Showing papers in "IEEE Transactions on Aerospace and Electronic Systems in 2016"
TL;DR: A combination of a convolutional neural network, which belongs to the deep learning research field, and support vector machines is presented as an efficient automatic target recognition system to improve its robustness against imaging errors and target variations.
Abstract: A combination of a convolutional neural network, which belongs to the deep learning research field, and support vector machines is presented as an efficient automatic target recognition system. Additional training methods that incorporate prior knowledge to the classifier and further improve its robustness against imaging errors and target variations are also presented. These methods generate artificial training data by elastic distortion and affine transformations that represent typical examples of image errors, like a changing range scale dependent on the depression angle or an incorrectly estimated aspect angle. With these examples presented to the classifier during the training, the system should become invariant against these variations and thus more robust. For the classification, the spotlight synthetic aperture radar images of the moving and stationary target acquisition and recognition database are used. Results are shown for the ten class database with a forced decision classification as well as with rejection class.
189 citations
TL;DR: A model-free–based terminal sliding-mode control strategy to control the attitude and position of a quadrotor whose model includes parameter variations, uncertainties, and external disturbances is developed.
Abstract: In this paper, a model-free–based terminal sliding-mode control (MFTSMC) strategy is developed to control the attitude and position of a quadrotor whose model includes parameter variations, uncertainties, and external disturbances. The proposed MFTSMC combines a model-free control approach with a sliding-mode technique and makes possible to eliminate the tracking error in a finite time. To demonstrate the performance and effectiveness of the proposed MFTSMC, numerical simulation results have been obtained and compared with corresponding results for PID, backstepping and sliding-mode controls.
168 citations
TL;DR: Novel Student's t based approaches for formulating a filter and smoother, which utilize heavy tailed process and measurement noise models, are found through approximations of the associated posterior probability density functions.
Abstract: Novel Student’s t based approaches for formulating a filter and smoother, which utilize heavy tailed process and measurement noise models, are found through approximations of the associated posterior probability density functions. Simulation results for manoeuvring target tracking illustrate that the proposed methods substantially outperform existing methods in terms of the root mean square error.
166 citations
TL;DR: An effective small-target detection approach based on weighted image entropy that aims to improve the signal-to-noise ratio for cases in which jamming objects in the scene have similar thermal intensity measure with respect to the background as small target is proposed.
Abstract: We propose an effective small-target detection approach based on weighted image entropy. The approach weights the local entropy measure by the multiscale grayscale difference followed by an adaptive threshold operation, which aims to improve the signal-to-noise ratio for cases in which jamming objects in the scene have similar thermal intensity measure with respect to the background as small target. The detection capability of the proposed approach has been validated on six real sequences, and the results demonstrate its significance and improvement.
164 citations
TL;DR: This paper reviews research into solving the two-dimensional (2D) rectangular assignment problem and combines the best methods to implement a k-best 2D rectangular assignment algorithm with bounded runtime.
Abstract: This paper reviews research into solving the two-dimensional (2D) rectangular assignment problem and combines the best methods to implement a k-best 2D rectangular assignment algorithm with bounded runtime. This paper condenses numerous results as an understanding of the "best" algorithm, a strong polynomial-time algorithm with a low polynomial order (a shortest augmenting path approach), would require assimilating information from many separate papers, each making a small contribution. 2D rectangular assignment Matlab code is provided.
135 citations
TL;DR: Simulation results indicate that cooperative space object tracking algorithms provide better results than algorithms using a single sensor, the consensus-based tracking algorithms can achieve performance close to that of the centralized algorithms, and the Cub-ICF and Cub-KCF outperform the conventional ICF and KCF for a challenging space objecttracking case shown in the paper.
Abstract: Cooperative tracking plays a key role in space situation awareness for scenarios with a limited number of observations or poor performance of a single sensor or both. To use the information from multiple networked sensors, both centralized and decentralized fusion algorithms can be used. Compared with centralized fusion algorithms, decentralized fusion algorithms are more robust in terms of communication failure and computational burden. One popular distributed estimation approach is based on the average consensus that asymptotically converges to the estimate by multiple exchanges of neighborhood information. Consensus-based algorithms have become popular in recent years due to the fact that they do not require global knowledge of the network or routing protocols. The main contributions of this paper are 1) an effective space-based object (SBO) measurement model that considers the geometric relation of the Sun, the space object, the SBO sensor, and the Earth; 2) two consensus-based filters, the information-weighted consensus filter (ICF) and the Kalman consensus filter (KCF), are used to track space objects by using multiple SBO sensors; and 3) the cubature rule-embedded ICF (Cub-ICF) and KCF (Cub-KCF) are proposed to improve the accuracy of the ICF and KCF. Three scenarios that contain one or two space objects and four SBOs are used to test proposed algorithms. We also compare the consensus-based space object tracking algorithms with the centralized extended information filter (centralized EIF) and the centralized cubature information filter (centralized Cub-IF). The simulation results indicate that 1) cooperative space object tracking algorithms provide better results than algorithms using a single sensor, 2) the consensus-based tracking algorithms can achieve performance close to that of the centralized algorithms, and 3) the Cub-ICF and Cub-KCF outperform the conventional ICF and KCF for a challenging space object tracking case shown in the paper. The proposed Cub-ICF and Cub-KCF algorithms should facilitate the application of using consensus-based filters for cooperative space object tracking.
113 citations
TL;DR: This paper investigates the fixed-time fault-tolerant control problem of spacecraft rendezvous and docking with a freely tumbling target in the presence of external disturbance and thruster faults and proposes two fixed- time position controllers to guarantee that the closed-loop system is stable in finite time.
Abstract: This paper investigates the fixed-time fault-tolerant control problem of spacecraft rendezvous and docking with a freely tumbling target in the presence of external disturbance and thruster faults. More specifically, based on the attitude of the target spacecraft, a line-of-sight coordinate frame is defined first, and the dynamical equations relative to the tumbling target are derived to describe the relative position (not six degrees of freedom). Then two fixed-time position controllers are proposed to guarantee that the closed-loop system is stable in finite time in the sense of a fixed-time concept, even in the presence of simultaneous external disturbance and thruster faults. Numerical simulations illustrate that the chaser spacecraft can successfully perform the rendezvous using the proposed controllers.
109 citations
TL;DR: A hybrid method to model and analyze the dynamic coupling of a space robotic system avoids the singularity problem for solving differential equations; at the velocity level, each type of coupling motion was separately modeled and analyzed for different requirements.
Abstract: Resolving linear and angular momentum conservation equations in different ways, a hybrid method was proposed to model and analyze the dynamic coupling of a space robotic system. This method dealt with the coupling problems for the base’s centroid position at the position level and attitude at the velocity level. Based on the base centroid virtual manipulator concept, the coupled space was addressed to represent the base’s centroid position coupling. For different cases, the reachable coupled space, attitude-constrained coupled space, and free coupled space were defined. However, the coupling for the base’s velocities was decomposed into joint-to-base rotation, joint-to-base translation, end-to-base rotation, and end-to-base translation coupling types. The dependence of the rotation and translation coupling was revealed, and the coupling factors were determined to measure the coupling degree. Then, the coupling effect for different loads, installation positions, and joint configurations was analyzed. Coupled maps were established to plan the trajectory for minimizing disturbance. Compared with previous works, dynamic coupling at the position level avoids the singularity problem for solving differential equations; at the velocity level, each type of coupling motion was separately modeled and analyzed for different requirements. The proposed method is useful for practicalapplications, such as designing a new manipulator or using an existing robotic system.
101 citations
TL;DR: A novel method for predicting long-term target states based on mean-reverting stochastic processes using the Ornstein-Uhlenbeck (OU) process and to a time scaling law for the related uncertainty that in the long term is shown to be orders of magnitude lower than under the nearly constant velocity (NCV) assumption.
Abstract: We present a novel method for predicting long-term target states based on mean-reverting stochastic processes. We use the Ornstein-Uhlenbeck (OU) process, leading to a revised target state equation and to a time scaling law for the related uncertainty that in the long term is shown to be orders of magnitude lower than under the nearly constant velocity (NCV) assumption. In support of the proposed model, an analysis of a significant portion of real-world maritime traffic is provided.
89 citations
TL;DR: The spectrum sharing problem between a pulsed, search radar and 802.11 wireless local area network (WLAN) as the secondary is formulated and the protection region for such a search radar for a single secondary user (initially) as well as a random spatial distribution of multiple secondary users is computed.
Abstract: Co-existence between unlicensed networks that share a spectrum spatio-temporally with terrestrial (e.g., Air Traffic Control) and shipborne radars1 in 3 GHz band is attracting significant interest. Similar to every primary-secondary coexistence scenario, interference from unlicensed devices to a primary receiver must be within acceptable bounds. In this work, we formulate the spectrum sharing problem between a pulsed, search radar (primary) and 802.11 wireless local area network (WLAN) as the secondary.We compute the protection region for such a search radar for 1) a single secondary user (initially) as well as 2) a random spatial distribution of multiple secondary users. Furthermore, we also analyze the interference to the Wi-Fi devices from the radar's transmissions to estimate the impact on achievable WLAN throughput as a function of distance to the primary radar.
83 citations
TL;DR: To efficiently and reliably solve such a highly nonlinear (nonconvex) problem, it is presented how to obtain its convex relaxation and then proposed a regularization technique which is critical to guarantee the exactness of this relaxation.
Abstract: This paper investigates optimal flight of aerodynamically controlled missiles in the terminal phase with both angle of attack and bank angle as control inputs. Practical constraints on impact angle and dynamic pressure are all considered. To efficiently and reliably solve such a highly nonlinear (nonconvex) problem, we first present how to obtain its convex relaxation and then propose a regularization technique which is critical to guarantee the exactness of this relaxation. Numerical examples are given to illustrate the effectiveness of the proposed method.
TL;DR: To describe complex dynamical variation and practical observation distortion of the extension in size, shape, and orientation, two random matrix-based models are proposed to estimate the kinematic state and the extension jointly.
Abstract: The approach of using a random matrix for extended object and group target tracking (EOT and GTT) is appealing when the scattering centers of the object or the group targets themselves are (partially) unresolvable. Designing and effectively applying this approach relies on effective modeling of the extended object and the target group. To describe complex dynamical variation and practical observation distortion of the extension in size, shape, and orientation, two random matrix-based models are proposed. True measurement noise can also be incorporated into our proposed measurement model easily. Facilitated by special properties of the models, an approximate Bayesian approach is proposed to estimate the kinematic state and the extension jointly. For maneuvering EOT and GTT, a multiple-model approach is derived by moment matching. To evaluate what is proposed, a scenario for maneuvering EOT is simulated. The results illustrate the effectiveness of the proposed models and approach.
TL;DR: These methods are developed for both solution separation and Chi-squared RAIM and capture the fact that exclusion enables continuity risk reduction in exchange for a higher integrity risk.
Abstract: This paper provides new integrity and continuity risk evaluation methods for fault detection and exclusion (FDE) using receiver autonomous integrity monitoring (RAIM). These methods are developed for both solution separation (SS) and Chi-squared RAIM: they capture the fact that exclusion enables continuity risk reduction in exchange for a higher integrity risk. The two approaches are implemented in an example advanced RAIM (ARAIM) application for worldwide vertical guidance of aircraft using multiconstellation Global Navigation Satellite Systems (GNSS).
TL;DR: An optimal control theory with state variable inequality constraint is used to design the guidance law, for which a control energy performance index with the weighting function of the range-to-go is minimized.
Abstract: In this paper, an impact angle control guidance law, which considers simultaneously the impact angle and seeker's look angle constraints, is proposed for a constant speed missile against a stationary target. An optimal control theory with state variable inequality constraint is used to design the guidance law, for which a control energy performance index with the weighting function of the range-to-go is minimized. Various forms of guidance and trajectory shaping are possible by selecting a proper gain of the weighting function. To handle the seeker's look angle limits when the missile trajectory is highly curved by controlling the impact angle, the proposed guidance law generates three types of acceleration commands as the guidance phases: the first acceleration command for an initial guidance phase makes an initial seeker's look angle reach the maximum look angle; the second one for a midguidance phase maintains the maximum look angle; the final one for a terminal guidance phase intercepts the target with the desired impact angle. The performance of the proposed guidance law was investigated with nonlinear simulations for various engagement conditions.
TL;DR: Simulations indicate improvements in both failure detection and recovery speed, contributing to improved accuracy and stability in HCV fault-tolerant navigation.
Abstract: A fault-detection algorithm for a redundant multisensor navigation system for hypersonic cruise vehicles (HCVs) is proposed. The algorithm comprehensively diagnoses failures according to the failure level monitored by the sequential probability ratio test (SPRT) and chi-square test as well as the failure trend monitored by the SPRT. A test statistics feedback-reset loop is also added to shorten the recovery time after failure ceases. Simulations indicate improvements in both failure detection and recovery speed, contributing to improved accuracy and stability in HCV fault-tolerant navigation.
TL;DR: A proof is presented of how a hexagon-shaped hexacopter can be designed to keep the ability to reject disturbance torques in all directions while counteracting the effect of a failure in any of its motors.
Abstract: A proof is presented of how a hexagon-shaped hexacopter can be designed to keep the ability to reject disturbance torques in all directions while counteracting the effect of a failure in any of its motors. The method proposed is simpler than previous solutions, because it does not require change of the motor rotation direction or in-flight mechanical reconfiguration of the vehicle. It consists of tilting the rotor a small fixed angle with respect to the vertical axis. Design guidelines are presented to calculate the tilt angle to achieve fault-tolerant attitude control without losing significant vertical thrust. It is also formally proved that the minimum number of unidirectional rotating motors needed to have fault tolerance is 6 and that this can be achieved by tilting their rotors. This proof is essentially a control allocation analysis that recovers in a simple way a result already known: the standard configuration (without tilting the motors) is not fault tolerant. A simulation example illustrates the theory.
TL;DR: This paper addresses the problem of assessing the TBD performance with real data and in a particularly severe clutter environment, i.e., sea-clutter, using a set of real data from a ground-based sea-search radar and develops an improved decision logic for plot confirmation.
Abstract: Track-before-detect (TBD) is a popular incoherent energy integration technique aimed at improving detectability of weak targets. A number of studies are available in the literature demonstrating its efficacy against disturbance (whether noise or clutter), but most of them refer to synthetic data, i.e., relying on computer simulations. In this paper, we tackle the problem of assessing the TBD performance with real data and in a particularly severe clutter environment, i.e., sea-clutter. Precisely, using a set of real data from a ground-based sea-search radar, we implement TBD directly on the plot-lists coming from the radar plot extractor (this be can done with acceptable complexity by using an ad hoc dynamic programming algorithm), and demonstrate its effectiveness in reducing sea-clutter. As a further contribution, we also develop an improved decision logic for plot confirmation.
TL;DR: A new OSR algorithm is introduced and its performance is compared to other current approaches for open set image classification.
Abstract: Training sets for supervised classification tasks are usually limited in scope and only contain examples of a few classes. In practice, classes that were not seen in training are given labels that are always incorrect. Open set recognition (OSR) algorithms address this issue by providing classifiers with a rejection option for unknown samples. In this work, we introduce a new OSR algorithm and compare its performance to other current approaches for open set image classification.
TL;DR: Performance of the developed HGMM estimation algorithms is evaluated on benchmark tracking scenarios, and simulation results demonstrate their superiority to the state-of-the-art MM estimation algorithms in terms of accuracy and computational complexity.
Abstract: Estimation for discrete-time stochastic systems with parameters varying in a continuous space is considered in this paper. Justified by an analysis of model approximation, a novel approach, called hybrid grid multiple model (HGMM), is proposed for state estimation. The model set used by HGMM is a combination of a fixed coarse grid and an adaptive fine grid to cover the mode space with a relatively small number of models. Next, two fundamental problems of the HGMM approach—model-set sequence-conditioned estimation and design of adaptive fine models—are addressed. Then, based on two model-set designs by moment matching, HGMM estimation algorithms are presented. Finally, performance of the developed HGMM estimation algorithms is evaluated on benchmark tracking scenarios, and simulation results demonstrate their superiority to the state-of-the-art MM estimation algorithms in terms of accuracy and computational complexity.
TL;DR: The air-to-ground link is analysed by considering random fading and shadowing effects impairing the wireless transmission, together with a homogeneous spatial distribution of the terrestrial users, to find the optimal altitude of the aerial base station to maximise the terrestrial coverage given a certain service region.
Abstract: In this paper, we investigate the achievable coverage and the information rate of an aerial base station. The air-to-ground link is analysed by considering random fading and shadowing effects impairing the wireless transmission, together with a homogeneous spatial distribution of the terrestrial users. The distribution of the buildings in the underlying coverage region is modeled using the International Telecommunication Union-Recommendation statistical city model. For a given urban environment/region, we analytically quantify the achievable coverage probability and the information rate of users on the ground at a particular base station altitude. Furthermore, we analytically find the optimal altitude of the aerial base station to maximise the terrestrial coverage given a certain service region. Simulations are conducted to verify our analysis, and a close match between the analytical and simulation results is observed.
TL;DR: In this article, the vector observation construction procedures for the strapdown inertial navigation system (SINS) are investigated with the main focus on the vector observations construction procedure for the SINS.
Abstract: In this paper, the optimization-based alignment (OBA) methods are investigated with the main focus on the vector observation construction procedures for the strapdown inertial navigation system (SINS). The contributions of this study are twofold. First, the OBA method is extended to be able to estimate the gyroscope biases coupled with the attitude based on the construction process of the existing OBA methods. This extension transforms the initial alignment into an attitude estimation problem that can be solved by using the nonlinear filtering algorithms. The second contribution is the comprehensive evaluation of the OBA methods and their extensions with different vector observations construction procedures in terms of convergence speed and steady-state estimate by using field test data collected from different grades of SINS. This study is expected to facilitate the selection of appropriate OBA methods for different grades of SINS.
TL;DR: An advanced version of the extensive cancellation algorithm (ECA) is proposed for robust disturbance cancellation and target detection in passive radar and the benefits of the proposed approach are demonstrated against real data sets accounting for quite different passive radar applications.
Abstract: In this paper an advanced version of the extensive cancellation algorithm (ECA) is proposed for robust disturbance cancellation and target detection in passive radar. Firstly some specific limitations of previous ECA versions are identified when dealing with a highly time-varying disturbance scenario in the presence of slowly moving targets. Specifically, the need to rapidly adapt the filter coefficients is shown to yield undesired effects on low Doppler target echoes, along with the expected partial cancellation. Therefore a sliding version of the ECA is presented which operates on partially overlapped signal batches. The proposed modification to the original ECA is shown to appropriately counteract the limitations above by taking advantage of a smooth estimate of the filter coefficients. An efficient implementation is also discussed to limit the corresponding computational load. The benefits of the proposed approach are demonstrated against real data sets accounting for quite different passive radar applications.
TL;DR: In this article, a new algorithm that is capable of accurately estimating the biases even in the absence of filter gain information from local platforms is proposed for distributed tracking systems with intermittent track transmission.
Abstract: Bias estimation or sensor registration is an essential step in ensuring the accuracy of global tracks in multisensor-multitarget tracking. Most previously proposed algorithms for bias estimation rely on local measurements in centralized systems or tracks in distributed systems along with additional information such as covariances, filter gains, or targets of opportunity. In addition, it is generally assumed that such data are made available to the fusion center at every sampling time. In practical distributed multisensor-tracking systems, where each platform sends local tracks to the fusion center, only state estimates and, perhaps, their covariances are sent to the fusion center at nonconsecutive sampling instants or scans. That is, not all the information required for exact bias estimation at the fusion center is available in practical distributed-tracking systems. In this paper, a new algorithm that is capable of accurately estimating the biases even in the absence of filter gain information from local platforms is proposed for distributed-tracking systems with intermittent track transmission. Through the calculation of the posterior Cram´er-Rao lower bound and various simulation results, it is shown that the performance of the new algorithm, which uses the tracklet idea and does not require track transmission at every sampling time or exchange of filter gains, can approach the performance of the exact bias estimation algorithm that requires local filter gains.
TL;DR: An innovative approach is developed, rendering MIMO radar able to satisfy transmit beamforming constraint in space-domain as well as the waveform orthogonality requirement in time domain.
Abstract: Coherent multiple-input multiple-output (MIMO) radar is capable of forming a specific transmit beam pattern favorable for radar operation, but the transmitted waveforms from different antenna elements need to be orthogonal for coherent beamforming. In this work, an innovative approach is developed for MIMO radar waveform design in space-time domain, rendering MIMO radar able to satisfy transmit beamforming constraint in space-domain as well as the waveform orthogonality requirement in time domain. The approach is validated through simulations.
TL;DR: The optimal α-AMF detectors under K-distributed clutter model are competitive in performance with the adaptive generalized likelihood ratio test linear threshold detectors (GLRT-LTDs) that are optimal for the compound-Gaussian clutter with the inverse Gamma texture.
Abstract: In this paper, shape-parameter-dependent matched filter (MF) detectors are proposed for moving target detection in K-distributed clutter, which are specified by a single parameter α [0, 1], the α-MF detectors for short. The α-MF detectors include the MF and normalized matched filter (NMF) detectors as special examples with α = 0 and 1. The parameter α can be chosen to match clutter characteristics. An empirical formula is given that the optimal parameter α equals the number of integrated pulses divided by it plus the shape parameter of K-distributed clutter. It is proved that the α-MF detectors are constant false alarm rate (CFAR) with respect to the scale parameter, clutter covariance matrix, and Doppler steering vector. The properties of adaptive α-MF (α-AMF) detectors are discussed. For K-distributed clutter, the optimal α-MF detectors are superior to the MF and NMF detectors and are comparable with the optimal K-distributed detectors (OKDs) of high computational cost. Finally, real high-resolution sea clutter data are available to verify the proposed detectors. The optimal α-AMF detectors under K-distributed clutter model are competitive in performance with the adaptive generalized likelihood ratio test linear threshold detectors (GLRT-LTDs) that are optimal for the compound-Gaussian clutter with the inverse Gamma texture.
TL;DR: A fast coherent integration method based on time reversing transform, second-order keystone transform and Lv’s distribution, i.e., TRT-SKT-LVD, is proposed and can achieve a good balance between the computational cost and detection ability.
Abstract: This paper addresses the coherent integration problem for detecting a maneuvering target with high-order range migration (RM), where the velocity, acceleration, and jerk result in, respectively, the first-order range migration (FRM), second-order range migration (SRM), and third-order range migration (TRM) within the coherent pulse interval. A fast coherent integration method based on time reversing transform (TRT), second-order keystone transform (SKT) and Lv’s distribution (LVD), i.e., TRT-SKT-LVD, is proposed. In this method, TRT operation is first presented to remove the TRM and FRM simultaneously. After that, the SKT is employed to correct the remaining SRM and then the coherent accumulation could be obtained via LVD. The proposed algorithm is simple and fast in that it can be easily implemented by using complex multiplications, the fast Fourier transform (FFT), and inverse FFT (IFFT). Compared with the existing methods, the presented method can realize the coherent integration without any searching procedure and can achieve a good balance between the computational cost and detection ability. Both simulated and real data demonstrate the effectiveness of the proposed algorithm.
TL;DR: By minimizing the Tsallis entropy of an ISAR image, the MTEPA can significantly improve the computational efficiency, while retaining the image focal quality of the restored ISAR images, as compared to MSEPA and MREA.
Abstract: Inverse synthetic aperture radar (ISAR) is a coherent imaging system formed by conducting signal processing on the received data consisting of radar cross section reflected from a maneuvering target. Autofocus is an essential step of ISAR imaging, whose performance has a great influence on the quality of the radar image. Minimum Shannon entropy phase adjustment (MSEPA) and minimum Renyi entropy-based algorithm (MREA) have been widely used to compensate for the phase error in ISAR autofocus. However, MSEPA and MREA have some drawbacks in terms of the noise sensitivity and computational efficiency. Tsallis entropy (nonextensive entropy) is a useful measure to describe the thermostatistical properties of physical systems. This paper concentrates on the performance of minimum Tsallis entropy phase adjustment (MTEPA) instead of the Shannon entropy. By minimizing the Tsallis entropy of an ISAR image, the MTEPA can significantly improve the computational efficiency, while retaining the image focal quality of the restored ISAR images, as compared to MSEPA and MREA. The order q of Tsallis entropy can be experimentally found by analyzing both an image quality metric and the computation time. In experimental results, the effectiveness of the MTEPA is illustrated and analyzed with simulated targets consisting of point scatterers and measured Boeing-747 data.
TL;DR: Compared with existing DCEM methods, ACE-BOC has much higher design flexibility in the number of signal components, power ratio among components, hardware complexity of both transmitters and receivers, and spectrum compatibility.
Abstract: In the signal design of new generation global navigation satellite systems, there is a strong demand for multiplexing multiple binary spreading signals on two adjacent frequencies into an integrated signal with a constant envelope. In this paper, a dual-frequency constant envelope multiplexing (DCEM) technique with high-design flexibility based on subcarrier waveform reconstruction, named asymmetric constant envelope binary offset carrier (ACE-BOC), is presented. This multiplexing technique can be seen as a generalized alternate BOC. It can combine four or fewer independent, bipolar, direct sequence spread spectrum signals onto two sidebands of a spectrum-split integrated signal, where each sideband consists of two or fewer signals with arbitrary power ratio modulated onto the quadrature components. The design principle, diversified generation methods of ACE-BOC signals, as well as the characteristics in both time and frequency domains are investigated. Multiplexing efficiency and receiving performance of this signal are also analyzed. Analysis with typical examples shows that, for both transmitters and receivers, ACE-BOC signals have multiple processing forms. Compared with existing DCEM methods, ACE-BOC has much higher design flexibility in the number of signal components, power ratio among components, hardware complexity of both transmitters and receivers, and spectrum compatibility. Such high-level design flexibility provides system designers great room in signal scheme optimization for varied navigation applications in the future.
TL;DR: In this paper, the authors derived update equations for the multisensor probability hypothesis density (PHD) filter and derived a computationally tractable approximation that combines a greedy measurement partitioning algorithm with the Gaussian mixture representation of the PHD.
Abstract: The single-sensor probability hypothesis density (PHD) and cardinalized probability hypothesis density (CPHD) filters have been developed in the literature using the random finite set framework. The existing multisensor extensions of these filters have limitations such as sensor-order dependence, numerical instability, or high computational requirements. In this paper, we derive update equations for the multisensor CPHD filter. The multisensor PHD filter is derived as a special case. Exact implementation of the multisensor CPHD involves sums over all partitions of the measurements from different sensors and is thus intractable. We propose a computationally tractable approximation that combines a greedy measurement partitioning algorithm with the Gaussian mixture representation of the PHD. Our greedy approximation method allows the user to control the trade-off between computational overhead and approximation accuracy.
TL;DR: The fast backprojection (FBP) algorithm is introduced for video-SAR image formation and avoids unnecessary duplication of processing for the overlapping parts between consecutive video frames and achieves O(N2 log N) complexity through a recursive procedure.
Abstract: Video synthetic aperture radar (video-SAR) is a land-imaging mode where a sequence of images is continuously formed when the radar platform either flies by or circles the scene. In this paper, the fast backprojection (FBP) algorithm is introduced for video-SAR image formation. It avoids unnecessary duplication of processing for the overlapping parts between consecutive video frames and achieves O(N2 log N) complexity through a recursive procedure. To reduce the processing complexity in video-SAR system, the scene is partitioned into the general region (GR) and the region of interest (ROI). In different regions, different aperture lengths are used. The proposed method allows a direct trade between processing speed and focused quality for the GR, meanwhile reserving particular details in the ROI. The effectiveness is validated both for a simulated scene and for X-band SAR measurements from the Gotcha data set.