Showing papers in "IEEE Transactions on Aerospace and Electronic Systems in 2012"
TL;DR: A new extension of the PHD and CPHD filters, which distinguishes between the persistent and the newborn targets is presented, which enables us to adaptively design the target birth intensity at each scan using the received measurements.
Abstract: The standard formulation of the probability hypothesis density (PHD) and cardinalised PHD (CPHD) filters assumes that the target birth intensity is known a priori In situations where the targets can appear anywhere in the surveillance volume this is clearly inefficient, since the target birth intensity needs to cover the entire state space This paper presents a new extension of the PHD and CPHD filters, which distinguishes between the persistent and the newborn targets This extension enables us to adaptively design the target birth intensity at each scan using the received measurements Sequential Monte-Carlo (SMC) implementations of the resulting PHD and CPHD filters are presented and their performance studied numerically The proposed measurement-driven birth intensity improves the estimation accuracy of both the number of targets and their spatial distribution
286 citations
TL;DR: This paper compares two algorithms for three-dimensional target localization from passive radar measurements, namely spherical interpolation (SI) and spherical intersection (SX), based on closed-form equations.
Abstract: This paper compares two algorithms for three-dimensional target localization from passive radar measurements. The algorithms use bistatic range measurements from multiple transmitter-receiver pairs to calculate the target position. The algorithms derived are based on the methods known for time-difference-of-arrival (TDOA) systems, namely spherical interpolation (SI) and spherical intersection (SX). Both algorithms rely on closed-form equations. A theoretical accuracy analysis of the algorithms is provided. This analysis is verified with Monte-Carlo simulations and a real-life example is presented.
226 citations
TL;DR: A novel joint sparse representation based multi-view automatic target recognition (ATR) method, which can not only handle multi- view ATR without knowing the pose but also has the advantage of exploiting the correlations among the multiple views of the same physical target for a single joint recognition decision.
Abstract: We introduce a novel joint sparse representation based multi-view automatic target recognition (ATR) method, which can not only handle multi-view ATR without knowing the pose but also has the advantage of exploiting the correlations among the multiple views of the same physical target for a single joint recognition decision. Extensive experiments have been carried out on moving and stationary target acquisition and recognition (MSTAR) public database to evaluate the proposed method compared with several state-of-the-art methods such as linear support vector machine (SVM), kernel SVM, as well as a sparse representation based classifier (SRC). Experimental results demonstrate that the proposed joint sparse representation ATR method is very effective and performs robustly under variations such as multiple joint views, depression, azimuth angles, target articulations, as well as configurations.
211 citations
TL;DR: It is proved that if the texture of compound-Gaussian clutter is modeled by an inverse-gamma distribution, the optimum detector is the optimum Gaussian matched filter detector compared to a data-dependent threshold that varies linearly with a quadratic statistic of the data.
Abstract: This paper deals with the problem of detecting a radar target signal against correlated non-Gaussian clutter, which is modeled by the compound-Gaussian distribution. We prove that if the texture of compound-Gaussian clutter is modeled by an inverse-gamma distribution, the optimum detector is the optimum Gaussian matched filter detector compared to a data-dependent threshold that varies linearly with a quadratic statistic of the data. We call this optimum detector a linear-threshold detector (LTD). Then, we show that the compound-Gaussian model presented here varies parametrically from the Gaussian clutter model to a clutter model whose tails are evidently heavier than any K-distribution model. Moreover, we show that the generalized likelihood ratio test (GLRT), which is a popular suboptimum detector because of its constant false-alarm rate (CFAR) property, is an optimum detector for our clutter model in the limit as the tails get extremely heavy. The GLRT-LTD is tested against simulated high-resolution sea clutter data to investigate the dependence of its performance on the various clutter parameters.
208 citations
TL;DR: Comment on the errors in the formulation of Theorem 1 and give a correct formulation of theorem.
Abstract: This paper presents a Gaussian-mixture (GM) implementation of the probability hypothesis density (PHD) filter for tracking extended targets. The exact filter requires processing of all possible measurement set partitions, which is generally infeasible to implement. A method is proposed for limiting the number of considered partitions and possible alternatives are discussed. The implementation is used on simulated data and in experiments with real laser data, and the advantage of the filter is illustrated. Suitable remedies are given to handle spatially close targets and target occlusion.
197 citations
TL;DR: A flatness-based flight trajectory planning/replanning strategy is proposed for a quadrotor unmanned aerial vehicle (UAV) to drive the system from an initial position to a final one without hitting the actuator constraints while minimizing the total time of the mission or minimize the total energy spent.
Abstract: A flatness-based flight trajectory planning/replanning strategy is proposed for a quadrotor unmanned aerial vehicle (UAV). In the nominal situation (fault-free case), the objective is to drive the system from an initial position to a final one without hitting the actuator constraints while minimizing the total time of the mission or minimizing the total energy spent. When actuator faults occur, fault-tolerant control (FTC) is combined with trajectory replanning to change the reference trajectory in function of the remaining resources in the system. The approach employs differential flatness to express the control inputs to be applied in the function of the desired trajectories and formulates the trajectory planning/replanning problem as a constrained optimization problem.
179 citations
TL;DR: It is shown that not only the long-time coherent integration gain can be obtained via the proposed SBRFT, but also the computational complexity and memory cost can be reduced to the level of the conventional Doppler filter banks processing, e.g., moving target detection (MTD).
Abstract: As a generalized Doppler filter bank processing, Radon-Fourier transform (RFT) has recently been proposed for long-time coherent integration detection of radar moving targets. The likelihood ratio test (LRT) detector is derived here for rectilinearly moving targets. It is found that the proposed LRT detector has the identical form as the existing RFT detector, which means that the RFT detector is an optimal detector for rectilinearly moving targets under the white Gaussian noise background. For the fast implementations of the RFT detector, instead of the joint 2-D trajectory searching and coherent integration in pulse-range domain, the 1-D fast Fourier transform (FFT)-based frequency bin RFT (FBRFT) method is proposed in the pulse-range frequency domain without loss of integration performance. Moreover, at the cost of a controllable performance loss, a suboptimal approach called subband RFT (SBRFT) is also proposed to reduce the storage memory. It is shown that not only the long-time coherent integration gain can be obtained via the proposed SBRFT, but also the computational complexity and memory cost can be reduced to the level of the conventional Doppler filter banks processing, e.g., moving target detection (MTD). Some numerical experiments are also provided to demonstrate the effectiveness of the proposed methods.
177 citations
TL;DR: A class of subspace-based methods for direction-of-arrival (DOA) estimation and tracking in the case of uniform linear arrays (ULAs) with mutual coupling with high flexibility and effectiveness is proposed.
Abstract: A class of subspace-based methods for direction-of-arrival (DOA) estimation and tracking in the case of uniform linear arrays (ULAs) with mutual coupling is proposed. By treating the angularly-independent mutual coupling as angularly-dependent complex array gains, the middle subarray is found to have the same complex array gains. Using this property, a new way for parameterizing the steering vector is proposed and the corresponding method for joint estimation of DOAs and mutual coupling matrix (MCM) using the whole array data is derived based on subspace principle. Simulation results show that the proposed algorithm has a better performance than the conventional subarray-based method especially for weak signals. Furthermore, to achieve low computational complexity for online and time-varying DOA estimation, three subspace tracking algorithms with different arithmetic complexities and tracking abilities are developed. More precisely, by introducing a better estimate of the subspace to the conventional tracking algorithms, two modified methods, namely modified projection approximate subspace tracking (PAST) (MPAST) and modified orthonormal PAST (MOPAST), are developed for slowly changing subspace, whereas a Kalman filter with a variable number of measurements (KFVM) method for rapidly changing subspace is introduced. Simulation results demonstrate that these algorithms offer high flexibility and effectiveness for tracking DOAs in the presence of mutual coupling.
167 citations
TL;DR: It is demonstrated through simulations that the noncoherent MIMO radar provides significant tracking performance improvement over a monostatic phased array radar with high range and azimuth resolutions.
Abstract: For a noncoherent multiple-input multiple-output (MIMO) radar system, the maximum likelihood estimator (MLE) of the target location and velocity, as well as the corresponding Cramer-Rao lower bound (CRLB) matrix, is derived. MIMO radar's potential in localization and tracking performance is demonstrated by adopting simple Gaussian pulse waveforms. Due to the short duration of the Gaussian pulses, a very high localization performance can be achieved, even when the matched filter ignores the Doppler effect by matching to zero Doppler shift. This leads to significantly reduced complexities for the matched filter and the MLE. Further, two interactive signal processing and tracking algorithms, based on the Kalman filter and the particle filter (PF), respectively, are proposed for noncoherent MIMO radar target tracking. For a system with a large number of transmit/receive elements and a high signal-to-noise ratio (SNR) value, the Kalman filter (KF) is a good choice; while for a system with a small number of elements and a low SNR value, the PF outperforms the KF significantly. In both methods, the tracker provides predictive information regarding the target location, so that the matched filter can match to the most probable target locations, reducing the complexity of the matched filter and improving the tracking performance. Since tracking is performed without detection, the presented approach can be deemed as a track-before-detect approach. It is demonstrated through simulations that the noncoherent MIMO radar provides significant tracking performance improvement over a monostatic phased array radar with high range and azimuth resolutions. Further, the effects of coherent integration of pulses are investigated for both the phased array radar and a hybrid MIMO radar, where only the pulses transmitted and received by colocated transceivers are coherently integrated and the other pulses are combined noncoherently. It is shown that the hybrid MIMO radar achieves significant tracking performance improvement when compared with the phased array radar, by using the extra Doppler information obtained through coherent pulse integration.
157 citations
TL;DR: A path planning algorithm is presented for uninhabited aerial vehicles trying to geolocate an emitter using passive payload sensors to generate a sequence of waypoints for each vehicle that minimizes localization uncertainty.
Abstract: A path planning algorithm is presented for uninhabited aerial vehicles (UAVs) trying to geolocate an emitter using passive payload sensors. The objective is to generate a sequence of waypoints for each vehicle that minimizes localization uncertainty. The path planning problem is cast as a nonlinear programming problem using an approximation of the Fisher information matrix (FIM) and solved at successive waypoints to generate vehicle trajectories. The effectiveness of the proposed algorithm is illustrated with simulation examples.
152 citations
TL;DR: The practical feasibility of a WiFi transmissions based passive bistatic radar (PBR) is analyzed here and the attractive possibility of avoiding the use of a dedicated receiving channel for the reference signal, by synthesizing it from the surveillance channel is investigated.
Abstract: The practical feasibility of a WiFi transmissions based passive bistatic radar (PBR) is analyzed here. The required data processing steps are described including the adopted techniques for 1) the control of the signal autocorrelation function (ACF) usually yielding a high sidelobe level, and 2) the removal of the undesired signal contributions which strongly limit the useful dynamic range. The performance of the proposed techniques is firstly evaluated against simulated data generated according to the IEEE 802.11 Standards. Moreover the results are presented against a real data set collected by an experimental setup when using the conventional dual (reference and surveillance) channels PBR receiving scheme. This allows us to demonstrate the potentialities of a WiFi-based PBR for local area surveillance applications, where vehicles and people can be detected and tracked. Based on the digital nature of the exploited signals of opportunity, the attractive possibility is also investigated of avoiding the use of a dedicated receiving channel for the reference signal, by synthesizing it from the surveillance channel. This approach is shown to yield comparable performance with respect to the conventional PBR approach while yielding a remarkable saving in terms of system complexity.
TL;DR: A new technique for modeling and simulating the coherent returns from radar sea clutter, based on the compound K-distribution model for clutter amplitude statistics, is described, and it is shown that simulations can reproduce the main statistical features observed in real measurements.
Abstract: This paper describes a new technique for modeling and simulating the coherent returns from radar sea clutter, based on the compound K-distribution model for clutter amplitude statistics. Using observations of recorded Doppler spectra, a simple method is proposed for characterizing the temporal variations of the Doppler spectrum observed in a single-range cell. It is shown that simulations based on this model can reproduce the main statistical features observed in real measurements.
TL;DR: This paper compares the use of trace, determinant, and eigenvalues of the covariance matrix or information matrix as scalar performance measures and demonstrates which matrix measures are appropriate for resource management applications.
Abstract: In target tracking, sensor resource management (SRM) assigns to each target a best combination of sensors, which requires performance analysis of track filter updates. Two popular implementations of track filters are the Kalman filter (or covariance filter) and the information filter. SRM with Kalman filters attempts to minimize the estimation error covariance matrix-based scalar performance measures, whereas SRM with information filters aims to maximize the information matrix-based counterpart. In this paper, we investigate issues related to scalar performance measures and, in particular, compare the use of trace, determinant, and eigenvalues of the covariance matrix or information matrix as scalar performance measures. The study demonstrates which matrix measures are appropriate for resource management applications. Furthermore, the study shows when the matrix measures lead to equivalent goals. While this analysis is agnostic to the type of measurement, the paper demonstrates how to accommodate bearing and range measurements. Overall, the analysis provides insight about how sensor measurements best reduce uncertainty so that we can properly exploit performance measures to satisfy requirements of practical tracking and SRM applications.
TL;DR: A distributed attitude coordination control scheme using terminal sliding mode (TSM) is proposed for a group of spacecraft in the presence of external disturbances, and a robust control term based on the hyperbolic tangent function is employed to suppress bounded external disturbances.
Abstract: A distributed attitude coordination control scheme using terminal sliding mode (TSM) is proposed for a group of spacecraft in the presence of external disturbances. A novel fast terminal sliding manifold is presented, and a robust control term based on the hyperbolic tangent function is employed to suppress bounded external disturbances. The finite-time stability of the overall closed-loop system is guaranteed by a Lyapunov-based approach, and numerical simulations are presented to demonstrate the performance of the proposed controller.
TL;DR: An algorithm based on matching pursuit (MP) is proposed for inverse synthetic aperture radar (ISAR) two-dimensional (2-D) imaging of uniformly rotating targets.
Abstract: An algorithm based on matching pursuit (MP) is proposed for inverse synthetic aperture radar (ISAR) two-dimensional (2-D) imaging of uniformly rotating targets. The ISAR echo is decomposed into many subsignals that are generated by discretizing spatial domain and synthesizing the ISAR data for every discretized spatial position. The subsignals that indeed contribute to the ISAR echo are selected by the MP, and their coefficients represent the superresolution image. The target rotation rate is estimated by combining MP with maximum contrast search.
TL;DR: Simulation results show that by adjusting the parameters of the objective function, solutions can be optimized according to the desired tradeoff between the conflicting objectives of detecting new targets and tracking previously detected targets.
Abstract: A new approach to route planning for joint search and track missions by coordinated unmanned aerial vehicles (UAVs) is presented. The cornerstone is a novel objective function that integrates naturally and coherently the conflicting objectives of target detection, target tracking, and vehicle survivability into a single scalar index for path optimization. This objective function is the value of information gained by the mission on average in terms of a summation, where the number of terms reflects the number of targets detected while how large each term is reflects how well each detected target is tracked. The UAV following the path that maximizes this objective function is expected to gain the most valuable information by detecting the most important targets and tracking them during the most critical times. Although many optimization algorithms exist, we use a modified particle swarm optimization algorithm along with our proposed objective function to determine which trajectory is the best on the average at detecting and tracking targets. For simplicity, perfect communication with centralized fusion is assumed and the problems of false alarm, data association, and model mismatch are not considered. For analysis, we provide several simplified examples along with a more realistic simulation. Simulation results show that by adjusting the parameters of the objective function, solutions can be optimized according to the desired tradeoff between the conflicting objectives of detecting new targets and tracking previously detected targets. Our approach can also be used to update plans in real time by incorporating the information obtained up to the time (and then reusing our approach).
TL;DR: A filter for joint detection and tracking of a single target using measurements from multiple sensors under the presence of detection uncertainty and clutter is proposed and a closed-form solution for the linear-Gaussian model is derived and an analytic implementation is proposed for nonlinear models based on the unscented transform.
Abstract: This paper proposes a filter for joint detection and tracking of a single target using measurements from multiple sensors under the presence of detection uncertainty and clutter. To capture the target presence/absence in the surveillance region as well as its kinematic state, we represent the target state as a set that can take on either the empty set or a singleton. The uncertainty in such a set is modeled by a Bernoulli random finite set (RFS), and Bayes optimal estimators for joint detection and tracking are presented. A closed-form solution for the linear-Gaussian model is derived and an analytic implementation is proposed for nonlinear models based on the unscented transform. We apply the technique to tracking targets constrained to move on roads with time difference of arrival/frequency difference of arrival (TDOA/FDOA) measurements.
TL;DR: A fault-tolerant GCC fusion algorithm is proposed by introducing an adaptive parameter, which can obtain robust fusion and the degree of robustness varies with that of incoherency between estimates to be fused.
Abstract: The problem of distributed fusion for estimation when the cross-correlation of errors of local estimates is unavailable is addressed. We discuss a general estimation fusion approach for this problem-generalized convex combination (GCC) - and classify various GCC fusion approaches in three categories. We develop three GCC fusion algorithms for the problem under consideration. First, based on a set-theoretic formulation of the problem, we propose a relaxed Chebyshev center covariance intersection (RCC-CI) algorithm to fuse the local estimates. Second, based on an information-theoretic criterion, we develop a fast covariance intersection (IT-FCI) algorithm with weights in a closed form. The proposed RCC-CI and IT-FCI algorithms are characterized by both the local estimates and the mean-square error (MSE) matrices being taken into account. Third, to fuse incoherent local estimates, we propose a fault-tolerant GCC fusion algorithm by introducing an adaptive parameter, which can obtain robust fusion and the degree of robustness varies with that of incoherency between estimates to be fused.
TL;DR: The work presented here takes the concept of admissible regions and attributable vectors along with a multiple hypothesis filtering approach to determine how well these SO orbits can be recovered for short-arc data in near realtime and autonomously.
Abstract: The population of space objects (SOs) is tracked with sparse resources and thus tracking data are only collected on these objects for a relatively small fraction of their orbit revolution (i.e., a short arc). This contributes to commonly mistagged or uncorrelated SOs and their associated trajectory uncertainties (covariances) to be less physically meaningful. The case of simply updating a catalogued SO is not treated here, but rather, the problem of reducing a set of collected short-arc data on an arbitrary deep space object without a priori information, and from the observations alone, determining its orbit to an acceptable level of accuracy. Fundamentally, this is a problem of data association and track correlation. The work presented here takes the concept of admissible regions and attributable vectors along with a multiple hypothesis filtering approach to determine how well these SO orbits can be recovered for short-arc data in near realtime and autonomously. While the methods presented here are explored with synthetic data, the basis for the simulations resides in actual data that has yet to be reduced, but whose characteristics are replicated as well as possible to yield results that can be expected using actual data.
TL;DR: It is shown that the proposed return link multiple access solution is providing a random access channel (RACH) aggregated spectral efficiency around 2 bit/s/Hz in the presence of power unbalance with reliable packet delivery over typical land mobile satellite (LMS) channels.
Abstract: The work presented here describes the key design drivers and performance of a high efficiency satellite mobile messaging system well adapted to the machine-to-machine communication services targeting, in particular, the vehicular market. It is shown that the proposed return link multiple access solution is providing a random access channel (RACH) aggregated spectral efficiency around 2 bit/s/Hz in the presence of power unbalance with reliable packet delivery over typical land mobile satellite (LMS) channels.
TL;DR: Simulation results show that the proposed integration system with the adaptive filter is more effective in estimating the position and attitude errors than a system that uses the extended Kalman filter.
Abstract: The inertial navigation system (INS)/GPS integration system is designed by employing an adaptive filter that can estimate measurement noise variance using the residual of the measurement To verify the efficiency of the proposed loosely-coupled INS/GPS integration system, simulations were performed by assuming that GPS information has large position errors The simulation results show that the proposed integration system with the adaptive filter is more effective in estimating the position and attitude errors than a system that uses the extended Kalman filter
TL;DR: A technique of hidden Markov modeling (HMM) of the shadow profile is developed here and indicates that the shadows provide useful discriminatory information that can be used to advance recognition capabilities in SAR ATR applications.
Abstract: The use of a target's shadow in synthetic aperture radar (SAR) imaging has garnered much attention for automated target recognition (ATR) applications. A technique of hidden Markov modeling (HMM) of the shadow profile is developed here. The basic HMM technique is refined using ensemble averaging, mission-based model selection criteria, multi-look scenarios, and data fusion. The algorithms are tested using DARPA's moving and stationary target acquisition and recognition (MSTAR) data. One of the drawbacks of using SAR shadows is that there exist certain, yet limited, target-radar configurations where the shadow simply does not robustly provide discriminatory target information. This limitation, however, can be easily overcome by imaging a target at multiple poses. With two orthogonal looks, the shadow-only classifier was seen to have an average classification performance of over 90% for a five target system. Additionally, the output of the shadow-only classifier is illustrated to be independent of a scattering center based classifier. All of the results indicate that the shadows provide useful discriminatory information that can be used to advance recognition capabilities in SAR ATR applications.
TL;DR: In this paper, several Global Positioning System/inertial navigation system (GPS/INS) algorithms are presented using both extended Kalman filter (EKF) and unscented Kalman Filter (UKF), and evaluated with respect to performance and complexity.
Abstract: In this paper, several Global Positioning System/inertial navigation system (GPS/INS) algorithms are presented using both extended Kalman filter (EKF) and unscented Kalman filter (UKF), and evaluated with respect to performance and complexity. The contributions of this study are that attitude estimates are compared with independent measurements provided by a mechanical vertical gyroscope using 23 diverse sets of flight data, and that a fundamental difference between EKF and UKF with respect to linearization is evaluated.
TL;DR: A novel formulation of sliding mode control (SMC) based proportional navigation (PN) guidance law does not need any knowledge of bounds of target acceleration and closed-loop stability for the guidance loop is established.
Abstract: A novel formulation of sliding mode control (SMC) based proportional navigation (PN) guidance law is presented. Unlike conventional SMC-based guidance laws, the law presented here does not need any knowledge of bounds of target acceleration. The target acceleration is estimated using the so-called inertial delay control (IDC). Closed-loop stability for the guidance loop is established. Simulations are carried out by considering highly-maneuvering targets and constant as well as varying missile velocity and the results are presented to demonstrate the effectiveness of the proposed formulation.
TL;DR: It is shown here that the most general form of transmit beamforming can be achieved in a decoupled form, using orthogonal (uncorrelated) waveforms and a multi-rank transmit beamformer.
Abstract: Methods for transmit beamforming in multiple-input multiple-output (MIMO) radar based on the design of multiple correlated waveforms have been proposed. This paper points out that this approach couples the spatial (beamformer) and temporal (waveform) parts of the problem, significantly complicating the design. It is shown here that the most general form of transmit beamforming can be achieved in a decoupled form, using orthogonal (uncorrelated) waveforms and a multi-rank transmit beamformer. This formulation allows the use of standard beamformer design procedures. Examples are provided to illustrate the design of multi-rank beamformers for search and tracking applications. The examples include single and multiple beamformers, adaptive beamformers, and wide beams for illumination. These examples are illustrated by simulation results.
TL;DR: It is proved that strapdown INS alignment, considering the unknown constant sensor biases, will be completely observable if the strap down INS is rotated successively about two different axes and will be nearly observable for finite known unobservable states (no more than two) if it is rotated about a single axis.
Abstract: Alignment of the strapdown inertial navigation system (INS) has strong nonlinearity, even worse when maneuvers, e.g., tumbling techniques, are employed to improve the alignment. There is no general rule to attack the observability of a nonlinear system, so most previous works addressed the observability of the corresponding linearized system by implicitly assuming that the original nonlinear system and the linearized one have identical observability characteristics. Strapdown INS alignment is a nonlinear system that has its own characteristics. Using the inherent properties of strapdown INS, e.g., the attitude evolution on the SO(3) manifold, we start from the basic definition and develop a global and constructive approach to investigate the observability of strapdown INS static and tumbling alignment, highlighting the effects of the attitude maneuver on observability. We prove that strapdown INS alignment, considering the unknown constant sensor biases, will be completely observable if the strapdown INS is rotated successively about two different axes and will be nearly observable for finite known unobservable states (no more than two) if it is rotated about a single axis. Observability from a global perspective provides us with insights into and a clearer picture of the problem, shedding light on previous theoretical results on strapdown INS alignment that were not comprehensive or consistent. The reporting of inconsistencies calls for a review of all linearization-based observability studies in the vast literature. Extensive simulations with constructed ideal observers and an extended Kalman filter are carried out, and the numerical results accord with the analysis. The conclusions can also assist in designing the optimal tumbling strategy and the appropriate state observer in practice to maximize the alignment performance.
TL;DR: It is shown that the analytically derived region of attraction agrees reasonably well with that obtained from the time-domain simulation and the predicted instability is experimentally validated.
Abstract: This paper presents a detailed analysis of the dynamic behaviour of a hybrid ac-dc electric power system under large disturbances. The system under study is representative of a power distribution network which is currently being developed for future "more electric" aircraft (MEA). Brayton-Moser mixed potential is employed along with Lyapunov stability theorems to determine an analytical estimation of the large-signal stability boundary of the system. Extensive time- domain simulations using detailed behavioural models of the power system components are undertaken. It is shown that the analytically derived region of attraction agrees reasonably well with that obtained from the time-domain simulation. The predicted instability is experimentally validated.
TL;DR: A time-of-arrival wall association algorithm is derived to relate multipath returns to their respective walls and targets, followed by a nonlinear least squares (NLS) optimization to localize the targets.
Abstract: In urban sensing and through-the-wall (TTW) radar, the existence of targets inside buildings results in multipath returns. These multipath returns are exploited to achieve target localization with a single sensor. A time-of-arrival (TOA) wall association algorithm is derived to relate multipath returns to their respective walls and targets, followed by a nonlinear least squares (NLS) optimization to localize the targets. Simulations and experimental data are used to validate the proposed algorithms.
TL;DR: It is shown that a generalized linear time-varying guidance law with an arbitrary pair of guidance coefficients can minimize a certain quadratic performance index subject to the terminal constraints.
Abstract: The work presented here demonstrates optimality of linear time-varying guidance laws for controlling impact angles as well as terminal misses using the inverse problem of optimal control theory. Under the assumptions of a stationary target and a lag-free missile with a constant speed and a small flight path angle, it is shown that a generalized linear time-varying guidance law with an arbitrary pair of guidance coefficients can minimize a certain quadratic performance index subject to the terminal constraints. Feasible sets of the guidance coefficients for capturability are investigated and explicitly stated by yielding the closed-form solutions of the guidance loop in this paper. These results imply that it is possible for more realistic settings of the guidance coefficients either to improve the guidance performance or to achieve some specific guidance objectives in practical environments. Furthermore, the time-to-go calculation methods, which consider the closed-loop solutions for arbitrary guidance coefficients, are included for implementation of the guidance law. Linear and nonlinear simulations are performed to validate the proposed approach.
TL;DR: The Wigner-Ville Hough transform is formulated to take into account the multiple pulses that are available in an observation interval at the intercept receiver, and a new algorithm, called the periodic WVHT (PWVHT), significantly outperforms the WvHT for LFMCW signals.
Abstract: The Wigner-Ville Hough transform (WVHT) is suboptimal in the detection and parameter estimation of linear frequency-modulated (LFM) continuous wave (LFMCW) low probability of intercept (LPI) radar waveforms because they are composed of concatenated LFM pulses. We formulate the detection and estimation problem to take into account the multiple pulses that are available in an observation interval at the intercept receiver. The new algorithm, called the periodic WVHT (PWVHT), significantly outperforms the WVHT for LFMCW signals.