Cognitive radio (CR) is a critical issue to solve the spectrum scarcity and to improve frequency spectrum utilization in wireless communication. Spectrum sensing is the first step for cognitive radio and it needs to detect signals presence under strict requirement such that secondary users (unlicensed users) can use the licensed spectral band without interfering primary users (licensed users). In this paper, we implement spectrum sensing in real environment and verify two present algorithms based on the time-covariance matrix. For the capability of sensing the spectrum, we make use of Software Defined Radio (SDR), which provides the ability to modify the hardware characteristics of the system and the flexibility for programming. Moreover, GNU Radio is an open source software toolkit which provides different functions that supports SDR. The need for low cost hardware platform for an SDR necessitated development of Universal Software Radio Peripheral (USRP). Combining GNU Radio and USRP, can be a very powerful tool to develop SDR based wireless communication systems and to carry out various experiments and testing. With the premise of high correlations of primary users and low correlations of additive white Gaussian noise (AWGN) between two different samples, we evaluated two kinds of present methods based on time-covariance matrix called Covariance (COV) method and Maximum-to-Minimum Eigenvalue (MME) method. To combat the noise uncertainty, COV uses the ratio between the time correlation and the signal energy as the signal detection indicator; and MME uses the ratio of maximum to minimum eigenvalues of the covariance matrix to detect the signal presence. We analyze the performance of these methods on GNU radio with USRP. Unlike energy detection (ED), COV and MME can do spectrum sensing without any prior knowledge of primary signal and noise power. Furthermore, it can combat noise uncertainty, of which ED is devoid. We present the performance of these two methods compared with ED for BPSK modulation as a primary signal to check the feasibility of these two algorithms.