Waveform design for efficient radar signal recovery

2021 ◽  
pp. 285-317
Author(s):  
Mohammad Alaee-Kerahroodi ◽  
Bhavani Shankar ◽  
Björn Ottersten
Keyword(s):  
Waveform Design ◽  
Radar Signal ◽  
Signal Recovery

scholarly journals Waveform Optimization of Compressed Sensing Radar without Signal Recovery

Information ◽  
2019 ◽  
Vol 10 (9) ◽  
pp. 271
Author(s):  
Quanhui Wang ◽  
Ying Sun
Keyword(s):  
Signal Processing ◽  
Compressed Sensing ◽  
Optimization Method ◽  
Statistical Characteristics ◽  
Iteration Step ◽  
Radar Signal ◽  
Signal Recovery ◽  
Waveform Optimization ◽  
Inference Problems ◽  
Output Sinr

Radar signal processing mainly focuses on target detection, classification, estimation, filtering, and so on. Compressed sensing radar (CSR) technology can potentially provide additional tools to simultaneously reduce computational complexity and effectively solve inference problems. CSR allows direct compressive signal processing without the need to reconstruct the signal. This study aimed to solve the problem of CSR detection without signal recovery by optimizing the transmit waveform. Therefore, a waveform optimization method was introduced to improve the output signal-to-interference-plus-noise ratio (SINR) in the case where the target signal is corrupted by colored interference and noise having known statistical characteristics. Two different target models are discussed: deterministic and random. In the case of a deterministic target, the optimum transmit waveform is derived by maximizing the SINR and a suboptimum solution is also presented. In the case of random target, an iterative waveform optimization method is proposed to maximize the output SINR. This approach ensures that SINR performance is improved in each iteration step. The performance of these methods is illustrated by computer simulation.

Radar Signal Recovery using Compressive Sampling Matching Pursuit Algorithm

2016 ◽  
Vol 67 (1) ◽  
pp. 94 ◽  
Author(s):  
M Sreenivasa Rao ◽  
K Krishna Naik ◽  
K Maheshwara Reddy
Keyword(s):  
Matching Pursuit ◽  
Sampling Rate ◽  
Recovery Process ◽  
Wide Frequency Range ◽  
Compressive Sampling ◽  
Radar Signal ◽  
Signal Recovery ◽  
Time Frequency ◽  
Receiver System ◽  
Radar Signals

In this study, we propose compressive sampling matching pursuit (CoSaMP) algorithm for sub-Nyquist based electronic warfare (EW) receiver system. In compressed sensing (CS) theory time-frequency plane localisation and discretisation into a N×N grid in union of subspaces is established. The train of radar signals are sparse in time and frequency can be under sampled with almost no information loss. The CS theory may be applied to EW digital receivers to reduce sampling rate of analog to digital converter; to improve radar parameter resolution and increase input bandwidth. Simulated an efficient approach for radar signal recovery by CoSaMP algorithm by using a set of various sample and different sparsity level with various radar signals. This approach allows a scalable and flexible recovery process. The method has been satisfied with data in a wide frequency range up to 40 GHz. The simulation shows the feasibility of our method.

scholarly journals Locally Optimal Radar Waveform Design for Detecting Doubly Spread Targets in Colored Noise

2017 ◽  
Author(s):  
Zhenghan Zhu ◽  
Steven Kay ◽  
R. S. Raghavan
Keyword(s):  
Impulse Response ◽  
Colored Noise ◽  
Waveform Design ◽  
Radar Signal ◽  
Wide Sense ◽  
Small Signal ◽  
Signal Power ◽  
Signal Design ◽  
Signal Waveform ◽  
Leibler Divergence

Radar transmit signal design is a critical factor for the radar performance. In this paper, we investigate the problem of radar signal waveform design under the small signal power conditions for detecting a doubly spread target, whose impulse response can be modeled as a random process, in a colored noise environment. The doubly spread target spans multiple range bins (range-spread) and its impulse response is time-varying due to fluctuation (hence also Doppler-spread), such that the target impulse response is both time-selective and frequency-selective. Instead of adopting the conventional assumption that the target is wide-sense stationary uncorrelated scattering,we assume that the target impulse response is both wide-sense stationary in range and in time to account for the possible correlation between the impulse responses corresponding to close range intervals. The locally most powerful detector, which is asymptotically optimal for small signal cases, is then derived for detecting such targets. The signal waveform is optimized to maximizing the detection performance of the detector or equivalently maximizing the Kullback-Leibler divergence. Numerical simulations validate the effectiveness of the proposed waveform design for the small signal power conditions and performance of optimum waveform design are shown in comparison to the frequency modulated waveform.

Sign in / Sign up

Export Citation Format

Share Document