Showing papers in "IEEE Transactions on Aerospace and Electronic Systems in 2007"

PHD filters of higher order in target number

Abstract: The multitarget recursive Bayes nonlinear filter is the theoretically optimal approach to multisensor-multitarget detection, tracking, and identification. For applications in which this filter is appropriate, it is likely to be tractable for only a small number of targets. In earlier papers we derived closed-form equations for an approximation of this filter based on propagation of a first-order multitarget moment called the probability hypothesis density (PHD). In a recent paper, Erdinc, Willett, and Bar-Shalom argued for the need for a PHD-type filter which remains first-order in the states of individual targets, but which is higher-order in target number. In this paper we show that this is indeed possible. We derive a closed-form cardinalized PHD (CPHD) filter, which propagates not only the PHD but also the entire probability distribution on target number.

MIMO radar waveform design based on mutual information and minimum mean-square error estimation

Abstract: This paper addresses the problem of radar waveform design for target identification and classification. Both the ordinary radar with a single transmitter and receiver and the recently proposed multiple-input multiple-output (MIMO) radar are considered. A random target impulse response is used to model the scattering characteristics of the extended (nonpoint) target, and two radar waveform design problems with constraints on waveform power have been investigated. The first one is to design waveforms that maximize the conditional mutual information (MI) between the random target impulse response and the reflected waveforms given the knowledge of transmitted waveforms. The second one is to find transmitted waveforms that minimize the mean-square error (MSE) in estimating the target impulse response. Our analysis indicates that under the same total power constraint, these two criteria lead to the same solution for a matrix which specifies the essential part of the optimum waveform design. The solution employs water-filling to allocate the limited power appropriately. We also present an asymptotic formulation which requires less knowledge of the statistical model of the target

Predictive & Adaptive MPPT Perturb and Observe Method

Abstract: The perturb and observe (P&O) best operation conditions are investigated in order to identify the edge efficiency performances of this most popular maximum power point tracking (MPPT) technique for photovoltaic (PV) applications. It is shown that P&O may guarantee top-level efficiency, provided that a proper predictive (by means of a parabolic interpolation of the last three operating points) and adaptive (based on the measure of the actual power) hill climbing strategy is adopted. The approach proposed is aimed at realizing, in addition to absolute best tracking performances, high robustness and promptness both in sunny and cloudy weather conditions. The power gain with respect to standard P&O technique is proved by means of simulation results and experimental measurements performed on a low power system. Besides the performance improvements, it is shown that the proposed approach allows possible reduction of hardware costs of analog-to-digital (A/D) converters used in the MPPT control circuitry.

Guidance law to control impact time and angle

Abstract: This paper proposes a new guidance law to control both impact time and impact angle for a flight vehicle's homing problem, which can be applied for an efficient salvo attack of antiship missiles or a cooperative mission of unmanned aerial vehicles (UAVs). The proposed law can lead vehicles to home on a target at a designated impact time with a prescribed impact angle. It comprises a feedback loop and an additional control command, the first to achieve the desired impact angle with zero miss distance, and the second to control the impact time. Numerical simulations demonstrate the performance of the proposed law in the accuracy of impact angle and impact time

Kullback-Leibler Approach to Gaussian Mixture Reduction

Abstract: A common problem in multi-target tracking is to approximate a Gaussian mixture by one containing fewer components; similar problems can arise in integrated navigation. A common approach is successively to merge pairs of components, replacing the pair with a single Gaussian component whose moments up to second order match those of the merged pair. Salmond [1] and Williams [2, 3] have each proposed algorithms along these lines, but using different criteria for selecting the pair to be merged at each stage. The paper shows how under certain circumstances each of these pair-selection criteria can give rise to anomalous behaviour, and proposes that a key consideration should the the Kullback-Leibler (KL) discrimination of the reduced mixture with respect to the original mixture. Although computing this directly would normally be impractical, the paper shows how an easily computed upper bound can be used as a pair-selection criterion which avoids the anomalies of the earlier approaches. The behaviour of the three algorithms is compared using a high-dimensional example drawn from terrain-referenced navigation.

Adaptive boosting for SAR automatic target recognition

Abstract: The paper proposed a novel automatic target recognition (ATR) system for classification of three types of ground vehicles in the moving and stationary target acquisition and recognition (MSTAR) public release database. First MSTAR image chips are represented as fine and raw feature vectors, where raw features compensate for the target pose estimation error that corrupts fine image features. Then, the chips are classified by using the adaptive boosting (AdaBoost) algorithm with the radial basis function (RBF) network as the base learner. Since the RBF network is a binary classifier, the multiclass problem was decomposed into a set of binary ones through the error-correcting output codes (ECOC) method, specifying a dictionary of code words for the set of three possible classes. AdaBoost combines the classification results of the RBF network for each binary problem into a code word, which is then "decoded" as one of the code words (i.e., ground-vehicle classes) in the specified dictionary. Along with classification, within the AdaBoost framework, we also conduct efficient fusion of the fine and raw image-feature vectors. The results of large-scale experiments demonstrate that our ATR scheme outperforms the state-of-the-art systems reported in the literature

Design Principles of MIMO Radar Detectors

Abstract: This paper considers the problem of multiple-input multiple-output (MIMO) radars employing space-time coding (STC) to achieve diversity. To this end, after briefly outlining the model of the received echo, a suitable detection structure is derived, and its performance is expressed in closed form as a function of the clutter statistical properties and of the space-time code matrix. Interestingly, this receiver requires prior knowledge of the clutter covariance, but the detection threshold is functionally independent thereof. At the transmitter design stage, we give two criteria for code construction: the first is based on the classical Chernoff bound, the second is an information-theoretic criterion. Interestingly, the two criteria lead to the same condition for code optimality, which in turn specializes, under the assumption of uncorrelated clutter and square code matrix, in some well-known full-rate space-time codes. A thorough performance assessment is also given, so as to establish the optimum achievable performance for MIMO radar systems.

Waveform Design for MIMO Radars

Abstract: We consider the problem of waveform design for multiple input/multiple output (MIMO) radars, where the transmit waveforms are adjusted based on target and clutter statistics. A model for the radar returns which incorporates the transmit waveforms is developed. The target detection problem is formulated for that model. Optimal and suboptimal algorithms are derived for designing the transmit waveforms under different assumptions regarding the statistical information available to the detector. The performance of these algorithms is illustrated by computer simulation.

ASPeCT: Unambiguous sine-BOC(n,n) acquisition/tracking technique for navigation applications

Abstract: This article presents a new tracking technique for sine-BOC(n,n) (or Manchester encoded) ranging signals, which is most likely to be a part of the new European Global Navigation Satellite System (GNSS), Galileo, signal plan. When traditional sine-BOC(n,n) tracking is considered, although offering excellent performance compared with current signals, it has the main drawback of potentially giving biased measurements. The new method presented herein allows the removal of this threat while maintaining the same level of performance. An adapted version of this technique can also be used for acquisition purposes

Iterated Unscented Kalman Filter for Passive Target Tracking

Abstract: It is of great importance to develop a robust and fast tracking algorithm in passive localization and tracking system because of its inherent disadvantages such as weak observability and large initial errors. In this correspondence, a new algorithm referred to as the iterated unscented Kalman filter (IUKF) is proposed based on the analysis and comparison of conventional nonlinear tracking problem. The algorithm is developed from UKF but it can obtain more accurate state and covariance estimation. Compared with the traditional approaches (e.g., extended Kalman filter (EKF) and UKF) used in passive localization, the proposed method has potential advantages in robustness, convergence speed, and tracking accuracy. The correctness as well as validity of the algorithm is demonstrated through numerical simulation and experiment results.

Maximum likelihood estimation for compound-gaussian clutter with inverse gamma texture

Abstract: The inverse gamma distributed texture is important for modeling compound-Gaussian clutter (e.g. for sea reflections), due to the simplicity of estimating its parameters. We develop maximum-likelihood (ML) and method of fractional moments (MoFM) estimates to find the parameters of this distribution. We compute the Cramer-Rao bounds (CRBs) on the estimate variances and present numerical examples. We also show examples demonstrating the applicability of our methods to real lake-clutter data. Our results illustrate that, as expected, the ML estimates are asymptotically efficient, and also that the real lake-clutter data can be very well modeled by the inverse gamma distributed texture compound-Gaussian model.

Novel data association schemes for the probability hypothesis density filter

Abstract: The probability hypothesis density (PHD) filter is a practical alternative to the optimal Bayesian multi-target Alter based on finite set statistics. It propagates the PHD function, a first-order moment of the full multi-target posterior density. The peaks of the PHD function give estimates of target states. However, the PHD filter keeps no record of target identities and hence does not produce track-valued estimates of individual targets. We propose two different schemes according to which PHD filter can provide track-valued estimates of individual targets. Both schemes use the probabilistic data-association functionality albeit in different ways. In the first scheme, the outputs of the PHD filter are partitioned into tracks by performing track-to-estimate association. The second scheme uses the PHD filter as a clutter filter to eliminate some of the clutter from the measurement set before it is subjected to existing data association techniques. In both schemes, the PHD filter effectively reduces the size of the data that would be subject to data association. We consider the use of multiple hypothesis tracking (MHT) for the purpose of data association. The performance of the proposed schemes are discussed and compared with that of MHT.

Multi-target state estimation and track continuity for the particle PHD filter

Abstract: Particle filter approaches for approximating the first-order moment of a joint, or probability hypothesis density (PHD), have demonstrated a feasible suboptimal method for tracking a time-varying number of targets in real-time. We consider two techniques for estimating the target states at each iteration, namely k-means clustering and mixture modelling via the expectation-maximization (EM) algorithm. We present novel techniques for associating the targets between frames to enable track continuity.

On Bistatic Inverse Synthetic Aperture Radar

Abstract: The use of multiple radar configurations can overcome some of the geometrical limitations that exist when obtaining radar images of a target using inverse synthetic aperture radar (ISAR) techniques. It is shown here how a particular bistatic configuration can produce three view angles and three ISAR images simultaneously. A new ISAR signal model is proposed and the applicability of employing existing monostatic ISAR techniques to bistatic configurations is analytically demonstrated. An analysis of the distortion introduced by the bistatic geometry to the ISAR image point spread function (PSF) is then carried out and the limits of the applicability of ISAR techniques (without the introduction of additional signal processing) are found and discussed. Simulations and proof of concept experimental data are also provided that support the theory.

Exact Bayesian and particle filtering of stochastic hybrid systems

Abstract: The standard way of applying particle filtering to stochastic hybrid systems is to make use of hybrid particles, where each particle consists of two components, one assuming Euclidean values, and the other assuming discrete mode values. This paper develops a novel particle filter (PF) for a discrete-time stochastic hybrid system. The novelty lies in the use of the exact Bayesian equations for the conditional mode probabilities given the observations. Therefore particles are needed for the Euclidean valued state component only. The novel particle filter is referred to as the interacting multiple model (IMM) particle filter (IMMPF) because it incorporates a filter step which is of the same form as the interaction step of the IMM algorithm. Through Monte Carlo simulations, it is shown that the IMMPF has significant advantage over the standard PF, in particular for situations where conditional switching rate or conditional mode probabilities have small values

Improved Global Range Alignment for ISAR

Abstract: An improved global range alignment is presented for inverse synthetic aperture radar (ISAR) imaging. The shifts of the echoes are modeled as a polynomial, and the coefficients of this polynomial are chosen to optimize a quality measure of range alignment. The shift in the time domain is carried out by introducing a phase ramp in the frequency domain in order to remove the limitation of integer steps. Because the quality measure of range alignment is calculated directly in the frequency domain, this method is computationally more efficient than the original global method.

User-level reliability monitoring in urban personal satellite-navigation

Abstract: Monitoring the reliability of the obtained user position is of great importance, especially when using the global positioning system (GPS) as a standalone system. In the work presented here, we discuss reliability testing, reliability enhancement, and quality control for global navigation satellite system (GNSS) positioning. Reliability testing usually relies on statistical tests for receiver autonomous integrity monitoring (RAIM) and fault detection and exclusion (FDE). It is here extended by including an assessment of the redundancy and the geometry of the obtained user position solution. The reliability enhancement discussed here includes rejection of possible outliers, and the use of a robust estimator, namely a modified Danish method. We draw special attention to navigation applications in degraded signal-environments such as indoors where typically multiple errors occur simultaneously. The results of applying the discussed methods to high-sensitivity GPS data from an indoor experiment demonstrate that weighted estimation, FDE, and quality control yield a significant improvement in reliability and accuracy. The accuracy actually obtained was by 40% better than with equal weights and no FDE; the rms value of horizontal errors was reduced from 15 m to 9 m, and the maximum horizontal errors were largely reduced.

PCRLB-based multisensor array management for multitarget tracking

Abstract: In this paper we consider the general problem of managing an array of sensors in order to track multiple targets in the presence of clutter. There are three complicating factors. The first is that because of physical limitations (e.g., communication bandwidth) only a small subset of the available sensors can be utilized at any one time. The second complication is that the associations of measurements to targets/clutter are unknown. The third complication is that the total number of targets in the surveillance region is unknown and possibly time varying. It are these second and third factors that extend previous work [ Tharmarasa, R., Kirubarajan, T., and Hernandez, M. L. Large-scale optimal sensor array management for multitarget tracking. IEEE Transactions on Systems, Man, and Cybernetics, to be published.]. Hence sensors must be utilized in an efficient manner to alleviate association ambiguities and to allow accurate estimation of the states of a varying number of targets. We pose the problem as a bi-criterion optimization with the two objectives of (1) controlling the posterior Cramer-Rao lower bound ((PCRLB) which provides a measure of the optimal achievable accuracy of target state estimation), and (2) maximizing the probability of detecting new targets. Only recently have expressions for multitarget PCRLBs been determined [Hue, C, Le Cadre, J.-P., and Perez, P]. Performance analysis of two sequential Monte Carlo methods and posterior Cramer-Rao bounds for multitarget tracking. In Proceedings of the 5th International Conference on Information Fusion, vol. 1, Annapolis, MD, July 2002, 464-473.], and the necessary simulation techniques are computationally expensive. However, in this paper we show the existence of a multitarget information reduction matrix (IRM) which can be calculated off-line in most cases. Additionally, we propose some approximations that further reduce the computational load. We present solution methodologies that, in simulations, are shown to determine efficient utilization strategies for the available sensor resources, with some sensors selected to track existing targets and others given the primary task of surveillance in order to identify new threats.

Doppler and direction-of-arrival (DDOA) radar for multiple-mover sensing

Abstract: A low-cost two-element receiving array concept is investigated for detecting multiple moving targets in indoor surveillance applications. Conventional direction-of-arrival (DOA) detection requires the use of an antenna array with multiple elements. Here we investigate the use of only two elements in the receiver array. The concept entails resolving the Doppler frequencies of the returned signals from the moving targets, and then measuring the phase difference at each Doppler frequency component to calculate the DOA of the targets. Simulations are performed to demonstrate the concept and to asses the DOA errors for multiple movers. An experimental system is designed and constructed to test the concept. The system consists of a two-element receiver array operating at 2.4 GHz. Measurement results of human subjects in indoor environments are presented, including through-wall scenarios.

Adaptive cancellation method for geometry-induced nonstationary bistatic clutter environments

Abstract: This paper describes and characterizes a new bistatic space-time adaptive processing (STAP) clutter mitigation method. The approach involves estimating and compensating aspects of the spatially varying bistatic clutter response in both angle and Doppler prior to adaptive clutter suppression. An important feature of the proposed method is its ability to extract requisite implementation information from the data itself, rather than rely on ancillary - and possibly erroneous or missing - system measurements. We justify the essence of the proposed method by showing its ability to align the dominant clutter subspaces of each range realization relative to a suitably chosen reference point as a means of homogenizing the space-time data set. Moreover, we numerically characterize performance using synthetic bistatic clutter data. For the examples considered herein, the proposed bistatic STAP method leads to maximum performance improvements between 17.25 dB and 20.75 dB relative to traditional STAP application, with average improvements of 6 dB to 10 dB.

Dimensionless score function for multiple hypothesis tracking

Abstract: This paper discusses several theoretical issues related to the score function for the measurement-to-track association/assignment decision in the track-oriented version of the multiple hypothesis tracker (MHT). This score function is the likelihood ratio: the ratio of the probability density function (pdf) of a measurement having originated from a track, to the pdf of this measurement having a different origin. The likelihood ratio score is derived rigorously starting from the fully Bayesian MHT (hypothesis oriented, based on combinatorial analysis of the general multitarget problem), which is shown to be amenable under some (reasonable) assumptions to the track-oriented MHT (TOMHT). The latter can be implemented efficiently using multidimensional assignment (MDA). The main feature of a likelihood ratio is the fact that it is a (physically) dimensionless quantity and, consequently, can be used for the association of different numbers of measurements and/or measurements of different dimension. The explicit forms of the likelihood ratio are discussed both for the commonly used Kalman tracking filter, as well as for the interacting multiple model (IMM) estimator. The issues of measurements of different dimension and different coordinate systems together with the selection of certain MHT design parameters - the spatial densities of the false measurements and new targets - are also discussed.

Integrated track splitting filter - efficient multi-scan single target tracking in clutter

Abstract: A fully automatic tracking algorithm must be able to deal with an unknown number of targets, unknown target initiation and termination times, false measurements and possibly time-varying target trajectory behaviour. An efficient algorithm for tracking in this environment is presented here. This approach makes use of estimates of the probability of target existence, which is an integral part of the algorithm. This allows for the efficient generation and management of possible target hypotheses, yielding an algorithm with performance that matches what can be obtained by multiple hypothesis tracking-based approaches, but at a significantly lower computational cost. This paper considers only the single target case for clarity. The extension to multiple targets is easily incorporated into this framework. Simulation studies are given that show the effectiveness of this approach in the presence of heavy and nonuniform clutter when tracking a target in an environment of low probability of detection and in an environment where the target performs violent manoeuvres.

Particle PHD filter multiple target tracking in sonar image

Abstract: Two contrasting approaches for tracking multiple targets in multi-beam forward-looking sonar images are considered. The first approach is based on assigning a Kalman filter to each target and managing the measurements with gating and a measurement-to-track data association technique. The second approach uses the recently developed particle implementation of the multiple-target probability hypothesis density (PHD) filter and a target state estimate-to-track data association technique. The two approaches are implemented and compared on both simulated sonar and real forward-looking sonar data obtained from an autonomous underwater vehicle (AUV) and demonstrate that the PHD filter with data association compares well with traditional approaches for multiple target tracking

Satellite fault diagnosis using a bank of interacting Kalman filters

Abstract: The main objective of this work is development and testing of a detection, isolation, and diagnosis algorithm based on interacting multiple model (IMM) filters for both partial (soft) and total (hard) reaction wheels faults in a spacecraft. This is shown to be accomplished under a number of different faulty mode scenarios for these actuators associated with the attitude control system (ACS) of a satellite. Various operating and faulty conditions due to changes and anomalies in the temperature, the power supply line voltage, and the loss of effectiveness of the torque and the current are considered in each reaction wheel associated with the three axes of the satellite. Once a fault mode is detected and isolated the recovery procedure can subsequently be engaged by invoking appropriate switching control strategies for the ACS. The application of a bank of interacting multiple Kalman filters for detection and diagnosis of anticipated reaction wheel failures in the ACS is described and developed. Compared with other model-based fault detection, diagnosis and isolation(FDDI) strategies developed in the control systems literature, our FDDI strategy is shown, through extensive numerical simulations, to be more accurate and robust with potential for extension to a number of other application areas.

Modified GLRT and AMF Framework for Adaptive Detectors

Abstract: The well-known general problem of signal detection in background interference is addressed for situations where a certain statistical description of the interference is unavailable, but is replaced by the observation of some secondary (training) data that contains only the interference. For the broad class of interferences that have a large separation between signal-and noise-subspace eigenvalues, we demonstrate that adaptive detectors which use a diagonally loaded sample covariance matrix or a fast maximum likelihood (FML) estimate have significantly better detection performance than the traditional generalized likelihood ratio test (GLRT) and adaptive matched filter (AMI') detection techniques, which use a maximum likelihood (ML) covariance matrix estimate. To devise a theoretical framework that can generate similarly efficient detectors, two major modifications are proposed for Kelly's traditional GLRT and AMF detection techniques. First, a two-set GLRT decision rule takes advantage of an a priori assignment of different functions to the primary and secondary data, unlike the Kelly rule that was derived without this. Second, instead of ML estimates of the missing parameters in both GLRT and AMF detectors, we adopt expected likelihood (EL) estimates that have a likelihood within the range of most probable values generated by the actual interference covariance matrix. A Gaussian model of fluctuating target signal and interference is used in this study. We demonstrate that, even under the most favorable loaded sample-matrix inversion (LSMI) conditions, the theoretically derived EL-GLRT and FL-AMF techniques (where the loading factor is chosen from the training data using the EL matching principle) gives the same detection performance as the loaded AMF technique with a proper a priori data-invariant loading factor. For the least favorable conditions, our EL-AMF method is still superior to the standard AMF detector, and may be interpreted as an intelligent (data-dependent) method for selecting the loading factor.

Asynchronous multirate multisensor information fusion algorithm

Abstract: This paper is concerned with the optimal dynamic information fusion problem for asynchronous multirate multisensors By establishing the state space models at each scale, a novel algorithm is developed to recursively fuse the data from multisensors, where the ratio between the sampling rates of different sensors is allowed to be any positive integer Without using the traditional interpolation or augmentation approaches for states or measurements, the state estimate is obtained based on global measurements, and the obtained state estimate is then proven to be the optimal in the sense of linear minimum variance It is shown that our main results improve and extend the existing information fusion algorithms for which the sampling rate ratio of different sensors is restricted to one or a power of two Finally, the feasibility and efficiency of the proposed algorithm is illustrated by a numerical simulation example

Gating Functions for Multipath Mitigation in GNSS BOC Signals

Abstract: A new multipath mitigation technique is proposed for binary offset carrier (BOC) signals in global navigation satellite systems (GNSS) using the concept of gating function originally conceived for the GPS coarse-acquisition (C/A) code. Specially-tailored pulses are utilized to diminish the number of false-lock points of the code discriminator response and to improve the multipath mitigation capability. The code loop includes only four real correlators (two extra correlators are required for the simplified bump-jumping algorithm with BOC(n,n) signals). Results obtained with BOC(n,n) and BOC(2n,n) signals show that this technique eliminates the multipath ranging errors for reflected rays with relative delays typically above twenty percent of the spreading code chip duration, thus comparing favorably with the conventional receiver correlation techniques.

Detection of satellite attitude sensor faults using the UKF

Abstract: A novel fault detection (FD) method for nonlinear systems using the residuals generated by the unscented Kalman filter (UKF) is proposed. The errors of the UKF are derived and sufficient conditions for the convergence of the UKF are presented. As the local approach is a powerful statistical technique for detecting changes in the mean of a Gaussian process, it is used to devise a hypothesis test to detect faults from residuals obtained from the UKF. Further, it is demonstrated that the selection of a sample number is important in improving the performance of the local approach. To illustrate the implementation and performance of the proposed technique, it is applied to detect sensor faults in the measurement of satellite attitude.

Pulse Compression Sidelobe Reduction by Minimization of L/sub p/-Norms

Abstract: Most modern radar systems make extensive use of pulse compression techniques. This paper presents a technique for the design of mismatched receive finite impulse response (FIR) filters based on the minimization of Lp -norms of the sidelobes. The goal of the minimization process is to reduce the range sidelobe levels of the convolution of the transmit pulse and the receive filter. A closed-form solution is derived for the least-squares case (which is equivalent to the L2-norm) and an expression for the optimization of the higher order norms is developed. The solutions for the higher order norms have to be obtained by means of iterative numerical methods. The effect of using receive filters which are longer than the transmit pulses is also investigated. Results are presented for linear FM transmit waveforms having time-bandwidth products ranging from 10 to 100 in combination with selected values of the norm order ranging from 2 to 200. Receive filter lengths up to three times the transmit pulse lengths are investigated. Results are presented which highlight the tradeoffs between sidelobe level, mismatch loss and mainlobe width. The effect of Doppler shift on the sidelobe response of these receive filters is also investigated.

Location and Imaging of Moving Targets using Nonuniform Linear Antenna Array SAR

Abstract: By using a linear antenna array, velocity synthetic aperture radar (VSAR) can detect, focus, and locate slowly moving targets well. However, it may mis-locate fast moving targets in the azimuth (cross-range) direction. In this correspondence, we propose a synthetic aperture radar (SAR) with a nonuniform linear antenna array and give a design of the antenna arrangement. It is shown that our proposed nonuniform linear antenna array SAR (NULA-SAR) can locate both slowly and fast moving targets correctly. An integrated NULA-SAR algorithm for moving target imaging is also presented, and it is verified by some simulations.