While navigating in rough weather, the ship encounters environmental forces and moments such as wind, waves, and sea currents. To maintain eco-efficient ("greener") and safer navigation, appropriate ships' operability and reliability are required. Improvement of ships' performance, and thus its efficiency, can be achieved with an on-board Decision Support System (DSS). The support of such systems is mainly reflected in the lessening of human error, provision of essential data and guidance for operational decisions, as well as the possibility of ship's dynamic positioning with sea waves in areas where operability is limited and prone to error. DSS consists of different integrated modules. A significant contribution is associated with advanced sea state estimation with appropriate signal processing algorithms and methodologies for obtaining data about Directional Wave Spectra (DWS). In recent years, DWS estimation is performed only according to available measured ship motion responses as a more efficient alternative to moored buoys, satellite, and radar measurements. The observed responses are usually the roll, pitch, and heave motions caused by sea waves. However, this so-called buoy analogy approach requires prior knowledge of the ship's performance in particular wave conditions. This information is given in the form of complex transfer functions called Response amplitude operators (RAO), and its accuracy may be insufficient due to incomplete knowledge of input conditions. Accordingly, signal processing and spectral analysis become even more prominent research subjects. Measured ship motion responses are non-stationary signals with time-varying frequency content. Also, these signals are often noisy due to the influence of external forces. Therefore, response signals need to be analysed and filtered to provide accurate information for further processing. For non-stationary signal processing, it is convenient to use Time-Frequency (TF) distribution methods with observation simultaneously in the time and frequency domains. Over the years, different TF methods have been proposed with the common intention of achieving a trade-off between good TF resolution and reduction of undesirable cross-terms in the case of quadratic distributions. The choice of an appropriate method and computer calculation in practical applications depends on the available measured data. Therefore, many adaptive, data-driven TF methods have been developed and proposed, some of them with additional post-processing algorithms. Although such methods provide improved results, estimation bias and variance control issues are often not considered. Besides, the components' resolution may be disturbed with an excessive smoothing process. More desirable results can be obtained by using different Multi-Window TF approaches where multiple window functions are used. These windows are orthogonal (for variance minimization) and optimally concentrated (for low bias estimation). Computational analysis is performed with a given number of window functions by finding optimal weighting coefficients and solving the optimization problem with additional parameters that minimize the variable projection problem. Following this approach, the authors achieved more reliable ship motion analysis results with an indication of possible optimization.