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Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE

Quantum synthetic aperture radar

Marco O. Lanzagorta, Oliverio Jitrik, Jeffrey K. Uhlmann, Salvador Elías Venegas-Andraca

Published May 1, 2017• 3 citations
Publisher

Abstract

Synthetic aperture radar (SAR) uses sensor motion to generate finer spatial resolution of a given target area. In this paper we explore the theoretical potential of quantum synthetic aperture quantum radar (QSAR). We provide theoretical analysis and simulation results which suggest that QSAR can provide improved detection performance over classical SAR in the high-noise low-brightness regime.

DOI
10.1117/12.2262645

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Synthetic aperture radar (SAR) uses sensor motion to generate finer spatial resolution of a given target area. In this paper we explore the theoretical potential of quantum synthetic aperture quantum radar (QSAR). We provide theoretical analysis and simulation results which suggest that QSAR can provide improved detection performance over classical SAR in the high-noise low-brightness regime.
Synthetic aperture radar (SAR) is a well established technology in the field of Earth remote sensing. Over the years, the resolution of SAR images has been steadily improving and the pixel count increasing as a result of advances in the sensor technology, and so have the computational resources required to process the raw data to a focused image. Because they are a necessary step in the study of the retrieved data, new high-resolution and low-complexity focusing algorithms are constantly explored in the SAR literature. The theory of quantum computing proposes a new computational framework that might allow to process a vast amount of data in a more efficient way. Relevant to our case is the advantage proven for the quantum Fourier transform (QFT), the quantum counterpart of a fundamental element of many SAR focusing algorithms. Motivated by this, in this work we propose a quantum version of the range-Doppler algorithm. We show how in general reference functions, a key element in many SAR focusing algorithms, can be mapped to quantum gates; we present the quantum circuit performing the SAR raw data focusing and we discuss in detail its computational complexity. We find that the core of the quantum range-Doppler algorithm has a computational complexity, namely the number of single- and two-qubit gates, of O(N)O(N)O(N), less than its classical counterpart with computational complexity O(Nlog⁡N)O(N \log N)O(NlogN).
The Radar is a kind of active remote-sensing device which has the abilities to detect and identify targets in all weathers. With the development of the radar system, the pursuit of higher SAR image quality makes the radar system more and more complicated so that the current radar systems need a revolution. Theoretical analyses show that a device processing quantum information has the potential to promote its sensitivity at utmost and the system performance would be increased as a result. This paper demonstrates the development history of quantum radar and predicts the feasibility of the quantum radar.
The field of quantum computing, especially quantum machine learning (QML), has been the subject of much research in recent years. Leveraging the quantum properties of superposition and entanglement promises exponential decrease in computation costs. With the promises of increased speed and accuracy in the quantum paradigm, many classical machine learning algorithms have been adapted to run on quantum computers, typically using a quantum-classical hybrid model. While some work has been done to compare classical and quantum classification algorithms in the Electro-Optical (EO) image domain, this paper will compare the performance of classical and quantum-hybrid classification algorithms in their applications on Synthetic Aperture Radar (SAR) data using the MSTAR dataset. We find that there is no significant difference in classification performance when training with quantum algorithms in ideal simulators as compared to their classical counterparts. However, the true performance benefits will become more apparent as the hardware matures.
In accord with imaging mechanism, by tackling the integrated quantum circuit without multiple preparations and measurements, a quantum algorithm with remarkable speedup for synthetic aperture radar (SAR) imaging is proposed to decrease the data storage and time consuming in this letter. First, the quantum algorithms for 2-D matched filtering and range cell migration correction (RCMC) are presented. Then, the relevant quantum circuits are designed and bonded together as an integrated quantum circuit for SAR imaging. It would be further applied to wide-swath imaging and sparse-driven radar imaging to form the corresponding quantum algorithm from quantum data to quantum data. The polynomial speedup in computational complexity is analyzed theoretically, and the needed quantum data are reduced exponentially compared to classical data. Experimental results obtained by computer simulation of quantum algorithm demonstrate that the proposed method can obtain the high-resolution SAR image and decrease at least10210^{2}102orders in complexity.
Synthetic Aperture Radar (SAR) has played an important role in Earth observation, with applications in disaster monitoring, agriculture, forestry, ice surveillance, defense, and planetary exploration. The reconstruction of SAR images is a costly operation that uses Fourier Transform, while quantum computing has some key advantages that potentially enhance the algorithm to make it faster. Therefore, in the first part of the thesis, a major image processing method used in SAR systems is studied, known as the Range Doppler Algorithm (RDA). RDA is implemented on a simulated data set and a real data set from ICEYE Oy. In the second half of the thesis, quantum computing basic is studied and reviewed, including qubits, 1-qubit gate and 2-qubits gates, as well as the Quantum Fourier Transform (QFT) that are specific to replace the classical Fourier Transform. A framework for implementing the Range Doppler Algorithm (RDA) on a quantum computer, referred to as the Quantum Range Doppler Algorithm (QRDA), is proposed. In addition, the required resources under ideal quantum-computing assumptions are analyzed, including time complexity (algorithmic scaling) and space complexity (the number of qubits required). Lastly, the potential applicability of the proposed. QRDA in the context of current quantum device, Noisy Intermediate-Scale Quantum (NISQ) machine, is discussed.
One of the major scientific thrusts from recent years has been to try to harness quantum phenomena to dramatically increase the performance of a wide variety of classical information processing devices. These advances in quantum information science have had a considerable impact on the development of standoff sensors such as quantum radar. In this paper we analyze the theoretical performance of low-brightness quantum radar that uses entangled photon states. We use the detection error probability as a measure of sensing performance and the interception error probability as a measure of stealthiness. We compare the performance of quantum radar against a coherent light sensor (such as lidar) and classical radar. In particular, we restrict our analysis to the performance of low-brightness standoff sensors operating in a noisy environment. We show that, compared to the two classical standoff sensing devices, quantum radar is stealthier, more resilient to jamming, and more accurate for the detection of low reflectivity targets.