Showing papers in "IEEE Transactions on Aerospace and Electronic Systems in 2008"

Transmit beamforming for MIMO radar systems using signal cross-correlation

Abstract: Proposed next-generation radar systems will have multiple transmit apertures with complete flexibility in the choice of the signals transmitted at each aperture. Here we propose the use of multiple signals with arbitrary cross-correlation matrix R, and show that R can be chosen to achieve or approximate a desired spatial transmit beampattern. Two specific problems are addressed. The first is the constrained optimization problem of finding the value of R which causes the true transmit beampattern to be close in some sense to a desired beampattern. This is approached using convex optimization techniques. The second is the problem of designing multiple constant-modulus waveforms with given cross-correlation R. The use of coded binary phase shift keyed (BPSK) waveforms is considered. A method for finding the code sequences based on random signaling with a structured correlation matrix is proposed. It is also shown that by restricting the class of admissible waveforms one reduces the set of possible signal correlation matrices.

Bayesian approach to extended object and cluster tracking using random matrices

Abstract: In algorithms for tracking and sensor data fusion the targets to be observed are usually considered as point source objects; i.e., compared with the sensor resolution their extension is neglected. Due to the increasing resolution capabilities of modern sensors, however, this assumption is often no longer valid as different scattering centers of an object can cause distinct detections when passing the signal processing chain. Examples of extended targets are found in short-range applications (littoral surveillance, autonomous weapons, or robotics). A collectively moving target group can also be considered as an extended target. This point of view is the more appropriate, the smaller the mutual distances between the individual targets are. Due to the resulting data association and resolution conflicts any attempt of tracking the individual objects within the group seems to be no longer reasonable. With simulated sensor data produced by a partly unresolvable aircraft formation the addressed phenomena are illustrated and an approximate Bayesian solution to the resulting tracking problem is proposed. Ellipsoidal object extensions are modeled by random matrices, which are treated as additional state variables to be estimated or tracked. We expect that the resulting tracking algorithms are also relevant for tracking large, collectively moving target swarms.

Target detection and parameter estimation for MIMO radar systems

Abstract: We investigate several target detection and parameter estimation techniques for a multiple-input multiple-output (MIMO) radar system. By transmitting independent waveforms via different antennas, the echoes due to targets at different locations are linearly independent of each other, which allows the direct application of many data-dependent beamforming techniques to achieve high resolution and excellent interference rejection capability. In the absence of array steering vector errors, we discuss the application of several existing data-dependent beamforming algorithms including Capon, APES (amplitude and phase estimation) and CAPES (combined Capon and APES), and then propose an alternative estimation procedure, referred to as the combined Capon and approximate maximum likelihood (CAML) method. Via several numerical examples, we show that the proposed CAML method can provide excellent estimation accuracy of both target locations and target amplitudes. In the presence of array steering vector errors, we apply the robust Capon beamformer (RCB) and doubly constrained robust Capon beamformer (DCRCB) approaches to the MIMO radar system to achieve accurate parameter estimation and superior interference and jamming suppression performance.

Position error bound for UWB localization in dense cluttered environments

Abstract: For most outdoor applications, systems such as global positioning system (GPS) provide users with accurate location estimates. However, similar range-only localization techniques in dense cluttered environments typically lack accuracy and reliability due, notably, to dense multipath, line-of-sight (LOS) blockage and excess propagation delays through materials. In particular, range measurements between a receiver and a transmitter are often positively biased. Furthermore, the quality of the range measurement degrades with distance, and the geometric configuration of the beacons also affects the localization accuracy. In this paper we derive a fundamental limit of localization accuracy for an ultrawide bandwidth (UWB) system operating in such environments, which we call the position error bound (PEB). The impact of different ranging estimation errors due to beacons distance and biases on the best positioning accuracy is investigated. The statistical characterization of biases coming from measurement campaigns can easily be incorporated into this analysis. We show that the relative importance of information coming from different beacons varies depending on the propagation conditions, such as whether the beacon is LOS or non-line-of-sight (NLOS). We show, in particular, that any a priori information knowledge on NLOS beacons can significantly improve the localization accuracy, especially in dense cluttered environments. Finally we put forth the concept of localization outage probability and epsi-localization accuracy outage, and use them to characterize the quality of localization throughout the area.

Extension of a two-step calibration methodology to include nonorthogonal sensor axes

Abstract: We present an extension of the nonlinear two-step estimation algorithm originally developed for the calibration of solid-state strapdown magnetometers. We expand the algorithm to include nonorthogonality within a sensor set for both two- and three-axis sensors. Nonorthogonality can result from manufacturing issues, installation geometry, and in the case of magnetometers, from soft iron bias errors. Simulation studies for both two- and three-axis sensors show convergence of the improved algorithm to the true values, even in the presence of realistic measurement noise. Finally the algorithm is experimentally validated on a low-cost solid-state three-axis magnetometer set, which shows definite improvement postcalibration. We note that the algorithm is general and can be applied to any two- or three-axis sensor set (such as accelerometers) with an error model consisting of scale, offset, and nonorthogonality errors.

Observability analysis and active control for airborne SLAM

Abstract: An unmanned aerial vehicle (UAV) is tasked to explore an unknown environment and to map the features it finds, but must do so without the use of infrastructure-based localisation systems such as GPS, or any a priori terrain data. The UAV navigates using a statistical estimation technique known as simultaneous localisation and mapping (SLAM) which allows for the simultaneous estimation of the location of the UAV as well as the location of the features it sees. SLAM offers a unique approach to vehicle localisation with potential applications including planetary exploration, or when GPS is denied (for example under intentional GPS jamming, or applications where GPS signals cannot be reached), but more importantly can be used to augment already existing systems to improve robustness to navigation failure. One key requirement for SLAM to work is that it must reobserve features, and this has two effects: firstly, the improvement of the location estimate of the feature; and secondly, the improvement of the location estimate of the platform because of the statistical correlations that link the platform to the feature. So our UAV has two options; should it explore more unknown terrain to find new features, or should it revisit known features to improve localisation quality. These options are instantiated into the online path planner for the UAV. We present the SLAM algorithm and evaluate two important properties about the algorithm which assist in developing a path planning module for the UAV. The first of these is the use of the probabilistic measure of "entropy" as an information-based measure of the certainty in the map and vehicle locations, and is used as a utility function for planning the UAVs trajectory and determining the order in which features in the map are observed. The second is an observability analysis of SLAM which presents the unobservable states which are dependent on vehicle maneuvers. The analysis dictates the type of manoeuvres required by the UAV while observing features in order to maintain accurate statistical estimates of the map and vehicle location. This has the effect of reducing the action space that the path planner needs to search over. Using these two properties, we demonstrate an online path planner that intelligently plans the vehicle's trajectory while exploring unknown terrain in order to maximise the quality of both the map and vehicle location. Results of the online path planning algorithm are presented using a 6-DoF simulator of our UAV. The results show that the vehicle localisation errors are constrained and that the number of features and the size of the map steadily grows during the flight.

Novel approach for ISAR image cross-range scaling

Abstract: Inverse synthetic aperture radar (ISAR) imaging systems produce electromagnetic images of targets in the range-Doppler domain. In order to rescale the image in a homogeneous range-cross range domain (meters by meters), the modulus of the target effective rotation vector must be known. Although in some cases it can be retrieved by means of ancillary data, in most cases the modulus of the target effective rotation vector must be estimated. A blind technique is proposed for estimating the modulus of the target effective rotation vector that exploits information carried by the chirp rate of scattering centres. A technique based on image segmentation, local polynomial Fourier transform (LPFT), and image contrast (IC) maximisation is used in order to extract the scattering centres and estimate their chirp rate. Simulated and real data analyses are provided to confirm the effectiveness of the proposed algorithm.

Track-before-detect procedures in a multi-target environment

Abstract: This paper addresses the problem of early detection of K ges 1 multiple moving targets in radar systems through the use of track-before-detect (TBD) techniques. At first, assuming prior knowledge of K, a binary generalized likelihood ratio test (GLRT) is derived, which shows that the multi-target TBD problem can be regarded as a K-path trellis search. Since optimal implementation of the GLRT has a nonlinear complexity either in the number of targets or in the number of integrated frames, suboptimum algorithms are investigated which allow to trade better estimation and tracking accuracy for a much lower implementation complexity. Next, the TBD problem with K unknown is discussed and a novel multi-hypothesis test strategy is derived as the solution to a constrained optimization problem, which subsumes the conventional binary GLRT as the special case of known K. Finally, numerical examples are provided to assess and compare the performances of the proposed TBD procedures.

Multiple-model probability hypothesis density filter for tracking maneuvering targets

Abstract: Tracking multiple targets with uncertain target dynamics is a difficult problem, especially with nonlinear state and/or measurement equations. With multiple targets, representing the full posterior distribution over target states is not practical. The problem becomes even more complicated when the number of targets varies, in which case the dimensionality of the state space itself becomes a discrete random variable. The probability hypothesis density (PHD) filter, which propagates only the first-order statistical moment (the PHD) of the full target posterior, has been shown to be a computationally efficient solution to multitarget tracking problems with a varying number of targets. The integral of PHD in any region of the state space gives the expected number of targets in that region. With maneuvering targets, detecting and tracking the changes in the target motion model also become important. The target dynamic model uncertainty can be resolved by assuming multiple models for possible motion modes and then combining the mode-dependent estimates in a manner similar to the one used in the interacting multiple model (IMM) estimator. This paper propose a multiple-model implementation of the PHD filter, which approximates the PHD by a set of weighted random samples propagated over time using sequential Monte Carlo (SMC) methods. The resulting filter can handle nonlinear, non-Gaussian dynamics with uncertain model parameters in multisensor-multitarget tracking scenarios. Simulation results are presented to show the effectiveness of the proposed filter over single-model PHD filters.

Geometric voting algorithm for star trackers

Abstract: We present an algorithm for recovering the orientation (attitude) of a satellite-based camera. The algorithm matches stars in an image taken with the camera to stars in a star catalogue. The algorithm is based on a geometric voting scheme in which a pair of stars in the catalogue votes for a pair of stars in the image if the angular distance between the stars of both pairs is similar. As angular distance is a symmetric relationship, each of the two catalogue stars votes for each of the image stars. The identity of each star in the image is set to the identity of the catalogue star that cast the most votes. Once the identity of the stars is determined, the attitude of the camera is computed using a quaternion-based method. We further present a fast tracking algorithm that estimates the attitude for subsequent images after the first algorithm has terminated successfully. Our method runs in comparable speed to state of the art algorithms but is still more robust than them. The system has been implemented and tested on simulated data and on real sky images.

Multi-target tracking in clutter without measurement assignment

Abstract: When tracking targets using radars and sonars, the number of targets and the origin of data is uncertain. Data may be false measurements or clutter, or they may be detections from an unknown number of targets whose possible trajectories and detection processes can only be described in a statistical manner. Optimal all-neighbor multi-target tracking (MTT) in clutter enumerates all possible joint measurement-to-track assignments and calculates the a posteriori probabilities of each of these joint assignments. The numerical complexity of this process is combinatorial in the number of tracks and the number of measurements. One of the key differences between most MTT algorithms is the manner in which they reduce the computational complexity of the joint measurement-to-track assignment process. We propose an alternative approach, using a form of soft assignment, that enables us to bypass this step entirely. Specifically, our approach treats possible detections of targets followed by other tracks as additional clutter measurements. It starts by approximating the a~priori probabilities of measurement origin. These probabilities are then used to modify the clutter spatial density at the location of the measurements. A suitable single target tracking (STT) filter then uses the modified clutter intensity for updating the track state. In effect, the STT filter is transformed into an MTT filter with a numerical complexity that is linear in the number of tracks and the number of measurements. Simulations show the effectiveness of this approach in a number of different multi-target scenarios.

Small-signal input impedance modeling of line-frequency rectifiers

Abstract: This paper presents a systematic method for modeling small-signal input impedance of line-frequency AC-DC converters. The objective is to develop proper models that can be used for stability analysis of AC power systems with significant DC loads powered by such converters. The proposed modeling method uses harmonic linearization and Fourier analysis techniques to describe the current and voltage mapping process through the converter switching circuit. The voltage and current mapping relations are then combined to give an impedance mapping model which converts the impedance of any circuit or system connected to the DC output of converter into a corresponding small-signal input impedance of the converter at the AC side. Similar relations can be used to map the AC source impedance into the DC side to give the equivalent dc source impedance for stability analysis of the DC subsystem. This paper focuses on the basic principle of the impedance mapping method and uses a single-phase diode rectifier circuit to demonstrate the modeling process. The resulting ac input impedance model is validated by detailed circuit simulation as well as experimental measurements.

Assignment costs for multiple sensor track-to-track association

Abstract: Successful track-to-track data association in a multisensor, multitarget scenario is predicated on a proper cost function. The cost function for associating two tracks is well established. This paper investigates different versions of the likelihood that more than two tracks represent the same target. These likelihoods lead to different cost functions for the general track-to-track association problem. Two of the likelihoods are approximations based on ad-hoc extensions of the well-known two-track expression. The next approximation is the generalized likelihood (GL), where the true state is replaced by the maximum likelihood (ML) estimate of the target state in the true likelihood. The final likelihood is derived exactly from a diffuse prior of the target state and is referred to as the diffuse prior likelihood (DPL). This paper reveals the connection between the DPL and GL. The last two likelihood expressions incorporate the cross-track error covariance matrices, which are not readily available in distributed track fusion. Therefore, the paper considers three forms of the DPL or GL: (1) the correlated tracks (CT) form by using the actual cross-covariance matrices, (2) the independent tracks (IT) form by assuming the cross-covariance matrices are zero, and (3) the approximated correlated tracks (ACT) form by approximating the cross-covariance matrices. Simulations compare the performance of all likelihood versions.

Carrier loop architectures for tracking weak GPS signals

Abstract: The performance of various carrier recovery loop architectures (phase lock loop (PLL), Doppler-aided PLL, frequency lock loop (FLL), and Doppler-aided FLL) in tracking weak GPS signals are analyzed and experimentally validated. The effects of phase or frequency detector design, oscillator quality, coherent averaging time, and external Doppler aiding information on delaying loss of lock are quantified. It is shown that for PLLs the metric of total phase jitter is a reliable metric for assessing low C/N performance of the tracking loop provided the loop bandwidth is not too small (~> 5 Hz). For loop bandwidths that are not too small, total phase jitter accurately predicts carrier-to-noise ratio (C/N) at which loss of lock occurs. This predicted C/N is very close to the C/N predicted by bit error rate (BER). However, unlike BER, total phase jitter can be computed in real-time and an estimator for it is developed and experimentally validated. Total phase jitter is not a replacement for BER, since at low bandwidths it is less accurate than BER in that the receiver loses lock at a higher C/N than predicted by the estimator. Similarly, for FLLs operating at small loop bandwidths, it is found that normalized total frequency jitter is not a reliable metric for assessing loss of lock in weak signal or low C/N conditions. At small loop bandwidths, while total frequency jitter may indicate that a loop is still tracking, the Doppler estimates provided by the FLL will be biased.

Sliding mode and shaped input vibration control of flexible systems

Abstract: In this paper, the vibration reduction problem is investigated for a flexible spacecraft during attitude maneuvering. A new control strategy is proposed, which integrates both the command input shaping and the sliding mode output feedback control (SMOFC) techniques. Specifically, the input shaper is designed for the reference model and implemented outside of the feedback loop in order to achieve the exact elimination of the residual vibration by modifying the existing command. The feedback controller, on the other hand, is designed based on the SMOFC such that the closed-loop system behaves like the reference model with input shaper, where the residual vibrations are eliminated in the presence of parametric uncertainties and external disturbances. An attractive feature of this SMOFC algorithm is that the parametric uncertainties or external disturbances of the system do not need to satisfy the so-called matching conditions or invariance conditions provided that certain bounds are known. In addition, a smoothed hyperbolic tangent function is introduced to eliminate the chattering phenomenon. Compared with the conventional methods, the proposed scheme guarantees not only the stability of the closed-loop system, but also the good performance as well as the robustness. Simulation results for the spacecraft model show that the precise attitudes control and vibration suppression are successfully achieved.

Maximum likelihood DOA estimation in unknown colored noise fields

Abstract: Direction-of-arrival (DOA) estimation in unknown noise environments is an important but challenging problem. Several methods based on maximum likelihood (ML) criteria and parameterization of signals or noise covariances have been established. Generally, to obtain the exact ML (EML) solutions, the DOAs must be jointly estimated along with other noise or signal parameters by optimizing a complicated nonlinear function over a high-dimensional problem space. Although the computation complexity can be reduced via derivation of suboptimal approximate ML (AML) functions using large sample assumption or least square criteria, nevertheless the AML estimators still require multi-dimensional search and the accuracy is lost to some extent. A particle swarm optimization (PSO) based solution is proposed here to compute the EML functions and explore the potential superior performances. A key characteristic of PSO is that the algorithm itself is highly robust yet remarkably simple to implement, while processing similar capabilities as other evolutionary algorithms such as the genetic algorithm (GA). Simulation results confirm the advantage of paring PSO with EML, and the PSO-EML estimator is shown to significantly outperform AML-based techniques in various scenarios at less computational costs.

Signal models for TDOA/FDOA estimation

Abstract: Much research has been done in the area of estimating time-difference-of-arrival (TDOA) and frequency-difference-of-arrival (FDOA) and their use in locating a radiating source. Early work in this area was focused on locating acoustic sources using passive sonar processing. Only later was TDOA/FDOA-based location considered for the case of passively locating electromagnetic sources. As a result of this, it is tempting to use results derived for the acoustic case when answering questions about the electromagnetic case. This correspondence shows that such borrowing can lead to incorrect results. The key factor that drives the significant differences between these two cases is the difference between the signal model assumptions for the two cases: wide-sense stationary (WSS) Gaussian process in the acoustic case and a deterministic signal in the electromagnetic case. Although the received signal equations may look identical (showing delay and Doppler shift), the resulting Fisher information, Cramer-Rao bound (CRB), and maximum likelihood estimator (MLE) are fundamentally different for the two signal scenarios.

GLRT subspace detection for range and Doppler distributed targets

Abstract: A generalized likelihood ratio test (GLRT) is derived for adaptive detection of range and Doppler-distributed targets. The clutter is modeled as a spherically invariant random process (SIRP) and its texture component is range dependent (heterogeneous clutter). We suppose here that the speckle component covariance matrix is known or estimated thanks to a secondary data set. Thus, unknown parameters to be estimated are local texture values, the complex amplitudes and Doppler frequencies of all scattering centers. To do so, we use superresolution methods. The proposed detector assumes a priori knowledge on the spatial distribution of the target and has the precious property of having a constant false alarm rate (CFAR) with the assumption of a known speckle covariance matrix or by the use of frequency agility.

Fault detection and diagnosis of aircraft actuators using fuzzy-tuning IMM filter

Abstract: This paper proposes a new interacting multiple model (IMM) filter for actuator fault detection Since each individual filter of the IMM filter uses the combined information of the estimation values from all the operating filters, it can effectively estimate system parameter variations, thereby it can diagnose the actuator damage with an unknown magnitude In this study, to diagnose the actuator failure fast and accurately, fuzzy logic is used to tune a transition probability among multiple models This makes the fault detection process smooth and reduces the possibility of false fault detection Also, a discrete fault tolerant command tracker is derived to cope with actuator damages To validate the performance of the proposed fault detection and diagnosis (FDD) algorithm, numerical simulations are performed for a high performance aircraft system

Best-known autocorrelation peak sidelobe levels for binary codes of length 71 to 105

Abstract: Best-known binary code autocorrelation peak sidelobe levels (PSLs) are updated for lengths 71 to 105. For lengths 71 to 82, codes with PSL 4 are found, establishing 4 as almost certainly the optimal value for these lengths. PSL-5 codes are produced for all lengths from 83 to 105, in many cases improving on best-known values.

Impact of GPS acquisition strategy on decision probabilities

Abstract: The first stage of processing within a GPS receiver consists of the signal acquisition process, the output of which provides a rough estimation of code delay and Doppler frequency. The strong dependence of the acquisition performance on the decision strategy is shown, establishing the role of decision probabilities. Three acquisition strategies are analyzed and a new model describing the performance of a hybrid acquisition system is developed. The theoretical models are validated by simulations, and secondary phenomena, generally neglected in the literature, are also discussed.

Tracking in clutter using IMM-IPDA-based algorithms

Abstract: We describe three single-scan probabilistic data association (PDA) based algorithms for tracking manoeuvering targets in clutter. These algorithms are derived by integrating the interacting multiple model (IMM) estimation algorithm with the PDA approximation. Each IMM model a posteriori state estimate probability density function (pdf) is approximated by a single Gaussian pdf. Each algorithm recursively updates the probability of target existence, in the manner of integrated PDA (IPDA). The probability of target existence is a track quality measure, which can be used for false track discrimination. The first algorithm presented, IMM-IPDA, is a single target tracking algorithm. Two multitarget tracking algorithms are also presented. The IMM-JIPDA algorithm calculates a posteriori probabilities of all measurement to track allocations, in the manner of the joint IPDA (JIPDA). The number of measurement to track allocations grows exponentially with the number of shared measurements and the number of tracks which share the measurements. Therefore, IMM-JIPDA can only be used in situations with a small number of crossing targets and low clutter measurement density. The linear multitarget IMM-IPDA (IMM-LMIPDA) is also a multitarget tracking algorithm, which achieves the multitarget capabilities by integrating linear multitarget (LM) method with IMM-IPDA. When updating one track using the LM method, the other tracks modulate the clutter measurement density and are subsequently ignored. In this fashion, LM achieves multitarget capabilities using the number of operations which are linear in the: number of measurements and the number of tracks, and can be used in complex scenarios, with dense clutter and a large number of targets.

Flower constellation set theory. Part I: Compatibility and phasing

Abstract: Flower Constellations are special satellite constellations whose satellites follow the same 3-dimensional space track with respect to assigned rotating reference frame. This paper presents the theoretical foundation of compatibility and phasing of the Flower Constellations. Compatibility is the synchronization property of a Flower Constellation with respect to a rotating reference frame while phasing dictates the satellite distribution property. Compatibility and phasing, which are ruled by a set of five independent integer parameters, constitute the two main properties of the Flower Constellations. In particular, the dual-compatible Flower Constellations theory, which allows a simultaneous synchronization of the Flower Constellation dynamics with two independent rotating reference frames, is introduced. Meaningful examples and potential applications are briefly discussed.

Multi-agent modeling and simulation of human behavior in aircraft evacuations

Abstract: Predicting the collective behavior of people during evacuations from aircraft is a significant challenge, complicated by human behavioral factors such as stress and panic. This paper presents a multi-agent system for simulating the impact of individual human factors on aircraft evacuations. Based on the model, a prototype system, AvatarSim, was developed to simulate emergency evacuations from aircrafts. The simulator can be used to model situations that are difficult to test in real-life due to safety considerations.

Interacting multiple model filter for tactical ballistic missile tracking

Abstract: An interacting multiple model (IMM) filter is presented for the real-time tracking of tactical ballistic missiles (TBMs). The novel aspects of the proposed IMM filter include the development of a constant axial force (CAF) Kalman filter, asymmetric IMM state interaction, and an entropy-based variation of the IMM mode probability update equation. Using data from a recent TBM defense (TBMD) test event, the proposed IMM filter is shown to yield consistent state estimates throughout the entire TBM trajectory, which includes a dual-stage boost during launch.

Refining the Klobuchar ionospheric coefficients based on GPS observations

Abstract: A method is proposed to improve the GPS broadcast ionospheric time-delay correction accuracy, using GPS observation data from the globally distributed international GNSS service (IGS) observation stations and the Crust Movement Observation Network of China (CMONOC). A new set of Klobuchar-Self coefficients can be estimated using the method. Primary results demonstrate that the refined Klobuchar-Self coefficients developed may provide better ionospheric delay corrections for single-frequency GPS receivers and improve standard single point positioning accuracies.

Implementing FFT-based digital channelized receivers on FPGA platforms

Abstract: This paper presents an in-depth study of the implementation and characterization of fast Fourier transform (FFT) pipelined architectures suitable for broadband digital channelized receivers. When implementing the FFT algorithm on field-programmable gate array (FPGA) platforms, the primary goal is to maximize throughput and minimize area. Feedback and feedforward architectures have been analyzed regarding key design parameters: radix, bitwidth, number of points and stage scaling. Moreover, a simplification of the FFT algorithm, the monobit FFT, has been implemented in order to achieve faster real time performance in broadband digital receivers. The influence of the hardware implementation on the performance of digital channelized receivers has been analyzed in depth, revealing interesting implementation trade-offs which should be taken into account when designing this kind of signal processing systems on FPGA platforms.

Polar format agorithm using chirp scaling for spotlight SAR image formation

Abstract: A novel implementation of the polar format algorithm (PFA) using the principle of chirp scaling (PCS) for spotlight synthetic aperture radar (SAR) image formation is addressed. A PFA completely free of interpolation has been achieved in moderate squinted imaging geometry. The presented approach consists of the range and azimuth scaling of the polar samples, in which only fast Fourier transforms (FFTs) and complex vector multiplications are involved. Following different processing chains, the dechirped and the chirped SAR signals are treated separately in this paper. Relative to the classic polyphase filter that uses the long sinc kernel, the new solution is able to attain a comparable result, but is much quicker by exploiting inherent properties of the linear frequency modulated (LFM) signal. Point target simulation has validated the presented methodology.

Practical fusion of quantized measurements via particle filtering

Abstract: Most treatments of decentralized estimation rely on some form of track fusion, in which local track estimates and their associated covariances are communicated. This implies a great deal of communication; and it was recently proposed that by an intelligent quantization directly of measurements, the communication needs could be considerably cut. However, several issues were not discussed. The first of these is that estimation with quantized measurements requires an update with a non-Gaussian distribution, reflecting the uncertainty within the quantization "bin."; In general this would be a difficult task for dynamic estimation, but Markov-chain Monte-Carlo (MCMC, and specifically here particle filtering) techniques appear quite appropriate since the resulting system is, in essence, a nonlinear filter. The second issue is that in a realistic sensor network it is to be expected that measurements should arrive out-of-sequence. Again, a particle filter is appropriate, since the recent literature has reported particle filter modifications that accommodate nonlinear-filter updates based on new past measurements, with the need to refilter obviated. We show results that indicate a compander/particle-filter combination is a natural fit, and specifically that quite good performance is achievable with only 2-3 bits per dimension per observation. The third issue is that intelligent quantization requires that both sensor and fuser share an understanding of the quantization rule. In dynamic estimation this is a problem since both quantizer and fuser are working with only partial information; if measurements arrive out-of-sequence the problem is compounded. We therefore suggest architectures, and comment on their suitabilities for the task. A scheme based on delta-modulation appears to be promising.

Aircraft power system harmonics involving single-phase PFC converters

Abstract: AC-DC converters with active power factor correction (PFC) are replacing uncontrolled diode rectification circuits on commercial jet airplanes in order to meet harmonic distortion limits imposed by new airborne electrical system power quality standards. The high line frequency of airborne AC power systems presents a major challenge for the design of PFC converters capable of meeting these standards. This paper investigates a new source of harmonic current distortion and the resulting system power quality problems related to dynamic interactions between PFC converters and the AC source. Experimental results are first presented to demonstrate the existence of such interactions and their effects on system power quality. Analytical and numerical simulation results are then presented to explain why such dynamic interactions can lead to significantly increased harmonic current distortion in steady state operation. Elimination of undesirable system interactions through proper damping of the PFC converter input filter is also presented and its effectiveness experimentally validated.