Showing papers in "IEEE Transactions on Aerospace and Electronic Systems in 2011"
TL;DR: A novel MPPT algorithm is proposed by introducing a particle swarm optimization (PSO) technique that uses only one pair of sensors to control multiple PV arrays, thereby resulting in lower cost, higher overall efficiency, and simplicity with respect to its implementation.
Abstract: Multiple photovoltaic (PV) modules feeding a common load is the most common form of power distribution used in solar PV systems. In such systems, providing individual maximum power point tracking (MPPT) schemes for each of the PV modules increases the cost. Furthermore, its v-i characteristic exhibits multiple local maximum power points (MPPs) during partial shading, making it difficult to find the global MPP using conventional single-stage (CSS) tracking. To overcome this difficulty, the authors propose a novel MPPT algorithm by introducing a particle swarm optimization (PSO) technique. The proposed algorithm uses only one pair of sensors to control multiple PV arrays, thereby resulting in lower cost, higher overall efficiency, and simplicity with respect to its implementation. The validity of the proposed algorithm is demonstrated through experimental studies. In addition, a detailed performance comparison with conventional fixed voltage, hill climbing, and Fibonacci search MPPT schemes are presented. Algorithm robustness was verified for several complicated partial shading conditions, and in all cases this method took about 2 s to find the global MPP.
527 citations
TL;DR: Detailed comparisons between the RFT and the well-known moving target detection (MTD) method are provided and it is shown that MTD is actually a special case of R FT and RFT is a kind of generalized Doppler filter bank processing for targets with across range unit (ARU) range walk.
Abstract: Based on the coupling relationship among radial velocity, range walk, and Doppler frequency of the moving target's echoes, a novel method is proposed, i.e., Radon-Fourier transform (RFT), to realize the long-time coherent integration for radar target detection. The RFT realizes the echoes spatial-temporal decoupling via joint searching along range and velocity directions, as well as the successive coherent integration via the Doppler filter bank. Besides, four equivalent RFTs are obtained with respect to the different searching parameters. Furthermore, a generalized form of RFT, i.e., generalized Radon-Fourier transform (GRFT), is also defined for target detection with arbitrary parameterized motion. Due to the similarity between the RFT and the well-known moving target detection (MTD) method, this paper provides detailed comparisons between them on five aspects, i.e., coherent integration time, filter bank structure, blind speed response, detection performance, and computational complexity. It is shown that MTD is actually a special case of RFT and RFT is a kind of generalized Doppler filter bank processing for targets with across range unit (ARU) range walk. Finally, numerical experiments are provided to demonstrate the equivalence among four kinds of RFTs. Also, it is shown that the RFT may obtain the coherent integration gain in the different noisy background and the target's blind speed effect may be effectively suppressed. In the meantime, both the weak target detection performance and the radar coverage of high-speed targets may be significantly improved via RFT without change of the radar hardware system.
414 citations
TL;DR: This paper develops an algorithm for optimizing the performance of the ground-to-relay links through control of the UAV heading angle, and proposes a smart handoff algorithm that updates node and relay assignments as the topology of the network evolves.
Abstract: In this paper, we investigate a communication system in which unmanned aerial vehicles (UAVs) are used as relays between ground-based terminals and a network base station. We develop an algorithm for optimizing the performance of the ground-to-relay links through control of the UAV heading angle. To quantify link performance, we define the ergodic normalized transmission rate (ENTR) for the links between the ground nodes and the relay, and derive a closed-form expression for it in terms of the eigenvalues of the channel correlation matrix. We show that the ENTR can be approximated as a sinusoid with an offset that depends on the heading of the UAV. Using this observation, we develop a closed-form expression for the UAV heading that maximizes the uplink network data rate while keeping the rate of each individual link above a certain threshold. When the current UAV relay assignments cannot meet the minimum link requirements, we investigate the deployment and heading control problem for new UAV relays as they are added to the network, and propose a smart handoff algorithm that updates node and relay assignments as the topology of the network evolves.
378 citations
TL;DR: It is shown that the proposed calibration technique is equivalent to the estimation of a rotation, scaling and translation transformation, and that the sensor alignment matrix is given by the solution of the orthogonal Procrustes problem.
Abstract: In this work a new algorithm is derived for the onboard calibration of three-axis strapdown magnetometers. The proposed calibration method is written in the sensor frame, and compensates for the combined effect of all linear time-invariant distortions, namely soft iron, hard iron, sensor nonorthogonality, and bias, among others. A maximum likelihood estimator (MLE) is formulated to iteratively find the optimal calibration parameters that best fit to the onboard sensor readings, without requiring external attitude references. It is shown that the proposed calibration technique is equivalent to the estimation of a rotation, scaling and translation transformation, and that the sensor alignment matrix is given by the solution of the orthogonal Procrustes problem. Good initial conditions for the iterative algorithm are obtained by a suboptimal batch least squares computation. Simulation and experimental results with low-cost sensors data are presented and discussed, supporting the application of the algorithm to autonomous vehicles and other robotic platforms.
314 citations
TL;DR: This paper proposes a method that uses an aircraft with a single-antenna GPS receiver and Pitot tube to estimate wind speed and direction and to calibrate the airspeed, useful for the operation of small unmanned aerial vehicles (UAVs).
Abstract: This paper proposes a method that uses an aircraft with a single-antenna GPS receiver and Pitot tube to estimate wind speed and direction and to calibrate the airspeed. This sensor combination alone does not determine the true attitude of the aircraft, so the wind parameters cannot be obtained directly from the measurements. However, if the aircraft flies at different headings, such as in banking turns or circle maneuvers, the wind magnitude and direction can be estimated from the geometrical relation between the wind and the measurements. An extended Kalman filter (EKF) is applied to estimate wind parameters. The EKF can also estimate the scaling factor used to convert dynamic pressure to airspeed. This is useful for the operation of small unmanned aerial vehicles (UAVs) because of difficulty in determining the airspeed scaling factor of a low-cost UAV. Simulations are performed for a constant 2-D wind. To test the effectiveness of the proposed method, flight tests of a small UAV are conducted. Simulations and flight test results show that the proposed method is effective.
251 citations
TL;DR: A detailed performance analysis for the novel radar long-time coherent integration method, i.e., Radon-Fourier transforms (RFT), and the causes and effective BSSL suppression methods are proposed.
Abstract: This paper gives a detailed performance analysis for the novel radar long-time coherent integration method, i.e., Radon-Fourier transforms (RFT). Some important properties of RFT, e.g., two-dimensional (2D) impulse response, 2D translational invariance, multitarget linear additivity, linear signal-to-noise ratio gain in additive white Gaussian noise (AWGN), as well as the 2D correlation function of transformed AWGN, are derived for continuous and discrete RFT, respectively. However, because of discrete pulse sampling, finite range resolution, and limited integration time, the "blind-speed sidelobes (BSSL)" of discrete RFT may inevitably appear in real applications. Although the BSSL are reduced with the increase of the blind-speed integer, they may still lead to false alarms or loss detections in a real multitarget scenario. Based on the analytic expression derived for BSSL, the causes of BSSL are analyzed and the effective BSSL suppression methods are proposed. Finally, numerical experiments are also provided to demonstrate the effectiveness of the proposed methods.
206 citations
TL;DR: A comprehensive theory of matched illumination waveforms for both deterministic and stochastic extended targets is presented and design of matched waveforms based on maximization of both signal-to-noise ratio (SNR) and mutual information (MI) is considered.
Abstract: A comprehensive theory of matched illumination waveforms for both deterministic and stochastic extended targets is presented. Design of matched waveforms based on maximization of both signal-to-noise ratio (SNR) and mutual information (MI) is considered. In addition the problem of matched waveform design in signal-dependent interference is extensively addressed. New results include SNR-based waveform design for stochastic targets, SNR-based design for a known target in signal-dependent interference, and MI-based design in signal-dependent interference. Finally we relate MI and SNR in the context of waveform design for stochastic targets.
186 citations
TL;DR: The implementation of Rényi divergence via the sequential Monte Carlo method is presented and the performance of the proposed reward function is demonstrated by a numerical example, where a moving range-only sensor is controlled to estimate the number and the states of several moving objects using the PHD filter.
Abstract: The context is sensor control for multi-object Bayes filtering in the framework of partially observed Markov decision processes (POMDPs). The current information state is represented by the multi-object probability density function (pdf), while the reward function associated with each sensor control (action) is the information gain measured by the alpha or Renyi divergence. Assuming that both the predicted and updated state can be represented by independent identically distributed (IID) cluster random finite sets (RFSs) or, as a special case, the Poisson RFSs, this work derives the analytic expressions of the corresponding Renyi divergence based information gains. The implementation of Renyi divergence via the sequential Monte Carlo method is presented. The performance of the proposed reward function is demonstrated by a numerical example, where a moving range-only sensor is controlled to estimate the number and the states of several moving objects using the PHD filter.
144 citations
TL;DR: The presented approach proves its suitability for the precise SAR focussing of the data acquired in general bistatic configurations for azimuth-invariance and topography-insensitivity.
Abstract: Due to the lack of an appropriate symmetry in the acquisition geometry, general bistatic synthetic aperture radar (SAR) cannot benefit from the two main properties of low-to-moderate resolution monostatic SAR: azimuth-invariance and topography-insensitivity. The precise accommodation of azimuth-variance and topography is a real challenge for efficent image formation algorithms working in the Fourier domain, but can be quite naturally handled by time-domain approaches. We present an efficient and practical implementation of a generalised bistatic SAR image formation algorithm with an accurate accommodation of these two effects. The algorithm has a common structure with the monostatic fast-factorised backprojection (FFBP), and is therefore based on subaperture processing. The images computed over the different subapertures are displayed in an advantageous elliptical coordinate system capable of incorporating the topographic information of the imaged scene in an analogous manner as topography-dependent monostatic SAR algorithms do. Analytical expressions for the Nyquist requirements using this coordinate system are derived. The overall discussion includes practical implementation hints and a realistic computational burden estimation. The algorithm is tested with both simulated and actual bistatic SAR data. The actual data correspond to the spaceborne-airborne experiment between TerraSAR-X and F-SAR performed in 2007 and to the DLR-ONERA airborne experiment carried out in 2003. The presented approach proves its suitability for the precise SAR focussing of the data acquired in general bistatic configurations.
127 citations
TL;DR: Simulations for a clutter-plus-jamming suppression application show that the proposed STAP algorithm outperforms the state-of-the-art reduced-rank schemes in convergence and tracking at significantly lower complexity.
Abstract: We develop a reduced-rank space-time adaptive processing (STAP) method based on joint iterative optimization of filters (JOINT) for airborne radar applications. The proposed method consists of a bank of full-rank adaptive filters, which forms the projection matrix, and an adaptive reduced-rank filter that operates at the output of the bank of filters. We describe the proposed method for both the direct-form processor (DFP) and the generalized sidelobe canceller (GSC) structures. Adaptive algorithms including the stochastic gradient (SG), the recursive least square (RLS), and their hybrid algorithms are derived for the efficient implementation of the JOINT STAP method. The computational complexity analysis of the proposed algorithms is shown in terms of the number of multiplications and additions per snapshot. Furthermore, the convexity analysis of the proposed method is carried out. Simulations for a clutter-plus-jamming suppression application show that the proposed STAP algorithm outperforms the state-of-the-art reduced-rank schemes in convergence and tracking at significantly lower complexity.
117 citations
TL;DR: This work provides two new adaptive detectors for Gaussian additive Noise and non-Gaussian additive noise which are modeled by the spherically invariant random vector (SIRV) and their statistical properties are derived and compared with simulations.
Abstract: In the general framework of radar detection, estimation of the Gaussian or non-Gaussian clutter covariance matrix is an important point. This matrix commonly exhibits a particular structure: for instance, this is the case for active systems using a symmetrically spaced linear array with constant pulse repetition interval. We propose using the particular persymmetric structure of the covariance matrix to improve the detection performance. In this context, this work provides two new adaptive detectors for Gaussian additive noise and non-Gaussian additive noise which is modeled by the spherically invariant random vector (SIRV). Their statistical properties are then derived and compared with simulations. The vast improvement in their detection performance is demonstrated by way of simulations or experimental ground clutter data. This allows for the analysis of the proposed detectors on both real Gaussian and non-Gaussian data.
TL;DR: An algorithm for high-speed small target detection and parameter estimation with narrowband radar is proposed and an improved method for fold factor estimation is presented.
Abstract: Target detection and parameter estimation are the principal problems of radar applications. The detection and parameter estimation for high-speed small targets are challenging for narrowband radar since the target may only occupy one range cell with small reflectivity. In addition, the high speed makes the target shift through range cells during the observation period, which makes it difficult to improve the target's reflecting energy coherent accumulation and signal-to-noise ratio (SNR). An algorithm for high-speed small target detection and parameter estimation with narrowband radar is proposed in this paper. Firstly three target motion parameters, i.e., the cross-range frequency modulation rate, ambiguous Doppler frequency, and fold factor are estimated one by one. Then based on the estimated parameters, a target detection method is proposed. In addition, we also analyze the detection performance under estimation error. After that, an improved method for fold factor estimation is presented. Simulation results have proved the validity of the proposed algorithm.
TL;DR: The preliminary performance assessment shows that the proposed TBD structures outperform the ML-PDA implementations especially in terms of probability of track detection (and for low signal-to-noise ratio (SNR) values).
Abstract: We propose and assess new algorithms for adaptive detection and tracking based on space-time data. At design stage we take into account possible spillover of target energy to adjacent range cells and assume a target kinematic model. Then, resorting to the generalized likelihood ratio test (GLRT) we derive track-before-detect (TBD) algorithms that can operate in scan-to-scan varying scenarios and, more important, that ensure the constant false track acceptance rate (CFTAR) property with respect to the covariance matrix of the disturbance. Moreover, we also propose CFTAR versions of the maximum likelihood-probabilistic data association (ML-PDA) algorithm capable of working with data from an array of sensors. The preliminary performance assessment, conducted resorting to Monte Carlo simulation, shows that the proposed TBD structures outperform the ML-PDA implementations especially in terms of probability of track detection (and for low signal-to-noise ratio (SNR) values).
TL;DR: A fault detection and diagnosis and a fault-tolerant control system for an unmanned aerial vehicle (UAV) subject to control surface failures and a nonlinear aircraft model designed for FTC researches has been proposed.
Abstract: A fault detection and diagnosis (FDD) and a fault-tolerant control (FTC) system for an unmanned aerial vehicle (UAV) subject to control surface failures are presented. This FDD/FTC technique is designed considering the following constraints: the control surface positions are not measured and some actuator faults are not isolable. Moreover, the aircraft has an unstable spiral mode and offers few actuator redundancies. Thus, to compensate for actuator faults, the healthy controls may move close to their saturation values and the aircraft may become uncontrollable; this is critical due to its open-loop unstability. A nonlinear aircraft model designed for FTC researches has been proposed. It describes the aerodynamic effects produced by each control surface. The diagnosis system is designed with a bank of unknown input decoupled functional observers (UIDFO) which is able to estimate unknown inputs. It is coupled with an active diagnosis method in order to isolate the faulty control. Once the fault is diagnosed, an FTC based on state feedback controllers aims at sizing the stability domain with respect to the flight envelope and actuator saturations while setting the dynamics of the closed-loop system. The complete system was demonstrated in simulation with a nonlinear model of the aircraft.
TL;DR: The performance of two nonlinear estimators is compared for the localization of a spacecraft and the behavior of the filters is compared on two sample missions: Earth-to-Moon transfer and geostationary orbit raising.
Abstract: The performance of two nonlinear estimators is compared for the localization of a spacecraft. It is assumed that range measurements are not available (as in deep space missions), and the localization problem is tackled on the basis of angles-only measurements. A dynamic model of the spacecraft accounting for several perturbing effects, such as Earth and Moon gravitational field asymmetry and errors associated with the Moon ephemerides, is employed. The measurement process is based on elevation and azimuth of Moon and Earth with respect to the spacecraft reference system. Position and velocity of the spacecraft are estimated using both the extended Kalman filter (EKF) and the unscented Kalman filter (UKF). The behavior of the filters is compared on two sample missions: Earth-to-Moon transfer and geostationary orbit raising.
TL;DR: This correspondence extends the Hough transform to N-dimensional data to efficiently combine multiple first-threshold crossings from moving targets to enhance the detection of targets in random clutter backgrounds through the application of track-before-detect (TBD) processing.
Abstract: The Hough transform (HT) algorithm detects straight-line features in two-dimensional data. This correspondence extends the HT to N-dimensional data to efficiently combine multiple first-threshold crossings from moving targets. The data dimensions can be the target position, its range and range-rate, and/or the first-threshold crossing times. This multi-dimensional HT (MHT) technique can be applied to enhance the detection of targets in random clutter backgrounds through the application of track-before-detect (TBD) processing.
TL;DR: Two novel group dynamical models within a continuous time setting using stochastic differential equations (SDE) that aim to mimic behavioural properties of groups are developed that can be combined with a group structure transition model to create realistic evolving group models.
Abstract: In this paper, we describe models and algorithms for detection and tracking of group and individual targets. We develop two novel group dynamical models within a continuous time setting using stochastic differential equations (SDE) that aim to mimic behavioural properties of groups. We also describe a possible way of modeling interactions between closely spaced targets using repulsive forces. These can be combined with a group structure transition model to create realistic evolving group models. We use a Markov chain Monte Carlo (MCMC)-particles algorithm to perform sequential inference. Computer simulations demonstrate the ability of the algorithm to detect and track targets within groups as well as to infer the correct group structure over time. The group tracking model is also applied to two sets of real ground moving target indicator (GMTI) radar data with group targets. The results show significant improvement in tracking accuracy over tracking without group models.
TL;DR: A complex-valued inverse Radon transform is put forward for imaging of rotating parts, which utilizes both the sinusoidal modulus and the phase of the radar echoes and analyze possible problems when applying these algorithms in real world situations and provide corresponding solutions.
Abstract: For a target with rotating parts, the ISAR image of its main body may be overshadowed by micro-Doppler signals, which are traditionally treated as interference and removed from radar echoes. However, it is potentially useful to perform imaging using micro-Doppler signals because they contain information on the geometrical structure of the rotating part. Inspired by the inverse Radon transform, well-known in the image processing community, this paper creates a data-recording model for two-dimensional (2D) ISAR imaging of rotating parts. Based on this model, the real-valued inverse Radon transform is applied to image formation in the range-slow time domain. Then we put forward a complex-valued inverse Radon transform for imaging of rotating parts, which utilizes both the sinusoidal modulus and the phase of the radar echoes. Additionally, we analyze possible problems when applying these algorithms in real world situations and provide corresponding solutions. Finally, the effectiveness of the algorithms is demonstrated using simulated and measured data.
TL;DR: A detailed analysis of the ambiguity function (AF) of a range of typical IEEE 802.11 signals obtained during a series of experimental trials finds that using Doppler with a suitable integration time can enable detection of typical personnel targets.
Abstract: Wireless transmission is becoming an increasingly widely available source of transmissions for passive radar detection. In this paper, we present a detailed analysis of the ambiguity function (AF) of a range of typical IEEE 802.11 signals obtained during a series of experimental trials. Theoretical analysis has been used to identify the average properties of basic signal types in terms of resolution and sidelobe levels in both the range and Doppler domains. The theoretical model of a range of typical 802.11 transmissions has been verified and range and Doppler resolutions have been investigated for a range of transmission types. It has been found that using Doppler with a suitable integration time can enable detection of typical personnel targets. A number of issues relating to the use of these transmissions have been identified during this study.
TL;DR: Simulation and trial test validate the performance of the proposed rapid fine strapdown INS alignment and make use of the forward and backward processes to repeatedly process the saved inertial measurement unit (IMU) data sequence to quickly obtain the initial strapdown attitude matrix.
Abstract: In order to solve the strapdown inertial navigation system (INS) alignment problem under the marine mooring condition, the rapid strapdown INS fine alignment method is proposed. This method uses the gravity in the inertial frame to deal with the lineal and angular disturbances. Also the forward and backward processes for strapdown INS calculation and filter estimation are designed. Making use of the forward and backward processes to repeatedly process the saved inertial measurement unit (IMU) data sequence could quickly obtain the initial strapdown attitude matrix. Simulation and trial test validate the performance of the proposed rapid fine strapdown INS alignment.
TL;DR: The Cramér-Rao lower bound (CRLB) for any unbiased estimator of the pulse delay is presented, and to retrieve the pulsar photon intensity function, the epoch folding procedure is characterized.
Abstract: How the relative position between two spacecraft can be estimated utilizing signals emitted from X-ray pulsars is explained. The mathematical models of X-ray pulsar signals are developed, and the pulse delay estimation problem is formulated. The Cramer-Rao lower bound (CRLB) for any unbiased estimator of the pulse delay is presented. To retrieve the pulsar photon intensity function, the epoch folding procedure is characterized. Based on epoch folding, two different pulse delay estimators are introduced, and their performance against the CRLB is studied. One is obtained by solving a least squares problem, and the other uses the cross correlation function between the empirical rate function and the true one. The effect of absolute velocity errors on position estimation is also studied. Numerical simulations are performed to verify the theoretical results.
TL;DR: Using a Neyman-Pearson (NP) framework for detection at the fusion center, the optimal fusion rule is derived and results demonstrating the efficiency of the copula-based fusion rule are shown.
Abstract: Detection of random signals under a distributed setting is considered. Due to the random nature of the spatial phenomenon being observed, the sensor decisions collected at the fusion center are correlated. Assuming that local detectors are single threshold binary quantizers, a novel approach for the fusion of correlated decisions is proposed using the theory of copulas. The proposed approach assumes only the knowledge of the marginal distribution of sensor observations but no prior knowledge of their joint distribution. Using a Neyman-Pearson (NP) framework for detection at the fusion center, the optimal fusion rule is derived. An example involving the detection of nuclear radiation is presented to illustrate the proposed approach, and results demonstrating the efficiency of the copula-based fusion rule are shown.
TL;DR: This paper studies DA games under the framework of a linear quadratic (LQ) formulation, and demonstrates with simulations that the LQ strategies based on the repetitive implementation can provide good control guidance laws for DA games.
Abstract: Techniques based on pursuit-evasion (PE) games are often applied in military operations of autonomous vehicles (AV) in the presence of mobile targets. Recently with increasing use of AVs, new scenarios emerge such as surveillance and persistent area denial. Compared with PE games, the actual roles of the pursuer and the evader have changed. In these emerging scenarios the evader acts as an intruder striking at some asset; at the same time the pursuer tries to destroy the intruder to protect the asset. Due to the presence of an asset, the PE game model with two sets of players (pursuers and evaders) is no longer adequate. We call this new problem a game of defending an asset(s) (DA). In this paper we study DA games under the framework of a linear quadratic (LQ) formulation. Three different DA games are addressed: 1) defending a stationary asset, 2) defending a moving asset with an arbitrary trajectory, and 3) defending an escaping asset. Equilibrium game strategies of the players are derived for each case. A repetitive scheme is proposed for implementation of the LQ strategies, and we demonstrate with simulations that the LQ strategies based on the repetitive implementation can provide good control guidance laws for DA games.
TL;DR: The aim is to design a robust fault detection filter such that, for all probabilistic packet dropouts, all unknown inputs and admissible uncertain parameters, the error between the residual and the fault signal is made as small as possible.
Abstract: This paper is concerned with the robust fault detection problem for a class of discrete-time networked systems with distributed sensors. Since the bandwidth of the communication channel is limited, packets from different sensors may be dropped with different missing rates during the transmission. Therefore, a diagonal matrix is introduced to describe the multiple packet dropout phenomenon and the parameter uncertainties are supposed to reside in a polytope. The aim is to design a robust fault detection filter such that, for all probabilistic packet dropouts, all unknown inputs and admissible uncertain parameters, the error between the residual (generated by the fault detection filter) and the fault signal is made as small as possible. Two parameter-dependent approaches are proposed to obtain less conservative results. The existence of the desired fault detection filter can be determined from the feasibility of a set of linear matrix inequalities that can be easily solved by the efficient convex optimization method. A simulation example on a networked three-tank system is provided to illustrate the effectiveness and applicability of the proposed techniques.
TL;DR: The problem of sensor resource management for multitarget tracking in decentralized tracking systems in which there is no central fusion center is considered and an efficient optimization-based algorithm is proposed here to address this problem in real time.
Abstract: The problem of sensor resource management for multitarget tracking in decentralized tracking systems is considered here. Inexpensive sensors available today are used in large numbers to monitor wide surveillance regions. However, due to frequency, power, and bandwidth limitations, there is an upper limit on the number of sensors that can be used by a fusion center (FC) at any one time. The problem is then to select the sensor subsets to be used at each sampling time in order to optimize the tracking performance (i.e., maximize the tracking accuracy of existing tracks and detect new targets as quickly as possible) under the given constraints. The architecture considered in this paper is decentralized in which there is no central fusion center (CFC); each FC communicates only with the neighboring FCs, as a result communication is restricted. In such cases each FC has to decide which sensors should be used by itself at each sampling time by considering which sensors can be used by neighboring FCs. An efficient optimization-based algorithm is proposed here to address this problem in real time. Simulation results illustrating the performance of the proposed algorithms are also presented to support its efficiency. The novelty lies in the discrete optimization formulation for large-scale sensor selection in decentralized networks.
TL;DR: This paper exploited the relation between the ambiguity function (AF) and the CRLB to calculate the Cramer-Rao lower bounds (CRLBs) for bistatic radar channels and compared with the monostatic counterparts as a function of the bistatics geometric parameters.
Abstract: In this paper we deal with the problem of calculating the Cramer-Rao lower bounds (CRLBs) for bistatic radar channels. To this purpose we exploited the relation between the ambiguity function (AF) and the CRLB. The bistatic CRLBs are analyzed and compared with the monostatic counterparts as a function of the bistatic geometric parameters. In the bistatic case both geometry factors and transmitted waveforms play an important role in the shape of the AF, and therefore in the estimation accuracy of the target range and velocity. In particular, the CRLBs depend on the target direction of arrival (DOA), the bistatic baseline length (BBL), and the distance between the target and the receiver. The CRLBs are then used to select the "optimum" bistatic channel (or set of channels) for the tracking of a radar target moving along a trajectory in a multistatic scenario.
TL;DR: A receding-horizon cooperative search algorithm is presented that jointly optimizes routes and sensor orientations for a team of autonomous agents searching for a mobile target in a closed and bounded region and it is shown that the team discovers the target in finite time with probability one.
Abstract: A receding-horizon cooperative search algorithm is presented that jointly optimizes routes and sensor orientations for a team of autonomous agents searching for a mobile target in a closed and bounded region. By sampling this region at locations with high target probability at each time step, we reduce the continuous search problem to a sequence of optimizations on a finite, dynamically updated graph whose vertices represent waypoints for the searchers and whose edges indicate potential connections between the waypoints. Paths are computed on this graph using a receding-horizon approach, in which the horizon is a fixed number of graph vertices. To facilitate a fair comparison between paths of varying length on nonuniform graphs, the optimization criterion measures the probability of finding the target per unit travel time. Using this algorithm, we show that the team discovers the target in finite time with probability one. Simulations verify that this algorithm makes effective use of agents and outperforms previously proposed search algorithms. We have successfully hardware tested this algorithm in two small unmanned aerial vehicles (UAVs) with gimbaled video cameras.
TL;DR: This work presents a new domain-specific architecture and protocol suite, including cross-layer optimizations between the physical, MAC, network, and transport layers, that provides selectable reliability for multiple applications within highly mobile tactical airborne networks.
Abstract: Highly-dynamic wireless environments present unique challenges to end-to-end communication networks, caused by the time-varying connectivity of high-velocity nodes combined with the unreliability of the wireless communication channel. Such conditions are found in a variety of networks, including those used for tactical communications and aeronautical telemetry. Addressing these challenges requires the design of new protocols and mechanisms specific to this environment. We present a new domain-specific architecture and protocol suite, including cross-layer optimizations between the physical, MAC, network, and transport layers. This provides selectable reliability for multiple applications within highly mobile tactical airborne networks. Our contributions for this environment include the transmission control protocol (TCP)-friendly transport protocol, AeroTP; the IP-compatible network layer, AeroNP; and the geolocation aware routing protocol AeroRP. Through simulations we show significant performance improvement over the traditional TCP/IP/MANET protocol stack.
TL;DR: The authors propose a novel technique for ATR using polarimetric ISAR (Pol-ISAR) images based on a model matching approach and results are obtained that show the effectiveness of such a technique.
Abstract: Automatic target recognition (ATR) is generally the reason why inverse synthetic aperture radar (ISAR) imaging systems are employed. Moreover, the use of fully polarimetric radar systems in radar imaging applications such as SAR and ISAR has enhanced both image quality and classification capabilities. The authors propose a novel technique for ATR using polarimetric ISAR (Pol-ISAR) images. The proposed method is based on a model matching approach. Results are obtained that show the effectiveness of such a technique.
TL;DR: A modification to the upper bound of the allowed reacquisition time for the current Wide Area Augmentation System (WAAS) Minimum Operational Performance Standards (MOPS) is recommended based on the availability analysis results and observed performance of a certified WAAS receiver.
Abstract: Strong ionospheric scintillation due to electron density irregularities inside the ionosphere is commonly observed in the equatorial region during solar maxima. Strong amplitude scintillation causes deep and frequent Global Positioning System (GPS) signal fading. Since GPS receivers lose carrier tracking lock at deep signal fading and the lost channel cannot be used for the position solution until reacquired, ionospheric scintillation is a major concern for GPS aviation in the equatorial area. Frequent signal fading also causes frequent reset of the carrier smoothing filter in aviation receivers. This leads to higher noise levels on the pseudo-range measurements. Aviation availability during a severe scintillation period observed using data from the previous solar maximum is analyzed. The effects from satellite loss due to deep fading and shortened carrier smoothing time are considered. Availability results for both vertical and horizontal navigation during the severe scintillation are illustrated. Finally, a modification to the upper bound of the allowed reacquisition time for the current Wide Area Augmentation System (WAAS) Minimum Operational Performance Standards (MOPS) is recommended based on the availability analysis results and observed performance of a certified WAAS receiver.