Heeseong Yang

An Improved Algebraic Solution for Moving Target Localization in Noncoherent MIMO Radar Systems

We propose an improved method for moving target localization with a noncoherent multiple-input multiple-output (MIMO) radar system having widely separated antennas. The method is based on the well-known two-stage weighted least squares (2SWLS) method, but in contrast to the recently proposed Group-2SWLS, it requires only one reference transmitter (or receiver) without grouping and combining. This change allows us to obtain a closed-form solution which is less likely to be degraded by bias. Furthermore, the proposed method can easily utilize not only time-of-arrival (TOA) and frequency-of-arrival (FOA) data but also time-difference-of-arrival (TDOA) and frequency-difference-of-arrival (FDOA) data. We also introduce new auxiliary variables for the purpose of numerical stability; our method using the auxiliary variables is shown to be numerically more stable than the Group-2SWLS, while attaining the Cramér-Rao lower bound (CRLB) at higher noise levels. The adoption of auxiliary variables requires no additional computations in contrast to the adoption of the concept of Turbo-2SWLS for the same purpose.

Hyperbolic Localization in MIMO Radar Systems

This letter addresses an effective algorithm for target localization in multiple-input-multiple-output (MIMO) radar systems with widely separated antennas. The algorithm derived uses time-of-arrival (TOA) measurements from multiple transmitter-receiver pairs and is based on a hyperbolic method suitable for radiation source localization in passive sensor networks. It does not have the local convergence problem as the conventional iterative method. Some combinations of the derived algorithm and the conventional iterative method are presented. In a numerical example, it is shown that the proposed methods can achieve the Cramer-Rao lower bound (CRLB) in the range of moderate processed measurement noise and obtain the better localization performance as the number of transmitters and receivers increases. Furthermore, some remarks are made on the robustness of the proposed methods.

Two-stage localization method in multistatic radar systems

In this paper, we present a new localization method in multistatic radar systems. Based on two-stage algorithm, which is used to estimate the location of a target that transmits a signal in passive sensor networks, we reformulate the target localization problem and derive the new two-stage algorithm which suits our situation. Simulation results demonstrate that the proposed method outperforms conventional methods and achieves the Cramer-Rao lower bound (CRLB) with the knowledge of noise statistics over the range of moderate noise power.

Nonnegative gridless compressive sensing for co-prime arrays

The direction-of-arrival estimation in co-prime arrays is formulated as gridless compressive sensing where the atoms can be anywhere in a continuous domain. A new algorithm, successive-atom-merging cyclic coordinate descent for atomic norm minimization, is proposed. The algorithm performs atom update in subdomains of the continuous space relying on atom merging, exhibits fast convergence and has practically feasible computational complexity. It is demonstrated in simulations that the proposed method is superior to the joint sparsity reconstruction method (JLASSO) and the MUSIC method with spatial smoothing (SS-MUSIC) in terms of several criteria.

Cyclic block coordinate minimization algorithms for DOA estimation in co-prime arrays

Abstract We derive several closed-form expressions that generalize co-prime array system model and study a nonnegative gridless compressive sensing formulation of the problem of estimating direction-of-arrival (DOA) based on the derived model. To solve the problem, two computationally efficient cyclic block coordinate minimization algorithms are proposed; the algorithms perform atomic norm minimization of an objective function through a sequence of computationally efficient atom merging and atom activation steps conducted in subdomains of a continuous atom search space. The convergence properties of the developed algorithms are analyzed. Numerical simulations demonstrate that the proposed techniques outperform the joint sparsity reconstruction method (JLASSO) and the ESPRIT method with spatial smoothing (SS-ESPRIT) in terms of various criteria. It is also demonstrated that our methods are significantly faster and yield competitive performance in terms of root mean square error (RMSE), detection probability, and false alarms when compared to the recent convex optimization based methods, i.e. the gridless SPICE with ESPRIT (GLS-ESPRIT), the atomic norm minimization with dimension reduction and ESPRIT (ANM-ESPRIT), and the nuclear norm minimization with ESPRIT (NNM-ESPRIT).

Spectral Analysis Method to Eliminate Spurious in FMICW HRR Millimeter-Wave Seeker

본 논문에서는 주파수 변조 단속 지속파(Frequency Modulated Interrupted Continuous Wave: FMICW) 시스템을 기반으로 한 고해상도(HRR: High Range Resolution) 레이더 탐색기에서 발생하는 스퓨리어스(Spurious)를 제거하기 위한 스펙트럼 분석 기법에 대해서 연구하고 새로운 제거 기법을 제안한다. 주파수 변조 지속파(Frequency Modulated Continuous Wave: FMCW)를 기반으로 하는 고해상도 레이더 시스템과 다르게 FMICW를 사용한 시스템은 주기적으로 나타나는 비연속적 IF(Intermediate Frequency) 신호에 의해 스펙트럼 상에서 스퓨리어스가 생기게 된다. 이러한 스퓨리어스를 제거하기 위해서 대역 통과 필터(band pass filter)를 사용하면 FMICW 시스템의 정확도가 이전 추정된 거리 값에 의존적이 되고 random interrupted sequence를 사용하면 noise floor가 증가하며, staggering process를 사용하면 중복된 정보를 위해 여러 개의 파형을 송신해야 되는 단점이 있다. 최근 소개된 IAA(Iterative Adaptive Approach) 또는 SPICE(SemiParametric Iterative Covariance-based Estimation method)와 같은 스펙트럼 분석 기법을 이용하면 이러한 단점 없이 FMICW 시스템에서의 스퓨리어스를 효과적으로 제거할 수 있다. IAA 또는 SPICE를 사용하기 위해서는 신뢰할 수 있는 데이터(reliable data)와 신뢰할 수 없는 데이터(unreliable data)를 구분하고, 신뢰할 수 있는 데이터만 이용하여야 하는데, 이를 위해서 STFT(Short Time Fourier Transform)가 적용된다.

Sweep Nonlinearity Estimation for High Range Resolution Millimeter-Wave Seeker Using Least Squares Method

In this thesis, to compensate the sweep nonlinearity occurring in the high resolution radar system using FMICW or FMCW, the method of the estimation of the nonlinearity is proposed. The nonlinear phase component caused by the nonlinear characteristic of the radar system is modelled as a linear combination of the sinusoidal functions consisting of various magnitudes and phases(systematic nonlinear phase error) and a random component(stochastic nonlinear phase error). From two IF signals that are measured respectively independently for two reference point targets lying in different distances which are known, a sparse linear equation is made and solved by least squares method to estimate the nonlinear phase component. The estimated component can be used for predistortion method to compensate the sweep nonlinearity.

Novel nonlinear phase distortion estimation in wideband linear frequency modulated waveform

Recently, frequency modulated continuous wave (FMCW) technique has drawn a lot of attention in various applications where the high resolution performance is needed due to its cost-eectiveness and low complexity as well as the high resolution performance. One of degradation factors in the technique is the characteristics of nonlinear phase distortion in transmitted waveform. The phase distortion degrades the resolution performance, that is, the contrast and resolution of the obtained range prole or image can be degraded. Especially, as the system with FMCW technique requires higher resolution performance and longer range coverage, the degradation problem becomes more severe hence it can be limited to be utilized for long-range applications like synthetic aperture radar (SAR). This paper proposes the novel algorithms to estimate the nonlinear phase distortion without any expensive devices but only one reference delay line and provides a favorable condition for parallel processing in the algorithms. The estimate of the distortion can be utilized for designing predistortion to compensate it. Simulation result obtained with an arbitrarily generated nonlinear phase distortion, demonstrates that the proposed scheme has outstanding estimation performance.