MATLAB Implementation of Point Target Quality Assessment

In radar signal processing or optical imaging fields, point target quality assessment serves as a crucial metric for evaluating system resolution and interference suppression capabilities. When implementing in MATLAB, the focus primarily lies on two core parameters: Integrated Sidelobe Ratio (ISLR) and Peak Sidelobe Ratio (PSLR).

Assessment Principle Point target responses typically exhibit a mainlobe (peak) and sidelobe structure. The Integrated Sidelobe Ratio reflects the relative intensity between the total energy in sidelobe regions and the mainlobe energy, demonstrating the system's anti-interference capability. The Peak Sidelobe Ratio represents the amplitude ratio between the highest sidelobe and the mainlobe, directly correlating with target resolvability.

Implementation Logic Data Preprocessing: Extract the response curve of the point target (e.g., one-dimensional range profile) and locate the mainlobe position through maximum value identification using MATLAB's max() function. Sidelobe Region Segmentation: Define guard bands near the mainlobe to avoid contamination, then extract left and right sidelobe data segments using array indexing operations. Parameter Calculation: Peak Sidelobe Ratio: Calculate by dividing the maximum sidelobe value by the mainlobe peak value, typically converted to decibel (dB) units using 20*log10(). Integrated Sidelobe Ratio: Compute the ratio between the integrated power in sidelobe regions (using trapz() for numerical integration) and the mainlobe power, also converted to dB scale. Result Visualization: Plot the response curve using plot() function, annotate mainlobe and sidelobe regions with text() or annotation commands, and display calculation results in a formatted table using fprintf() oruitable().

Extension Considerations In two-dimensional scenarios, the assessment can be extended to joint evaluation along azimuth and range directions using meshgrid() and contour plots. Window function selection (such as Hamming window through hamming() function) can optimize sidelobe suppression - the impact of windowing must be considered during evaluation by applying the window before parameter calculation.