Multi-sensor Asynchronous Sampling Information Fusion Estimation

With the multi-sensor data fusion technology widely used in military and civil-ian fields, it has been received intensive attention by international and domesticresearchers. However, the problem of synchronization data fusion has been gainedmore investigation, which requires that all sensors to measure a target synchronouslyand all measurements data are communicated to the fusion center synchronously.But in practice, we often encountered with the problem of the multi-rate asyn-chronous data fusion. For example, the sensors may have di?erent sampling peri-ods, di?erent inherent delays and di?erent communication delays, which will leadto asynchronous data fusion. So it is more important in theoretical meaning andapplying values to study the multi-rate asynchronous information fusion estimationproblems. In this paper, we study the information fusion estimation algorithms forthe multi-sensor asynchronous sampling systems, including the systems with inte-ger times sampling rates, with rational times sampling rates and with four multiplesampling rates.For the linear discrete-time stochastic system with integer times sampling rates,based on known state equation modeled in the finest scale, the original system istransformed into a new multi-sensor system based on every scale by augmentationand partition of the state and measurement equations. For the new stochasticsystems with single integer times sampling rate, the cross-covariance matrices offiltering errors between arbitrary two sensors are derived. At last, the multi-sensoroptimal information fusion state filters are presented based on the optimal fusionestimation algorithm in the linear minimum variance sense.For the linear discrete-time stochastic system with rational times samplingrates, it is formalized into a synchronous sampling system with single samplingrate by the model transformation. Then the local Kalman filter of each sensors isobtained. Meanwhile, the cross-covariance matrices of estimation errors betweenarbitrary sensors are derived. At last, the multi-sensor optimal information fusionstate filters are presented based on the optimal fusion estimation algorithm in thelinear minimum variance sense.For a multi-rate linear discrete stochastic system, there exist four rates: the state updating rate,the measurement sampling rates, the estimate updating ratesand the estimate output rate. Suppose that the state updating rate is di?erent fromthe measurement sampling rates. For the single sensor the estimate updating ratesare equal to the measurement sampling rates, the estimate output rate is an arbi-trary positive integer. First, through model transform, the multi-rate asynchronoussampling system is formalized into a synchronous sampling system. Then the statefilters of local sensors are given. At last, the multi-sensor optimal information fusionstate filters are presented based on the optimal fusion estimation algorithm in thelinear minimum variance sense.