Maneuvering Multi-target Tracking Algorithm Based on Modified Generalized Probabilistic Data Association

Aiming at the problem of strong nonlinear and effective echo confirm of multi-target tracking system in clutters environment, a novel maneuvering multitarget tracking algorithm based on modified generalized probabilistic data association is proposed in this paper. In view of the advantage of particle filter which can deal with the nonlinear and non-Gaussian system, it is introduced into the framework of generalized probabilistic data association to calculate the residual and residual covariance matrices, and the interconnection probability is further optimized. On that basis, the dynamic combination of particle filter and generalized probabilistic data association method is realized in the new algorithm. The theoretical analysis and experimental results show the filtering precision is obviously improved with respect to the tradition method using suboptimal filter.

Variational Bayesian Kalman filter using natural gradient

We propose a technique based on the natural gradient method for variational lower bound maximization for a variational Bayesian Kalman filter.The natural gradient approach is applied to the Kullback-Leibler divergence between the parameterized variational distribution and the posterior density of interest.Using a Gaussian assumption for the parametrized variational distribution,we obtain a closed-form iterative procedure for the Kullback-Leibler divergence minimization,producing estimates of the variational hyper-parameters of state estimation and the associated error covariance.Simulation results in both a Doppler radar tracking scenario and a bearing-only tracking scenario are presented,showing that the proposed natural gradient method outperforms existing methods which are based on other linearization techniques in terms of tracking accuracy.

A filtering-based bridge weigh-in-motion system on a continuous multi-girder bridge considering the influence lines of different lanes

A real-time vehicle monitoring is crucial for effective bridge maintenance and traffic management because overloaded vehicles can cause damage to bridges,and in some extreme cases,it will directly lead to a bridge failure.Bridge weigh-in-motion(BWIM)system as a high performance and cost-effective technology has been extensively used to monitor vehicle speed and weight on highways.However,the dynamic effect and data noise may have an adverse impact on the bridge responses during and immediately following the vehicles pass the bridge.The fast Fourier transform(FFT)method,which can significantly purify the collected structural responses(dynamic strains)received from sensors or transducers,was used in axle counting,detection,and axle weighing technology in this study.To further improve the accuracy of the BWIM system,the field-calibrated influence lines(ILs)of a continuous multi-girder bridge were regarded as a reference to identify the vehicle weight based on the modified Moses algorithm and the least squares method.In situ experimental results indicated that the signals treated with FFT filter were far better than the original ones,the efficiency and the accuracy of axle detection were significantly improved by introducing the FFT method to the BWIM system.Moreover,the lateral load distribution effect on bridges should be considered by using the calculated average ILs of the specific lane individually for vehicle weight calculation of this lane.