Showing papers in "IEEE Transactions on Aerospace and Electronic Systems in 2010"

Source Localization and Sensing: A Nonparametric Iterative Adaptive Approach Based on Weighted Least Squares

Abstract: Array processing is widely used in sensing applications for estimating the locations and waveforms of the sources in a given field. In the absence of a large number of snapshots, which is the case in numerous practical applications, such as underwater array processing, it becomes challenging to estimate the source parameters accurately. This paper presents a nonparametric and hyperparameter, free-weighted, least squares-based iterative adaptive approach for amplitude and phase estimation (IAA-APES) in array processing. IAA-APES can work well with few snapshots (even one), uncorrelated, partially correlated, and coherent sources, and arbitrary array geometries. IAA-APES is extended to give sparse results via a model-order selection tool, the Bayesian information criterion (BIC). Moreover, it is shown that further improvements in resolution and accuracy can be achieved by applying the parametric relaxation-based cyclic approach (RELAX) to refine the IAA-APES&BIC estimates if desired. IAA-APES can also be applied to active sensing applications, including single-input single-output (SISO) radar/sonar range-Doppler imaging and multi-input single-output (MISO) channel estimation for communications. Simulation results are presented to evaluate the performance of IAA-APES for all of these applications, and IAA-APES is shown to outperform a number of existing approaches.

Fully Automatic Computation of Diagonal Loading Levels for Robust Adaptive Beamforming

Abstract: The main drawback of the conventional diagonal loading (DL) approaches is that there is no clear guideline on how to choose the DL level reliably or how to select user parameters appropriately. An algorithm that can be used to compute the DL level completely automatically from the given data without the need of specifying any user parameter is considered. In this algorithm an enhanced covariance matrix estimate obtained via a shrinkage method, instead of the sample covariance matrix, is used in the standard Capon beamforming formulation. The performance of the resulting beamformer is illustrated via numerical examples, and it is compared with several other adaptive beamformers.

MIMO Radar Moving Target Detection in Homogeneous Clutter

Abstract: A multiple-input multiple-output (MIMO) radar approach employing widely dispersed transmit and receive antennas is studied for the detection of moving targets. The MIMO radar transmits orthogonal waveforms from the different transmit antennas so these waveforms can be separated at each receive antenna. For a moving target in colored Gaussian noise-plus-clutter, we quantify the gains from having widely dispersed antennas that allow the overall system to "view" the target simultaneously from several different directions. The MIMO radar performance is contrasted with that of a traditional phased-array approach, which employs closely spaced antennas for this purpose. The MIMO radar approach is well suited to handle targets that have small radial velocities for scenarios in which colocated sensors cannot separate the target from the background clutter. Both a centralized processing and a simple distributed processing form of the MIMO radar approach are developed and studied, and the gains from the centralized version, which come at the price of additional complexity, are clearly demonstrated and explained intuitively. The constant false alarm rate (CFAR) property of an adaptive version of the MIMO moving target detector is also demonstrated for homogeneous clutter.

Intrapulse Radar-Embedded Communications

Abstract: The embedding of a covert communication signal amongst the ambient scattering from an incident radar pulse has previously been achieved by modulating a Doppler-like phase shift sequence over numerous pulses (i.e., on an inter-pulse basis). In contrast, this paper considers radar-embedded communications on an intrapulse basis whereby an incident radar waveform is converted into one of K communication waveforms, each of which acts as a communication symbol representing some predetermined information (e.g., a bit sequence). To preserve a low intercept probability, this manner of radar-embedded communications necessitates prudent selection of the set of communication waveforms as well as interference cancellation on receive. A general mathematical model and subsequent optimization problem is established for the design of the communication waveforms, from which three design strategies are developed. Also, receiver design issues are discussed, and an interference-canceling maximum likelihood receiver is presented. Performance results are presented in terms of the communication symbol error rate as well as a correlation-based metric from which intercept probability can be inferred. It is demonstrated that, given persistent radar illumination with a pulse repetition frequency (PRF) of 1-2 kHz, intrapulse radar-embedded communications can theoretically achieve data-rates commensurate with speech coding (for the interval of the radar dwell time) with the potential for even higher data-rates if additional diversity is appropriately incorporated.

Survey of Maneuvering Target Tracking. Part II: Motion Models of Ballistic and Space Targets

Abstract: This paper is the second part in a series that provides a comprehensive survey of maneuvering target tracking without addressing the so-called measurement-origin uncertainty. It surveys motion models of ballistic targets used for target tracking. Models for all three phases (i.e., boost, coast, and reentry) of motion are covered.

Closed-Form Expression for the Poisson-Binomial Probability Density Function

Abstract: The Poisson-binomial probability density function (pdf) describes the numbers of successes in N independent trials, when the individual probabilities of success vary across trials. Its use is pervasive in applications, such as fault tolerance, signal detection, target tracking, object classification/identification, multi-sensor data fusion, system management, and performance characterization, among others. We present a closed-form expression for this pdf, and we discuss several of its advantages regarding computing speed and implementation and in simplifying analysis, with examples of the latter including the computation of moments and the development of new trigonometric identities for the binomial coefficient and the binomial cumulative distribution function (cdf). Finally we also pose and address the inverse Poisson-binomial problem; that is, given such pdf, how to find (within a permutation) the probabilities of success of the individual trials.

Modeling the Effects of Ionospheric Scintillation on GPS Carrier Phase Tracking

Abstract: A characterization is given for the behavior of Global Positioning System phase tracking loops in the presence of severe equatorial ionospheric scintillation. The purpose of this work is to develop a simple, general, and realistic scintillation effects model that can be used to improve the scintillation performance of phase tracking loops. The new characterization of scintillation effects proposed herein employs a differentially detected bit error model to predict cycle slipping rates that approximately agree with data-driven simulation tests.

Canonical Scattering Feature Models for 3D and Bistatic SAR

Abstract: This paper develops three-dimensional (3D), bistatic parametric models that describe canonical radar scattering responses of several geometric objects. These models find use in inverse scattering-based processing of high-frequency radar returns. Canonical feature models are useful for extracting geometry from synthetic-aperture radar (SAR) scattering measurements and as feature primitives for automatic target recognition (ATR) and scene visualization. Previous work has considered monostatic feature models for two-dimensional (2D) radar processing; we extend this work to consider bistatic and 3D radar apertures. In the work presented here, we generalize geometric theory of diffraction (GTD) solutions for several scattering mechanisms in a plane. Products of these planar mechanisms in azimuth and elevation are used to produce 3D bistatic scattering models for six canonical shapes: a rectangular plate, dihedral, trihedral, cylinder, top-hat, and sphere. The derived models are characterized by a small number of parameters, and are shown to agree with results obtained from high-frequency, asymptotic scattering simulations.

Micro-Doppler Signature Extraction from Ballistic Target with Micro-Motions

Abstract: Precession and nutation of the warhead and the wobble of decoys are the typical micro-motions of ballistic targets, and micro-Doppler analysis is a new way to investigate micro-motions. Based on the difference of micro-motions between the warhead and decoys, micro-Doppler signatures might be extracted for radar target identification, which is seen as a good method and a technique of great potential. We build micro-motion models of a ballistic missile target, including precession, nutation, and wobble; develop a novel model of micro-Doppler modulations based on the proposed concept of the micro-motion matrix; derive the formulas of micro-Doppler induced by the three micro-motions; and verify them by simulation studies. In order to further approach the actual case, the effective point scatterer model and the occlusion effect are considered in micro-Doppler, and the simulated results are shown compared with ones under the fixed point scatterer model and without the occlusion effect. In addition the precession experiment is performed in a microwave chamber, and the measured result is in accordance with the simulated result and the computed result.

Integrated Cubic Phase Function for Linear FM Signal Analysis

Abstract: In this paper, an integrated cubic phase function (ICPF) is introduced for the estimation and detection of linear frequency-modulated (LFM) signals. The ICPF extends the standard cubic phase function (CPF) to handle cases involving low signal-to-noise ratio (SNR) and multi-component LFM signals. The asymptotic mean squared error (MSE) of an ICPF-based estimator as well as the output SNR of an ICPF-based detector are derived in closed form and verified by computer simulation. Comparison with several existing approaches is also included, which shows that the ICPF serves as a good candidate for LFM signal analysis.

Pseudo-Correlation-Function-Based Unambiguous Tracking Technique for Sine-BOC Signals

Abstract: The sine-BOC (binary offset carrier) modulation is used in several signals of the new European Global Navigation Satellite System, Galileo, and modernized GPS. It provides these signals with enhanced robustness against multipath and increases the precision of the range measurement. However, this modulation presents some drawbacks. The most severe is the ambiguity problem in acquisition and tracking, which introduces a large bias in the pseudo-range measurement. In order to solve this problem, an unambiguous tracking technique for sine-BOC signals is proposed. This technique is based on a pseudo correlation function (PCF) which does not have any side peak and thus completely removes all of the false lock points on the discriminator output. Impacts of thermal noise and multipath on the proposed technique are investigated. Theoretical and numerical results obtained with BOC(n,n) and BOC(2n,n) signals show that this technique has a good noise mitigation performance and an average multipath performance.

Auxiliary Particle Implementation of Probability Hypothesis Density Filter

Abstract: Optimal Bayesian multi-target filtering is, in general, computationally impractical owing to the high dimensionality of the multi-target state. The probability hypothesis density (PHD) filter propagates the first moment of the multi-target posterior distribution. While this reduces the dimensionality of the problem, the PHD filter still involves intractable integrals in many cases of interest. Several authors have proposed sequential Monte Carlo (SMC) implementations of the PHD filter. However these implementations are the equivalent of the bootstrap particle filter, and the latter is well known to be inefficient. Drawing on ideas from the auxiliary particle filter (APF), we present an SMC implementation of the PHD filter, which employs auxiliary variables to enhance its efficiency. Numerical examples are presented for two scenarios, including a challenging nonlinear observation model.

Knowledge-Aided Bayesian Radar Detectors & Their Application to Live Data

Abstract: This paper considers the problem of adaptive radar detection in Gaussian clutter with unknown spectral properties. We employ a Bayesian approach based on a suitable model for the probability density function (pdf) of the unknown clutter covariance matrix. We devise two detectors based on the generalized likelihood ratio test (GLRT) criterion both one-step and two-step. The suggested decision rules achieve the same performance as the non-Bayesian GLRT detectors when the size of the training set is sufficiently large. However, our new detectors significantly outperform their non-Bayesian counterparts when the training set is small. The analysis is also supported by results on real L-band clutter data from the MIT Lincoln Laboratory phase one radar and on high fidelity radar data from the knowledge-aided sensor signal processing and expert reasoning (KASSPER) program.

RAIM with Optimal Integrity and Continuity Allocations Under Multiple Failures

Abstract: Among the receiver autonomous integrity monitoring (RAIM) algorithms treating multiple failures, multiple hypothesis solution separation algorithms (MHSS) - a type of solution separation algorithm - offer several advantages: First, the link between threat model, upper bound on the position error - the protection level and probability of hazardously misleading information is an easy and straightforward one; second, the calculation of the protection level does not involve complex steps. One of the critical steps in this algorithm is the allocation of the integrity and continuity budgets among the failure modes, as it determines the overall performance of the algorithm. After describing the baseline MHSS approach, we present an algorithm that simultaneously allocates the integrity and continuity budget among the failure modes to obtain the minimum protection level per satellite geometry. Then, we show how slope-based RAIM and solution separation RAIM are related through a little-known formula, which both unifies and highlights the differences between the two approaches. Finally, we apply the algorithm to evaluate the performance of RAIM for vertical guidance for a dual constellation, and find that even with a very large prior probability of satellite failure, vertical guidance can be achieved worldwide with high availability.

Joint Data Association, Registration, and Fusion using EM-KF

Abstract: In performing surveillance using a sensor network, data association and registration are two essential processes which associate data from different sensors and align them in a common coordinate system. While these two processes are usually addressed separately, they actually affect each other. That is, registration requires correctly associated data, and data with sensor biases will result in wrong association. We present a novel joint sensor association, registration, and fusion approach for multisensor surveillance. In order to perform registration and association together, the expectation-maximization (EM) algorithm is incorporated with the Kalman filter (KF) to give simultaneous state and parameter estimates. Computer simulations are carried out to evaluate the performances of the proposed joint association, registration, and fusion method based on EM-KF.

Vision-Based Estimation for Guidance, Navigation, and Control of an Aerial Vehicle

Abstract: While a Global Positioning System (GPS) is the most widely used sensor modality for aircraft navigation, researchers have been motivated to investigate other navigational sensor modalities because of the desire to operate in GPS denied environments. Due to advances in computer vision and control theory, monocular camera systems have received growing interest as an alternative/collaborative sensor to GPS systems. Cameras can act as navigational sensors by detecting and tracking feature points in an image. Current methods have a limited ability to relate feature points as they enter and leave the camera field of view (FOV). A vision-based position and orientation estimation method for aircraft navigation and control is described. This estimation method accounts for a limited camera FOV by releasing tracked features that are about to leave the FOV and tracking new features. At each time instant that new features are selected for tracking, the previous pose estimate is updated. The vision-based estimation scheme can provide input directly to the vehicle guidance system and autopilot. Simulations are performed wherein the vision-based pose estimation is integrated with a nonlinear flight model of an aircraft. Experimental verification of the pose estimation is performed using the modelled aircraft.

Maneuvering Target Tracking in the Presence of Glint using the Nonlinear Gaussian Mixture Kalman Filter

Abstract: The problem of maneuvering target tracking in the presence of glint noise is addressed in this work. The main challenge in this problem stems from its nonlinearity and non-Gaussianity. A new estimator, named as nonlinear Gaussian mixture Kalman filter (NL-GMKF) is derived based on the minimum-mean-square error (MMSE) criterion and applied to the problem of maneuvering target tracking in the presence of glint. The tracking performance of the NL-GMKF is evaluated and compared with the interacting multiple modeling (IMM) implemented with extended Kalman filter (EKF), unscented Kalman filter (UKF), particle filter (PF) and the Gaussian sum PF (GSPF). It is shown that the NL-GMKF outperforms these algorithms in several examples with maneuvering target and/or glint noise measurements.

Data-Driven Testbed for Evaluating GPS Carrier Tracking Loops in Ionospheric Scintillation

Abstract: A large set of equatorial ionospheric scintillation data has been compiled, used to characterize features of severe scintillation that impact Global Positioning System phase tracking, and used to develop a scintillation testbed for evaluating tracking loops. The data-driven testbed provides researchers a tool for studying, and the receiver developers a tool for testing, the behavior of carrier tracking loops under realistic scintillation conditions. It is known that severe equatorial scintillation causes cycle slipping and, in the worst cases, complete loss of carrier lock. Testbed results indicate that cycle slips are primarily caused by the abrupt, near half-cycle phase changes that occur during the deep power fades of severe equatorial scintillation. For a class of standard tracking loops, parameter values that minimize scintillation-induced cycle slipping are identified.

Estimation Fusion with General Asynchronous Multi-Rate Sensors

Abstract: The asynchronous estimation fusion problem is investigated for an arbitrary number of sensors with arbitrary sampling rates. By constructing an augmented measurement equation at the fusion time instant, a centralized asynchronous fusion algorithm is developed based on the Kalman filter first without ignoring the correlation between the process noise and the augmented measurement noise. It is optimal in the minimum mean-squared error (MMSE) sense. A distributed asynchronous fusion algorithm is then proposed by reconstructing the optimal centralized fusion result with asynchronous local estimates and their error covariance matrices. It is equivalent to the centralized fusion algorithm under the full-rate communication assumption and outperforms the latter when at least one sensor communicates with the fusion center at a lower rate than its sampling rate. Compared with the existing distributed fusion algorithms for asynchronous sensors, the proposed distributed fusion algorithm avoids the complicated calculation of cross-covariance matrices between each pair of asynchronous local estimates. The communication burden can also be reduced since neither sensor measurement matrices nor local filtering gains need to be transmitted to the fusion center. Moreover all available local estimates are utilized as well as the fused one-step prediction. Some practical considerations of the proposed distributed fusion algorithm are also discussed. Performance of the proposed centralized and distributed fusion algorithms are illustrated through numerical simulations.

Ultrawideband Random Noise Radar Design for Through-Wall Surveillance

Abstract: We have developed an ultrawideband (UWB) random noise radar for through-wall surveillance applications. The operating frequency is in the ultrahigh frequency range, and the entire system is built around the concept of software defined radio. The radar receiver performance is statistically evaluated using both simulation studies and actual measurement results. We also discuss the phenomena of interference level and radar cross section (RCS) of the human target using the receiver operating characteristics (ROC). Experimental results presented show that random noise radars are useful for detecting and tracking humans obscured by building walls.

SAR Imaging with Noise Radar

Abstract: Two ground-based coherent imaging systems designed on the basis of noise radar technology (NRT) using continuous waveform with a linear synthetic aperture and pulse coherent noise waveform with a circular synthetic aperture are presented. A short description of noise waveform synthetic aperture radar (SAR) theory and data processing algorithms are given. Experimental results obtained show that the quality of the imagery is comparable to that of frequency-modulation continuous-wave (FMCW) or stepped frequency radar systems. Experimental validation of the technique at different locations provides confidence in the ability of noise waveform SAR to achieve high-resolution imaging at low cost. The exploitation of the interferometric phase combining pairs of images spaced in time is shown to allow the detection of displacements in the scene with a submillimeter accuracy.

Novel Range-Spread Target Detectors in Non-Gaussian Clutter

Abstract: Range-spread target detection in spherically invariant random vector clutter is addressed, and different detectors with constant false alarm rate (CFAR) property are devised by exploiting order statistics theory. Firstly, with a known normalized clutter covariance matrix, the generalized likelihood ratio test based on order statistics (OS-GLRT) utilizes some largest observations from the range cells occupied by the most likely target scatterers. OS-GLRT is robust when the estimated number of scatterers is somewhat larger than the actual, but is degraded for smaller estimations. To improve the robustness of OS-GLRT, an OS-GLRT with dynamic threshold (DOS-GLRT) is designed, which adjusts detection threshold dynamically. By replacing the ideal normalized clutter covariance matrix with the constrained approximate maximum likelihood (ML) estimated matrix based on secondary data only, the adaptive OS-GLRT and adaptive DOS-GLRT are also obtained. The performance assessment conducted by Monte Carlo simulation confirms the effectiveness of the proposed detectors.

GNSS Acquisition in the Presence of Continuous Wave Interference

Abstract: The extreme weakness of a global navigation satellite system (GNSS) signal makes it vulnerable to a wide variety of interfering signals, falling within the GNSS frequency bands. One of the main classes of these disturbing signals is represented by continuous wave interference (CWI), which include all signals that can be effectively represented as pure sinusoids. This paper deals with the performance of GNSS signal acquisition in the presence of CWI. An original model describing the impact of CWI on signal acquisition is proposed and the expressions of both false alarm and detection probabilities are mathematically formulated and validated by Monte Carlo simulations. The paper also investigates the role of the interference amplitude and frequency along with the impact of different system parameters on the detection and false alarm probabilities.

MMSE-Based Filtering in Presence of Non-Gaussian System and Measurement Noise

Abstract: The problem of sequential Bayesian estimation in linear non-Gaussian problems is addressed. In the Gaussian sum filter (GSF), the non-Gaussian system noise, the measurement noise, and the posterior state densities are modeled by the Gaussian mixture model (GMM). The GSF is optimal under the minimum-mean-square error (MMSE) criterion, however it is impractical due to the exponential model order growth of the system probability density function (pdf). The proposed recursive estimator, named the Gaussian mixture Kalman filter (GMKF), combines the GSF and the model order reduction procedure. The posterior state density at each iteration is approximated by a lower order density. This model order reduction procedure minimizes the estimated Kullback-Leibler divergence (KLD) of the reduced order density from the original density at each step. The estimation performance of the proposed GMKF is compared with the interactive multiple modeling (IMM), particle filter (PF), Gaussian sum PF (GSPF), and the GSF with mixture reduction (MR) method via simulations. It is shown in several examples that the proposed GMKF outperforms the other tested algorithms in terms of estimation accuracy. The superior estimation performance of the GMKF is obtained at the expense of its computational complexity, which is higher than the IMM and the MR algorithms.

On the Convex Parameterization of Constrained Spacecraft Reorientation

Abstract: Guiding the rotational motion of a spacecraft subject to constraints on its permissible orientation often leads to nonconvex optimal control problems. In this paper, we consider convex parameterizations of sets associated with the constrained rigid body orientations. We then elaborate on ramifications of such a parameterization in the development of steering laws for autonomous spacecraft reorientation that are based on convex optimization algorithms. Such problems appear in almost every space science mission equipped with heat- or light-sensitive instruments, e.g., cryogenically cooled infrared telescopes, star trackers, and low-energy ion composition analyzers. An example, demonstrating the viability of the proposed algorithm for constrained multiple spacecraft reorientations concludes the work presented here.

Radar Micro-Doppler Signature Classification using Dynamic Time Warping

Abstract: This paper describes the first feasibility study using dynamic time warping (DTW) to classify the micro-Doppler signature (μ-DS ) for radar automatic target recognition (ATR). Real radar data has been used in the testing, and the performance of the DTW classifier has been benchmarked against the conventional k-nearest neighbour (k-NN) algorithm. The basic theory behind the μ-DS is introduced, and aspects of the phenomenon that could cause difficulties for classifiers are highlighted. We explain how DTW can cope with these difficulties and achieve successful classification of three target classes. A correct classification rate exceeding 0.8 has been achieved, leading to the conclusion that this technique shows considerable promise for application in radar ATR systems.

Impact of Sea Clutter Nonstationarity on Disturbance Covariance Matrix Estimation and CFAR Detector Performance

Abstract: Adaptive detection of signals embedded in non-Gaussian clutter is an important challenge for radar engineers. We present an analysis of sea clutter nonstationarity with respect to clutter covariance matrix estimation and its impact on the constant false alarm rate (CFAR) property of the normalized adaptive matched filter (NAMF). Three covariance matrix estimators, e.g., the sample covariance matrix (SCM), the normalized sample covariance matrix (NSCM), and the approximate maximum likelihood (AML) estimators, have been investigated. The impact of nonstationarity, which emerges in the statistical analysis of the clutter data, is measured in terms of probability of false alarm and probability of detection. Performance analysis is presented using both simulated data and measured sea clutter data recorded by two different X-band radars, namely, the Fynmeet radar and the IPIX radar.

GMTI Tracking via the Gaussian Mixture Cardinalized Probability Hypothesis Density Filter

Abstract: The cardinalized probability hypothesis density (CPHD) filter is a recursive Bayesian algorithm for estimating multiple target states with a varying target number in clutter. In particular the Gaussian mixture variant (GMCPHD), which provides closed-form prediction and update equations for the filter in the case of linear Gaussian systems, is a candidate for real time multi-target tracking. The following three issues are addressed. First we show the equivalence between the GMCPHD filter and the standard multi hypothesis tracker (MHT) in the case of a single target. Second by using a Gaussian sum approach, we extend the GMCPHD filter to incorporate digital road maps for road constrained targets. The use of such external information leads to more precise tracks and faster and more reliable target number estimates. Third we model the effect of Doppler blindness by a target state-dependent detection probability, which leads to a more stable target-number estimation in the case of low-Doppler targets.

Development of a Hand-Launched Small UAV for Ground Reconnaissance

Abstract: The engineering design of a hand-launched, small unmanned aerial vehicle (SUAV), including guidance strategy and control design, together with real data from practical flight tests, is presented. The main goal in this work is the implementation of a low cost, portable, and reliable aerial platform for ground reconnaissance. The vehicle was specially designed so that the number of necessary sensors and actuators was reduced, without precluding the feasibility of the assigned mission.

Optimality of Proportional Navigation Based on Nonlinear Formulation

Abstract: The proportional navigation (PN) guidance law is derived for an ideal pursuer with a stationary target, by obtaining an optimal feedback solution minimizing a performance index of the range-weighted control energy. Without any linearization of system equations, the optimal PN with an arbitrary navigation constant is obtained. Thus the exact analyses on optimality of PN based on nonlinear formulation is presented here.