Target Localization in MIMO OFDM Radars Adopting Adaptive Power Allocation among Selected Sub-Carriers

Multiple-input multiple-output (MIMO) radar has been introduced to enhance the performance of classical radar systems. Nevertheless, radar cross sections (RCS) fluctuations remains a known problem in radars. Target localization using narrowband signal produces reduced accuracy due to RCS fluctuations. One of the solution to this problem is utilization of frequency diversity of wideband signal. This paper presents target localization in MIMO radars using an adaptive orthogonal frequency division multiplexing (OFDM) waveform for effective frequency diversity utilization. Each transmitting antenna transmits an OFDM signal in different time slots and received by the each receiving antenna in the receiver array. A joint direction-of-departure (DOD) and direction-of-arrival (DOA) estimation scheme is applied to each of the OFDM sub-carrier using two-way multiple signal classification (MUSIC) algorithm. The estimation results at each sub-carrier are combined based on majority decision using angle histogram (non-parametric approach) to formulate the final wideband angle estimation. In addition, an adaptive power allocation among the sub-carriers is implemented, where the system evaluates the signal quality at each sub-carrier and consequently formulates a feedback to the MIMO transmitting side. The following transmission will comprise of OFDM waveform that focuses the transmit power at selected sub-carriers only. The sub-carrier selection is based on singular values obtained from singular value decomposition operation at each of the sub-carrier. The performance of the proposed scheme is evaluated through numerical simulations as well as validation by experiments in a radio anechoic chamber. It was demonstrated that the usage of larger number of sub-carriers improves the angle estimation accuracy

Design and Analysis of Superresolution algorithm and signal separation technique for an OFDM-based MIMO radar

Extend the OFDM-based SISO radar to MIMO configuration to estimate direction of arrival (azimuth and elevation); Design a RF front end for MIMO radar transmitter and verify the DOA algorithm.Recently, the concept of MIMO radar has been proposed. MIMO radar has the capability to transmit orthogonal (or incoherent) waveforms at multiple transmit antennas. It offers promising potentials for multipath fading, resolution enhancement and interference suppression. Many research about MIMO radar in signal processing have been conducted. However, the implementation of MIMO radar in practice is still not common. In this thesis, the SISO OFDM radar and communication system (RadCom), a previous project at Institut für Hochfrequenztechnik und Elektronik, KIT, Germany, is extended to MIMO configuration using the idea of spectrally interleaved multi-carrier signals to estimate the direction of arrival of targets in 2D and 3D radar. The main aim of this thesis is to implement and evaluate a MIMO OFDM-based radar system. The thesis consits of two parts, the software and hardware part. In the first part of the thesis, the MIMO radar is studied. A signal modeling is derived along with the analysis of suitable antenna geometries for 2D and 3D radar. The MIMO radar with the ability to form vitual array can increase significantly the resolution of direcion-of-arrival (DOA). For that purpose, numerous algorithms based on different mathematical approaches exist. The numerical results show that the MUSIC algorithm based on subspace method is simple to implement and have good resolution. We combine the OFDM-based signal model with MUSIC to perform 2D and 3D radar sensing. The DOA estimation with MUSIC along with the simulation results are presented. The second half of this thesis focuses on hardware design for MIMO radar systems. A RF front end for 4 transmitters with direct conversion architecture was considered. The transmitters include low pass filters (LPFs), I/Q upcoverter, PLL synthesizer, 4:1 Wilkinson divider. Inverse Chebyshev LPFs at 50 Mhz with lumped elements were built and measured. In addition, a sufficient 4:1 Wilkinson divider was simulated and fabricated. The other individual elements of the transmitter was measured and analyzed. Several measurement have been taken to test one whole transmitter. In addition, the signal generated by the FPGA, which is planned to intergrate with the transmitters, are analyzed. Finally, the possible future works have been pointed out

Compressive Sensing for MIMO Radar

Multiple-input multiple-output (MIMO) radar systems have been shown to achieve superior resolution as compared to traditional radar systems with the same number of transmit and receive antennas. This paper considers a distributed MIMO radar scenario, in which each transmit element is a node in a wireless network, and investigates the use of compressive sampling for direction-of-arrival (DOA) estimation. According to the theory of compressive sampling, a signal that is sparse in some domain can be recovered based on far fewer samples than required by the Nyquist sampling theorem. The DOA of targets form a sparse vector in the angle space, and therefore, compressive sampling can be applied for DOA estimation. The proposed approach achieves the superior resolution of MIMO radar with far fewer samples than other approaches. This is particularly useful in a distributed scenario, in which the results at each receive node need to be transmitted to a fusion center for further processing

Biologically Inspired Sensing and MIMO Radar Array Processing

The contributions of this dissertation are in the fields of biologically inspired sensing and multi-input multi-output: MIMO) radar array processing. In our research on biologically inspired sensing, we focus on the mechanically coupled ears of the female Ormia ochracea. Despite the small distance between its ears, the Ormia has a remarkable localization ability. We statistically analyze the localization accuracy of the Ormia\u27s coupled ears, and illustrate the improvement in the localization performance due to the mechanical coupling. Inspired by the Ormia\u27s ears, we analytically design coupled small-sized antenna arrays with high localization accuracy and radiation performance. Such arrays are essential for sensing systems in military and civil applications, which are confined to small spaces. We quantitatively demonstrate the improvement in the antenna array\u27s radiation and localization performance due to the biologically inspired coupling. On MIMO radar, we first propose a statistical target detection method in the presence of realistic clutter. We use a compound-Gaussian distribution to model the heavy tailed characteristics of sea and foliage clutter. We show that MIMO radars are useful to discriminate a target from clutter using the spatial diversity of the illuminated area, and hence MIMO radar outperforms conventional phased-array radar in terms of target-detection capability. Next, we develop a robust target detector for MIMO radar in the presence of a phase synchronization mismatch between transmitter and receiver pairs. Such mismatch often occurs due to imperfect knowledge of the locations as well as local oscillator characteristics of the antennas, but this fact has been ignored by most researchers. Considering such errors, we demonstrate the degradation in detection performance. Finally, we analyze the sensitivity of MIMO radar target detection to changes in the cross-correlation levels: CCLs) of the received signals. Prior research about MIMO radar assumes orthogonality among the received signals for all delay and Doppler pairs. However, due to the use of antennas which are widely separated in space, it is impossible to maintain this orthogonality in practice. We develop a target-detection method considering the non-orthogonality of the received data. In contrast to the common assumption, we observe that the effect of non-orthogonality is significant on detection performance