Sparse sensor arrays for active sensing - Array configurations and signal processing

Robin Rajamäki

Sparse sensor arrays for active sensing - Array configurations and signal processing

Julkaisun otsikon käännös: Harvat anturiryhmät aktiivisessa aistimisessa - Anturigeometrioita ja signaalinkäsittelyä

Tutkimustuotos: Doctoral Thesis › Collection of Articles

Abstrakti

Monianturijärjestelmät ovat avainasemassa monessa sovelluksessa, kuten tutkassa, kaikuluotauksessa, lääketieteellisessä ultraäänessä ja langattomassa viestinnässä. Usean anturin käyttäminen tarjoaa erinäisiä hyötyjä, kuten suunnatun lähetyksen ja vastaanoton, paremman signaali-kohinasuhteen ja mahdollisuuden torjua häiriöitä. Perinteisissä monianturijärjestelmissä anturit on sijoitettu tasavälein janalle tai suorakulmion muotoon. Tällainen anturiryhmä voi kuitenkin tulla kalliiksi, kun sen koko ja antureiden lukumäärä kasvaa suureksi. Sitä vastoin harvat anturiryhmät, joiden anturit on sijoitettu epäjaksollisesti, vaativat huomattavasti vähemmän resursseja vertailukelpoisen suorituskyvyn saavuttamiseksi esimerkiksi sirottajien tai signaalilähteiden paikantamisessa. Tämän mahdollistaa virtuaalinen anturiryhmä, joka tyypillisesti koostuu fyysisten antureiden sijaintien parittaisista erotuksista tai summista, eli niin sanotusta erotus- tai summajoukosta. Virtuaaliseen anturiryhmään perustuvassa harvojen anturiryhmien suunnittelussa on ennen keskitytty lähes yksinomaan passiiviseen aistimiseen. Aktiivista aistimista, jossa anturit sekä lähettävät että vastaanottavat, on tutkittu huomattavasti vähemmän. Aktiivisella aistimisella on kuitenkin tärkeitä ja laajalle levinneitä sovelluksia esimerkiksi tutka- ja ultraäänikuvantamisessa. Summajoukko ilmenee luonnollisesti aktiivisen aistimisen signaalimallissa, kun taas erotusjokko on tärkeämmässä asemassa passiivisessa aistimisessa. Tässä väitöskirjassa ehdotetaan uusia harvoja anturigeometrioita ja signaalinkäsittelymenetelmiä aktiiviseen aistimiseen ja kuvantamiseen. Työssä kehitetään sekä lineaarisia että tasomaisia geometrioita, jotka saavuttavat suuren yhtenäisen summajoukon edullisesti käyttämällä merkittävästi vähemmän antureita kuin perinteiset geometriat. Nämä harvat anturigeometriat pystyvät sekä havaitsemaan huomattavasti antureiden lukumäärää enemmän siroittajia, että tuottamaan säteilykuvioita, jotka ovat perinteisesti vain tasavälisen geometrian saavutettavissa. Useat ehdotetuista anturigeometrioista ovat symmetrisiä, mistä seuraa, että niiden summa- ja erotusjoukot ovat samat ja että ne soveltuvat sekä aktiiviseen että passiiviseen aistimiseen. Väitöskirjassa kehitetään myös menetelmiä lineaariseen kuvantamiseen, jossa kuvanlaatua parannetaan yhdistämällä useita komponenttikuvia, jotka mahdollisesti vastaavat erillisiä lähetyksiä ja vastaanottoja. Työssä muotoillaan myös optimointitehtävä, jonka ratkaisulla saavutetaan haluttu säteilykuvio ennalta määrättyyn tarkkuuteen käyttämällä mahdollisimman vähän komponenttikuvia. Ongelman ratkaisemiseksi johdetaan algoritmeja ja määritellään rajoja optimaaliselle komponenttikuvien määrälle tyypillisimmät monianturijärjestelmäarkkitehtuurit huomioon ottaen. Numeeriset simulaatiot vahvistavat johdetut teoreettiset tulokset ja havainnollistavat kompromisseja eri järjestelmäparametrien välillä. Kontribuutioilla on käytännön arvoa anturiryhmien suunnittelussa aktiivista aistimista varten.

Julkaisun otsikon käännös	Harvat anturiryhmät aktiivisessa aistimisessa - Anturigeometrioita ja signaalinkäsittelyä
Alkuperäiskieli	Englanti
Pätevyys	Tohtorintutkinto
Myöntävä instituutio	Aalto-yliopisto
Valvoja/neuvonantaja	Koivunen, Visa, Vastuuprofessori
Kustantaja	Aalto University
Painoksen ISBN	978-952-64-0473-8
Sähköinen ISBN	978-952-64-0474-5
Tila	Julkaistu - 2021
OKM-julkaisutyyppi	G5 Artikkeliväitöskirja

Tutkimusalat

harvat anturiryhmät
aktiivinen aistiminen
virtuaaliryhmä
summajoukko

Pääsy asiakirjaan

http://urn.fi/URN:ISBN:978-952-64-0474-5

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@phdthesis{bcd10f1fa454499f9963062160891be1,

title = "Sparse sensor arrays for active sensing - Array configurations and signal processing",

abstract = "Multisensor systems are a key enabling technology in, e.g., radar, sonar, medical ultrasound, and wireless communications. Using multiple sensors provides spatial selectivity, improves the signal-to-noise ratio, and enables rejecting unwanted interference. Conventional multisensor systems employ a simple array of uniformly spaced sensors with a linear or rectangular geometry. However, a uniform array spanning a large electrical aperture may become prohibitively expensive, as many sensors and costly RF-IF front ends are needed. In contrast, sparse sensor arrays require drastically fewer resources to achieve comparable performance in terms of spatial resolution and the number of identifiable scatterers or sources. This is facilitated by the co-array: a virtual array structure consisting of the pairwise differences or sums of physical sensor positions. Most recent works on co-array-based sparse array design focus exclusively on passive sensing. Active sensing, where sensors transmit signals and observe their backscattered component, have been investigated less, despite their importance in ubiquitous applications such as radar and ultrasound imaging. The sum co-array naturally arises from the active sensing signal model, whereas the difference co-array is often more relevant in passive sensing. This dissertation proposes novel sparse array designs and signal processing methods for active sensing and imaging. We introduce linear and planar sparse array configurations that achieve a large contiguous sum co-array for diverse aspect ratios using significantly fewer sensors than conventional arrays. These low-cost arrays resolve vastly more scatterers than sensors in both azimuth and elevation, and synthesize beampatterns that are normally achieved by uniform arrays only. Several of the proposed configurations are symmetric, which implies that their sum and difference co-arrays are equivalent, and that they are suitable for both active and passive sensing. We also develop methods for coherent linear imaging, where image quality is improved by summing multiple component images, possibly corresponding to separate transmissions and receptions. We formulate a new optimization problem for achieving any feasible transmit-receive beampattern to a desired accuracy using as few component images as possible. We derive algorithms and closed-form expressions approximately solving this problem, and establish bounds on the number of component images of the optimal solution. We consider fully digital, hybrid, and fully analog beamforming architectures, as well as various waveform diversity cases, including phased array and orthogonal MIMO. Hybrid and analog beamforming further reduce the number of RF-IF front ends and related hardware costs, whereas waveform diversity governs the number of component images acquired per transmission. Numerical experiments verify the analytical results and characterize the trade-offs between the various system parameters. The contributions are of practical value in the design of sensor arrays for active sensing.",

keywords = "sparse sensor arrays, active sensing, sum co-array, harvat anturiryhm{\"a}t, aktiivinen aistiminen, virtuaaliryhm{\"a}, summajoukko, sparse sensor arrays, active sensing, sum co-array",

author = "Robin Rajam{\"a}ki",

year = "2021",

language = "English",

isbn = "978-952-64-0473-8",

series = "Aalto University publication series DOCTORAL DISSERTATIONS",

publisher = "Aalto University",

number = "108",

address = "Finland",

school = "Aalto University",

}

TY - BOOK

T1 - Sparse sensor arrays for active sensing - Array configurations and signal processing

AU - Rajamäki, Robin

PY - 2021

Y1 - 2021

N2 - Multisensor systems are a key enabling technology in, e.g., radar, sonar, medical ultrasound, and wireless communications. Using multiple sensors provides spatial selectivity, improves the signal-to-noise ratio, and enables rejecting unwanted interference. Conventional multisensor systems employ a simple array of uniformly spaced sensors with a linear or rectangular geometry. However, a uniform array spanning a large electrical aperture may become prohibitively expensive, as many sensors and costly RF-IF front ends are needed. In contrast, sparse sensor arrays require drastically fewer resources to achieve comparable performance in terms of spatial resolution and the number of identifiable scatterers or sources. This is facilitated by the co-array: a virtual array structure consisting of the pairwise differences or sums of physical sensor positions. Most recent works on co-array-based sparse array design focus exclusively on passive sensing. Active sensing, where sensors transmit signals and observe their backscattered component, have been investigated less, despite their importance in ubiquitous applications such as radar and ultrasound imaging. The sum co-array naturally arises from the active sensing signal model, whereas the difference co-array is often more relevant in passive sensing. This dissertation proposes novel sparse array designs and signal processing methods for active sensing and imaging. We introduce linear and planar sparse array configurations that achieve a large contiguous sum co-array for diverse aspect ratios using significantly fewer sensors than conventional arrays. These low-cost arrays resolve vastly more scatterers than sensors in both azimuth and elevation, and synthesize beampatterns that are normally achieved by uniform arrays only. Several of the proposed configurations are symmetric, which implies that their sum and difference co-arrays are equivalent, and that they are suitable for both active and passive sensing. We also develop methods for coherent linear imaging, where image quality is improved by summing multiple component images, possibly corresponding to separate transmissions and receptions. We formulate a new optimization problem for achieving any feasible transmit-receive beampattern to a desired accuracy using as few component images as possible. We derive algorithms and closed-form expressions approximately solving this problem, and establish bounds on the number of component images of the optimal solution. We consider fully digital, hybrid, and fully analog beamforming architectures, as well as various waveform diversity cases, including phased array and orthogonal MIMO. Hybrid and analog beamforming further reduce the number of RF-IF front ends and related hardware costs, whereas waveform diversity governs the number of component images acquired per transmission. Numerical experiments verify the analytical results and characterize the trade-offs between the various system parameters. The contributions are of practical value in the design of sensor arrays for active sensing.

AB - Multisensor systems are a key enabling technology in, e.g., radar, sonar, medical ultrasound, and wireless communications. Using multiple sensors provides spatial selectivity, improves the signal-to-noise ratio, and enables rejecting unwanted interference. Conventional multisensor systems employ a simple array of uniformly spaced sensors with a linear or rectangular geometry. However, a uniform array spanning a large electrical aperture may become prohibitively expensive, as many sensors and costly RF-IF front ends are needed. In contrast, sparse sensor arrays require drastically fewer resources to achieve comparable performance in terms of spatial resolution and the number of identifiable scatterers or sources. This is facilitated by the co-array: a virtual array structure consisting of the pairwise differences or sums of physical sensor positions. Most recent works on co-array-based sparse array design focus exclusively on passive sensing. Active sensing, where sensors transmit signals and observe their backscattered component, have been investigated less, despite their importance in ubiquitous applications such as radar and ultrasound imaging. The sum co-array naturally arises from the active sensing signal model, whereas the difference co-array is often more relevant in passive sensing. This dissertation proposes novel sparse array designs and signal processing methods for active sensing and imaging. We introduce linear and planar sparse array configurations that achieve a large contiguous sum co-array for diverse aspect ratios using significantly fewer sensors than conventional arrays. These low-cost arrays resolve vastly more scatterers than sensors in both azimuth and elevation, and synthesize beampatterns that are normally achieved by uniform arrays only. Several of the proposed configurations are symmetric, which implies that their sum and difference co-arrays are equivalent, and that they are suitable for both active and passive sensing. We also develop methods for coherent linear imaging, where image quality is improved by summing multiple component images, possibly corresponding to separate transmissions and receptions. We formulate a new optimization problem for achieving any feasible transmit-receive beampattern to a desired accuracy using as few component images as possible. We derive algorithms and closed-form expressions approximately solving this problem, and establish bounds on the number of component images of the optimal solution. We consider fully digital, hybrid, and fully analog beamforming architectures, as well as various waveform diversity cases, including phased array and orthogonal MIMO. Hybrid and analog beamforming further reduce the number of RF-IF front ends and related hardware costs, whereas waveform diversity governs the number of component images acquired per transmission. Numerical experiments verify the analytical results and characterize the trade-offs between the various system parameters. The contributions are of practical value in the design of sensor arrays for active sensing.

KW - sparse sensor arrays

KW - active sensing

KW - sum co-array

KW - harvat anturiryhmät

KW - aktiivinen aistiminen

KW - virtuaaliryhmä

KW - summajoukko

KW - sparse sensor arrays

KW - active sensing

KW - sum co-array

M3 - Doctoral Thesis

SN - 978-952-64-0473-8

T3 - Aalto University publication series DOCTORAL DISSERTATIONS

PB - Aalto University

ER -

Sparse sensor arrays for active sensing - Array configurations and signal processing

Abstrakti

Tutkimusalat

Pääsy asiakirjaan

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