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无线传感器网络的定位算法研究

摘要第5-8页
Abstract第8-10页
List of Abbreviations第11-18页
Chapter 1. Introduction第18-34页
    1.1. Background第18-24页
        1.1.1. Architecture of Sensor Network第18-19页
        1.1.3. Applications of WSNs第19-21页
        1.1.4. Challenges and Hurdles第21-24页
    1.2. Thesis Motivation第24-26页
    1.3 Recent Progresses in Related Areas第26-30页
        1.3.1. Energy Saving第26-28页
        1.3.2. Localization Algorithms in NLOS Environments第28-30页
    1.4. Contributions第30-32页
    1.5. Dissertation Outline第32-34页
Chapter 2. The Key Existing Localization Techniques for Wireless Sensor Networks第34-48页
    2.1. Range-free Localization Techniques第34-38页
        2.1.1. Centroid Localization (CL)第34-35页
        2.1.2. Area-Based Point-In-Triangulation Test (APIT) Localization Scheme第35-36页
        2.1.3. Secure Range-independent Localization (SeRLoc)第36页
        2.1.4. Ad-Hoc Positioning System (APS)第36-38页
    2.2. Range-based Localization Techniques第38-47页
        2.2.1. TOA Estimation第38-41页
        2.2.2. TDOAEstimation第41-43页
        2.2.3. RSS Estimation第43-45页
        2.2.4. DOA Estimation第45-47页
    2.3. Conclusions第47-48页
Chapter 3. An NSGA-Ⅱ Based Localization Algorithm for Wireless Sensor Networks第48-70页
    3.1. Introduction第48-50页
    3.2. System Model第50-52页
        3.2.1. Communication Protocol第51页
        3.2.2. Measurement Models第51-52页
    3.3. Energy Consumption第52-54页
        3.3.1. Energy Consumption in Reception第52-53页
        3.3.2. Energy Consumption in Transmittion第53-54页
    3.4. Positioning Performance第54-59页
        3.4.1. RSS-only CRB第55-56页
        3.4.2. The Hybrid DRSS-RSS CRB第56页
        3.4.3. The Hybrid RSS/DOA CRB第56-58页
        3.4.4. The Hybrid DRSS-RSS/DOA CRB第58-59页
    3.5. B-FIM Constrained Optimization for Localization第59-61页
        3.5.1. Bayesian Fisher Information Matrix (B-FIM)第59-60页
        3.5.2. B-FIM Constrained Optimization Formulation第60-61页
    3.6. Genetic-Based Multi-objective Optimization for Localization第61-64页
        3.6.1. Multi-objective Optimization第61-62页
        3.6.2. The NSGA-Ⅱ Based Multi-objective Optimization Problem Formulation第62-64页
    3.7. Simulation Results第64-69页
        3.7.1. Simulation Results for B-FIM Constraint based Localization Algorithm第65-66页
        3.7.2. Simulation Results for NSGA-Ⅱ based Localization Algorithm第66-69页
    3.8. Conclusions第69-70页
Chapter 4. Energy-Efficient Localization in WSNs:A Leader-Follower Stackelberg GameApproach第70-82页
    4.1. Introduction第70-71页
    4.2. System Model and Game Formulation第71-72页
        4.2.1. System Model第71页
        4.2.2. Game Formulation第71-72页
    4.3. Game for Hybrid DOA/RSS Based Localization第72-78页
        4.3.1. Target's Best Response Strategy第73-75页
        4.3.2. CANs's Best Response Strategy第75-78页
    4.4. Simulation Results第78-80页
    4.5. Conclusions第80-82页
Chapter 5. NBP-Based Localization Algorithm in NLOS Environments第82-99页
    5.1. Introduction第82-84页
    5.2. System Model第84-86页
        5.2.1. Network Model第84-85页
        5.2.2. Problem Formulation第85-86页
    5.3. NBP-Based Localization第86-87页
    5.4. NLOS Localization with Priori Error Information第87-94页
        5.4.1. Idealized Case:Known NLOS Status and Distribution Parameters第87-89页
        5.4.2. Known NLOS Probability and Distribution Parameters第89-90页
        5.4.3. Known Distribution Parameters Only第90-94页
    5.5. Simulation Results第94-98页
        5.5.1. Relationship between the Number of Neighbors of the Sensor and the Belief of ItsEstimate Position第94-96页
        5.5.2. RMSE as the Function of Noise Standard Deviation第96-97页
        5.5.3. RMSE as a Function of the Number of NLOS Anchors第97-98页
    5.6. Conclusions第98-99页
Chapter 6. Conclusions and Future Work第99-102页
    6.1. Conclusions第99-100页
    6.2. Future Work第100-102页
致谢第102-103页
Publication第103-104页
Bibliography第104-110页

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