Wenqiang Pu

Joint Beam Selection and Power Allocation for Multiple Target Tracking in Netted Colocated MIMO Radar System

In this paper, a joint beam selection and power allocation (JBSPA) strategy is developed for multiple target tracking in netted colocated multiple-input multiple-output radar system. Each radar in this network adopts a multibeam working mode, in which multiple simultaneous transmit beams are synthesized. The basis of the JBSPA strategy is to use the optimization technique to control the limited beam and power resource of each radar in order to achieve accurate target state estimation. The Bayesian Cramér-Rao lower bound is derived, normalized, and subsequently utilized, as the optimization criterion for the JBSPA strategy. The resulting optimization problem consists of two adaptable parameters, one for beam selection and the other for power allocation. By introducing an auxiliary vector, a fast two-step solution technique is presented to jointly decide the number of beams generated by each radar, as well as the assignment and transmit power of each beam, subject to some resource constraints. Simulation results verify the superiority of the proposed JBSPA algorithm, in terms of the worst-case tracking accuracy of the multiple targets.

Benefit Analysis of Data Fusion for Target Tracking in Multiple Radar System

With the recent development in radar technology, a multiple radar system (MRS) has become an attractive platform for target tracking. Technically speaking, data fusion among multiple radars can definitely enhance the tracking performance. However, the enhancement may not always be significant, as the improvement depends on several factors, such as the signal-to-noise ratio, the deployment, and the resolution of each radar. In this paper, a benefit analysis of data fusion for target tracking in MRS is developed. In particular, the analysis is on whether, for a given target in a given environment, the fusion between two radars is worthy to be implemented. First, the performance enhancement achieved by individual radar, in terms of the Bayesian Cramér-Rao lower bound, is derived as a recursive procedure. On this basis, a scalar parameter is then defined, according to which the decision on whether to fuse the data from two radars or use individual radar instead to track a target can be made. Finally, simulation results demonstrate the correctness of fusion rule defined in this paper.

Comparison of two binaural beamforming approaches for hearing aids

Beamforming algorithms in binaural hearing aids are crucial to improve speech understanding in background noise for hearing impaired persons. In this study, we compare and evaluate the performance of two recently proposed minimum variance (MV) beamforming approaches for binaural hearing aids. The binaural linearly constrained MV (BLCMV) beamformer applies linear constraints to maintain the target source and mitigate the interfering sources, taking into account the reverberant nature of sound propagation. The inequality constrained MV (ICMV) beamformer applies inequality constraints to maintain the target source and mitigate the interfering sources, utilizing estimates of the direction of arrivals (DOAs) of the target and interfering sources. The similarities and differences between these two approaches is discussed and the performance of both algorithms is evaluated using simulated data and using real-world recordings, particularly focusing on the robustness to estimation errors of the relative transfer functions (RTFs) and DOAs. The BLCMV achieves a good performance if the RTFs are accurately estimated while the ICMV shows a good robustness to DOA estimation errors.

Cooperative Target Assignment and Dwell Allocation for Multiple Target Tracking in Phased Array Radar Network

Abstract Motivated by networked anti-missile defense applications, a cooperative target assignment and dwell allocation (CTADA) algorithm is developed for multiple target tracking (MTT) in phased array radar (PAR) network. The basis of the CTADA scheme is to not only optimize the target-to-radar assignment, but also effectively allocate the limited time resource of each PAR to its responsible targets, such that the MTT performance could be efficiently improved in overload situations (the number of targets greatly exceeds the number of PARs). We formulate the resource allocation framework as a mathematical optimization problem, and use the normalized Bayesian Cramer-Rao lower bound as its objective function. The resulting optimization problem consists of two adaptable parameters, one for target-to-radar assignment and the other for dwell allocation. By exploiting the unique relationship between these two adaptable parameters, an efficient two-step solution technique, which consists of a convex relaxation step and a heuristic dividing step, is developed for the CTADA optimization problem. Simulation results verify the superiority of the proposed CTADA algorithm, in terms of the worst case tracking accuracy of the multiple targets.

Joint Threshold Adjustment and Power Allocation for Cognitive Target Tracking in Asynchronous Radar Network

In this paper, a joint threshold adjustment and power allocation (JTAPA) algorithm is developed for target tracking in asynchronous radar network (ARN). The basis of the JTAPA strategy is to feed back the target track information from the fusion center to local radar sites to enhance both the target detection capability and the resource utilization efficiency of the ARN. For the detector, we develop a threshold adjustment (TA) algorithm for better detection performance, based on the predicted target location information fed back from the fusion center. For the transmitter, we build an asynchronous power allocation (APA) model based on the perceptual information, and use an optimization technique to control the limited power resource for the next period of time. The goal of the APA scheme is to achieve better target tracking accuracy with a given power budget. The Bayesian Cramér–Rao lower bound is derived, normalized, and subsequently utilized, as the optimization criterion for the APA strategy. The resulting nonconvex optimization problem is solved through relaxation incorporating the spectral projected gradient technique. Simulation results demonstrate that the integration of the TA and APA processes can evidently improve the tracking performance.

Decentralized 3-D Target Tracking in Asynchronous 2-D Radar Network: Algorithm and Performance Evaluation

The problem of 3-D target tracking in asynchronous 2-D radar network is considered. To deal with this problem, this paper presents a decentralized asynchronous track-to-track fusion (DAT2TF) algorithm. The DAT2TF algorithm is implemented by reconstructing the optimal centralized fusion result with asynchronous local estimates and their error covariance matrices. The derivations show that this algorithm actually operates in a centralized sense, but is not optimal due to two approximations about the local target motion model and the polar to Cartesian measurement conversion procedure. To evaluate the estimation performance of the DAT2TF algorithm, a decentralized Bayesian Cramér–Rao lower bound is also developed. Simulation results show that the proposed approach is effective and efficient, when compared with the particle filter-based centralized fusion architecture, in terms of the tracking accuracy and the computation load.

Optimal estimation of sensor biases for asynchronous multi-sensor data fusion

An important step in a multi-sensor surveillance system is to estimate sensor biases from their noisy asynchronous measurements. This estimation problem is computationally challenging due to the highly nonlinear transformation between the global and local coordinate systems as well as the measurement asynchrony from different sensors. In this paper, we propose a novel nonlinear least squares formulation for the problem by assuming the existence of a reference target moving with an (unknown) constant velocity. We also propose an efficient block coordinate decent (BCD) optimization algorithm, with a judicious initialization, to solve the problem. The proposed BCD algorithm alternately updates the range and azimuth bias estimates by solving linear least squares problems and semidefinite programs. In the absence of measurement noise, the proposed algorithm is guaranteed to find the global solution of the problem and the true biases. Simulation results show that the proposed algorithm significantly outperforms the existing approaches in terms of the root mean square error.

An optimization model for electroencephalography-assisted binaural beamforming

In a multi-speakers noisy environment, the incoherent correlation between the electroencephalography (EEG) signal and the attended speech envelope provides a user-informed way to design the beamformer for preserving the attended speech and suppressing others. In this work, we exploit such incoherent correlation property in terms of Pearson correlation and propose a unified optimization model for simultaneously designing beamformer and aligning attention preference of the user to each speech source. To balance different design considerations, the proposed optimization formulation makes a trade-off among aligning attention preference, controlling speech distortion, and reducing noise in a weighted manner in the objective function. Specifically, the attention preference is aligned by maximizing the Pearson correlation between the envelope of beamforming output and the linearly transformed EEG signal. To control speech distortion with flexibility, the spatial response of the beamformer to each source is penalized in a min-max sense. And the noise is further reduced by minimizing its mean squares at beamforming output. Experiments on collected EEG signal in two speakers environment demonstrate the effectiveness of the proposed model.In a multi-speakers noisy environment, the incoherent correlation between the electroencephalography (EEG) signal and the attended speech envelope provides a user-informed way to design the beamformer for preserving the attended speech and suppressing others. In this work, we exploit such incoherent correlation property in terms of Pearson correlation and propose a unified optimization model for simultaneously designing beamformer and aligning attention preference of the user to each speech source. To balance different design considerations, the proposed optimization formulation makes a trade-off among aligning attention preference, controlling speech distortion, and reducing noise in a weighted manner in the objective function. Specifically, the attention preference is aligned by maximizing the Pearson correlation between the envelope of beamforming output and the linearly transformed EEG signal. To control speech distortion with flexibility, the spatial response of the beamformer to each source is penal...

Exact Fisher Information Matrix With State-Dependent Probability of Detection

In this paper, the exact Fisher information matrix (EFIM) is derived for target localization with range measurements under imperfect detection. We treat the probability of detection (PD) as a target state-dependent parameter, and take a partial derivative of PD with respect to the target state during the calculation of the EFIM. By introducing an additional information impact parameter (AIIP), we define a parameter to reveal the divergence between EFIM and the original Fisher information matrix. We analytically prove that the AIIP is bandwidth invariant, and subsequently we know that the divergence parameter is inversely linear with the square of the signal bandwidth. Moreover, we also analyze the connection of the divergence parameter with the signal-to-noise ratio (SNR) and the false alarm rate by simulation. The results suggest that approximating PD as a state-free parameter is feasible for most of the real radar applications with low false alarm rate, large bandwidth, and moderate SNR, except for some specific continuous wave radar systems, which operate with a small bandwidth and high false alarm rate.