Showing papers in "IEEE Transactions on Aerospace and Electronic Systems in 2014"

Radar waveform design in a spectrally crowded environment via nonconvex quadratic optimization

Abstract: Radar signal design in a spectrally crowded environment is a very challenging and topical problem due to the increasing demand for both military surveillance/remote-sensing capabilities and civilian wireless services. This paper deals with the synthesis of optimized radar waveforms ensuring spectral compatibility with the overlayed licensed electromagnetic radiators. A priori information, for instance, provided by a radio environmental map (REM), is exploited to force a spectral constraint on the radar waveform, which is thus the result of a constrained optimization process aimed at improving some radar performances (such as detection, sidelobes, resolution, tracking). The feasibility of the waveform optimization problem is extensively studied, and a solution technique leading to an optimal waveform is proposed. The procedure requires the relaxation of the original problem into a convex optimization problem and involves a polynomial computational complexity. At the analysis stage, the waveform performance is studied in terms of trade-off among the achievable signal to interference plus noise ratio (SINR), spectral shape, and the resulting autocorrelation function (ACF).

SAR Automatic Target Recognition Using Discriminative Graphical Models

Abstract: The problem of automatically classifying sensed imagery such as synthetic aperture radar (SAR) into a canonical set of target classes is widely known as automatic target recognition (ATR). A typical ATR algorithm comprises the extraction of a meaningful set of features from target imagery followed by a decision engine that performs class assignment. While ATR algorithms have significantly increased in sophistication over the past two decades, two outstanding challenges have been identified in the rich body of ATR literature: 1) the desire to mine complementary merits of distinct feature sets (also known as feature fusion), and 2) the ability of the classifier to excel even as training SAR images are limited. We propose to apply recent advances in probabilistic graphical models to address these challenges. In particular we develop a two-stage target recognition framework that combines the merits of distinct SAR image feature representations with discriminatively learned graphical models. The first stage projects the SAR image chip to informative feature spaces that yield multiple complementary SAR image representations. The second stage models each individual representation using graphs and combines these initially disjoint and simple graphs into a thicker probabilistic graphical model by leveraging a recent advance in discriminative graph learning. Experimental results on the benchmark moving and stationary target acquisition and recognition (MSTAR) data set confirm the benefits of our framework over existing ATR algorithms in terms of improvement in recognition rates. The proposed graphical classifiers are particularly robust when feature dimensionality is high and number of training images is small, a commonly observed constraint in SAR imagery-based target recognition.

Extended Object Tracking with Random Hypersurface Models

Abstract: The random hypersurface model (RHM) is introduced for estimating a shape approximation of an extended object in addition to its kinematic state. An RHM represents the spatial extent by means of randomly scaled versions of the shape boundary. In doing so, the shape parameters and the measurements are related via a measurement equation that serves as the basis for a Gaussian state estimator. Specific estimators are derived for elliptic and star-convex shapes.

Approximate evaluation of marginal association probabilities with belief propagation

Abstract: Data association, the problem of reasoning over correspondence between targets and measurements, is a fundamental problem in tracking. This paper presents a graphical model formulation of data association and applies an approximate inference method, belief propagation (BP), to obtain estimates of marginal association probabilities. We prove that BP is guaranteed to converge, and bound the number of iterations necessary. Experiments reveal a favourable comparison to prior methods in terms of accuracy and computational complexity.

Multipath exploitation in through-the-wall radar imaging using sparse reconstruction

Abstract: Multipath exploitation and compressive sensing (CS) have both been applied independently to through-the-wall radar imaging (TWRI). Fast and efficient data acquisition is desired in scenarios where multipath effects cannot be neglected. Hence, we combine the two methods to achieve good image reconstruction in multipath environments from few spatial and frequency measurements. Ghost targets appear in the scene primarily due to specular reflections from interior walls and multiple reflections within the front wall. Assuming knowledge of the room geometry, we can invert the multipath model and eliminate ghosts by means of CS. We develop effective methods for the reconstruction of stationary scenes, which employ a group sparse CS approach. Additionally, we separate the target and wall contributions to the image by a sparse reconstruction approach joining wall and target models, which allows suppression of the ghosts and increased signal-to-clutter ratio (SCR) at the target locations. Effectiveness of the proposed approach is demonstrated using both simulated and real data.

New chirp sequence radar waveform

Abstract: The general requirement in the automotive radar application is to measure the target range R and radial velocity v r simultaneously and unambiguously with high accuracy and resolution even in multitarget situations, which is a matter of the appropriate waveform design. Based on a single continuous wave chirp transmit signal, target range R and radial velocity v r cannot be measured in an unambiguous way. Therefore a so-called multiple frequency shift keying (MFSK) transmit signal was developed, which is applied to measure target range and radial velocity separately and simultaneously. In this case the radar measurement is based on a frequency and additionally on a phase measurement, which suffers from a lower estimation accuracy compared with a pure frequency measurement. This MFSK waveform can therefore be improved and outperformed by a chirp sequences waveform. Each chirp signal has in this case very short time duration T chirp . Therefore the measured beat frequency f B is dominated by target range R and is less influenced by the radial velocity v r . The range and radial velocity estimation is based on two separate frequency measurements with high accuracy in both cases. Classical chirp sequence waveforms suffer from possible ambiguities in the velocity measurement. It is the objective of this paper to modify the classical chirp sequence to get an unambiguous velocity measurement even in multitarget situations.

Polyphase-coded FM (PCFM) radar waveforms, part I: implementation

Abstract: Polyphase radar codes promise enhanced performance and flexibility due to greater design freedom. While the search for better codes continues, the implementation issues of transmitter bandlimiting and nonlinear distortion have precluded their widespread use in high-power systems. This paper introduces a modified continuous phase modulation implementation that converts an arbitrary polyphase code into a nonlinear frequency-modulated waveform that is constant envelope and spectrally well contained. Experimental results assess the receive sampling and pulse compression effects.

Energy-efficient maneuvering and communication of a single UAV-based relay

Abstract: We focus on energy-efficient circular maneuvering and communication for a small unmanned aerial vehicle (UAV)-based relay, acting as a communication relay for connecting two stationary ground nodes. Based on an energy-efficiency metric, which is defined as the ratio of network capacity to the power consumption of both maneuvering and communication, we formulate an energy efficiency maximization problem with two variables: speed and load factor, of the UAV-based relay, and then obtain a closed-form suboptimal solution.

Polyphase-coded FM (PCFM) radar waveforms, part II: optimization

Abstract: This paper addresses polyphase code optimization with respect to the nonlinear frequency modulation waveform generated by the continuous phase modulation implementation. A greedy search leveraging the complementary metrics of peak sidelobe level, integrated sidelobe level, and spectral content yield extremely low range sidelobes relative to waveform time-bandwidth product. Transmitter distortion is also incorporated into the optimization via modeling and actual hardware. Thus the physical radar emission can be designed to address spectrum management and enable the physical realization of advanced waveform-diverse schemes.

Active power management system for an unmanned aerial vehicle powered by solar cells, a fuel cell, and batteries

Abstract: 200W class, low-speed, long-endurance unmanned aerial vehicle (UAV) that employs solar cells, a fuel cell, and a battery pack as its power sources is considered. This study applies an active power management method that directs each individual source to generate the appropriate power, depending on the power supply and demand, instead of the passive method in which the power sources irresponsibly generate power, depending on their characteristics. The power management system (PMS) under active management determines the power output from each source. The flight test of the UAV with a PMS onboard is conducted for 3.8 h. The active PMS verifies its own feasibility as it successfully keeps the power sources within their proper operational bounds and maintains a target state-of-charge of 45%, while responding to the various conditions associated with the power required. In addition, through a comparison of flight test results with a power simulation of the passive method, the usefulness, advantages, and disadvantages of an active power management method over a passive method are investigated.

3D interferometric ISAR imaging of noncooperative targets

Abstract: Inverse synthetic aperture radar (ISAR) images are frequently used in target classification and recognition applications. Nevertheless, the interpretation of ISAR images remains problematic for several reasons. One of these is the fact that the image plane cannot be defined by the user but instead depends on the target's own motions and on its relative position with respect to the radar. In order to overcome the problem of interpreting two-dimensional (2D) ISAR images, a method for three-dimensional (3D) reconstruction of moving targets is presented. This method is based on the use of a dual interferometric ISAR system. The interferometric phases measured from two orthogonal baselines are used to jointly estimate the target's effective rotation vector and the heights of the scattering centers with respect to the image plane. The scattering center extraction from the ISAR image is performed by applying a multichannel CLEAN technique. Finally, a 3D image of the moving target is reconstructed from the 3D spatial coordinates of the scattering centers. The effectiveness and robustness of the proposed algorithm is first proven theoretically and then tested against several radar-target scenarios as well as in the presence of noise.

Augmented L 1 adaptive tracking control of quad-rotor unmanned aircrafts

Abstract: This paper addresses a new trajectory tracking controller for quad-rotor aircrafts in the presence of time-varying aerodynamic effect and bounded external disturbance using the novel L 1 adaptive control methodology augmented with nonlinear feed-forward compensations. The proposed augmented L 1 adaptive controller achieves uniformly bounded transient and asymptotic tracking of the output signal for any designated bounded reference trajectory. Finally, simulations of tracking a circular reference trajectory are performed to illustrate the validity of the proposed controller.

Sub-Nyquist radar prototype: Hardware and algorithm

Abstract: Traditional radar sensing typically employs matched filtering between the received signal and the shape of the transmitted pulse. Matched filtering (MF) is conventionally carried out digitally, after sampling the received analog signals. Here, principles from classic sampling theory are generally employed, requiring that the received signals be sampled at twice their baseband bandwidth. The resulting sampling rates necessary for correlation-based radar systems become quite high, as growing demands for target distinction capability and spatial resolution stretch the bandwidth of the transmitted pulse. The large amounts of sampled data also necessitate vast memory capacity. In addition, real-time data processing typically results in high power consumption. Recently, new approaches for radar sensing and estimation were introduced, based on the finite rate of innovation (FRI) and Xampling frameworks. Exploiting the parametric nature of radar signals, these techniques allow significant reduction in sampling rate, implying potential power savings, while maintaining the system's estimation capabilities at sufficiently high signal-to-noise ratios (SNRs). Here we present for the first time a design and implementation of an Xampling-based hardware prototype that allows sampling of radar signals at rates much lower than Nyquist. We demonstrate by real-time analog experiments that our system is able to maintain reasonable recovery capabilities, while sampling radar signals that require sampling at a rate of about 30 MHz at a total rate of 1 MHz.

Robust PCA micro-doppler classification using SVM on embedded systems

Abstract: In this paper a novel feature extraction technique for micro-Doppler classification and its real time implementation using SVM on an embedded low-cost DSP are presented. The effectiveness of the proposed technique is improved through the exploitation of the outlier rejection capabilities of the Robust PCA in place of the classic PCA.

Subarray-based frequency diverse array radar for target range-angle estimation

Abstract: Phased-array is widely used in communication, radar, and navigation systems, but the beam steering is fixed in an angle for all range cells. Frequency diverse array (FDA) provides a range-dependent beamforming, but it cannot estimate directly both the range and angle of a target. This paper proposes a subarray-based FDA radar, with an aim to localize the target in the range-angle domain.We divide the whole FDA array into two subarrays, which employ two different frequency increments. In doing so, the target's range and angle are estimated directly from the transmit-receive beamforming output peak. The estimation performance is examined by analyzing the minimum mean variance square error (MMSE) and the Cramer-Rao lower bound (CRLB) versus signal-to-noise ratio (SNR). The corresponding transmit-receive beampattern and signal-to-interference plus noise ratio (SINR) are also formalized. Moreover, the CRLB can be used to optimally design the frequency increments. The effectiveness is verified by simulation results.

Aerial-terrestrial communications: terrestrial cooperation and energy-efficient transmissions to aerial base stations

Abstract: Hybrid aerial-terrestrial communication networks based on low-altitude platforms are expected to meet optimally the urgent communication needs of emergency relief and recovery operations for tackling large-scale natural disasters. The energy-efficient operation of such networks is important given that the entire network infrastructure, including the battery-operated ground terminals, exhibits requirements to operate under power-constrained situations. In this paper, we discuss the design and evaluation of an adaptive cooperative scheme intended to extend the survivability of the battery-operated aerial-terrestrial communication links. We propose and evaluate a real-time adaptive cooperative transmission strategy for dynamic selection between direct and cooperative links based on the channel conditions for improved energy efficiency. We show that the cooperation between mobile terrestrial terminals on the ground could improve energy efficiency in the uplink, depending on the temporal behavior of the terrestrial and aerial uplink channels. The corresponding delay in having cooperative (relay-based) communications with relay selection is also addressed. The simulation analysis corroborates that the adaptive transmission technique improves overall energy efficiency of the network whilst maintaining low latency, enabling real-time applications.

Tri-feature-based detection of floating small targets in sea clutter

Abstract: It is always a challenging problem for marine surface surveillance radar to detect sea-surface floating small targets. Conventional detectors using incoherent integration and adaptive clutter suppression have low detection probabilities for such targets with weak returns and unobservable Doppler shifts. In this paper, three features of a received vector at a resolution cell-the relative amplitude, relative Doppler peak height, and relative entropy of the Doppler amplitude spectrum-are exploited to give returns with targets from sea clutter. Real datasets show that each feature alone has some discriminability, and the three features jointly exhibit strong discriminability. Due to diversity of targets in practice, it is impossible to get features of returns with all kinds of targets. We recast detection of sea-surface floating small targets as a one-class anomaly detection problem in the 3D feature space. A fast convexhull learning algorithm is proposed to learn the decision region of the clutter pattern from feature vectors of clutter-only observations. As a result, a tri-feature-based detector is developed. The experiment results for the IPIX datasets show that the proposed detector at an observation time of several seconds attains better detection performance than several existing detectors.

Decorrelated unbiased converted measurement Kalman filter

Abstract: Converted measurement tracking is a technique that filters in the coordinate system where the underlying process of interest is linear and Gaussian and requires the measurements to be nonlinearly transformed to fit. The goal of the transformation is to allow for tracking in the coordinate system that is most natural for describing system dynamics. There are two potential issues that arise when performing converted measurement tracking. The first is conversion bias that occurs when the measurement transformation introduces a bias in the expected value of the converted measurement. The second is estimation bias that occurs because the estimate of the converted measurement error covariance is correlated with the measurement noise, leading to a biased Kalman gain. First, previously proposed unbiased conversions are examined. Following this, the decorrelated unbiased converted measurement approach is presented. Results show that to overcome conversion bias and estimation bias, an unbiased measurement conversion should be employed that calculates the converted measurement error covariance using the predicted measurement. The conversion approaches are evaluated in tracking scenarios relevant to radar and sonar measurements.

Rank-Constrained Maximum Likelihood Estimation of Structured Covariance Matrices

Abstract: This paper develops and analyzes the performance of a structured covariance matrix estimate for the important practical problem of radar space-time adaptive processing in the face of severely limited training data. Traditional maximum likelihood (ML) estimators are effective when training data are abundant, but they lead to poor estimates, degraded false alarm rates, and detection loss in the realistic regime of limited training. The problem is exacerbated by recent advances, which have led to high-dimensionalof the observations arising from increased antenna elements, as well as higher temporal resolution (time epochs and finally=). This work addresses the problem by incorporating constraints in the ML estimation problem obtained from the geometry and physics of the airborne phased array radar scenario. In particular, we exploit the structure of the disturbance covariance and, importantly, knowledge of the clutter rank to derive a new rank-constrained maximum likelihood (RCML) estimator of clutter and disturbance covariance. We demonstrate that despite the presence of the challenging rank constraint, the estimation can be transformed to a convex problem and derive closed-form expressions for the estimated covariance matrix. Performance analysis using the knowledge-aided sensor signal processing and expert reasoning data set (where ground truth covariance is made available) shows that the proposed estimator outperforms state-of-the-art alternatives in the sense of a higher normalized signal-to-interference and noise ratio. Crucially, the RCML estimator excels for low training, including the notoriously difficult regime of K≤N training samples.

Feedback control for spacecraft reorientation under attitude constraints via convex potentials

Abstract: A novel guidance algorithm is proposed for the attitude reorientation of a rigid body spacecraft in the presence of multiple types of attitude-constrained zones. In this direction, two types of attitude-constrained zones are first developed using unit quaternions, namely, the attitude-forbidden and -mandatory zones. The paper then utilizes a convex parameterization of forbidden and mandatory zones for constructing a strictly convex logarithmic barrier potential that is subsequently used for the synthesis of feedback attitude control laws while the inevitable unwinding phenomenon is given a simple and effective remedy. Model-independent and model-dependent control laws are then implemented by using the Lyapunov direct method and the modified integrator backstepping method. The paper concludes with a set of simulation results to evaluate the effectiveness and demonstrate the viability of the proposed methodology.

Detection of RF Electronics by Multitone Harmonic Radar

Abstract: Multitone harmonic radar is presented. The radar transmits multiple closely-spaced tones and receives nonlinear mixing products as well as harmonics. Harmonic and multitone responses are recorded from commercially-available RF devices. An original method for discriminating between electronic targets, by receiving at least two nonlinear mixing products near a harmonic, is presented. Target detection is demonstrated experimentally for a novel pulsed two-tone harmonic radar. Experimental results are extrapolated to estimate radar design parameters to achieve a realistic standoff range.

TDOA based direct positioning maximum likelihood estimator and the cramer-rao bound

Abstract: The maximum likelihood estimator (MLE) and its performance for the localization of a stationary emitter using a network of spatially separated passive stationary sensors is presented. The conventional approach for localization using multiple sensors is to first estimate the time differences of arrival (TDOAs) independently between pairs of sensors and then find the location of the emitter using the intersection point of the hyperbolas defined by these TDOAs. It has recently been shown that this two-step approach is suboptimal and an alternate direct position determination (DPD) approach has been proposed. In the work presented here we take the DPD approach to derive the MLE and show that the MLE outperforms the conventional two-step approach.We analyze the two commonly occurring cases of signal waveform unknown and signal waveform known with unknown transmission time. This paper covers a wide variety of transmitted signals such as narrowband or wideband, lowpass or bandpass, etc. Sampling of the received signals has a quantization-like effect on the location estimate and so a continuous time model is used instead.We derive the Fisher information matrix (FIM) and show that the proposed MLE attains the Cramer-Rao lower bound (CRLB) for high signal-to-noise ratios (SNRs).

Kalman and smooth variable structure filters for robust estimation

Abstract: The extended Kalman filter (EKF) and the unscented Kalman filter (UKF) are among the most popular estimation methods. The smooth variable structure filter (SVSF) is a relatively new sliding mode estimator. In an effort to use the accuracy of the EKF and the UKF and the robustness of the SVSF, the filters have been combined, resulting in two new estimation strategies, called the EK-SVSF and the UK-SVSF, respectively. The algorithms were validated by testing them on a well-known target tracking computer experiment.

Diagnosis of Airspeed Measurement Faults for Unmanned Aerial Vehicles

Abstract: Airspeed sensor faults are common causes for incidents with unmanned aerial vehicles (UAV) with pitot tube clogging or icing being the most common causes. Timely diagnosis of such faults or other artifacts in signals from airspeed sensing systems could potentially prevent crashes. This paper employs parameter adaptive estimators to provide analytical redundancies and a dedicated diagnosis scheme is designed. Robustness is investigated on sets of flight data to estimate distributions of test statistics. The result is robust diagnosis with adequate balance between false alarm rate and fault detectability.

Cognitive random stepped frequency radar with sparse recovery

Abstract: Random stepped frequency (RSF) radar, which transmits random-frequency pulses, can suppress the range ambiguity, improve convert detection, and possess excellent electronic counter-countermeasures (ECCM) ability [1]. In this paper, we apply a sparse recovery method to estimate the range and Doppler of targets. We also propose a cognitive mechanism for RSF radar to further enhance the performance of the sparse recovery method. The carrier frequencies of transmitted pulses are adaptively designed in response to the observed circumstance. We investigate the criterion to design carrier frequencies, and efficient methods are then devised. Simulation results demonstrate that the adaptive frequency-design mechanism significantly improves the performance of target reconstruction in comparison with the nonadaptive mechanism.

Relative pose estimation for cylinder-shaped spacecrafts using single image

Abstract: This paper describes a novel framework to determine the relative pose and range of a solid-of-revolution-shaped spacecraft from a single image without any artificial beacons. The translation and the symmetry axis of the spacecraft can be estimated from the imaged cross sections of the spacecraft body. Then the pose and range of the spacecraft are fully determined by means of the images of its solar panels and asymmetric feature. Our method has been validated by both synthetic and real images.

Distributed attitude control for multiple spacecraft with communication delays

Abstract: The work presented here considers the attitude synchronization problem for a group of spacecraft in the presence of communication delays. Based on the backstepping control and finite-time control techniques, a novel nonsmooth distributed cooperative attitude control algorithm is proposed for multiple spacecraft with attitude described by quaternion. Rigorous proof shows that attitude synchronization can be achieved asymptotically under the proposed control law if the communication topology graph among the spacecraft is strongly connected. Finally, a simulation example is given to demonstrate the efficiency of the proposed method.

Maximum a posteriori–based angular superresolution for scanning radar imaging

Abstract: Because a scanning radar system works as a noncoherent sensor, it is suitable for any geometry situation, and it has significant and extensive applications, such as surveillance, autonomous landing of aircraft, navigation, and guidance. After the pulse compression technique of improving range resolution was presented, angular resolution became crucial for a scanning radar system. In this paper, a scheme of angular superresolution based on maximum a posteriori (MAP) framework is proposed. First, the received signal in azimuth is modeled as a mathematical convolution of the antenna pattern and the targets' scattering. Then, the principle of the angular superresolution algorithm, superresolution performance analysis, and computational implementation are presented. The algorithm can endure more significant disturbance of noise than conventional approaches. Simulation validates that the method can improve the radar angular resolution at least four times, even when the signal-to-noise ratio (SNR) is 10 dB. Furthermore, real data processing has proved the effectiveness of the proposed method.

Neighborhood Geometric Center Scaling Embedding for SAR ATR

Abstract: Feature extraction from high-dimensional synthetic aperture radar images is one of the key steps for SAR automatic target recognition. In this paper, we propose a new approach to SAR image feature extraction that is named neighborhood geometric center scaling embedding, which is based on manifold learning theory. In our framework, neighborhood geometric center scaling is introduced to construct neighborhood relationships. The samples are endowed with clear clustering directions in dimensionality reduction, and the classification is better conducted in the feature space than in the original space. Moreover, by introducing neighborhood geometric center scaling, the influence of neighbor parameters on recognition performance is reduced effectively. The experiment based on the Moving and Stationary Target Acquisition and Recognition database shows that the proposed method has better recognition performance and higher stability than other methods.

Power allocation and waveform design for the compressive sensing based MIMO radar

Abstract: By exploring sparsity in the target space, compressive sensing (CS) based multi-input multi-output (MIMO) radar systems achieve either the same localization performance as traditional methods but with significantly fewer measurements, or significantly improved performance with the same number of measurements The work presented here investigates the performance gain of CS-MIMO radars, stemming from optimal power allocation among the transmit antennas, or optimal waveform design In both cases, the optimization criterion is the minimization of the coherence between the target returns from different search cells, or equivalently, the coherence of the columns of the sensing matrix