為了可以提升生活品質和安全性，近期用於公共與私領域室內感測應用的智慧感測器被廣泛使用。雷達遠距離感測的原理是基於都卜勒效應來進行目標物偵測。然而，環境中的電磁波不僅會被目標反射，還可能被其他靜止物體反射，而導致接收訊號容易因為雜波干擾而失真。因此，需要對進一步的訊號處理著手進行研究，以提高雷達系統的感測靈敏度。
在本研究中，使用了基於自我注入鎖定的雷達技術，且提出了一系列用於室內人體感測應用的演算法，以實現高靈敏度的感測。第一步驟是進行存在感測，此技術設計了一訊號處理方法來檢測目標物是否存在於感測環境中。接著提出一種基於時域的兩階段廣義概似比檢測演算法，且此演算法除了可以降低計算成本和觀測時間，還能克服頻譜中的洩漏問題，提升存在感測的準確性。在確定目標物存在後，若能進一步分析並收集受測者之生理訊號，對人們日常生活品質和安全性具有重要意義，因此，提出了一種新穎的短時生命徵象感測演算法，其使用正交餘弦轉換與可變窗長之技術。相較於傳統傅立葉轉換的頻率解析度其可降低50%，因此，所提出的方法能夠使用更短的窗長分析，並且在準確性與計算效率之間取得平衡。最後，為了進一步準確地分析心跳周期的心律間隔，即心率變異度，需要在時域內對雷達訊號進行分析。為此，提出了一種PRT波塑形之演算法，用於增強雷達訊號之心跳特徵並獲取心率變異度資訊，且由於該方法在時域內處理，可以避免頻域分析中窗長與頻率解析度之間的取捨問題。

Recently, smart sensors used in public and private indoor-detection applications have been widely adopted to improve human quality of life and safety. The detection principle of remote radar sensing is based on the Doppler effect of a target. However, electromagnetic waves that propagate through the environment may not only be reflected by the target but also by other static objects, which leads to distortion in the received signals owing to clutter interference. Hence, the signal processing must be investigated to improve the detection sensitivity of radar systems.
In this study, a self-injection-locked-based radar was used to achieve good detection sensitivity, and a serial algorithm was proposed for indoor human-sensing applications. The first step concerns occupancy detection, in which signal processing is designed to detect whether a subject exists in the sensing environment. Subsequently, an algorithm for a two-stage generalized likelihood ratio test is analyzed in the time domain, which reduces the computational cost and observation time. Furthermore, it can overcome the leakage problem in the spectrum and increase occupancy detection accuracy. After determining the presence of the subject, further analysis to collect vital signs helps improve human safety and quality of life. Hence, a novel short-time vital sign detection algorithm was proposed using a quadrature cosine transform and varying window-length techniques. Owing to a 50% frequency-resolution reduction of the conventional Fourier transform, the proposed method can strike a balance between accuracy and computational efficiency using a shorter window length. Finally, accurate heartbeat intervals or heart rate variability (HRV) monitoring is required to analyze radar signals in the time domain. Consequently, the proposed PRT-shaping algorithm for enhanced heartbeat features, and the extraction of HRV, was processed in the time domain to avoid the trade-off between window length and frequency resolution typically encountered during frequency-domain analyses.

Contents

論文審定書 i
Acknowledgements ii
摘要 iii
Abstract iv
List of Figures vii
List of Tables xi
Chapter 1 Introduction 1
1.1 Research Motivation 1
1.2 Noncontact Radar System 3
1.2.1 Continuous Wave Radar 3
1.2.2 Self-Injection-Locked Based Radar 4
1.3 Existing Signal Processing 5
1.4 Dissertation Overview 8
Chapter 2 Occupancy Detection Algorithm 9
2.1 Frequency-Modulated Phase and Self-Injection-Locked Radar System 9
2.1.1 System Architecture 9
2.1.2 Detection Principle 10
2.2 Proposed Occupancy Detection Algorithm 12
2.2.1 Occupancy Detection Theory 12
2.2.2 Probability Density Function Based Generalized Likelihood Ratio Test 13
2.2.3 Proposed Method and Simulation 15
2.3 Two-Stage GLRT Algorithm and Experimental Results 20
2.3.1 Single-Target Detection 20
2.3.2 Multi-Target Detection 25
2.4 Discussion 30
2.5 Summary 32
Chapter 3 Short-Time Vital Sign Detection Algorithm 33
3.1 Phase and Quadrature Self-Injection-Locked Radar System 33
3.1.1 System Architecture 33
3.1.2 Quadrature Signals 35
3.2 Proposed Short-Time Vital Sign Detection Algorithm 36
3.2.1 Quadrature Cosine Transform 36
3.2.2 Simulation for Frequency-Domain Analysis 42
3.2.3 Varying Window Length Technique 45
3.3 Experimental Results 52
3.4 Discussion 55
3.5 Summary 57
Chapter 4 Heart-Rate Variability Monitoring Algorithm 58
4.1 Phase and Quadrature Self-Injection-Locked Radar System 59
4.1.1 Detection Principle for Phase Recovery 59
4.1.2 System Validation Test 60
4.2 Proposed Heart-Rate Variability Monitoring Algorithm 64
4.2.1 Radar Heartbeat Signal 64
4.2.2 PRT shaping Algorithm 65
4.2.3 PRT Model Discussion 70
4.3 Experimental Results 71
4.4 Discussion 78
4.5 Summary 80
Chapter 5 Conclusions 81
Bibliography 83
Vita 92

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