光達是一種廣泛應用在大地測量學、大氣物理學，以及自動駕駛車的遙測技術。光達以雷射光照射物體並由光波傳送到接收的時間差，亦即飛行時間，以及光速來推算目標物的深度。在眾多種類的光達系統中，混沌光達系統因其傳送波強度隨時間的變化類似雜訊而能透過計算參考訊號和目標訊號的相關性來推算飛行時間，因此最為廣泛應用。然而，系統的運算複雜度會隨著偵測距離及傳送訊號的長度而增大，這現象是混沌光達系統中頗受關注的議題。因此，這個研究提出一個應用在混沌光達系統的深度感測的低複雜度演算法。本篇論文提出的演算法能在極少的表現損失下減少運算複雜度到原本演算法的百分之十五。而時差測距模組的硬體架構設計也會在本篇論文提出，此論文提出的演算法能降低硬體架構的功率消耗。

LiDAR, Light Detection and Ranging, is a remote-sensing technique applied generally to geodesy, atmospheric physics and even autonomous cars. The approach for depth- sensing of LiDAR systems is illuminating the target with laser signal and deriving the time difference, named time of flight (TOF), between the reflected signal (target signal) and the transmitted one (reference signal). Among numerical kinds of LiDAR systems, the chaotic LiDAR (CLiDAR) system is used most commonly for its noise-like wave- form beams which enable us to find the time of flight simply by correlating the target signal with the reference one. Nevertheless, the amount of computation increases with the extension of the detectable range and the length of the signal. The phenomenon is a concerned issue of CLiDAR. Therefore, the study proposed a low-complexity al- gorithm for depth sensing of CLiDAR. The proposed algorithm reduces the amount of computation to 15% of the traditional one with tiny loss of performance. The hardware of the time-of-flight calculation unit is implemented in this research, and the proposed algorithm can reduce its power consumption.

1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
1.2 Chaotic LiDAR System . . . . . . . . . . . . . . . . . . . . . . . . 4
1.3 Research Motivation . . . . . . . . . . . . . . . . . . . . . . . . 4
1.4 Thesis Organization . . . . . . . . . . . . . . . . . . . . . . . . 5
2 Depth Sensing Algorithm of Chaotic LiDAR . . . . . . . . . . . . . . . 7
2.1 Determination of Time of Flight . . . . . . . . . . . . . . . . . . 7
2.1.1 Correlation of Reference and Target Signal . . . . . . . . . . . . 8
2.1.2 Spline Interpolation . . . . . . . . . . . . . . . . . . . . . . .10
2.2 Depth Sensing Algorithm of Chaotic LiDAR . . . . . . . . . . . . . .15
3 Proposed Local-search Algorithm for Chaotic LiDAR . . . . . . . . . .19
3.1 Proposed Depth Sensing Algorithm for Chaotic LiDAR . . . . . . . . .19
3.1.1 Spline Interpolation to the Maximum-value Segment . . . . . . . .19
3.1.2 Depth Sensing by Local Search with Moving Averaged Threshold . . .24
3.2 Computational Complexity Analysis . . . . . . . . . . . . . . . . .29
3.2.1 Spline Interpolation . . . . . . . . . . . . . . . . . . . . . . .29
3.2.2 Traditional Depth-sensing Algorithm . . . . . . . . . . . . . . .29
3.2.3 Spline Interpolation to the Maximum-value Segment . . . . . . . .30
3.2.4 Proposed Depth-sensing Algorithm . . . . . . . . . . . . . . . . .30
3.3 Simulation Result and Discussion . . . . . . . . . . . . . . . . . .31
3.3.1 Environment Setup . . . . . . . . . . . . . . . . . . . . . . . .32
3.3.2 Simulation Result and Analysis . . . . . . . . . . . . . . . . . .34
4 Architecture Design . . . . . . . . . . . . . . . . . . . . . . . . .41
4.1 Hardware Architecture . . . . . . . . . . . . . . . . . . . . . . .41
4.1.1 Correlation Unit . . . . . . . . . . . . . . . . . . . . . . . . .42
4.1.2 Index Selection Unit . . . . . . . . . . . . . . . . . . . . . . .44
4.1.3 Interpolation Unit . . . . . . . . . . . . . . . . . . . . . . . .45
4.2 Timing Schedule . . . . . . . . . . . . . . . . . . . . . . . . . . 47
4.3 Simulation and Synthesis Result . . . . . . . . . . . . . . . . . . 51
4.3.1 Specification Setup . . . . . . . . . . . . . . . . . . . . . . . 51
4.3.2 Simulation Result . . . . . . . . . . . . . . . . . . . . . . . . 52
4.3.3 Synthesis Result and Throughput Analysis . . . . . . . . . . . . .53
5 Conclusion. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55

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