30 research outputs found
Mathematical optimization techniques for cognitive radar networks
This thesis discusses mathematical optimization techniques for waveform design in cognitive radars. These techniques have been designed with an increasing level of sophistication, starting from a bistatic model (i.e. two transmitters and a single receiver) and ending with a cognitive network (i.e. multiple transmitting and multiple receiving radars). The environment under investigation always features strong signal-dependent clutter and noise. All algorithms are based on an iterative waveform-filter optimization. The waveform optimization is based on convex optimization techniques and the exploitation of initial radar waveforms characterized by desired auto and cross-correlation properties. Finally, robust optimization techniques are introduced to account for the assumptions made by cognitive radars on certain second order statistics such as the covariance matrix of the clutter.
More specifically, initial optimization techniques were proposed for the case of bistatic radars. By maximizing the signal to interference and noise ratio (SINR) under certain constraints on the transmitted signals, it was possible to iteratively optimize both the orthogonal transmission waveforms and the receiver filter. Subsequently, the above work was extended to a convex optimization framework for a waveform design technique for bistatic radars where both radars transmit and receive to detect targets. The method exploited prior knowledge of the environment to maximize the accumulated target return signal power while keeping the disturbance power to unity at both radar receivers.
The thesis further proposes convex optimization based waveform designs for multiple input multiple output (MIMO) based cognitive radars. All radars within the system are able to both transmit and receive signals for detecting targets. The proposed model investigated two complementary optimization techniques. The first one aims at optimizing the signal to interference and noise ratio (SINR) of a specific radar while keeping the SINR of the remaining radars at desired levels. The second approach optimizes the SINR of all radars using a max-min optimization criterion.
To account for possible mismatches between actual parameters and estimated ones, this thesis includes robust optimization techniques. Initially, the multistatic, signal-dependent model was tested against existing worst-case and probabilistic methods. These methods appeared to be over conservative and generic for the considered signal-dependent clutter scenario. Therefore a new approach was derived where uncertainty was assumed directly on the radar cross-section and Doppler parameters of the clutters. Approximations based on Taylor series were invoked to make the optimization problem convex and {subsequently} determine robust waveforms with specific SINR outage constraints.
Finally, this thesis introduces robust optimization techniques for through-the-wall radars. These are also cognitive but rely on different optimization techniques than the ones previously discussed. By noticing the similarities between the minimum variance distortionless response (MVDR) problem and the matched-illumination one, this thesis introduces robust optimization techniques that consider uncertainty on environment-related parameters.
Various performance analyses demonstrate the effectiveness of all the above algorithms in providing a significant increase in SINR in an environment affected by very strong clutter and noise
Foundations of MIMO Radar Detection Aided by Reconfigurable Intelligent Surfaces
A reconfigurable intelligent surface (RIS) is a nearly-passive flat layer
made of inexpensive elements that can add a tunable phase shift to the
impinging electromagnetic wave and are controlled by a low-power electronic
circuit. This paper considers the fundamental problem of target detection in a
RIS-aided multiple-input multiple-output (MIMO) radar. At first, a general
signal model is introduced, which includes the possibility of using up to two
RISs (one close to the radar transmitter and one close to the radar receiver)
and subsumes both a monostatic and a bistatic radar configuration with or
without a line-of-sight view of the prospective target. Upon resorting to a
generalized likelihood ratio test (GLRT), the design of the phase shifts
introduced by the RIS elements is formulated as the maximization of the
probability of detection in the location under inspection for a fixed
probability of false alarm, and suitable optimization algorithms are proposed.
The performance analysis shows the benefits granted by the presence of the RISs
and shed light on the interplay among the key system parameters, such as the
radar-RIS distance, the RIS size, and location of the prospective target. A
major finding is that the RISs should be better deployed in the near-field of
the radar arrays at both the transmit and the receive side. The paper is
concluded by discussing some open problems and foreseen applications.Comment: Paper submitted to IEEE Transactions on Signal Processing; revised
version after first-round revie
Principles of minimum variance robust adaptive beamforming design
Robustness is typically understood as an ability of adaptive beamforming algorithm to achieve high performance in the situations with imperfect, incomplete, or erroneous knowledge about the source, propagation media, and antenna array. It is also desired to achieve high performance with as little as possible prior information. In the last decade, several fruitful principles to minimum variance distortionless response (MVDR) robust adaptive beamforming (RAB) design have been developed and successfully applied to solve a number of problems in a wide range of applications. Such principles of MVDR RAB design are summarized here in a single paper. Prof. Gershman has actively participated in the development and applications of a number of such MVDR RAB design principles
Multi-static Parameter Estimation in the Near/Far Field Beam Space for Integrated Sensing and Communication Applications
This work proposes a maximum likelihood (ML)-based parameter estimation
framework for a millimeter wave (mmWave) integrated sensing and communication
(ISAC) system in a multi-static configuration using energy-efficient hybrid
digital-analog arrays. Due to the typically large arrays deployed in the higher
frequency bands to mitigate isotropic path loss, such arrays may operate in the
near-field regime. The proposed parameter estimation in this work consists of a
two-stage estimation process, where the first stage is based on far-field
assumptions, and is used to obtain a first estimate of the target parameters.
In cases where the target is determined to be in the near-field of the arrays,
a second estimation based on near-field assumptions is carried out to obtain
more accurate estimates. In particular, we select beamfocusing array weights
designed to achieve a constant gain over an extended spatial region and
re-estimate the target parameters at the receivers. We evaluate the
effectiveness of the proposed framework in numerous scenarios through numerical
simulations and demonstrate the impact of the custom-designed flat-gain
beamfocusing codewords in increasing the communication performance of the
system.Comment: 16 page
OFDM passive radar employing compressive processing in MIMO configurations
A key advantage of passive radar is that it provides a means of performing position detection and tracking without the need for transmission of energy pulses. In this respect, passive radar systems utilising (receiving) orthogonal frequency division multiplexing (OFDM) communications signals from transmitters using OFDM standards such as long term evolution (LTE), WiMax or WiFi, are considered. Receiving a stronger reference signal for the matched filtering, detecting a lower target signature is one of the challenges in the passive radar. Impinging at the receiver, the OFDM waveforms supply two-dimensional virtual uniform rectangul ararray with the first and second dimensions refer to time delays and Doppler frequencies respectively. A subspace method, multiple signals classification (MUSIC) algorithm, demonstrated the signal extraction using multiple time samples. Apply normal measurements, this problem requires high computational resources regarding the number of OFDM subcarriers. For sub-Nyquist sampling, compressive sensing (CS) becomes attractive. A single snap shot measurement can be applied with Basis Pursuit (BP), whereas l1-singular value decomposition (l1-SVD) is applied for the multiple snapshots. Employing multiple transmitters, the diversity in the detection process can be achieved. While a passive means of attaining three-dimensional large-set measurements is provided by co-located receivers, there is a significant computational burden in terms of the on-line analysis of such data sets. In this thesis, the passive radar problem is presented as a mathematically sparse problem and interesting solutions, BP and l1-SVD as well as Bayesian compressive sensing, fast-Besselk, are considered. To increase the possibility of target signal detection, beamforming in the compressive domain is also introduced with the application of conve xoptimization and subspace orthogonality. An interference study is also another problem when reconstructing the target signal. The networks of passive radars are employed using stochastic geometry in order to understand the characteristics of interference, and the effect of signal to interference plus noise ratio (SINR). The results demonstrate the outstanding performance of l1-SVD over MUSIC when employing multiple snapshots. The single snapshot problem along with fast-BesselK multiple-input multiple-output configuration can be solved using fast-BesselK and this allows the compressive beamforming for detection capability