Qun Wan

Millimeter-wave spotlight circular synthetic aperture radar (scsar) imaging for Foreign Object Debris on airport runway

Foreign Object Debris (FOD) on airport runway is one of the greatest threats to aviation safety. The major characteristics of FOD are diversity and smallness. Synthetic aperture radar (SAR) has a natural advantage in high resolution imaging, and is, therefore, very suitable for small and weak targets imaging on airport runway. Firstly, a new millimeter-wave FOD imaging system called Spotlight Circular Synthetic Aperture Radar (SC-SAR) is introduced in this paper. The SC-SAR system not only can solve the practical limitations for ground-based applications but also fully exploit the advantage of existing ground-based SAR systems for FOD imaging. Secondly, based on the geometry and imaging principle of SC-SAR, the adjusted version of the Range-Doppler imaging formula was presented. Finally, the simulation results validate feasibility of SC-SAR imaging system and algorithm.

Source Association, DOA, and Fading Coefficients Estimation for Multipath Signals

This paper addresses the source association (SA), direction of arrival (DOA), and fading coefficients (FCs) estimation problem in multipath environment. First, we establish a rank reduction property for a multipath signal model with the existence of multiple groups of coherent signals. Subsequently, based on this property, effective algorithms for SA, DOA, and FCs estimation have been developed. The proposed DOA and FCs estimation methods exploit the multipath structure information to achieve improved accuracy. The new DOA estimation methods work well even in the case that the DOAs of the multipath signals associated with different sources are (nearly) overlapped. Meanwhile, the new methods are applicable to arbitrary array geometry while without decreasing the effective array aperture. Then, the stochastic Cramér–Rao bound on DOA and FCs estimation of multipath model (MCRB) exploiting the multipath structure information is derived in closed form. Numerical simulations have been provided to demonstrate the effectiveness of the proposed methods.

DOA and Gain-Phase Errors Estimation for Noncircular Sources With Central Symmetric Array

The problem of direction-of-arrival (DOA) estimation for noncircular sources impinging on a central symmetric array (CSA) in the presence of sensor gain-phase uncertainties is addressed in this paper. A noniterative method is proposed and the corresponding stochastic Cramér–Rao bound is derived. The proposed method is realized through two steps. First, an eigenstructure-based technique is presented to estimate the spatial signatures. Second, the DOAs are obtained by adopting an element-wise division approach to the estimated spatial signatures, based on which, the sensor gain-phase errors are given in closed-form. The ambiguity of DOA estimation is analyzed as well. The proposed method offers a number of advantages in comparison with the existing methods that apply to CSA. First, the DOA estimator is independent of the sensor phases. Second, the proposed method applies to incoherent sources. Third, the proposed method is capable of providing 360° azimuthal coverage under certain conditions. Fourth, an additional performance gain is achieved by taking the property of noncircular sources into consideration. Numerical simulations are provided to verify the effectiveness of the proposed method.

Semidefinite Programming Methods for Alleviating Sensor Position Error in TDOA Localization

This paper develops a unified solution for time-difference-of-arrival (TDOA) localization in the presence of sensor position errors. This technique starts with maximum likelihood estimation (MLE), which is known to be nonconvex. A semidefinite programming technique to effectively transform the MLE problem into a convex optimization is proposed, together with a unified solution for four scenarios: 1) without a calibration emitter; 2) with a single calibration emitter, whose position is subject to measurement errors; 3) with a single calibration emitter, whose position is perfectly known; and 4) with a single calibration emitter, whose position is completely unknown. The results are finally extended to the case of multiple calibration emitters, whose positions are also subject to errors. Similar to the existing schemes that are known to have good performances, the proposed solution also reaches the Cramér–Rao lower bound when sensor position errors and TDOA measurement noise are sufficiently small. However, as TDOA measurement noise or sensor position errors increase, comparison with the existing state-of-the-art methods for each scenario shows that the proposed solution performs significantly better.

3D hybrid TOA-AOA source localization using an active and a passive station

Hybrid source localization methods have proved to be able to improve the accuracy of position estimation. But several existing hybrid algorithms deal with two-dimensional (2D) scenarios only or require numerical search. This paper introduces a three-dimensional (3D) passive localization method using the active time of arrival (TOA) measurement and the one TOA and two angle of arrival (AOA) pairs observed at two stations. A closed-form solution is derived for the 3D source location with hybrid measurements from two stations. The experimental results demonstrate that the proposed method outperforms the state-of-art ones in terms of root mean-square error (RMSE), especially in large measurement error situation.

Joint Synchronization and Localization in Wireless Sensor Networks Using Semidefinite Programming

A new joint synchronization and localization method for wireless sensor networks using two-way exchanged time-stamps is proposed in this paper. The goal is to jointly localize and synchronize the source node, assuming that the locations and clock parameters of the anchor nodes are known. We first form the measurement model and derive the Cramér–Rao lower bound (CRLB). An analysis of the advantages and disadvantages of a recent scheme on joint synchronization and localization motivates us to develop a maximum likelihood estimator (MLE) that effectively resolves the issues of this existing scheme. A novel semidefinite programming method is then proposed to transform the nonconvex MLE problem into a convex optimization problem. Extensive simulation results are obtained to compare the synchronization and localization performances of proposed scheme and a few state-of-the-art existing schemes.

A Type-2 Block-Component-Decomposition Based 2D AOA Estimation Algorithm for an Electromagnetic Vector Sensor Array

This paper investigates a two-dimensional angle of arrival (2D AOA) estimation algorithm for the electromagnetic vector sensor (EMVS) array based on Type-2 block component decomposition (BCD) tensor modeling. Such a tensor decomposition method can take full advantage of the multidimensional structural information of electromagnetic signals to accomplish blind estimation for array parameters with higher resolution. However, existing tensor decomposition methods encounter many restrictions in applications of the EMVS array, such as the strict requirement for uniqueness conditions of decomposition, the inability to handle partially-polarized signals, etc. To solve these problems, this paper investigates tensor modeling for partially-polarized signals of an L-shaped EMVS array. The 2D AOA estimation algorithm based on rank-(L1,L2,·) BCD is developed, and the uniqueness condition of decomposition is analyzed. By means of the estimated steering matrix, the proposed algorithm can automatically achieve angle pair-matching. Numerical experiments demonstrate that the present algorithm has the advantages of both accuracy and robustness of parameter estimation. Even under the conditions of lower SNR, small angular separation and limited snapshots, the proposed algorithm still possesses better performance than subspace methods and the canonical polyadic decomposition (CPD) method.

Target localization in noncoherent distributed MIMO radar system using squared range-sum measurements

This paper considers the problem of target localization from the bistatic range-sum measurements in noncoherent distributed multiple-input multiple-output (MIMO) radar system. The localization procedure includes two parts. First, it divides the measurements into several groups based on the different transmitter or receiver, and for each group, we use least squares (LS) approach based on squared range-sum measurements (SRS-LS) and weighted least squares (WLS) approach based on squared range-sum measurements (SRS-WLS) to estimate the target location. Second, we use the estimates from different groups to form a composite estimate. Simulation results are include to show the performance of the proposed algorithms. It is shown that the SRS-LS is very close to the Cramér-Rao lower bound (CRLB), and the SRS-WLS can reach the CRLB in a range of moderate measurements noise.

Robust adaptive beamforming for noncircular signal against array steering vector mismatch and interference nonstationarity

The performance of adaptive arrays is severely degraded if the weights are in the presence of interference nonstationarity and signal steering vector mismatch. Because of this, we proposed a new robust null broadening adaptive beamforming algorithm. The method is realized by the combination of projection transform and diagonal loading technique. We got a new sample covariance matrix through diagonal loading technique and the received data transform technique which is based on the concept of subspace projection. We applied the proposed algorithm to noncircular signals which are usually encountered in the context of radio communications. According to the theoretical analysis, the projection transform operation can improve the orthogonality between signal subspaces and noise subspaces. The proposed approach can effectively broaden the jammer nulls and strengthen the null depth. Simulation results demonstrate that the proposed algorithm can provide strong robustness against both signal steering vector mismatch and jammer motion.