The paper proposes a novel approach to radar imaging using compressed sensing, where the time-frequency plane is discretized into an \(N \times N\) grid. By transmitting a sufficiently "incoherent" pulse, such as the Alltop sequence, and employing compressed sensing techniques, the target scene can be reconstructed with high resolution. The authors present theoretical upper bounds on the sparsity \(K\) of the target scene and verify through numerical simulations that even better performance can be achieved in practice. This approach offers significant potential for improving resolution over classical radar systems. The paper also discusses the theoretical foundations of compressed sensing, matrix identification, and the properties of the Alltop sequence, and compares the resolution limits of compressed sensing radar with those of classical radar. Numerical simulations demonstrate that compressed sensing radar can achieve higher resolution, especially when multiple targets are present. The paper concludes by highlighting the potential applications of this approach in various fields, including underwater acoustic communication and high-resolution radar imaging.The paper proposes a novel approach to radar imaging using compressed sensing, where the time-frequency plane is discretized into an \(N \times N\) grid. By transmitting a sufficiently "incoherent" pulse, such as the Alltop sequence, and employing compressed sensing techniques, the target scene can be reconstructed with high resolution. The authors present theoretical upper bounds on the sparsity \(K\) of the target scene and verify through numerical simulations that even better performance can be achieved in practice. This approach offers significant potential for improving resolution over classical radar systems. The paper also discusses the theoretical foundations of compressed sensing, matrix identification, and the properties of the Alltop sequence, and compares the resolution limits of compressed sensing radar with those of classical radar. Numerical simulations demonstrate that compressed sensing radar can achieve higher resolution, especially when multiple targets are present. The paper concludes by highlighting the potential applications of this approach in various fields, including underwater acoustic communication and high-resolution radar imaging.