Ngoc Hung Nguyen

Adaptive waveform selection for multistatic target tracking

This paper studies the problem of adaptive waveform selection for target tracking by a multistatic radar system consisting of a dedicated transmitter and multiple receivers. The transmitted waveforms are selected to minimize the tracking mean square error by minimizing a tracking cost function obtained using the Craḿer-Rao lower bound of radar estimates. The superior performance of the proposed adaptive waveform selection algorithm over conventional waveforms is illustrated with simulation examples.

Optimal Geometry Analysis for Multistatic TOA Localization

Multistatic time-of-arrival (TOA) localization has recently attracted great attention due to performance advantages offered by multistatic radar. In target localization, the relative sensor-target geometry is an important factor that can significantly affect the localization performance. In this paper, we analyze the optimal geometries for a two-dimensional TOA localization configuration commonly presented in the literature with a single transmitter and multiple receivers. Our analysis is based on minimizing the area of estimation confidence region, which is equivalent to maximizing the determinant of the Fisher information matrix. If the coordinate system is rotated such that the bearing angle of the transmitter with respect to the target is zero, an optimal geometry is obtained when a subset of the receivers are collinear with the target at a bearing angle of π/3 rad and the remaining receivers at a bearing angle of -π/3 rad. The arrangement of the receivers on either branch of the bearing angles, i.e., π/3 rad or -π/3 rad, is decided based on the measurement noise variances at the receivers. We conclude the study with numerical solutions generated by the genetic algorithm and simulation examples for UAV path optimization to verify the accuracy of the analytical findings.

Optimal sensor placement for Doppler shift target localization

A key component of cognitive radar is intelligent signal processing which encompasses an algorithmic decision-making process for adaptive transmitted waveform selection and online optimal path planning for moving radar platforms in order to deal with non-stationary and uncertain surrounding environments. This paper focuses on optimal radar trajectories in the cognitive radar context. In particular, the optimal sensor placement problem for stationary target localization by multiple moving Doppler-shift radars is considered. A solution based on maximizing the determinant of the Fisher information matrix is proposed and analyzed. The analytical findings of the paper are demonstrated by way of extensive simulations incorporating unmanned aerial vehicles equipped with Doppler radar in a number of different scenarios.

Adaptive waveform and Cartesian estimate selection for multistatic target tracking

This paper considers the problem of target tracking by a multistatic radar system. In order to use linear Kalman filtering for tracking, the time delay, Doppler shift and arrival angle measurements from multiple receivers are transformed into target position and velocity estimates in Cartesian coordinates before being processed by the Kalman filter. A new multistatic tracking algorithm is proposed with the novel feature of joint adaptive selection of transmitted waveform and Cartesian estimate to minimize the mean-square error of the tracking estimate. Cramer-Rao lower bounds (CRLBs) of Cartesian estimates are derived and exploited in the development of the joint adaptive selection scheme. Three main performance advantages of the proposed algorithm are (i) reduction of bandwidth and power consumption in transmitter-receiver communication links by utilizing a single Cartesian estimate in tracking process, (ii) optimization of the tracking performance by jointly selecting waveform and Cartesian estimate, and (iii) the inherent benefits of linear estimation from use of the linear Kalman filter including stability. Simulation examples demonstrate the dependence of the CRLBs of the Cartesian estimates on the transmitted waveform, target position and velocity, radar geometry, and particular elliptic-to-Cartesian transformation. Simulations also highlight the superior performance of our proposed algorithm over existing techniques. HighlightsA new multistatic tracking algorithm is proposed.The novel feature is joint adaptive selection of waveform and Cartesian estimate.The CRLBs of Cartesian estimates are derived and exploited in the selection scheme.The elliptic-to-Cartesian transformations are derived in a general geometry.The performance advantages of the proposed algorithm are demonstrated.

Adaptive waveform scheduling for target tracking in clutter by multistatic radar system

An adaptive waveform scheduling algorithm is presented for target tracking by a multistatic radar system with two key components: (i) a distributed multistatic tracking algorithm for target tracking in cluttered environments, and (ii) the next transmitted waveform selected to minimize the tracking mean squared error. The scheduling algorithm is developed based on the minimization of the trace of the expected tracking error covariance matrix. A simulation example is presented to demonstrate the superiority of the proposed waveform scheduling algorithm over conventional fixed waveforms.

Optimal geometry analysis for elliptic target localization by multistatic radar with independent bistatic channels

This paper derives the optimal angular geometry for elliptic time-of-arrival localization by a multistatic radar consisting of multiple independent bistatic channels. The optimal geometry analysis is conducted based on minimization of the area of the estimation confidence region which is equivalent to maximizing the determinant of the Fisher information matrix. It is shown that the optimal angular geometry is obtained when the transmitter and receiver of each bistatic channel are collinear with the target where the target is placed at the either end. The optimization problem therefore boils down to determination of the optimal angular separation between different bistatic channels which is mathematically equivalent to the optimal angular sensor separation for angle-of-arrival localization. These analytical results are confirmed by simulation examples.

Single-platform passive emitter localization with bearing and Doppler-shift measurements using pseudolinear estimation techniques

The maximum-likelihood (ML) estimator for single-platform Doppler-bearing emitter localization does not admit a closed-form solution and must be implemented using computationally demanding iterative numerical search algorithms. The iterative ML solution is vulnerable to convergence problems due to the nonconvex nature of the ML cost function and the threshold effect. To alleviate these problems, this paper presents new closed-form Doppler-bearing emitter localization algorithms in the 2D-plane based on pseudolinear estimation techniques; namely, the pseudolinear estimator (PLE), the bias-compensated PLE and the weighted instrumental variable (WIV) estimator. The bias-compensated PLE aims to remove the instantaneous estimation bias inherent in the PLE. The WIV estimator incorporates the bias-compensated PLE to achieve an asymptotically unbiased estimate of the emitter position. The proposed WIV estimator is proved to be asymptotically efficient for sufficiently small measurement noise. Through simulation examples its performance is shown to be almost identical to that of the ML estimator, exhibiting small bias and approaching the Cramer-Rao lower bound at moderate noise levels. HighlightsThe problem of single-platform Doppler-bearing emitter localization is considered.New localization algorithms are proposed based on pseudolinear estimation technique.Proposed algorithms are closed-form with low complexity and inherent stability.The proposed WIV estimator enjoys the desirable property of asymptotic unbiasedness.It is analytically shown to be asymptotically efficient for small measurement noise.

Adaptive distributed waveform selection for target tracking by a multistatic radar system

We propose an algorithm that performs joint target tracking and adaptive waveform selection at all the receivers of a multistatic radar system in a collaborative and fully distributed manner. The receivers track the target cooperatively utilizing an extended version of the recently-proposed diffusion Kalman filter and perform adaptive waveform selection by minimizing the mean-square tracking error. The centralized adaptive waveform-selection algorithms for multi-static radar systems require collection of the data measured by all the receivers in the system using possibly long receiver-transmitter communication links. On the other hand, the proposed algorithm solely relies on the local communications among the neighboring receivers to disseminate the information across the system. Moreover, all the receivers perform the same tasks of target tracking and optimal waveform selection and roughly converge to the same estimated values. Thus, the information of the best waveform to be transmitted as well as the targets track can be passed to the transmitter from any receiver. Simulation results show that the proposed algorithm outperforms the conventional fixed-waveform algorithms and performs close to the centralized adaptive waveform-selection algorithm.

Multistatic pseudolinear target motion analysis using hybrid measurements

This paper presents a new hybrid pseudolinear estimator (PLE) for target motion analysis of a constant-velocity target in the two-dimensional plane using angle-of-arrival, time-difference-of-arrival and frequency-difference-of-arrival measurements obtained from spatially distributed stationary passive receivers. The hybrid PLE is developed by linearizing the nonlinear measurement equations in the unknown target motion parameters. The resulting estimator is not only closed-form and has low computational complexity, but is also free from nuisance parameters, therefore avoiding the problems arising from the dependence of the nuisance parameters on the target motion parameters. However, the noise injected into the PLE data matrix causes biased estimates. To address this, a bias-compensated PLE is proposed based on an asymptotic bias analysis of the hybrid PLE. This estimator is then incorporated into a weighted instrumental variable (WIV) estimator to obtain asymptotically unbiased estimates of the target motion parameters. The WIV estimator is shown to be asymptotically efficient both analytically and through numerical simulation examples. Furthermore, it is observed that the WIV estimator performs similar to the computationally demanding maximum likelihood estimator, closely achieving the Cramer-Rao lower bound and producing negligible bias at moderate noise levels. HighlightsNew pseudolinear estimators are proposed for multistatic target motion analysis.Hybrid sensor measurements of AOA, TDOA and FDOA are exploited.Proposed estimators are closed-form with low complexity and inherent stability.Complexity analysis confirms computational advantage of proposed estimators.WIV estimator is analytically and numerically shown to be asymptotically efficient.

Optimal sensor-target geometries for Doppler-shift target localization

Doppler-shift target localization has recently attracted renewed interest due to its wide range of applications. In this paper we analyze the optimal sensor-target geometries for the Doppler-shift target localization problem where the position and velocity of a moving target are estimated from Doppler-shift measurements taken at stationary sensors. The analysis is based on minimizing the estimation uncertainty, which is equivalent to maximizing the determinant of the Fisher information matrix. In particular, the optimal geometries that maximize the estimation accuracy for target position only, velocity only, and both position and velocity, are investigated. The analytical findings are verified by numerical examples.