Previous studies on target recognition/detection techniques have largely been conducted using signals in the time domain along with corresponding analytical performance analyses.
This study analytically guides the performance of target detection and recognition techniques using the concept of the probability density function. The Monte-Carlo simulation is generally used to analyze the performance of natural frequency-based target recognition and detection techniques. That is, after setting the variance of an additive noise, the pertinent target recognition and detection technique is performed by producing noisy signals by repetitively producing an independent zero mean noise with a specific SNR(Signal to Noise Ratio). For an accurate performance analysis, the number of repetitions must be increased significantly, which requires a considerable amount of computation time. Therefore, this study aims to extend a time domain radar target recognition and detection technique to frequency-domain. In addition, analytical performance analysis schemes have been proposed. Through the analytical performance analysis, the performance of the detection and recognition technique can be analyzed without a Monte-Carlo simulation. The amount of computation required for the analytical method is quite small compared with the performance analysis method using the Monte-Carlo simulation. For detection, non-recursive detection scheme in the time domain has been extended to the frequency domain. A recursive approach has been proposed to reduce the computational complexity of the detection algorithm. For recognition, the time domain performance analysis scheme has been extended to the frequency domain, where both schemes are based on the zero threshold value. In addition, for improvement of the recognition performance, optimal threshold selection scheme both for the time domain and the frequency domain have been proposed. In addition, a multiple target recognition scheme has been proposed. This study aims to expand on previous studies and thereby further the research on target recognition/detection techniques and the analytical performance analysis thereof.