Accurate real-time estimation of the gait phase (GP) is crucial for many control methods in exoskeletons and prostheses. A class of approaches to GP estimation construct the phase portrait of a segment or joint angle, and use the normalized polar angle of this diagram to estimate the GP. Although several studies have investigated such methods, quantitative information regarding their performance is sparse. In this work, we assess the performance of 3 portrait-based methods in flat and inclined steady walking conditions, using quantitative metrics of accuracy, repeatability and linearity. Two methods use portraits of the hip angle versus angular velocity (AVP), and hip angle versus integral of the angle (IAP). In a novel third method, a linear transformation is applied to the portrait to improve its circularity (CSP). An independent heel-strike (HS) detection algorithm is employed in all algorithms, rather than assuming HSs to occur at a constant point on the portrait. The novel method shows improvements in all metrics, notably significant root-mean-square error reductions compared to IAP (-3%, p < 0.001) and AVP (-2.4%, p < 0.001) in slope, and AVP (-1.61%, p = 0.0015) in flat walking. A non-negligible inter-subject variability is observed between phase angles at HS (equivalent to up to 8.4% of error in the GP), highlighting the importance of explicit HS detection for portrait-based methods.