Anomaly detection based weighted combination kernel RX algorithm and its parameter selection

In order to combine the spectral shape difference information and the polynomial kernel function global information, exploit the object feature fully and improve the accuracy of anomaly detection, anomaly detection method was proposed based on weighted combination kernel RX algorithm. A spectral angle kernel function was added to Gaussian kernel function in the anomaly detection method. Because the kernels' parameter and the weighting parameter will affect the efficiency of the algorithm, the random function selection, the hill climbing method and the particle swarm optimization algorithm were implemented for setting the above parameters. Experiment results show that at a constant false alarm rate, it is the best to set the parameters by means of the particle swarm algorithm. Target detection rate is 83.5% by using the weighted combination kernel RX algorithm, higher than that by means of the traditional kernel RX algorithm.