Implementation of Sliding Window Detection for Small Targets Under Gaussian Noise

This project implements a sliding window detection method for small targets under Gaussian noise using MATLAB. The input data consists of 1000 points, with a sliding window size of 30 that shifts one data point per iteration. The core algorithm involves three main operations: first, squaring each of the 30 data points within the current window; second, summing all squared values; and finally, dividing the result by the window size to normalize the output. This approach essentially computes the moving average of squared signal values, which enhances signal-to-noise ratio for better detection performance. Experimental verification confirms that this method effectively detects radar target signals. The implementation can be achieved using MATLAB's built-in functions like movmean for moving averages combined with element-wise squaring operations (.^2). This technique has broad applications in target detection fields, enabling improved understanding and analysis of radar data while enhancing detection accuracy and reliability. The code structure typically involves initializing parameters, processing data through the sliding window algorithm, and applying thresholding to identify potential targets.