Target tracking using distributed sensor network is in general a challenging problem because it always needs to deal with real-time processing of noisy information. In this paper the problem of using nonlinear sensors...Target tracking using distributed sensor network is in general a challenging problem because it 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. The problem is formulated as a prudent design of nonlinear filters for a linear system subject to noisy nonlinear 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 the random walk model. It can be shown that the nonlinear filter will have optimal convergence if the number of the sensors are large enough and the convergence rate will be highly improved if the sensors are deployed appropriately. This actually raises an interesting issue on active sensing： how to optimally move the sensors if they are considered as mobile multi-agent systems？ Finally, a simulation example is given to illustrate and validate the construction of our filter.展开更多
文摘Target tracking using distributed sensor network is in general a challenging problem because it 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. The problem is formulated as a prudent design of nonlinear filters for a linear system subject to noisy nonlinear 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 the random walk model. It can be shown that the nonlinear filter will have optimal convergence if the number of the sensors are large enough and the convergence rate will be highly improved if the sensors are deployed appropriately. This actually raises an interesting issue on active sensing： how to optimally move the sensors if they are considered as mobile multi-agent systems？ Finally, a simulation example is given to illustrate and validate the construction of our filter.
