写一个基于粒子滤波的多目标追踪代码

以下是一个简单的基于粒子滤波的多目标追踪的代码示例：

import numpy as np

class Particle:
    def __init__(self, x, y, weight):
        self.x = x
        self.y = y
        self.weight = weight

class MultiObjectTracker:
    def __init__(self, num_particles, measurement_noise):
        self.num_particles = num_particles
        self.particles = []
        self.measurement_noise = measurement_noise

    def initialize_particles(self):
        # 初始化粒子集合
        for _ in range(self.num_particles):
            x = np.random.uniform(0, 1)
            y = np.random.uniform(0, 1)
            weight = 1 / self.num_particles
            particle = Particle(x, y, weight)
            self.particles.append(particle)

    def predict(self, motion_noise):
        # 预测阶段，根据运动模型更新粒子位置
        for i in range(self.num_particles):
            noise_x = np.random.normal(0, motion_noise)
            noise_y = np.random.normal(0, motion_noise)
            self.particles[i].x += noise_x
            self.particles[i].y += noise_y

    def update(self, measurements):
        # 权重更新阶段，根据观测数据更新粒子权重
        for i in range(self.num_particles):
            dx = measurements[0] - self.particles[i].x
            dy = measurements[1] - self.particles[i].y
            distance = np.sqrt(dx**2 + dy**2)
            weight = np.exp(-distance / (2 * self.measurement_noise**2))
            self.particles[i].weight = weight

    def resample(self):
        # 重采样阶段，根据粒子权重进行重采样操作
        weights = [particle.weight for particle in self.particles]
        indices = np.random.choice(range(self.num_particles), size=self.num_particles, replace=True, p=weights)
        self.particles = [self.particles[i] for i in indices]

    def estimate(self):
        # 目标估计阶段，计算加权平均值作为目标的估计位置
        estimated_x = np.mean([particle.x for particle in self.particles])
        estimated_y = np.mean([particle.y for particle in self.particles])
        return estimated_x, estimated_y

    def track(self, measurements, motion_noise):
        self.predict(motion_noise)
        self.update(measurements)
        self.resample()
        return self.estimate()

# 示例用法
tracker = MultiObjectTracker(num_particles=100, measurement_noise=0.1)
tracker.initialize_particles()

measurements = [0.2, 0.3]  # 观测数据
motion_noise = 0.05  # 运动噪声

estimated_x, estimated_y = tracker.track(measurements, motion_noise)
print("Estimated position:", estimated_x, estimated_y)

请注意，这只是一个简单的示例代码，实际应用中可能需要根据具体情况进行更复杂的模型设计和算法优化。另外，代码中使用了numpy库来进行随机数生成和矩阵计算，需要确保已安装该库。