A Pulse Compression Waveform for Weather Radars With Solid-State Transmitters

Abstract

Due to the contradiction between the high sensitivity requirement and low transmission power of weather radars with solid-state transmitters, a pulse compression technique is necessary. For the purpose of range sidelobe suppression, methods based on amplitude modulation and a mismatched filter are commonly used for target detection radars, which are not applicable for weather observations because of its drawbacks such as main lobe expansion and power loss. This letter presents a nonlinear frequency modulation pulse compression waveform to achieve a very low range sidelobe level. A mathematical model for waveform design is established, in which the time-frequency relation is expressed as the combination of a linear function and a sine series. Thus, a set of parameters can fully characterize the time-frequency curve. By using a simulated annealing algorithm, the optimal parameter set can be obtained, which shows that a peak sidelobe level under −60 dB is achievable. Finally, this kind of waveform is implemented on hardware, and its performance is verified.Index Terms-Multiobjective optimization, nonlinear frequency modulation (NLFM), pulse compression, range sidelobe level, simulated annealing (SA).

Algorithms for Designing Unimodular Sequences with High Doppler Tolerance for Simultaneous Fully Polarimetric Radar

“…It can be seen from the figure that both algorithms can suppress the side-lobes to almost zero in a specified lag interval. The $PCL$ and I of the sequences designed by the two algorithms are about −100 dB, which is low enough for the applications of simultaneous polarimetric radar [28]. The main difference between the two algorithms is the consumed time.…”

Staring Spotlight SAR with Nonlinear Frequency Modulation Signal and Azimuth Non-Uniform Sampling for Low Sidelobe Imaging

“…To obtain the impulse response with low sidelobes, amplitude weighting by a selected window function during pulse compression is usually applied, which will make the spectrum shape of the LFM signal similar with taper weighting as shown in on the left side of Figure 1. Since window weighting changes the spectrum shape of the LFM signal and noise, according to the principle of the matched filter, the output SNR after pulse compression is not the maximum value, and window weighting results in the SNR loss in focused SAR images [3,21]. An improved method to obtain the tapering shape spectrum for the impulse response with low sidelobes is to generate the non-uniform discrete frequency distribution as shown in the middle part of Figure 1.…”

“…. , P/2 − 1 (21) where ∆t a ≤ 1/B total indicates the azimuth time sampling interval after upsampling in azimuth pre-filtering, B total is the total Doppler bandwidth of the imaged scene, and P is the number of azimuth samples after upsampling. In order to efficiently apply FFT to implement azimuth convolution between the SAR raw data and the selected chirp signal, P should be [30]:…”

A Novel Optimization Strategy of Sidelobe Suppression for Pulse Compression Weather Radar

“…A window function is often utilized to suppress range sidelobes, resulting in the expansion of the main lobe and the loss of the signal to noise ratio (SNR) [4,5]. However, the reflectivity of precipitations ranges from about −10 to 75 dBZ [6,7] and the radar sensitivity is one of the most critical factors for distributed precipitations in weather observations. Therefore, in order to reduce the SNR loss of LFM signal after windowing, the NLFM waveform was developed decades ago.…”