Statistical MIMO Radar
Abstract
Inspired by recent advances in multiple-input multiple-output (MIMO) communications, we introduce the statistical MIMO radar concept. Unlike beamforming, array radar, or STAP, which presuppose a high correlation between signals either transmitted or received by an array, the proposed MIMO radar exploits the independence between signals at the array elements. Whereas correlation-based array techniques are capable of providing degrees of freedom for spatial filtering, they have no bearing on the effects of target scattering. Radar targets generally consist of many small elemental scatterers that are fused by the radar waveform and the processing at the receiver to result in echoes with fluctuating amplitude and phase. In conventional radar, target radar cross-section (RCS) fluctuations are regarded as a nuisance that degrades radar performance. The novelty of statistical MIMO radar is that it takes the opposite view, namely, it capitalizes on target RCS scintillations and glint to improve the radar s performance. MIMO radar utilizes multiple antennas at both the transmitter and receiver. It can be applied in monostatic or bistatic modes. The antennas at each end of the radar system have to be sufficiently separated such that the target provides uncorrelated reflection coefficients between each transmit/receive pair of antennas. We demonstrate that the MIMO radar greatly improves detection and estimation performance due to the absence of target fades. Specifically, statistical MIMO radar overcomes target RCS fluctuations by averaging over many decorrelated channels between transmit and receive antennas. Subsequently, the received signal is a superposition of independently faded signals, and the average SNR of the received signal is more or less constant. This is equivalent to converting a Swerling case I RCS to a Swerling case II, but without the loss of time. Moreover, MIMO spatial diversity also eliminates the deep interference nulls in the elevation coverage.
Document Details
- Document Type
- Technical Report
- Publication Date
- Dec 20, 2004
- Accession Number
- ADA433622
Entities
People
- Alex Haimovich
- Dmitry Chizhik
- Eran Fishler
- Len Cimini
- Reinaldo Valenzuela
- Rick Blum
Organizations
- New Jersey Institute of Technology