基于神经网络的目标跟踪模型与卡尔曼滤波的比较

文件列表：
├文件夹1：[nn-tracking-master]
│  ├(1)align_data.m
│  ├文件夹1：[bayesopt]
│  │  ├(1).folder
│  │  ├(2).gitignore
│  │  └█
│  ├文件夹2：[best]
│  │  ├文件夹1：[predict_1]
│  │  │  ├(1)time_step_0.1_hs_26_sl_92_lstm_x1_snr_x3.png
│  │  │  ├(2)time_step_0.1_hs_26_sl_92_lstm_x1_snr_x3.txt
│  │  │  ├(3)time_step_0.1_hs_26_sl_92_lstm_x1_snr_x3_bayesopt_0.038585.mat
│  │  │  ├(4)time_step_0.1_hs_33_sl_63_relu_x2_snr_x3.png
│  │  │  ├(5)time_step_0.1_hs_33_sl_63_relu_x2_snr_x3.txt
│  │  │  ├(6)time_step_0.1_hs_33_sl_63_relu_x2_snr_x3_bayesopt_0.043559.mat
│  │  │  ├(7)time_step_0.1_hs_99_sl_89_gru_x1_snr_x3.png
│  │  │  ├(8)time_step_0.1_hs_99_sl_89_gru_x1_snr_x3.txt
│  │  │  ├(9)time_step_0.1_hs_99_sl_89_gru_x1_snr_x3_bayesopt_0.041931.mat
│  │  │  └█
│  │  ├(1)time_step_0.1_hs_33_sl_42_relu_x2_snr_x3.png
│  │  ├(2)time_step_0.1_hs_33_sl_42_relu_x2_snr_x3.txt
│  │  ├(3)time_step_0.1_hs_33_sl_42_relu_x2_snr_x3_bayesopt_0.032756.mat
│  │  ├(4)time_step_0.1_hs_45_sl_45_relu_x2_snr_x5.png
│  │  ├(5)time_step_0.1_hs_45_sl_45_relu_x2_snr_x5.txt
│  │  ├(6)time_step_0.1_hs_45_sl_45_relu_x2_snr_x5_bayesopt_0.03872.mat
│  │  ├(7)time_step_0.1_hs_88_sl_10_tanh_x2.png
│  │  ├(8)time_step_0.1_hs_88_sl_10_tanh_x2.txt
│  │  ├(9)time_step_0.1_hs_88_sl_10_tanh_x2_bayesopt_0.063691.mat
│  │  ├(10)time_step_0.5_hs_77_sl_27_leru_x1.mat
│  │  ├(11)time_step_0.5_hs_77_sl_27_leru_x1.png
│  │  ├(12)time_step_0.5_hs_77_sl_27_leru_x1.txt
│  │  └█
│  ├(2)calc_errors.m
│  ├(3)deep_lstm_nn.m
│  ├(4)EKF.m
│  ├(5)example_feedforward.m
│  ├(6)gen_sin.m
│  ├(7)hybrid_lstm_nn.m
│  ├(8)KF.m
│  ├(9)noise_ff_nn.m
│  ├(10)noise_lstm_nn.m
│  ├(11)plot_2var_dep.m
│  ├(12)plot_results.m
│  ├(13)prepare_train_data.m
│  ├(14)print_data_stats.m
│  ├(15)rand_fill.m
│  ├(16)rescale_data.m
│  ├(17)rnnBlock.m
│  ├(18)single_nn_optim.m
│  ├(19)single_nn_test.m
│  ├(20)test_network.m
│  ├(21)time_series_forecasting_test.m
│  ├(22)ts_extrap.m
│  ├(23)ts_ff_nn.m
│  ├(24)ts_kf.m
│  ├(25)ts_lstm_nn.m
│  └█
└█

运行例图：



基于神经网络的目标跟踪模型与卡尔曼滤波的比较.zip (2.34 MB, 下载次数: 0, 售价: 30 积分)

2023-11-20 02:44 上传

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