Improvement of radar performance using LFM pulse compression technique

Abstract

One fundamental aspect in designing a good radar system is the performance of range resolution. The good range resolution performance is required in radar system to detect small targets at a long distance. It is already known that the longer pulse will increase energy and improve target detection. Conversely, shorter pulse will increase the range resolution of radar. Recently, one pulse compression technique frequently used is linear frequency modulated (LFM) pulse wave. This paper presents the LFM pulse compression technique for improving radar performance and its simulation in Matlab and GNU radio using audio and radio waves. The performance of radar resolution using LFM pulse compression technique is observed from the parameter of pulse compression ratio (PCR). A higher PCR value indicates a better radar resolution. From the result, it shows that the values of PCR in Matlab R ⃝ simulation using audio and radio waves are 6000 and 300000, respectively. Moreover, in GNU radio implementation, the values PCR using audio and radio waves are 0.275 and 50, respectively.

Analysis of Non Linear Frequency Modulation (NLFM) Waveforms for Pulse Compression Radar

“…The equations of LFM original signal can be seen in (2) until (5). Substituting is done between (2) and (18) and then between (4) with (17), (18) and (19) where the result of substituting is at (20) -(22).…”

“…Various methods have been developed for pulse compression to improve radar performances. The waveform design of pulse compression is usually using frequency or phase modulation [5]. LFM is pulse compression type which is commonly used in current radar systems because of its simplicity and Doppler tolerant [6].…”

Event classification for natural gas pipeline safety monitoring based on long short-term memory network and Adam algorithm

“…8,9 In this article, pulse compression technology is applied to detect and locate natural gas pipeline events. [10][11][12][13] Since the characteristics of detection signals are inconspicuous after matched filtering, traditional neural networks which require feature extraction in advance are no longer capable. Due to the ability of long-time correlation series processing without feature extraction, the LSTM network along with adaptive moment estimation (Adam) algorithm is introduced to classify the one-dimensional detection signals of abnormal events and normal components such as pipeline elbows.…”

Time-Resolved Optical Spectroscopy for Laser Chirp Characterization and Self-Heterodyne Generation of LFM and NLFM Microwave Pulses

“…By comparing Equations 2-6 and 2-13, one can note that, different from the conventional case of a sinusoidal pulse, the range resolution for a compressed pulse does not depend on the pulse duration T , therefore, T can be chosen large enough to result in a satisfactory SNR whilst a large ∆f guarantees a good range resolution. Thus, the use of LFM pulses combined with matched filters represents a great improvement in radar performance, which is analyzed in detail in [10].…”

“…Since the same unbalanced (308 m) MZI was used, this characterization also includes the minor phase fluctuation contribution due to environmental interference. However, to circumvent this problem, a narrower linewidth DFB-LD along with techniques to reduce its phase noise in chirping operation [10] could be used for more demanding radar applications.…”