关键技术和算法：快速静态定位方法的探索

# 定义位置与信号强度的匹配关系fingerprint = {    "位置A": {"Wi-Fi1": -70, "Wi-Fi2": -60},    "位置B": {"Wi-Fi1": -60, "Wi-Fi2": -80},    "位置C": {"Wi-Fi1": -80, "Wi-Fi2": -70}}# 测量当前信号强度measure = {"Wi-Fi1": -75, "Wi-Fi2": -65}# 匹配当前信号强度与位置def fingerprint_location(fingerprint, measure):    min_distance = float("inf")    location = ""    for fp in fingerprint:        distance = 0        for signal in fingerprint[fp]:            distance += abs(fingerprint[fp][signal] - measure[signal])  # 计算欧氏距离        if distance < min_distance:            min_distance = distance            location = fp    return location# 调用指纹定位函数result = fingerprint_location(fingerprint, measure)print("当前位置：", result)

# 已知参考点的坐标和信号延迟anchors = {    "参考点A": {"x": 0, "y": 0, "delay": 1},    "参考点B": {"x": 3, "y": 0, "delay": 2},    "参考点C": {"x": 0, "y": 4, "delay": 3}}# 测量到达对象的信号延迟measure = {"参考点A": 2, "参考点B": 4, "参考点C": 5}# 计算对象的坐标def trilateration(anchors, measure):    A = []    b = []    for anchor in anchors:        x = anchors[anchor]["x"]        y = anchors[anchor]["y"]        delay = measure[anchor] * 0.5  # 转换为时间        A.append([x, y, -delay])        b.append(x ** 2 + y ** 2 - delay ** 2)    result = np.linalg.lstsq(A, b, rcond=None)[0]  # 最小二乘法求解    return result[0], result[1]# 调用三角定位函数x, y = trilateration(anchors, measure)print("对象坐标：({0}, {1})".format(x, y))