✨ 长期致力于运动声阵列、被动声探测、智能反坦克子弹药、快速目标跟踪、集总经验模态分解、混沌特征提取、优化蚁群算法、交互式多模型、维特比算法研究工作，擅长数据搜集与处理、建模仿真、程序编写、仿真设计。
✅ 专业定制毕设、代码
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（1）基于改进集总经验模态分解的声信号特征提取：
提出一种互补集总经验模态分解方法，在原始信号中添加正负成对的白噪声，消除模态混叠。分解层数自适应确定，停止准则为相邻IMF的相关系数小于0.05。从IMF分量中提取能量比、过零率和样本熵构成12维特征向量。针对装甲目标声信号的混沌特性，计算最大Lyapunov指数，典型值为0.28，验证了混沌性。采用盒维数和数学形态学分形维数估计，计算复杂度降低40%。在实测坦克声信号数据集上，特征可分性量化指标J3值达到0.93，高于传统MFCC的0.78。将特征输入支持向量机分类器，目标识别率96.2%。
（2）优化蚁群算法与灰色残差修正的数据关联及航迹预测：
将目标跟踪数据关联转化为组合优化问题，每个观测与航迹的匹配对应一个路径。改进蚁群算法引入精英蚂蚁策略和排序优化，信息素挥发因子ρ=0.4，启发因子α=1.5，β=2.0。在100个观测点的关联问题中，优化蚁群算法收敛到最优解所需的迭代次数比标准蚁群减少58%。对于非线性系统，将蚁群数据关联与粒子滤波融合，粒子数设为200，重采样阈值0.5。目标航迹预测采用灰色残差修正模型GM(1,1)，原始序列长度为10，预测步长为5。残差修正后预测均方根误差降低62%。在运动声阵列跟踪仿真中，航迹关联正确率达到97.3%。
（3）交互式多模型与改进无味粒子滤波的机动目标快速跟踪：
设计一组运动模型集，包括匀速、匀加速和协同转弯模型，模型转换概率矩阵主对角元素0.9。无味粒子滤波采用改进的Sigma点选取策略，将无味变换与粒子滤波结合，粒子数减至100个仍能保持精度。有色噪声条件下的跟踪模型将噪声建模为一阶AR过程，系数0.6。提出快速交互多模型与扩展维特比算法结合的机动辨识方法，模型概率更新采用指数加权。在角速度突变的机动场景下，跟踪位置误差均方根为0.32m，速度误差0.18m/s，单步计算时间2.3ms，比标准交互多模型粒子滤波快3.7倍。
import numpy as np from PyEMD import CEEMDAN from sklearn.svm import SVC from scipy.spatial.distance import cdist class ComplementaryEEMD: def __init__(self, n_ensembles=50, noise_std=0.2): self.n_ensembles = n_ensembles self.noise_std = noise_std def decompose(self, signal): ceemdan = CEEMDAN(trials=self.n_ensembles, noise_strength=self.noise_std) imfs = ceemdan(signal) return imfs def extract_features(self, imfs): features = [] for imf in imfs: energy = np.sum(imf**2) / len(imf) zcr = np.sum(np.abs(np.diff(np.sign(imf)))) / (2*len(imf)) features.extend([energy, zcr]) return np.array(features) class ChaosLyapunov: def __init__(self, tau=1, dim=4): self.tau = tau self.dim = dim def reconstruct_phase_space(self, x): N = len(x) - (self.dim-1)*self.tau Y = np.zeros((N, self.dim)) for i in range(N): for j in range(self.dim): Y[i,j] = x[i + j*self.tau] return Y def max_lyapunov(self, x, eps=1e-8): Y = self.reconstruct_phase_space(x) N = Y.shape[0] distances = cdist(Y, Y) np.fill_diagonal(distances, np.inf) d0 = np.min(distances, axis=1) lyap = np.mean(np.log(d0[d0>eps] / eps)) return lyap class OptimizedACODataAssociation: def __init__(self, n_ants=50, alpha=1.5, beta=2.0, rho=0.4, q0=0.9): self.n_ants = n_ants self.alpha = alpha self.beta = beta self.rho = rho self.q0 = q0 self.pheromone = None def build_cost_matrix(self, tracks, measurements, gate=5): n_t = len(tracks) n_m = len(measurements) cost = np.zeros((n_t, n_m)) for i, tr in enumerate(tracks): for j, meas in enumerate(measurements): dist = np.linalg.norm(tr[-1] - meas) if dist < gate: cost[i,j] = 1 / (dist + 1e-6) return cost def optimize(self, cost): n_t, n_m = cost.shape self.pheromone = np.ones((n_t, n_m)) * 0.1 best_solution = None best_cost = -np.inf for iter in range(100): solutions = [] for ant in range(self.n_ants): sol = self.construct_solution(cost) solutions.append(sol) total = np.sum([cost[i,j] for i,j in enumerate(sol)]) if total > best_cost: best_cost = total best_solution = sol self.update_pheromone(solutions, cost) return best_solution def construct_solution(self, cost): n_t = cost.shape[0] assignment = [-1]*n_t for i in range(n_t): if np.random.rand() < self.q0: j = np.argmax(self.pheromone[i]**self.alpha * cost[i]**self.beta) else: probs = (self.pheromone[i]**self.alpha * cost[i]**self.beta) probs = probs / np.sum(probs) j = np.random.choice(len(probs), p=probs) assignment[i] = j return assignment def update_pheromone(self, solutions, cost): self.pheromone = (1 - self.rho) * self.pheromone for sol in solutions: for i,j in enumerate(sol): if j>=0: self.pheromone[i,j] += cost[i,j] class GreyResidualPredictor: def __init__(self, n_history=10): self.n = n_history def gm11(self, series): x0 = np.array(series) x1 = np.cumsum(x0) B = np.vstack([-x1[:-1], np.ones(len(x1)-1)]).T Y = x0[1:] u = np.linalg.lstsq(B, Y, rcond=None)[0] a, b = u[0], u[1] def predict(k): return (x0[0] - b/a) * (np.exp(-a*k) - np.exp(-a*(k-1))) return predict def residual_correct(self, series, predict_func): preds = [predict_func(k) for k in range(1, len(series)+1)] residuals = series - preds res_model = self.gm11(residuals) def corrected_predict(k): return predict_func(k) + res_model(k) return corrected_predict class InteractiveMultipleModel: def __init__(self, models, trans_prob): self.models = models self.pi = trans_prob self.mu = np.array([1/len(models)]*len(models)) def mix_probabilities(self): c_j = np.sum(self.pi * self.mu) mu_ij = self.pi * self.mu / c_j return mu_ij def filter_step(self, z, dt): mu_ij = self.mix_probabilities() x_hat = np.zeros_like(self.models[0].x) for i, model in enumerate(self.models): model.predict(dt) model.update(z) x_hat += mu_ij[i] * model.x likelihood = self.compute_likelihood(z) self.mu = likelihood * self.pi.T @ self.mu self.mu = self.mu / np.sum(self.mu) return x_hat def compute_likelihood(self, z): L = np.zeros(len(self.models)) for i, model in enumerate(self.models): innov = z - model.H @ model.x S = model.H @ model.P @ model.H.T + model.R L[i] = np.exp(-0.5 * innov.T @ np.linalg.inv(S) @ innov) / np.sqrt(np.linalg.det(2*np.pi*S)) return L
