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Target tracking using distributed sensor.network is in general a challenging problem becauseit always needs to deal with real-time processing of noisy information.In this paper the problem ofusing nonlinear sensors such as distance and direction sensors for estimating a moving target is studied.The problem is formulated as a prudent design of nonlinear filters for a linear system subject to noisynonlinear measurements and partially unknown input,which is generated by an exogenous system.In the worst case where the input is completely unknown,the exogenous dynamics is reduced to therandom walk model.It can be shown that the nonlinear filter will have optimal convergence if thenumber of the sensors are large enough and the convergence rate will be highly improved if the sensorsare deployed appropriately.This actually raises an interesting issue on active sensing:how to optimallymove the sensors if they are considered as mobile multi-agent systems? Finally,a simulation exampleis given to illustrate and validate the construction of our filter.
Target tracking using distributed sensor.network is in general a challenging problem because of always needs to deal with real-time processing of noisy information. In this paper the problem of using nonlinear sensors such as distance and direction sensors for estimating a moving target is studied. problem is formulated as a prudent design of nonlinear filters for a linear system subject to noisynonlinear measurements and partially unknown input, which is generated by an exogenous system. In the worst case where the input is completely unknown, the exogenous dynamics is reduced to therandom walk model .It can be shown that the nonlinear filter will have optimal convergence if thenmber of the sensor are large enough and the convergence rate will be highly improved if the sensorsare to be deployed on.This actually raises an interesting issue on active sensing: how to optimallymove the sensors if they are considered as mobile multi-agent systems? Finally, a simulation exampleis given to illustrate and validate the construction of our filter.