though shown in target tracking here is not limited but applies to other areas of radar, ATR, air traffic control & collision avoidance, network centric warfare etc. also. Latest knowledge based research has been incorporated in a broader sense to cover ANNs, CI, fuzzy etc. also. View full abstract»

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  • An algorithm or the neural fusion of IRST & radar for airborne target detection

    Page(s): 44 - 47
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    This paper investigates in to the possibility of using a BAM correlating encoding based neural fusion of IRST and radar at the point of the IRST's maximum range. During training phase (in peace time or at a safe place or range), intermittent appearance of a target on IRST display can be recorded in a temporal array. Corresponding intermittent appearance on radar will also be recorded on another array. Treating IRST array as horizontal array and radar array as vertical one, these two binary arrays will be made bipolar by replacing 0s with 1s and multiplied and square or rectangular arrays obtained. A large number of sets can be obtained like this representing the entire representative situations and corresponding square matrices added to form a general weight matrix. Data corresponding to the intermittent appearances of targets and other objects on radar display will be kept in the forms of binary arrays as database. In application phase, if a target is detected through the radar at the maximum range where target appears on the IRST display, radar can be switched off. IRST display will show intermittent appearances of the target, which may be difficult to track or even to discriminate from nearby bird or far off planet/star. The data collected for a number of frames for a single target's estimated intermittent appearance will be stored in an array as binary data. This binary array will be multiplied with the general weight matrix and resulting vertical matrix after thresholding represents an estimated radar data. This approximated radar binary array can be compared with stored radar representations and nearest class can be declared the class of the object present in the scene. As a further improvement, this whole experiment can be performed in a peaceful condition and the estimated radar representation obtained can be compared with exact radar representation and error calculated. Another neural model (like multilayer perceptron) can be used to provide a feedback to correct the errors in the radar estimation. The process basically works as an adaptive filter and predicts a radar array corresponding to the IRST array. The success of the algorithm depends on the training (selecting representative situations) and the implementation methods. Optical implementatio- n with optical associative memories can also be experimented for faster processing. View full abstract»

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  • Linear complex-field coding for cooperative networking

    Page(s): 48 - 51
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    Commonly used protocols involving J cooperating communicators are based on repetition encoding and achieve diversity of order J with bandwidth efficiency 1/J. We introduce a protocol capable of achieving the same diversity with bandwidth efficiency essentially equal to 1/2. The protocol is based on linear complex-field coded (LCFC) relay transmissions over orthogonal frequency division multiplexed (OFDM) subcarriers. Cooperators provide diversity by repeating delayed versions of the original packet, thereby generating a frequency-selective multipath channel. The so enabled diversity is collected by standard LCFC-OFDM decoders. Analysis and corroborating simulations establish that the novel protocol achieves diversity order equal to the number of users. View full abstract»

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  • Distributed space-time block coding for cooperative networks with multiple-antenna nodes

    Page(s): 52 - 55
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    In this paper, we present an extension to our previous work on distributed space-time block coding where we considered cooperative networks with a large set of single-antenna decode-and-forward relays N. Here, we consider the general case where each relay node is equipped with NT antennas and the destination node has NR antennas. It is assumed that at any given time only a small, a priori unknown subset of nodes S C N is active. In the proposed scheme, the signal transmitted by an active node is the product of an information-carrying space-time block code (STBC) matrix, which is identical for all nodes, and a unique node signature matrix. It is shown that existing STBCs designed for Nc ≥ 2 co-located antennas are a favorable choice for the code matrix guaranteeing a diversity order of d = min {NcNR, NTNSNR} if ns nodes are active. Simulation results show that a considerable gain can be achieved by using multiple-antenna nodes even if the antennas are highly correlated. View full abstract»

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  • Optimal rate allocation for the vector Gaussian CEO problem

    Page(s): 56 - 59
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    Consider the problem of estimating a vector source with a bandwidth constrained sensor network in which sensors make distributed observations on the source and collaborate with a fusion center (FC) to generate a final estimate. Due to power and bandwidth limitations, each sensor must compress its data and transmit to the FC only the minimum amount of information necessary to ensure the final estimate meets a given distortion bound. The optimal power allocation for the class of linear decentralized analog compression schemes was considered in Z-Q Luo et al. (2005) and proved to be NP-hard in general. In this paper, we consider the optimal rate allocation problem in the so called Berger-Tung achievable rate distortion region. In contrast to the power allocation for the linear analog compression schemes, we show that the optimal rate allocation can be formulated as a convex optimization problem which can be efficiently solved by interior point methods. Our convex reformulation technique is also applicable to the vector Gaussian multiterminal source coding problem. View full abstract»

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  • Cooperative models for synchronization, scheduling and transmission in large scale sensor networks: an overview

    Page(s): 60 - 63
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    What is the difference between classical remote sensing and sensor networks? What kind of data models that one can assume in the context of sensor networks? Can the sensors in the network concurrently contribute to the sensing objective, without creating network conflicts? It is becoming apparent that methodologies designed to resolve network resource allocation conflicts in the communications among open systems have several bottlenecks when applied to sustain networking among concurrent sensing nodes. Can we structure the network activities so that they are always directly beneficial to the sensing task? The goal of this paper is to articulate these questions and indicate how some resource allocation conflicts can be removed embracing collaborative networking approaches among the sensors. View full abstract»

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  • A two-stage fastmap-MDS approach for node localization in sensor networks

    Page(s): 64 - 67
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    Given a set of pairwise distance estimates between nodes, it is often of interest to generate a map of node locations. This is an old problem that has attracted renewed interest in the signal processing community, due to the recent emergence of wireless sensor networks and ad-hoc networks. Sensor maps are useful for estimating the spatial distribution of measured phenomena, as well as for routing purposes. Both centralized and decentralized solutions have been developed, along with ways to cope with missing data, accounting for the reliability of individual measurements, etc. We revisit the basic version of the problem, and propose a two-stage algorithm that combines algebraic initialization and gradient descent. In particular, we borrow an algebraic solution from the database literature and adapt it to the sensor network context, using a specific choice of anchor/pivot nodes. The resulting estimates are fed to gradient descent iteration. The overall algorithm offers better performance at lower complexity than existing centralized full-connectivity solutions. Also, its performance is relatively close to the corresponding Cramer-Rao bound, especially for small values of range error variance. View full abstract»

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  • A computational multi-kernel approach to multi-sensor problems using cross-referencing (CREF)

    Page(s): 68 - 70
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    This paper attempts to show how multi-sensor imagery data may be used to generate image reconstructions that are superior to any produced from individual sensors, and to provide a plausibility argument as to why this comes about. The case of two sensors will be considered for convenience and brevity. View full abstract»

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  • Dual methods for sensor testing of industrial containers. I. The classical approach

    Page(s): 71 - 73
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    An efficient approximation method is developed for computing the bending displacements of a plate with transverse shear deformation lying on an elastic foundation, typical of the materials and structures used in the manufacturing and nondestructive testing of mechanical sensors for industrial containers. View full abstract»

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  • Dual methods for sensor testing of industrial containers. II. A nonclassical approach

    Page(s): 74 - 76
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    A dual approximation method is developed for computing the displacements in bending of a plate with transverse shear deformation, in which the domain of the dual functional does not require restrictions on its elements even in the absence of an elastic foundation. This is important in the study of materials and structures used in the manufacturing and nondestructive testing of mechanical sensors for industrial containers. View full abstract»

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  • Integral methods for mechanical sensor design and performance testing in plates with transverse shear deformation and transverse normal strain

    Page(s): 77 - 80
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    A modified boundary integral equation method is used to solve a specific type of mixed boundary value problem in an enhanced theory of bending of elastic plates in which the effects of transverse shear deformation and transverse normal strain are taken into account. The boundary conditions consist of a combination of transverse displacement and bending and twisting moments. The discussion covers both interior and exterior problems, for which existence and uniqueness results are derived. This type of problem has direct application in the calibration and performance testing of mechanical sensors for large industrial structures. View full abstract»

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  • Bootstrap based nonparametric curve and confidence band estimates for spectral densities

    Page(s): 81 - 84
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    We consider the problem of global bandwidth optimisation and confidence interval estimation for spectral density estimates obtained by applying a nonparametric curve estimator to the periodogram. The use of a local quadratic regression smoother is examined as a possible way to reduce the bias inherent in classical kernel spectral density estimators, which are simply local mean regression smoothers. It is found that while quadratic smoothers are much less sensitive to a poor choice of bandwidth, they do not always outperform mean smoothers. View full abstract»

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  • Multiple target tracking with constrained motion using particle filtering methods

    Page(s): 85 - 88
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    In this paper, we propose the constrained motion proposal (COMP) algorithm that incorporates target kinematic constraint information into a particle filter to track multiple targets. We represent deterministic or stochastic constraints on target motion as a likelihood function that is incorporated into the particle filter proposal density. Using Monte Carlo simulations, we demonstrate that this approach improves tracking performance while reducing computational cost relative to the independent partition particle filter with and without a constraint likelihood function. View full abstract»

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