Haowen Chen

MIMO Radar Sensitivity Analysis of Antenna Position for Direction Finding

Sensitivity analysis plays an integral role in many engineering design problems. The purpose of this paper is to investigate the direction finding sensitivities (DFSs) with respect to antenna position uncertainties (APUs) for multiple-input multiple-output (MIMO) radar with colocated antennas. These uncertainties cause differences between the MIMO radar virtual array manifold used by direction finding (DF) algorithms and the true array manifold, which is named as “calibrated errors.” To evaluate the effects of such errors on DF, the DFSs relative to APUs are considered from two following approaches. First, we use the first-order sensitivity analysis for MIMO radar. For a given arbitrary antenna geometry, the formulas of DFSs using maximum likelihood (ML) algorithm are developed for relatively small APUs. In addition, the formula for computing the ambiguity thresholds of the ML algorithm as a function of target separation and other DF system parameters are derived for relatively large APUs. Alternatively, the DFSs are only concerned with antenna geometry, i.e., the virtual array manifold, being regardless of any certain DF algorithm. Herein, we extend Manikass method to MIMO radar. To assess the importance of each antenna in a given MIMO radar system, we derive the antenna importance function (AIF) which is defined as the amount of varieties of manifold vectors from the APUs. Furthermore, to compare the robustness to APUs for different antenna geometries, we derive the overall system sensitivity (OSS) for MIMO radar systems. In numerical example section, we show the previous DFS analysis results by several representative MIMO radar antenna geometries. The presented sensitivity analysis could be as the guideline of MIMO radar system analysis and design.

A Joint Scheme of Antenna Selection and Power Allocation for Localization in MIMO Radar Sensor Networks

We consider a joint scheme of antenna subset selection and optimal power allocation for localization in multiple-input-multiple-output radar sensor networks. Sensor management is accomplished by solving a constrained optimization problem that is formulated to minimize the error in estimating target position, while conserving transmitter number and power budget. We propose a suboptimal scheme for approximately solving this problem. The scheme separates the optimization into two steps, in which each step is transformed into a second-order cone programming (SOCP) by convex relaxation. Simulation results show that the proposed suboptimal two-step SOCP solution provides better localization performance for other strategies and is close to the optimal solution with a simulated annealing technique.

A Sinusoidal Frequency Modulation Fourier Transform for Radar-Based Vehicle Vibration Estimation

The intricate vibration of a working vehicle provides an important signature to the vehicle type. Small vibrations introduce phase modulation in radar echoes, which is referred to as micro-Doppler (m-D) phenomenon and can be modeled as sinusoidal frequency-modulated (SFM) signal. Such phase modulation induced by vibrations consists of multiple frequency components; moreover, the modulation is usually rather weak. Present parametric estimators are difficult to estimate so many parameters of every frequency component, while nonparametric approaches suffer from low precision. This paper considers the analysis of SFM signal with weak and multiple frequency components modulation on phase term. We first define the SFM signal space to bridge a gap between the SFM signal analysis and classical signal processing methods. Based on the defined signal space, a novel m-D analysis method, i.e., the sinusoidal frequency modulation Fourier transform (SFMFT), is presented. With the operations acting directly on the phase term of SFM signal, SFMFT gives the frequency spectrum of vibration traces. Unlike the existing methods, which apply a sliding short-time window to perform an instantaneous approximation, the proposed method makes use of the global data, which can provide a longer integral period gain, and consequently improves the estimation performance significantly. Simulation results indicate that the proposed method outperforms the existing methods in the spectrum accuracy, the range of estimable vibration amplitude/frequency, and the computation complexity.

Antenna Geometry Conditions for MIMO Radar With Uncoupled Direction Estimation

We mainly focus on the role played by the antenna geometry in the direction estimation performance for a direction finding (DF) bistatic multiple-input multiple-output (MIMO) radar. First, we introduce the signal and the noise models which satisfy the space-time separability conditions. Then, we derive an expression of the Cramér-Rao lower bound (CRLB) on target parameters with the antenna locations for a single point target in the far-field scenario. We show that, under the space-time separability conditions, the temporal dimension parameters (range and range rate) and spatial dimension parameters [direction-of-departure (DOD) and the direction-of-arrival (DOA)] are uncoupled. We also show that DOD and DOA are uncoupled because of their spatial independence. In addition, we derive a set of the necessary and sufficient geometrical constraints for the uncoupled direction estimation which is based on the diagonal Fisher information matrix (FIM). We present that, when both the transmit and the receive antenna location parameters have equivalent auto-moment-of-inertia tensors of x and y axes, and zero cross-moment-of-inertia tensors of x, y and z axes, the bistatic MIMO radar system with uncoupled direction estimation can be obtained. The corresponding conditions are extended to monostatic MIMO radar case for their commonness of the colocated antenna geometry. In numerical example section, we show several representative antenna geometries to illustrate the antenna geometry conditions derived in this paper.

Fourier-Sparsity Integrated Method for Complex Target ISAR Imagery

In existing sparsity-driven inverse synthetic aperture radar (ISAR) imaging framework a sparse recovery (SR) algorithm is usually applied to azimuth compression to achieve high resolution in the cross-range direction. For range compression, however, direct application of an SR algorithm is not very effective because the scattering centers resolved in the high resolution range profiles at different view angles always exhibit irregular range cell migration (RCM), especially for complex targets, which will blur the ISAR image. To alleviate the sparse recovery-induced RCM in range compression, a sparsity-driven framework for ISAR imaging named Fourier-sparsity integrated (FSI) method is proposed in this paper, which can simultaneously achieve better focusing performance in both the range and cross-range domains. Experiments using simulated data and real data demonstrate the superiority of our proposed framework over existing sparsity-driven methods and range-Doppler methods.

EFFECTS OF GEOMETRY CONFIGURATIONS ON AMBIGUITY PROPERTIES FOR BISTATIC MIMO RADAR

Bistatic multiple-input multiple-output (MIMO) radar can improve the system performance for obtaining the waveform diversity and larger degrees of freedom (DoF), and efiectively counteract the stealthy target for its transmit antennas and receive antennas separated placement. Similarly with the conventional bistatic radar, the geometry conflgurations of bistatic MIMO radar also play an important role in radar systems performance. Aimed at considering these efiects of geometry conflgurations on the performance for bistatic MIMO radar, in this paper the extended ambiguity function is deflned as the coherent cumulation of the matching output of all channels, where the information of the system geometry conflguration is included in the received signal model. This new ambiguity function can be used to characterize the local and global resolution properties of the whole radar systems instead of only considering transmitted waveforms in Woodwards. In addition, some examples with the varying system conflgurations or target parameters are given to illustrate their efiects, where the spatial stepped-frequency signal set (a quasi-orthogonal waveform set) is used. The simulation results demonstrate that the more approaching monostatic MIMO radar case, the better ambiguity properties of time-delay and Doppler for bistatic MIMO radar.

Radar Coincidence Imaging for Off-Grid Target Using Frequency-Hopping Waveforms

Radar coincidence imaging (RCI) is a high-resolution staring imaging technique without the limitation of the target relative motion. To achieve better imaging performance, sparse reconstruction is commonly used. While its performance is based on the assumption that the scatterers are located at the prediscretized grid-cell centers, otherwise, off-grid emerges and the performance of RCI degrades significantly. In this paper, RCI using frequency-hopping (FH) waveforms is considered. The off-grid effects are analyzed, and the corresponding constrained Cramer-Rao bound (CCRB) is derived based on the mean square error (MSE) of the “oracle” estimator. For off-grid RCI, the process is composed of two stages: grid matching and off-grid error (OGE) calibration, where two-dimension (2D) band-excluded locally optimized orthogonal matching pursuit (BLOOMP) and alternating iteration minimization (AIM) algorithms are proposed, respectively. Unlike traditional sparse recovery methods, BLOOMP realizes the recovery in the refinement grids by overwhelming the shortages of coherent dictionary and is robust to noise and OGE. AIM calibration algorithm adaptively adjusts the OGE and, meanwhile, seeks the optimal target reconstruction result.

Manifold Sensitivity Analysis for MIMO Radar

The purpose of this letter is to investigate the sensitivity relative to the antenna position uncertainties (APUs) for multiple-input multiple-output (MIMO) radar with colocated antennas. Manifold study is introduced to MIMO radar virtual array as an analyzing tool. To assess the importance of each antenna in a MIMO radar system, we extend Manikass sensor importance function to MIMO radar. Furthermore, to compare the robustness to APUs of different antenna geometries, we extend the overall-system-sensitivity criterion to MIMO radar system. We show that, with the same sensor geometry, MIMO radar has better robustness performance than the corresponding phased array because of waveform diversity.

MIMO Radar Systems Design Based on Maximum Channel Capacity

In this paper, we consider the problem of bistatic multiple- input multiple-output (MIMO) radar systems design for parameters estimation. Maximum channel capacity is used as criterion for the problem of optimal systems design under transmitted power constraint and channel constraint. We obtain that the system design based on maximum channel capacity can be expressed as a joint optimization problem. Given the number of transmit antenna, the number of receive antenna and signal-noise ratio (SNR), the maximum channel capacity can be determined. This maximum channel capacity can be obtained from a unique appropriate power allocation and antenna placement strategy, which is very important for system design.

Low-Grazing Angle Detection in Compound-Gaussian Clutter with Hybrid MIMO Radar

This paper focuses on the target detection in low-grazing angle using a hybrid multiple-input multiple-output (MIMO) radar systems in compound-Gaussian clutter, where the multipath effects are very abundant. The performance of detection can be improved via utilizing the multipath echoes. First, the reflection coefficient considering the curved earth effect is derived. Then, the general signal model for MIMO radar is introduced in low-grazing angle; also, the generalized likelihood test (GLRT) and generalized likelihood ratio test-linear quadratic (GLRT-LQ) are derived with known covariance matrix. Via the numerical examples, it is shown that the derived GLRT-LQ detector outperforms the GLRT detector in low-grazing angle, and both performances can be enhanced markedly when the multipath effects are considered.