MIMO Radar Using Compressive Sampling (0911.4752v1)

Abstract: A MIMO radar system is proposed for obtaining angle and Doppler information on potential targets. Transmitters and receivers are nodes of a small scale wireless network and are assumed to be randomly scattered on a disk. The transmit nodes transmit uncorrelated waveforms. Each receive node applies compressive sampling to the received signal to obtain a small number of samples, which the node subsequently forwards to a fusion center. Assuming that the targets are sparsely located in the angle- Doppler space, based on the samples forwarded by the receive nodes the fusion center formulates an l1-optimization problem, the solution of which yields target angle and Doppler information. The proposed approach achieves the superior resolution of MIMO radar with far fewer samples than required by other approaches. This implies power savings during the communication phase between the receive nodes and the fusion center. Performance in the presence of a jammer is analyzed for the case of slowly moving targets. Issues related to forming the basis matrix that spans the angle-Doppler space, and for selecting a grid for that space are discussed. Extensive simulation results are provided to demonstrate the performance of the proposed approach at difference jammer and noise levels.

• The paper proposes a distributed MIMO radar system leveraging compressive sampling and ℓ1-optimization to reconstruct sparse target information from fewer samples.
• Simulations demonstrate superior performance over traditional methods, achieving high resolution and jam-resistance using significantly fewer data samples and receivers.
• The research has practical implications for reducing energy use and costs in radar systems, and opens future research into integrating compressive sampling with other radar functionalities.

Overview of "MIMO Radar Using Compressive Sampling"

The paper "MIMO Radar Using Compressive Sampling" by Yao Yu, Athina P. Petropulu, and H. Vincent Poor explores an innovative approach to improving the efficiency and resolution of MIMO radar systems. This research harnesses the concept of compressive sampling (CS) to significantly decrease the number of samples required for effective radar operation, thus leading to energy savings during data transmission phases.

Central Concept

The authors propose a MIMO radar system wherein both transmitters and receivers are nodes of a wireless network, randomly distributed on a disk. Each transmit node launches uncorrelated waveforms, while each receive node employs CS to process the incoming signal into a compressed number of samples. These samples are then forwarded to a fusion center which solves an ℓ1-optimization problem to derive angle and Doppler information about the targets.

Key to this approach is the assumption that targets are sparsely located in the angle-Doppler space, allowing CS to reconstruct the sparse signal using fewer samples compared to conventional radar systems.

Performance Evaluation

The paper provides extensive numerical simulations to establish the performance gains of the proposed MIMO radar system over traditional methods, especially in scenarios of slowly moving targets and adverse conditions such as the presence of a jammer. Notable comparisons are made with conventional approaches such as Capon, APES, GLRT, and MUSIC methods, particularly highlighting superior resolution capabilities with fewer data samples.

1. Stationary Targets: For stationary targets, the proposed method outperforms traditional techniques by maintaining high peak-to-ripple and peak-to-jammer ratios even with fewer receive nodes (as low as one) and fewer samples (M = 30, compared to L = 512 for other methods).
2. Moving Targets: The proposed system shows robustness against motion-induced complexities by accurately estimating angle-Doppler parameters. It provides improved performance over the conventional matched filtering method in scenarios where the targets' speed introduces Doppler shifts, which are negligible considering the chosen pulse duration and other parameters.
3. Jam-Resistance: An essential contribution is the formulation of analytical expressions for the signal-to-jammer ratio and the development of a modified measurement matrix that enhances resistance against jamming.

Implications

This research holds significant implications for practical radar applications, potentially leading to decreased operational costs and longer lifespans for radar-equipped networks by reducing energy consumption through compressive sampling. Theoretical implications also include potential advancements in the fusion of CS with radar technologies beyond MIMO contexts, leveraging the sparsity in other domains.

Future Directions

Future research could explore the integration of range detection capabilities and adaptation to wideband radar signals. Additionally, addressing potential challenges related to node synchronization and localization errors could further enhance the reliability and robustness of the proposed system. The paper also opens pathways for bridging the gap between theory and application in distributed systems using CS.

In summary, through leveraging CS, the paper contributes a forward-looking vision to the evolution of radar systems, rendering them more agile, efficient, and ultimately more suitable for dynamic, distributed environments.