Weihua Wu

In this study, we extend traditional (single-target) hybrid systems to multi-target hybrid systems with a focus on the multi-maneuvering-target tracking sy

To track multiple maneuvering targets hidden in the Doppler blind zone (DBZ), we have proposed an MM-GLMB-DBZ tracker based on the latest multiple model generalized labeled multi-Bernoulli (MM-GLMB) filter. To further enhance the tracking performance, this paper combines sensor control techniques to the MM-GLMB-DBZ tracker. Macroscopically, the proposed algorithm consists of the MM-GLMB-DBZ tracker and a controller. Unlike conventional control approaches where separate prediction and update implementations are usually adopted, the proposed control algorithm constructs a systematic process flow for the joint prediction and update implementation of GLMB-like filters. Moreover, inside the core controller module, we apply the previously designed safety indicator and reward function for avoiding the DBZ, and derive the Cauchy-Schwarz divergence (CSD) compatible with the tracker. Hence, this control algorithm considers such factors as the safety of the sensor itself, the DBZ avoidance, and the improvement of the tracking accuracy. Numerical examples verify the effectiveness of the proposed control scheme, showing that it is significantly better than the random control strategy, the original MM-GLMB-DBZ tracker without the control technology, and the state-of-the-art control approach.

To improve the performance of tracking multiple maneuvering targets hidden in the Doppler blind zone (DBZ), we put forward the idea of using sensor control technique to suppress the DBZ masking problem for the first time, by utilizing the principle that the absolute Doppler of a target with respect to a sensor is affected by the target-to-sensor relative geometry and extending multi model probability hypothesis density (MM-PHD) filter for DBZ masking to the partially observable Markov decision process (POMDP) framework. First, the process flow of sensor control is systematically constructed based on our existing work. Second, in the core sensor controller module, we devise three objective functions (including a new safety indicator ensuring sensor safety, a novel reward rule for the DBZ avoidance, and the Cauchy-Schwarz divergence (CSD) compatible with the multi-maneuvering-target tracking) and a decision-making logic for the selection of control commands. Finally, the feasibility and effectiveness of the proposed control scheme are verified through numerical examples, and it is demonstrated that it is obviously superior to the random control strategy and the earlier work without using the control technology.

For a ground moving target indication (GMTI) radar, the presence ofDoppler blind zone (DBZ) results in many short tracks with frequent label switching, which seriously deteriorates the tracking performance. When the DBZ masking is coupled with targets maneuvering, tracking multiple maneuvering targets hidden in the DBZ becomes very challenging, which is reflected in the fact that there is no public research on this issue. To overcome this complicated problem, we propose a practical and fully functional GMTI multi-maneuvering-target tracker based on the multiple model probability hypothesis density (MM-PHD) filter. Unlike the standard MM-PHD filter, the proposed tracker utilizes the Doppler information and incorporates the minimum detectable velocity (MDV) to suppress the DBZ masking. Furthermore, to cope with the problems of the fixed initiation and no label output of the standard MM-PHD filter, the resulting MM-PHD filter with the Doppler and MDV information is augmented with measurement-driven adaptive track initiation and track label propagation, which are necessary for a practical tracker and also required for evaluating the overall GMTI tracking performance. Finally, numerical examples show that the proposed tracker outperforms significantly the existing ones, thus verifying its effectiveness.

Tracking multiple maneuvering targets hidden in the Doppler blind zone (DBZ) is a challenging problem. To overcome the complicated problem, we proposed a tracker based on the multiple model probability hypothesis density (MM-PHD) filter. However, the PHD filter is only the first-order moment approximation of the multi-target Bayesian filter, and it cannot output track labels in principle. In order to improve the tracking performance, another novel tracker is proposed in this paper. To track multiple maneuvering targets, the proposed tracker is built on the latest multiple model generalized labeled multi-Bernoulli (MM-GLMB) filter. Moreover, it incorporates the minimum detectable velocity (MDV) to suppress the DBZ masking. Finally, a measurement-driven adaptive track initiation is introduced to address the fixed track initiation problem of the standard MM-GLMB filter. It is demonstrated through numerical examples that the proposed tracker outperforms the existing work significantly, especially in terms of both accuracy and robustness of cardinality estimation.

When there is the correlation between Doppler and slant range, previous literatures have presented some sequential filter algorithms. However, they are only applied to simplified fixed radar's local coordinate system. As a result, a Sequential converted measurement Kalman filter (SCMKF) with Doppler measurements based on the Earth centered earth fixed (ECEF) coordinate system applicable to a moving airborne platform which has time varying attitude is proposed. Firstly, the correlated Doppler and range are decorrelated using the Cholesky factorization, then the converted position measurements are obtained by a series of coordinate transformation with unchanged range component and other observations, such as azimuth and elevation angles; the corresponding error covariances are derived which are used to the Converted measurement Kalman filter (CMKF). Finally the sequential filter is implemented for changed pseudo-Doppler measurements. The proposed method is validated through Monte Carlo test compared with the performance of CMKF with just converted position measurements and traditional SCMKF with Doppler which ignores the correlation between Doppler and range noises, and the conclusion is obtained that utilizing Doppler information correctly can improve tracking performance, nevertheless, the improvement gain of filter accuracy is limited, which can provide some references for engineering application.