To achieve mono-static sensing in the integrated sensing and communication network, an additional receiving panel for sensing is needed to cover the blind area around the base station. However, high-resolution estimation requires a wideband signal with large-scale antennas, making the receiver expensive and power hungry with traditional high sampling-rate and bit-depth analog-to-digital converters (ADCs). In this paper, a low complexity sensing receiver with one-bit quantization is proposed. In addition, an adaptive time-varying threshold architecture is designed to solve the dynamic range problem of one-bit ADC. An algorithm combining tensor unfolding and one-bit compressive sensing is proposed first to estimate the parameters of strong signals, based on which they could be reconstructed and the thresholds of one-bit ADCs are tuned adaptively using an adjustable switch network. After that, parameters of the weak target could be estimated in a sequential manner. The Cramér-Rao lower bound of the weak signal is derived to verify the effectiveness of the method. Numerical results show that our method eliminates the dynamic range problem in theory and after adaptive quantization, the weak-to-strong power ratio has an improvement of 27 dB, which is mainly affected by the estimation accuracy of the strong signals.