Wang Guohong

Optimal decision fusion with fuzzy priori probabilities

When a priori probabilities are fuzzy, the optimal decision fusion in the sense of minimum error of probability in a fusion center is considered. The fusion center receives decisions from various distributed sensors. The optimal decision fusion scheme at the fusion center is derived by using the URI (utility ranking index) and TDC (total distance criterion) fuzzy ordering criteria. It is found that the decision structure at the fusion center in this case is identical to that used by Chair and Varshney (1986) except that the threshold is different, and that an improvement in the performance of the system by using the presented schemes is achieved beyond that obtained by using the maximum membership degree (MMD) and maximum likelihood (ML) criteria.

Analytical performance evaluation of association of active and passive tracks for airborne sensors

An association algorithm for tracks generated by active and passive sensors was presented and its performance in terms of the probability of correct and false association was analytically evaluated by Farina and Miglioli (Signal Process. 69(3) (1998) 209). Based on the work in Farina and Miglioli, this paper concentrates on the derivation of simplified performance evaluation formula for deterministic target trajectories. The main contribution of this paper is: (a) the simplified formula of false association probability for a fixed value of correct association probability is presented, and (b) the simplified formula of correct association probability for a fixed value of false association probability is also derived. The advantage of the work is that the performance evaluation can be done with simple calculation instead of performing integral and using trial-and-error method. Numerical results and comparisons show that the presented methods are feasible and effective.

A novel unscented filter TBD algorithm FOR weak radar target

In this paper, the unscented particle filter (UPF) is adopted to realize the tracking before detection (TBD) of weak radar target. In order to construct an accurate approximation to true proposal distribution, the state at each time scan is predicted according to the unscented Kalman filter (UKF). Unfortunately, in the framework of TBD, the UPF is used to handle with the raw measurements, which are unthresholded. In this situation, how to select the only measurement to compute the innovation is an important and difficult issue. Thus, it is difficult to perform the state update, and ineffective selection will lead to poor performance or even fail to detect targets. To address this problem, a modified UPF-TBD algorithm (MUPF-TBD) is proposed, in which the observation innovation is generated by a dummy observation. And the dummy observation can be generated from the measurement prediction of the state estimation at last step. Thus, using the feedback information, this new proposed method can obtain an accurate approximation to the system and as a result, improve the estimation performance. An application example is given to draw a comparison between this new algorithm and the existing algorithm. Simulation results illustrate the effectiveness of this approach.

Passive location algorithm of two sensors based on multihypothesis extended Kalman filter

Multitarget tracking with bearings only measurements of two passive sensors is a very important problem, which has not been solved. To counter this problem a method is proposed in this paper, This method firstly used the bearing measurements of two passive sensors to estimate the initial range interval of targets, which are divided into several subintervals. At each subinterval an extended Kalman filter and a multihypothesis method are used to estimate the state of targets. At the same time the bearing measurements are associated. Combined state estimate is obtained as weighted sums of the state estimate of each subinterval. Simulation results show that through using the algorithm discussed in this paper two passive sensors can locate and track multiple targets at the same time.

The track initiation algorithm based on Hough transform and space accumulation

Track initiation algorithm based on Hough transform is an effective method on condition of strong clutter environment. But due to the influence of measurement error and the selection of parameter space threshold, the problems of clutter points mix in target tracks and multiple tracks initialized by only one target are existed. In order to solve these problems, this paper proposes a track initiation algorithm based on Hough transform and space accumulation. The algorithm firstly using Hough transform for the preliminary screening of the original data. And then the obtained primary measurement data would be accumulated on data space. Finally, the threshold detection is used to judge whether to initial track. Algorithm effectively reduces the clutter points mixed in target tracks and solves the problem of multiple tracks initialized by only one target. Simulation results show that the algorithm can initial the target track effectively.

PPHDF-AI for multitarget tracking with unknown target SNR

The performance of multitarget tracking can be improved by using target amplitude information (AI) for that targets can be identified earlier through the enhanced discrimination between target and false alarm. However, one of the limitations of this application is that the signal-to-noise ratio (SNR) of target is usually unknown in practice. This paper proposes a particle probability hypothesis density filter (PPHDF) with amplitude information (PPHDF-AI) by extending the state vector and measurement vector of particle filter (PF) can be easily. By the extension of dynamic equation and measurement equation, the proposed algorithm can estimate targets SNR as well as their individual states. Simulation results demonstrate that the proposed method is quite suitable for situation that target SNR is unknown, and is superior to traditional methods.