Sandeep Gogineni

Target Estimation Using Sparse Modeling for Distributed MIMO Radar

Multiple-input multiple-output (MIMO) radar systems with widely separated antennas provide spatial diversity by viewing the targets from different angles. In this paper, we use a novel approach to accurately estimate properties (position, velocity) of multiple targets using such systems by employing sparse modeling. We also introduce a new metric to analyze the performance of the radar system. We propose an adaptive mechanism for optimal energy allocation at the different transmit antennas. We show that this adaptive energy allocation mechanism significantly improves in performance over MIMO radar systems that transmit fixed equal energy across all the antennas. We also demonstrate accurate reconstruction from very few samples by using compressive sensing at the receivers.

Polarimetric MIMO Radar With Distributed Antennas for Target Detection

Multiple-input-multiple-output (MIMO) radar systems with widely separated antennas enable viewing the target from different angles, thereby providing spatial diversity gain. Polarimetric design of the transmit waveforms based on the properties of the target scattering matrix provides better performance than transmitting waveforms with only fixed horizontal or vertical polarizations. We propose a radar system that combines the advantages of both systems by transmitting polarized waveforms from multiple distributed antennas, in order to detect a point-like stationary target. The proposed system employs 2-D vector sensors at the receivers, each of which measures the horizontal and vertical components of the received electric field separately. We design the Neyman-Pearson detector for such systems. We derive approximate expressions for the probability of false alarm (PFA) and the probability of detection (PD). Using numerical simulations, we demonstrate that optimal design of the antenna polarizations provides improved performance over MIMO systems that transmit waveforms of fixed polarizations over all the antennas. We also demonstrate that having multiple widely separated antennas gives improved performance over single-input-single-output (SISO) polarimetric radar. We also demonstrate that processing the vector measurements at each receiver separately gives improved performance over systems that linearly combine both the received signals to give scalar measurements.

Frequency-Hopping Code Design for MIMO Radar Estimation Using Sparse Modeling

We consider the problem of multiple-target estimation using a colocated multiple-input multiple-output (MIMO) radar system. We employ sparse modeling to estimate the unknown target parameters (delay, Doppler) using a MIMO radar system that transmits frequency-hopping waveforms. We formulate the measurement model using a block sparse representation. We adaptively design the transmit waveform parameters (frequencies, amplitudes) to improve the estimation performance. Firstly, we derive analytical expressions for the correlations between the different blocks of columns of the sensing matrix. Using these expressions, we compute the block coherence measure of the dictionary. We use this measure to optimally design the sensing matrix by selecting the hopping frequencies for all the transmitters. Secondly, we adaptively design the amplitudes of the transmitted waveforms during each hopping interval to improve the estimation performance. To perform this amplitude design, we initialize it by transmitting constant-modulus waveforms of the selected frequencies to estimate the radar cross section (RCS) values of all the targets. Next, we make use of these RCS estimates to optimally select the waveform amplitudes. We demonstrate the performance improvement due to the optimal design of waveform parameters using numerical simulations. Further, we employ compressive sensing to conduct accurate estimation from far fewer samples than the Nyquist rate.

Cramér-Rao Bounds for UMTS-Based Passive Multistatic Radar

Owing to the favorable ambiguity function properties and the increased deployment, mobile communications systems are useful for passive bistatic radar applications. Further, simultaneously using multiple illuminators in a multistatic configuration will improve the radar performance, providing spatial diversity and increased resolution. We compute modified Cramér-Rao lower bounds (MCRLB) for the target parameter (delay, Doppler) estimation error using universal mobile telecommunications system (UMTS) signals as illuminators of opportunity for passive multistatic radar systems. We consider both coherent and non-coherent processing modes. These expressions for MCRLB are an important performance metric in that they enable the selection of the optimal illuminators for estimation.

Monopulse MIMO Radar for Target Tracking

We propose a multiple input multiple output (MIMO) radar system with widely separated antennas that employs monopulse processing at each of the receivers. We use Capon beamforming to generate the two beams required for the monopulse processing. We also propose an algorithm for tracking a moving target using this system. This algorithm is simple and practical to implement. It efficiently combines the information present in the local estimates of the receivers. Since most modern tracking radars already use monopulse processing at the receiver, the proposed system does not need much additional hardware to be put to use. We simulated a realistic radar-target scenario to demonstrate that the spatial diversity offered by the use of multiple widely separated antennas gives significant improvement in performance when compared with conventional single input single output (SISO) monopulse radar systems. We also show that the proposed algorithm keeps track of rapidly maneuvering airborne and ground targets under hostile conditions like jamming.

Game theoretic design for polarimetric MIMO radar target detection

Polarimetric radar systems allow the flexibility of transmitting arbitrarily polarized waveforms that match the scattering profiles of the target. Since different types of targets have varying profiles, the advantages of a polarimetric radar system can fully be exploited only when the type of target is accurately estimated. However, accurate estimation requires a significant amount of training data, which can be expensive. We propose a polarimetric design scheme for distributed multiple input multiple output (MIMO) radar target detection. We formulate the selection of transmit polarizations using a game theoretic framework by examining the impact of all possible transmit schemes on the detection performance with different available target profiles (see also Gogineni and Nehorai, 2011 [1]). This approach does not require training data, and we show a significant performance improvement due to the polarimetric design. Other radar design problems can also be solved using this game theoretic approach.

Ambiguity Function Analysis for UMTS-Based Passive Multistatic Radar

There has been a growing interest in passive radar systems in the research community over the last decade because of the several merits they offer, including ease of deployment, low cost, and non-detectability of the receivers. During the same period, the idea of distributed MIMO radar and its advantages under the coherent and non-coherent operating scenarios has been extensively studied. Keeping these benefits it mind, in this paper, we consider a UMTS-based passive multistatic radar with distributed antennas. We compute the ambiguity profiles of this radar system under both the coherent and non-coherent modes. The non-coherent processing mode improves the target detection performance by obtaining spatially diverse looks of the target. On the other hand, coherent processing enhances the resolution of target localization. We use numerical examples to demonstrate our analytical results.

Target estimation using compressive sensing for distributed MIMO radar

Distributed Multiple Input Multiple Output (MIMO) radar systems enable viewing the targets from different angles, thereby providing spatial diversity gain. In this paper, we propose an approach to accurately estimate the parameters (position, velocity) of multiple targets using such systems from fewer number of samples by employing compressive sensing. We also introduce a new metric to analyze the performance of the radar system. We show the improvement in performance over conventional Single Input Single Output (SISO) radar systems due to the spatial diversity offered by MIMO radar. We also demonstrate that the sampling rates can be significantly reduced by using compressive sensing at the receivers.

Adaptive design for distributed MIMO radar using sparse modeling

Multiple Input Multiple Output (MIMO) radar systems with widely separated antennas provide spatial diversity gain by viewing the targets from different angles. In this paper, we propose an approach to accurately estimate the properties (position, velocity) of multiple targets using such systems by employing sparse modeling. We also propose a new metric to analyze the performance of the radar system. We develop an adaptive mechanism for optimal energy allocation at different transmitters. We show that this adaptive mechanism outperforms MIMO radar systems that transmit fixed equal energy across all the antennas.

Target tracking using monopulse MIMO radar with distributed antennas

We propose a Multiple Input Multiple Output (MIMO) radar system with distributed antennas that employs monopulse processing at the receivers. We also propose an algorithm to track a moving target using this system. This algorithm is simple and practical to implement. It efficiently combines the information present in the local estimates of the receivers. Since most modern tracking radars already use monopulse processing at the receiver, the proposed system does not need much additional hardware. Using numerical simulations, we demonstrate the advantages of the proposed system over conventional single antenna monopulse radar. We also show that the proposed algorithm keeps track of rapidly maneuvering airborne and ground targets.