Research Of Algorithms In A Target Tracking System Based On Wireless Sensor Networks

Recent advances in digital electronics, microprocessor micro-electro-mechanics and wireless communication have enabled the deployment of large scales sensor networks where thousands of small sensors are distributed over a vast field to obtain fine-grained high-precision sensing data. Due to many attractive characteristic of sensor nodes such as small size and low cost, sensor networks are adopted in many military and civil applications, such as target tracking, surveillance, environmental control and health care. Target tracking is one of the most important applications in wireless sensor networks. Energy efficiency, routing protocol, target location and trajectory prediction are considered most important problem when designing a system for target tracking. In this dissertation, we design the architecture of target tracking based on wireless sensor network, and research on four key issues of the above.A key issue in the design of target tracking based on wireless sensor network is to devise mechanisms to make efficient use of its energy, and thus, extend its lifetime. The information about the amount of available energy in each node is the key to balance the network load. Sending energy information packet periodically is the most commonly used method to obtain the remained energy in each node. But on many occasions, the energy consumption is much more than the energy savings using this method. This dissertation studies approaches to obtain the energy by the prediction. For the prediction, a probabilistic energy model base on Markov chains is firstly discussed. In this model, each sensor node can be modeled by a Markov chain, the node operation modes are represented by the states of a Markov chain and, if a sensor node has M operation modes, it is modeled by a Markov chain with M states. Then we present an algorithm of Energy Prediction based on Stationary Distribution of discrete-time Markov chain, and discuss the energy prediction based on continuous-time in addition. The results of simulation show the efficiency of the algorithm.Some nodes run out of energy quickly, causing the network to extend communication distance of the propagation path, and thus lead to increase energy consumption of the network and reduce the life of network. For this problem, we presented a routing algorithm based on energy prediction. In the algorithm, each node predicted the remaining energy of its neighbor nodes, the routing selection was optimized. The result of simulation output and analysis shows that the EPR could optimize the routing balance the energy consuming of sensor nodes and prolong the network survival. In the other hand, data communication in wireless sensor networks often has timing constraints in the form of deadlines, which represent a new generation of real-time data communications from traditional networked systems. In this work we discuss energy problems of an existing real-time routing protocol LNA. Based on LNA, we propose an Energy-efficient Real-Time Routing (ERTR) protocol, which supports energy-efficient real-time data transmitting in wireless sensor networks. ERTR maximize the number of messages that can reach the sink where each message has its own due-date by minimizing the maximal lateness of all messages. ERTR also average the energy of node to increase the energy efficiency of wireless sensor networks according to the maximum entropy principle. The results of simulation show the algorithm could balance the network energy.we propose a target localization algorithm based on Support Vector Machine (SVM). This algorithm firstly divided the entire monitoring area into several sub-regional, then calculated the optimal SVM for each sub-regional by the known location of the node. Simulation shows the algorithm could accurately estimate the location of the target. Furthermore, location accuracy is depends on the SVM classification accuracy, how to choose the optimal SVM is a key issue to our target location algorithm. We present one quantitative criteria for the SVM classification using statistical analysis method. The results of simulation show the rationality and the effectiveness of the criteria.we propose a multiple objects tracking algorithm based on Kalman filter. The algorithm divided multiple objects tracking problem into a set of single-target tracking problem using nearest neighbor method, and used Kalman filter to track single-target. In addition, considering the convergence of Kalman filter, we discuss the solutions to non-linear stochastic differential equations.