# CPD方案BOM对比分析
# 注意：以下 BOM 成本与部署周期均为工程估算值，非厂商公开报价

cpd_solutions = {
    "Calterah UWB (软件升级)": {
        "额外硬件": "无（复用无钥匙进入UWB）",
        "新增BOM": "$0（仅软件开发）",
        "额外重量": "0g",
        "线束改动": "无",
        "OTA更新": "支持",
        "部署周期": "3-6个月",
        "限制": "测距精度~10cm，呼吸检测灵敏度待验证"
    },
    "TI IWR6843 (60GHz专用雷达)": {
        "额外硬件": "1×IWR6843AOP + 天线 + 外壳",
        "新增BOM": "$15-25",
        "额外重量": "~50g",
        "线束改动": "需CAN总线连接",
        "OTA更新": "支持",
        "部署周期": "12-18个月（新硬件集成）",
        "限制": "需在车顶/顶灯位置安装"
    },
    "摄像头+rider融合": {
        "额外硬件": "利用现有OMS摄像头",
        "新增BOM": "$0-5（算法授权）",
        "额外重量": "0g",
        "线束改动": "无",
        "OTA更新": "支持",
        "部署周期": "6-12个月",
        "限制": "遮挡场景失效，需雷达backup"
    },
    "UWB+60GHz融合": {
        "额外硬件": "UWB + 60GHz雷达",
        "新增BOM": "$15-25",
        "额外重量": "~50g",
        "线束改动": "需新增雷达线束",
        "OTA更新": "支持",
        "部署周期": "12-18个月",
        "限制": "成本最高，但覆盖最全面"
    }
}

for solution, details in cpd_solutions.items():
    print(f"\n{'='*50}")
    print(f"方案: {solution}")
    for key, value in details.items():
        print(f"  {key}: {value}")

"""
Calterah UWB CPD 呼吸检测模拟验证

UWB雷达检测呼吸的原理：
1. 发射UWB脉冲，接收反射信号
2. 提取胸腔微动（呼吸引起的0.1-0.5mm位移）
3. 通过Range-FFT和相位提取获取微动信号
4. 带通滤波（0.1-0.5Hz）提取呼吸频率

注意：此为原理验证代码，实际部署需Calterah SDK
"""

import numpy as np
from scipy import signal
from typing import Tuple

class UWBCPDSimulator:
    """
    UWB CPD呼吸检测模拟器
    
    模拟Calterah CAL1106AQ的CPD检测流程：
    1. UWB脉冲发射与接收
    2. Range-FFT提取目标距离bin
    3. 相位提取获取微动信号
    4. 带通滤波提取呼吸频率
    5. 活体判定
    """
    
    def __init__(self, sampling_rate=1000, range_resolution=0.1):
        """
        Args:
            sampling_rate: 采样率 (Hz)
            range_resolution: 距离分辨率 (m)
        """
        self.fs = sampling_rate
        self.range_res = range_resolution
        
        # 模拟参数
        self.target_distance = 0.8  # 婴儿在后排约0.8m
        self.breath_freq_adult = 0.25  # 成人呼吸 ~15次/分
        self.breath_freq_infant = 0.45  # 婴儿呼吸 ~27次/分
        self.heartbeat_freq = 1.2  # 心跳 ~72次/分
        
    def simulate_uwb_signal(self, duration: float = 10.0,
                            is_occupied: bool = True,
                            is_infant: bool = True) -> dict:
        """
        模拟UWB雷达接收信号
        
        Args:
            duration: 采集时长（秒）
            is_occupied: 座椅是否有人
            is_infant: 是否为婴儿
        
        Returns:
            信号数据字典
        """
        t = np.arange(0, duration, 1/self.fs)
        n_samples = len(t)
        
        # 目标距离bin
        target_bin = int(self.target_distance / self.range_res)
        
        if is_occupied:
            # 呼吸引起的胸腔位移
            breath_freq = self.breath_freq_infant if is_infant else self.breath_freq_adult
            breath_displacement = 0.3e-3  # 0.3mm 呼吸位移
            
            # 呼吸信号
            breath_signal = breath_displacement * np.sin(2 * np.pi * breath_freq * t)
            
            # 心跳信号（更微弱）
            heartbeat_signal = 0.05e-3 * np.sin(2 * np.pi * self.heartbeat_freq * t)
            
            # 总微动信号
            micro_motion = breath_signal + heartbeat_signal
            
            # 噪声
            noise = np.random.normal(0, 0.02e-3, n_samples)
            
            # 接收信号 = 直耦 + 目标反射 + 噪声
            # RLS抑制后主要剩微动
            signal_phase = 4 * np.pi * micro_motion / 3e8 * 7.5e9  # UWB中心频率7.5GHz
            received = np.exp(1j * signal_phase) + noise
            
        else:
            # 空座椅：只有静态反射和噪声
            received = np.ones(n_samples, dtype=complex) * 0.1
            received += np.random.normal(0, 0.02, n_samples)
            micro_motion = np.zeros(n_samples)
        
        return {
            'time': t,
            'received': received,
            'target_bin': target_bin,
            'micro_motion': micro_motion,
            'is_occupied': is_occupied,
            'is_infant': is_infant
        }
    
    def extract_breathing(self, received_signal: np.ndarray) -> Tuple[np.ndarray, float]:
        """
        从UWB接收信号中提取呼吸频率
        
        Args:
            received_signal: 接收信号 (complex)
        
        Returns:
            filtered_signal: 滤波后的呼吸信号
            breath_freq: 估计的呼吸频率 (Hz)
        """
        # 提取相位（微动信息在相位中）
        phase = np.angle(received_signal)
        
        # 解包裹
        phase_unwrapped = np.unwrap(phase)
        
        # 带通滤波：0.1-1.0 Hz（覆盖婴儿和成人呼吸+心跳）
        nyq = self.fs / 2
        low = 0.1 / nyq
        high = 1.0 / nyq
        b, a = signal.butter(4, [low, high], btype='band')
        filtered = signal.filtfilt(b, a, phase_unwrapped)
        
        # FFT提取频率
        freqs = np.fft.rfftfreq(len(filtered), 1/self.fs)
        fft_mag = np.abs(np.fft.rfft(filtered))
        
        # 找呼吸频率峰值（0.1-0.6Hz范围）
        breath_mask = (freqs >= 0.1) & (freqs <= 0.6)
        if np.any(breath_mask) and np.max(fft_mag[breath_mask]) > 0.01:
            peak_idx = np.argmax(fft_mag[breath_mask])
            breath_freq = freqs[breath_mask][peak_idx]
        else:
            breath_freq = 0.0
        
        return filtered, breath_freq
    
    def detect_presence(self, received_signal: np.ndarray,
                        threshold: float = 0.001) -> dict:
        """
        活体检测判定
        
        Args:
            received_signal: 接收信号
            threshold: 活体判定阈值
        
        Returns:
            判定结果
        """
        filtered, breath_freq = self.extract_breathing(received_signal)
        
        # 信号能量
        energy = np.var(filtered)
        
        # 判定
        is_alive = energy > threshold and breath_freq > 0.05
        
        return {
            'is_present': is_alive,
            'breath_frequency': breath_freq,
            'breath_rate_per_min': breath_freq * 60 if breath_freq > 0 else 0,
            'signal_energy': energy,
            'confidence': min(energy / threshold / 2, 1.0)
        }


# 测试验证
if __name__ == "__main__":
    print("=" * 60)
    print("Calterah UWB CPD 呼吸检测模拟验证")
    print("=" * 60)
    
    simulator = UWBCPDSimulator()
    
    # 场景1: 婴儿在后排
    print("\n场景1: 婴儿在后排座椅")
    data = simulator.simulate_uwb_signal(duration=10.0, is_occupied=True, is_infant=True)
    result = simulator.detect_presence(data['received'])
    print(f"  信号能量: {result['signal_energy']:.6f}")
    print(f"  呼吸频率: {result['breath_frequency']:.3f} Hz ({result['breath_rate_per_min']:.1f} 次/分)")
    print(f"  活体检测: {'✅ 检测到活体' if result['is_present'] else '❌ 未检测到'}")
    print(f"  置信度: {result['confidence']:.1%}")
    print(f"  真实呼吸: {simulator.breath_freq_infant:.3f} Hz ({simulator.breath_freq_infant*60:.1f} 次/分)")
    
    # 场景2: 空座椅
    print("\n场景2: 空座椅")
    data = simulator.simulate_uwb_signal(duration=10.0, is_occupied=False)
    result = simulator.detect_presence(data['received'])
    print(f"  信号能量: {result['signal_energy']:.6f}")
    print(f"  呼吸频率: {result['breath_frequency']:.3f} Hz")
    print(f"  活体检测: {'✅ 检测到活体' if result['is_present'] else '❌ 未检测到（正确）'}")
    print(f"  置信度: {result['confidence']:.1%}")
    
    # 场景3: 成人在后排
    print("\n场景3: 成人在后排座椅")
    data = simulator.simulate_uwb_signal(duration=10.0, is_occupied=True, is_infant=False)
    result = simulator.detect_presence(data['received'])
    print(f"  信号能量: {result['signal_energy']:.6f}")
    print(f"  呼吸频率: {result['breath_frequency']:.3f} Hz ({result['breath_rate_per_min']:.1f} 次/分)")
    print(f"  活体检测: {'✅ 检测到活体' if result['is_present'] else '❌ 未检测到'}")
    print(f"  置信度: {result['confidence']:.1%}")
    print(f"  真实呼吸: {simulator.breath_freq_adult:.3f} Hz ({simulator.breath_freq_adult*60:.1f} 次/分)")