Object detection and parameter estimation are two fundamental problems of radars. The process firstly detects existence states of objects， and then estimates parameters to identify the existence state. In this paper， we establish a generalized system model combining detection and estimation. Radar information， detection information and estimation information are strictly defined. It is proved that the radar information is the sum of the detection information and the estimation information. The joint posterior probability distribution of the existence state and time delay is derived for constant amplitude scattering objects. Radar entropy error is defined as power of posterior entropy. Both radar information and entropy error can be used to evaluate radar performance. A stochastic method of joint object detection and parameter estimation is proposed by sampling a posteriori （SAP） probability distribution. The main contribution is the proof of radar theorem which points out the theoretical radar limit for comparison of various methods. The radar theorem shows that the entropy error is achievable. Conversely， the empirical entropy error of any radar is no less than the theoretical entropy error. It is further proved that the concatenated system with the optimal detector and the optimal estimator can approach the theoretical limit of radar. The radar theorem and separation theorem answer the theoretical problem of joint detection-estimation， and will give a great promotion to the development of radar technology.