In this manuscript, the problem of detecting multiple targets and jointly estimating their spatial coordinates (namely, the range, the Doppler and the direction of arrival of their electromagnetic echoes) in a colocat...
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In this manuscript, the problem of detecting multiple targets and jointly estimating their spatial coordinates (namely, the range, the Doppler and the direction of arrival of their electromagnetic echoes) in a colocated multiple-input multiple-output radar system employing orthogonal frequency division multiplexing is investigated. It is well known its optimal solution, namely the joint maximum likelihood estimator of an unknown number of targets, is unfeasible because of its huge computational complexity. Moreover, until now, sub-optimal solutions have not been proposed in the technical literature. In this manuscript a novel approach to the development of reduced complexity solutions is illustrated. It is based on the idea of separating angle estimation from range-Doppler estimation, and of exploiting known algorithms for solving these two sub-problems. A detailed analysis of the accuracy and complexity of various detection and estimation methods based on this approach is provided. Our numerical results evidence that one of these methods is able to approach optimal performance in the maximum likelihood sense with a limited computational effort in different scenarios.
Sparse array designs have focused mostly on angular resolution (mainlobe width), peak sidelobe level and directivity factor of virtual arrays for multiple-inputmultiple-output (MIMO) radar. The notion of the MIMO rad...
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ISBN:
(纸本)9798350374520;9798350374513
Sparse array designs have focused mostly on angular resolution (mainlobe width), peak sidelobe level and directivity factor of virtual arrays for multiple-inputmultiple-output (MIMO) radar. The notion of the MIMO radar virtual array is based on the direct path assumption in that the direction-of-departure (DOD) and direction-of-arrival (DOA) of the targets are equal. However, the DOD and DOA of targets in multipath scenarios are likely to be very different. The identification of multipath targets requires DOD-DOA imaging using the transmit and receive arrays, not the virtual array. To improve the imaging of both direct path and multipath targets, we introduce several new criteria for MIMO radar sparse linear array (SLA) designs for multipath scenarios. Under the new criteria, we adopt a cyclic optimization strategy under a coordinate descent framework to design the MIMO SLAs. We present several numerical examples to demonstrate the effectiveness of the proposed approaches.
This study focusses on the target detection problem in the distributed multiple-inputmultiple-output (MIMO) radar. The observation environment is assumed to be partially homogeneous in one transmitter-receiver path a...
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This study focusses on the target detection problem in the distributed multiple-inputmultiple-output (MIMO) radar. The observation environment is assumed to be partially homogeneous in one transmitter-receiver path and simultaneously non-homogeneous for different paths. By characterising the disturbance signals with a series of auto-regressive (AR) processes, two parametric detectors based on the Wald test are developed. The first one utilises an iterative scheme to estimate the unknown parameters that were involved in the derivation, and the second one obtains these estimations sequentially. One of the most attractive features of the two distributed MIMO radar detectors is that they can be used in the presence/absence of the training data. More specifically, they can take advantage of the training data to boost the detection performances effectively even if the training data are very limited. The simulation results of several scenarios demonstrate the remarkable detection performances of authors' proposed methods compared with that of the competitors. Furthermore, the amplitude estimation performances of the two detectors are evaluated via the corresponding Cramer-Rao bound (CRB).
In general, target localization in distributed multiple-inputmultiple-output (MIMO) radar systems requires synchronization among the sensors, which may not be feasible when the sensors are widely separated. In this p...
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In general, target localization in distributed multiple-inputmultiple-output (MIMO) radar systems requires synchronization among the sensors, which may not be feasible when the sensors are widely separated. In this paper, the target localization problem is addressed under asynchronous distributed MIMO radar systems in the presence of clock and frequency offsets with the aid of a cooperative target, whose position and velocity are known with measurement errors. Specifically, the influence of the cooperative target position and velocity errors on the unknown target localization accuracy is analyzed through the Cramer-Rao lower bound (CRLB) derivation firstly. Then two methods, namely, the offset estimation based localization method and the offset elimination based localization method, are proposed to estimate the unknown target location. The former estimates the clock and frequency offsets firstly and then locates the unknown target based on the offset estimates, and the latter achieves the unknown target location estimate based on the differential measurements of the two targets. Through the theoretical derivation and numerical simulations, the two proposed methods are demonstrated to be approximately unbiased and can attain the CRLB under mild noise. Moreover, the simulation results show that the proposed methods enjoy higher localization accuracy than the state-of-the-art algorithms.
In this paper, we consider the joint detection and localization (in the delay-Doppler domain) of targets by using multiple-input multiple-output radars. Specifically, inspired by the fact that most of existing contrib...
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In this paper, we consider the joint detection and localization (in the delay-Doppler domain) of targets by using multiple-input multiple-output radars. Specifically, inspired by the fact that most of existing contributions do not encompass target energy spillover between adjacent matched filter samples, we firstly analyze all the energy configurations associated with the target position within the delay-Doppler cell under test. Then, at the design stage, we formulate the detection problem by considering all data samples that might contain target components. The resulting problem is solved by applying the generalized likelihood ratio test (along with the two-step modification) when possible or ad hoc approximations of it dictated by the mathematical intractability of specific energy configurations. In these last cases, we devise two estimation procedures grounded on either a cyclic optimization of the likelihood function or the minimization of a suitable upper bound. Remarkably, the resulting architectures are capable of estimating the target position within the cell under test in the delay-Doppler domain. Finally, the behavior of the proposed architectures in terms of detection and localization performance is assessed over synthetic data and in comparison with conventional detectors that do not consider the spillover of target energy. The examples clearly show the superiority of these new detectors over the considered competitors as well as their capability of returning reliable estimates of target delay-Doppler position.
This paper investigates the Bayesian detection problem for a moving target that is embedded in a homogeneous Gaussian clutter with an unknown but stochastic covariance matrix for frequency diverse array multiple-input...
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This paper investigates the Bayesian detection problem for a moving target that is embedded in a homogeneous Gaussian clutter with an unknown but stochastic covariance matrix for frequency diverse array multiple-inputmultiple-output (FDA-MIMO) radar. First, we propose a Bayesian detector based on structured generalized likelihood ratio test (SGLRT), namely BSGLRT, criteria that requires no training data. Then, we present a detector based on the Bayesian unstructured generalized likelihood ratio test (BUGLRT) to reduce the three dimensions (range-angle-Doppler) search into one-dimension Doppler searches for low-complexity implementation in practical applications. Moreover, the robustness of the BSGLRT and BUGLRT detectors is also analyzed. Numerical results reveal that the proposed Bayesian detectors and estimators, i.e. BSGLRT and BUGLRT, outperform their non-Bayesian counterparts in Gaussian clutter with a small number of snapshots and/or low signal-to-clutter rate (SCR) for FDA-MIMO radar.
We focus on the target detection problem of the multiple-inputmultiple-output (MIMO) radar in clutter. Most of the existing MIMO radar detection works rely on the clutter models, which may limit their scopes of appli...
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We focus on the target detection problem of the multiple-inputmultiple-output (MIMO) radar in clutter. Most of the existing MIMO radar detection works rely on the clutter models, which may limit their scopes of application. To address this issue, a deep learning based MIMO radar target detection framework is proposed in this paper, which utilises the powerful representation and discrimination capabilities of deep neural networks (DNNs) to improve the detection performance in a data-driven manner and does not require any prior information about the clutter distribution. In most classification problems, DNNs are trained with the cross entropy loss, which promotes DNNs to achieve higher accuracy. However, the main concern in the radar target detection problem is the probability of detection (PD) under a given probability of false alarm. In this framework, a novel loss is proposed to directly maximise the average PD of the DNN, or equivalently maximise the area under curve of the DNN. Under the DL-based MIMO radar target detection framework, a deep convolutional neural network (CNN) architecture is introduced, and an input feature of the received signal for the CNN based on the conventional generalised likelihood ratio test statistics is designed to further improve the detection performance. Finally, extensive simulation results are presented to validate the detection performance and the robustness of the proposed methods.
Two efficient solutions are proposed for the localization problem of an object in multiple-inputmultiple-output (MIMO) radar systems, when the transmitter positions and offsets are unknown. The localization problem i...
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Two efficient solutions are proposed for the localization problem of an object in multiple-inputmultiple-output (MIMO) radar systems, when the transmitter positions and offsets are unknown. The localization problem is first recast into a convex form by applying the semidefinite relaxation (SDR) technique, the solution of which converges to global optimum. We also propose a closed-form solution warranting global convergence, in which the object position is estimated by two stages. In the stage-one solution, the auxiliary variables are introduced to transform the nonlinear problem into a linear form. The stage-two solution is further designed to refine the estimates obtained from the stage-one solution. The minimum number of receivers and the complexity are also analyzed for the proposed solutions. The simulated results show that the SDR and closed-form solutions provide good estimates for the object position and transmitter positions. Their performance can sufficiently approach the Cramer-Rao Lower Bound (CRLB) accuracy at the high signal-to-noise ratio (SNR).
In this paper, an interference suppression method based on beamforming is proposed. By combining minimum redundancy array (MRA) and frequency diverse array (FDA) multiple-inputmultiple-output (MIMO) radar, an FDA-MRM...
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ISBN:
(数字)9781665441667
ISBN:
(纸本)9781665441667
In this paper, an interference suppression method based on beamforming is proposed. By combining minimum redundancy array (MRA) and frequency diverse array (FDA) multiple-inputmultiple-output (MIMO) radar, an FDA-MRMO radar suppression interference method is proposed. The MRA is adopted at its transmit port, breaking the limitation on the suppressible number of the mainlobe deceptive interference. The MRA is employed to obtain extended DOF by using a difference coarray, resulting in the suppressible number of order O(2M) with M transmit element number. The mainlobe interference is suppressed by the transmit dimension nonadaptive method, and the sidelobe barrage interference is suppressed by the Generalized sidelobe canceller(GSC) method in the receive dimension. Simulation results demonstrate the effectiveness of the proposed method.
In this paper, an operative radar network based on microwave photonics techniques is presented. The proposed system employs a centralized architecture, with a central unit driving multiple remote radar peripherals. Th...
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ISBN:
(纸本)9781728153681
In this paper, an operative radar network based on microwave photonics techniques is presented. The proposed system employs a centralized architecture, with a central unit driving multiple remote radar peripherals. The wide distribution of the peripherals, the possibility to operate in dual-band mode and the coherent management of the transmitted and received signals are discussed. The surveillance system is deployed in a real freight port, exploiting the available optical fiber network, for the monitoring of the maritime traffic. Preliminary results demonstrate the possibility of exploiting geometric and frequency diversity on the target detection. Finally, the first experimental multi-sensor, multi-band inverse synthetic aperture radar target imaging results are presented.
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