Properties of the MIMO radar ambiguity function

MIMO (multiple-input multiple-output) radar is an emerging technology which has drawn considerable attention. Unlike the traditional SIMO (single-input multiple-output) radar, which transmits scaled versions of a single waveform in the antenna elements, the MIMO radar transmits independent waveforms in each of the antenna elements. It has been shown that MIMO radar systems have many advantages such as high spatial resolution, improved parameter identifiability, and enhanced flexibility for transmit beampattern design. In the traditional SIMO radar, the range and Doppler resolutions can be characterized by the radar ambiguity function. It is a major tool for studying and analyzing radar signals. Recently, the ambiguity function has been extended to the MIMO radar case. In this paper, some mathematical properties of the MIMO radar ambiguity function are derived. These properties provide insights into the MIMO radar waveform design

MIMO Radar Ambiguity Properties and Optimization Using Frequency-Hopping Waveforms

The concept of multiple-input multiple-output (MIMO) radars has drawn considerable attention recently. Unlike the traditional single-input multiple-output (SIMO) radar which emits coherent waveforms to form a focused beam, the MIMO radar can transmit orthogonal (or incoherent) waveforms. These waveforms can be used to increase the system spatial resolution. The waveforms also affect the range and Doppler resolution. In traditional (SIMO) radars, the ambiguity function of the transmitted pulse characterizes the compromise between range and Doppler resolutions. It is a major tool for studying and analyzing radar signals. Recently, the idea of ambiguity function has been extended to the case of MIMO radar. In this paper, some mathematical properties of the MIMO radar ambiguity function are first derived. These properties provide some insights into the MIMO radar waveform design. Then a new algorithm for designing the orthogonal frequency-hopping waveforms is proposed. This algorithm reduces the sidelobes in the corresponding MIMO radar ambiguity function and makes the energy of the ambiguity function spread evenly in the range and angular dimensions

Target localization in MIMO radar systems

MIMO (Multiple-Input Multiple-Output) radar systems employ multiple antennas to transmit multiple waveforms and engage in joint processing of the received echoes from the target. MIMO radar has been receiving increasing attention in recent years from researchers, practitioners, and funding agencies. Elements of MIMO radar have the ability to transmit diverse waveforms ranging from independent to fully correlated. MIMO radar offers a new paradigm for signal processing research. In this dissertation, target localization accuracy performance, attainable by the use of MIMO radar systems, configured with multiple transmit and receive sensors, widely distributed over an area, are studied. The Cramer-Rao lower bound (CRLB) for target localization accuracy is developed for both coherent and noncoherent processing. The CRLB is shown to be inversely proportional to the signal effective bandwidth in the noncoherent case, but is approximately inversely proportional to the carrier frequency in the coherent case. It is shown that optimization over the sensors\u27 positions lowers the CRLB by a factor equal to the product of the number of transmitting and receiving sensors. The best linear unbiased estimator (BLUE) is derived for the MIMO target localization problem. The BLUE\u27s utility is in providing a closed-form localization estimate that facilitates the analysis of the relations between sensors locations, target location, and localization accuracy. Geometric dilution of precision (GDOP) contours are used to map the relative performance accuracy for a given layout of radars over a given geographic area. Coherent processing advantage for target localization relies on time and phase synchronization between transmitting and receiving radars. An analysis of the sensitivity of the localization performance with respect to the variance of phase synchronization error is provided by deriving the hybrid CRLB. The single target case is extended to the evaluation of multiple target localization performance. Thus far, the analysis assumes a stationary target. Study of moving target tracking capabilities is offered through the use of the Bayesian CRLB for the estimation of both target location and velocity. Centralized and decentralized tracking algorithms, inherit to distributed MIMO radar architecture, are proposed and evaluated. It is shown that communication requirements and processing load may be reduced at a relatively low performance cost

Fundamental aspects of netted radar performance

Netted radar employs several spatially distributed transmitters and receivers for information retrieval. This system topology offers many advantages over traditional monostatic and bistatic systems which use a single transmitter and a single receiver. For example, it provides better utilization of reflected energy, more flexible system arrangement and enhanced information retrieval capability. Therefore, the netted radar system is of emerging interests among radar researchers. This work investigates several fundamental aspects that determine netted radar performance. This includes netted radar sensitivity, the netted radar ambiguity function and the netted radar ground plane effect. Mathematical models are developed to provide a mean to examine different aspects of netted radar performance. Software simulations examine netted radar performance over a range of parameter variations. Simulation results show that netted radar can offer better performance over traditional monnostatic and bistatic radar in many cases. Some elementary field trials have been conducted using a prototype netted radar system developed within the UCL radar group to examine aspects of netted radar performance in practice. The field trials are focused on netted radar range and sensitivity which are fundamental. The field trial results show that the theoretical benefits that netted radar can offer are generally realizable in practice

Frequency Diverse Array Radar: Signal Characterization and Measurement Accuracy

Radar systems provide an important remote sensing capability, and are crucial to the layered sensing vision; a concept of operation that aims to apply the right number of the right types of sensors, in the right places, at the right times for superior battle space situational awareness. The layered sensing vision poses a range of technical challenges, including radar, that are yet to be addressed. To address the radar-specific design challenges, the research community responded with waveform diversity; a relatively new field of study which aims reduce the cost of remote sensing while improving performance. Early work suggests that the frequency diverse array radar may be able to perform several remote sensing missions simultaneously without sacrificing performance. With few techniques available for modeling and characterizing the frequency diverse array, this research aims to specify, validate and characterize a waveform diverse signal model that can be used to model a variety of traditional and contemporary radar configurations, including frequency diverse array radars. To meet the aim of the research, a generalized radar array signal model is specified. A representative hardware system is built to generate the arbitrary radar signals, then the measured and simulated signals are compared to validate the model. Using the generalized model, expressions for the average transmit signal power, angular resolution, and the ambiguity function are also derived. The range, velocity and direction-of-arrival measurement accuracies for a set of signal configurations are evaluated to determine whether the configuration improves fundamental measurement accuracy

Signal Processing Algorithms for MIMO Radar

Radar is a system that uses electromagnetic waves to detect, locate and measure the speed of reflecting objects such as aircraft, ships, spacecraft, vehicles, people, weather formations, and terrain. It transmits the electromagnetic waves into space and receives the echo signal reflected from objects. By applying signal processing algorithms on the reflected waveform, the reflecting objects can be detected. Furthermore, the location and the speed of the objects can also be estimated. Radar was originally an acronym for "RAdio Detection And Ranging". Today radar has become a standard English noun. Early radar development was mostly driven by military and military is still the dominant user and developer of radar technology. Military applications include surveillance, navigation, and weapon guidance. However, radar now has a broader range of applications including meteorological detection of precipitation, measuring ocean surface waves, air traffic control, police detection of speeding traffic, sports radar speed guns, and preventing car or ship collisions. Recently, the concept of MIMO radar has been proposed. The MIMO radar is a multiple antenna radar system which is capable of transmitting arbitrary waveform from each antenna element. In the traditional phased array radar, the transmitting antennas are limited to transmit scaled versions of the same waveform. However the MIMO radar allows the multiple antennas to transmit arbitrary waveforms. Like MIMO communications, MIMO radar offers a new paradigm for signal processing research. MIMO radar possesses significant potentials for fading mitigation, resolution enhancement, and interference and jamming suppression. Fully exploiting these potentials can result in significantly improved target detection, parameter estimation, target tracking and recognition performance. The MIMO radar technology has rapidly drawn considerable attention from many researchers. Several advantages of MIMO radar have been discovered by many different researchers such as increased diversity of the target information, excellent interference rejection capability, improved parameter identifiability, and enhanced flexibility for transmit beampattern design. The degrees of freedom introduced by MIMO radar improves the performance of the radar systems in many different aspects. However, it also generates some issues. It increases the number of dimensions of the received signals. Consequently, this increases the complexity of the receiver. Furthermore, the MIMO radar transmits an incoherent waveform on each of the transmitting antennas. This in general reduces the processing gain compared to the phased array radar. The multiple arbitrary waveforms also affects the range and Doppler resolution of the radar system. The main contribution of this thesis is to study the signal processing issues in MIMO radar and propose novel algorithms for improving the MIMO radar system. In the first part of this thesis, we focus on the MIMO radar receiver algorithms. We first study the robustness of the beamformer used in MIMO radar receiver. It is known that the adaptive beamformer is very sensitive to the DOA (direction-of-arrival) mismatch. In MIMO radar, the aperture of the virtual array can be much larger than the physical receiving array in the SIMO radar. This makes the performance of the beamformer more sensitive to the DOA errors in the MIMO radar case. In this thesis, we propose an adaptive beamformer that is robust against the DOA mismatch. This method imposes constraints such that the magnitude responses of two angles exceed unity. Then a diagonal loading method is used to force the magnitude responses at the arrival angles between these two angles to exceed unity. Therefore the proposed method can always force the gains at a desired interval of angles to exceed a constant level while suppressing the interferences and noise. A closed form solution to the proposed minimization problem is introduced, and the diagonal loading factor can be computed systematically by a proposed algorithm. Numerical examples show that this method has an excellent SINR (signal to noise-plus-interference ratio) performance and a complexity comparable to the standard adaptive beamformer. We also study the space-time adaptive processing (STAP) for MIMO radar systems. With a slight modification, STAP methods developed originally for the single-input multiple-output (SIMO) radar (phased array radar) can also be used in MIMO radar. However, in the MIMO radar, the rank of the jammer-and-clutter subspace becomes very large, especially the jammer subspace. It affects both the complexity and the convergence of the STAP algorithm. In this thesis, we explore the clutter space and its rank in the MIMO radar. By using the geometry of the problem rather than data, the clutter subspace can be represented using prolate spheroidal wave functions (PSWF). Using this representation, a new STAP algorithm is developed. It computes the clutter space using the PSWF and utilizes the block diagonal property of the jammer covariance matrix. Because of fully utilizing the geometry and the structure of the covariance matrix, the method has very good SINR performance and low computational complexity. The second half of the thesis focuses on the transmitted waveform design for MIMO radar systems. We first study the ambiguity function of the MIMO radar and the corresponding waveform design methods. In traditional (SIMO) radars, the ambiguity function of the transmitted pulse characterizes the compromise between range and Doppler resolutions. It is a major tool for studying and analyzing radar signals. The idea of ambiguity function has recently been extended to the case of MIMO radar. In this thesis, we derive several mathematical properties of the MIMO radar ambiguity function. These properties provide some insights into the MIMO radar waveform design. We also propose a new algorithm for designing the orthogonal frequency-hopping waveforms. This algorithm reduces the sidelobes in the corresponding MIMO radar ambiguity function and makes the energy of the ambiguity function spread evenly in the range and angular dimensions. Therefore the resolution of the MIMO radar system can be improved. In addition to designing the waveform for increasing the system resolution, we also consider the joint optimization of waveforms and receiving filters in the MIMO radar for the case of extended target in clutter. An extended target can be viewed as a collection of infinite number of point targets. The reflected waveform from a point target is just a delayed and scaled version of the transmitted waveform. However, the reflected waveform from an extended target is a convolved version of the transmitted waveform with a target spreading function. A novel iterative algorithm is proposed to optimize the waveforms and receiving filters such that the detection performance can be maximized. The corresponding iterative algorithms are also developed for the case where only the statistics or the uncertainty set of the target impulse response is available. These algorithms guarantee that the SINR performance improves in each iteration step. The numerical results show that the proposed iterative algorithms converge faster and also have significant better SINR performances than previously reported algorithms.</p