Remote sensing using MIMO systems

A technique for sensing a moving object within a physical environment using a MIMO communication link includes generating a channel matrix based upon channel state information of the MIMO communication link. The physical environment operates as a communication medium through which communication signals of the MIMO communication link propagate between a transmitter and a receiver. A spatial information variable is generated for the MIMO communication link based on the channel matrix. The spatial information variable includes spatial information about the moving object within the physical environment. A signature for the moving object is generated based on values of the spatial information variable accumulated over time. The moving object is identified based upon the signature.

TECHNICAL FIELD

This disclosure relates generally to multiple-input and multiple-output communication technology and remote sensing.

BACKGROUND INFORMATION

Multiple-input, multiple-output (“MIMO”) refers to a technology that uses multiple antennas at the transmitter or receiver to improve communication performance. In wireless communications, MIMO technology can provide improved throughput and link range for a given bandwidth and transmit power. MIMO achieves these improvements by intelligently balancing the total transmit power over the multiple antennas. MIMO technology is used in various wireless communication standards including IEEE 802.11n (Wifi), 4G cellular communications, 3rd Generation Partnership Project Long Term Evolution, and WiMAX, to name a few. MIMO use three different techniques to increase throughput and link range: precoding (beamforming), spatial multiplexing, and diversity coding. Beamforming uses phase and/or gain weights applied to the signal transmitted from each antenna at the transmitter to maximize the signal received at the receiver. Spatial multiplexing uses a high rate signal split into multiple lower rate streams with each stream transmitted over a different spatial channel resulting from the spatial offset between the multiple antennas. Diversity coding applies space-time coding to a single data stream. MIMO technology has been applied to radar systems. However, MIMO radar operates using the same fundamental principle as traditional radar, which interrogates a specific physical region via a pulsed RF waveform and analyzes the reflected pulse to remote wirelessly sense the presence of an object within the physical region.

DETAILED DESCRIPTION

Embodiments of a system and method for multi-input and multiple-output (“MIMO”) remote sensing to obtain spatial information about an object within the physical environment of the MIMO communication link are described herein. In the following description numerous specific details are set forth to provide a thorough understanding of the embodiments. One skilled in the relevant art will recognize, however, that the techniques described herein can be practiced without one or more of the specific details, or with other methods, components, materials, etc. In other instances, well-known structures, materials, or operations are not shown or described in detail to avoid obscuring certain aspects.

FIG. 1is a diagram illustrating a MIMO communication system100configured to perform remote sensing of an object105within a physical environment110, in accordance with an embodiment of the disclosure. The illustrated embodiment of MIMO communication system100includes a MIMO transmitter (“TX”)115and a MIMO receiver (“RX”)120. The illustrated embodiment of MIMO TX115includes an antenna array125, MIMO transmitter circuitry130, and a processing system135. The illustrated embodiment of MIMO RX120includes an antenna array140, MIMO receiver circuitry145, and a processing system150. AlthoughFIG. 1illustrates one-way communications between a transmitter and receiver, it should be appreciated that embodiments of the present invention are equally applicable for use with bi-directional transceivers, in which case, MIMO TX115would include additional receiver circuitry and MIMO RX120would include additional transmitter circuitry.

The techniques disclosed herein use the spatial characteristics of MIMO communication technology to remotely sense or identify object105within physical environment110. This MIMO remote sensing technique operates differently than MIMO radar. MIMO radar interrogates a specific physical region via a pulsed RF waveform and analyzes the reflected pulses to remote sense the presence of an object within the physical region. In contrast, embodiments of the disclosed technique treat the physical environment110as a communication medium through which communication signals155propagate between MIMO transmitter115and MIMO receiver120. Communication signals115represent different spatial communication channels between MIMO transmitter115and MIMO receiver120that collectively establish a MIMO communication link between the transmitter and the receiver. As object105moves into physical environment110, it perturbs the spatial communication channels by creating one or more new spatial communication channels (e.g., spatial communication channel160), attenuating one or more existing spatial communication channels, or enhancing one or more existing communication channels. In essence, object105becomes part of the communication medium.

The channel perturbations can be measured via appropriate analysis of the channel state information or channel matrix associated with the MIMO communication link. Since the individual spatial communication channels of a MIMO communication link are spatially distinct or propagate along different spatial paths, spatial information variables can be derived from the channel matrix. The spatial information variables can be used to generate signatures that contain spatial information about object105moving through physical environment110. For example, the signatures may be distinctively related to the size, shape, and/or matter composition distribution of object105. In the case of an RF antenna array, the matter composition distribution of object105may be indirectly sensed, since the spatial distribution of RF absorptive material versus RF reflective material versus RF transparent material will appear in the signatures generated based upon the spatial information variables.

Embodiments of antenna arrays125and140collectively include three or more independent antennas to implement a MIMO communication link. However, typically each antenna array125and140will include two or more antennas for emitting and/or receiving the communication signals155that establish the various spatial communication channels of a MIMO communication link. Antenna arrays125and140may be implemented as RF antennas (e.g., monopole antennas), microwave antennas, optical antennas (e.g., light emitters and photo-detectors), or acoustical antennas (e.g., sonar antennas). However, in each implementation, the antennas are used to establish a MIMO communication link.

Embodiments of the present invention are capable of piggybacking remote sensing off of an active MIMO communication link that is being used to transmit useful data (e.g., communication services such voice, data, video) while simultaneously remote sensing objects within physical environment110. MIMO TX115and MIMO RX120may broadcast communication signals155according to various wireless communication standards, such as IEEE 802.11n (Wifi), 4G cellular communications (e.g., LTE), 3rd Generation Partnership Project Long Term Evolution, WiMAX, or otherwise. In other embodiments, MIMO TX115and MIMO RX120may broadcast signals155according to proprietary or non-standardized MIMO signaling protocols.

As mentioned above, the spatial information variables are calculated based upon analyzing the MIMO channel matrix. The MIMO channel matrix is generated based upon channel state information (“CSI”). When generated at the receiver, the CSI is often referred to as CSIR. When generated at the transmitter, the CSI is often referred to as CSIT. The MIMO channel matrix may be generated based solely on CSIR, solely on CSIT, or on a combination of both CSIR and CSIT when a feedback data transmission path is available. CSI may take the form of instantaneous CSI by measuring real-time current channel conditions, statistical CSI which measures a statistical characterization of the channel over a longer period of time, or a combination of both. Other non-standardized techniques for obtain channel state information and/or generating the MIMO channel matrix may be used.

MIMO communication signaling exploits the multiple spatial channels available within the channel matrix. In order to optimize the data throughput of the MIMO communication link, a MIMO system preprocesses the transmitted signal and post processes the received signal. This uses knowledge of the channel, as well as various mathematical manipulations on that channel information, as is known in the art. An M×N MIMO communication system has M transmitting elements and N receiving elements. The equation (without including the noise contribution) that describes the time dependency of this system is written as:
{right arrow over (y)}(t)N×1=½H(t)N×M{right arrow over (x)}(t)M×1(Eq. 1)
where y is the received signal vector, x is the transmitted signal vector, and H is the channel matrix. For simplicity, we focus on the time varying aspect of the channel, which represents an important channel parameter for MIMO remote sensing. The MIMO device (e.g., MIMO TX115or MIMO RX120) generates the channel matrix H by sounding the channel with a preset data pattern to measure channel properties and derive the CSI. The channel sounding is a routine part of a MIMO communication protocol to establish the MIMO communication link and balance the transmit power across the spatial communication channels using the channel matrix H. The MIMO remote sensing technique described herein exploits the channel matrix H for the additional purpose of sensing object105. During regular operation of a MIMO communication system, the channel matrix H is continuously updated on a periodic basis (e.g., microsecond basis), which provides continuous real-time sensing updates.

Once H is known, a factorization (e.g., singular value decomposition or SVD) can be performed to determine the singular values (e.g., eigenvalues) associated with each of the individual channels (also referred to as eigenchannels). H can be written as,
HN×M=ŪN×NDN×MVM×M*  (Eq. 2)
where U and V are unitary matrices, (i.e., VV*=I), and * means the conjugate transpose. From SVD theory, the nonzero diagonal elements of D, when squared, are the eigenvalues of the matrix H*H when N≧M or of the matrix H H* when M≧N.

These eigenvalues are associated with what can be viewed as a spatial communication channel between the transmitter and receiver and indicate how to distribute the power amongst the spatial communication channels in order to maximize the data throughput, or capacity C, using a water-filling technique. The equation for capacity, C is,

C=maxQ:Tr⁡(Q)=P⁢log⁢I_N+HQH_*(Eq.⁢3)
where Q is the input covariance matrix, and Tr(Q) is the trace of Q. The power allocation for the water-filling strategy is then described as,

Pi=(μ-1σi2)+,1≤i≤min⁡(M,N)(Eq.⁢4)
where μ is the water-fill level, σ2iis the ith singular value, P, is the power in the ith eigenmode of the channel, and (x)+ is defined as max(x,0).

For MIMO remote sensing, the capacity C of the MIMO system may be used to observe or measure how H changes over time, as opposed merely to improve data transfer. Thus, at least two properties of H*H (or H H*) may be monitored to remotely sense object105: (1) the singular values themselves, and/or (2) the channel capacity C. Generically, these properties are referred to herein as spatial information variables. It is also contemplated that the condition number of H*H could provide another measurable property for MIMO remote sensing.

FIG. 2is a flow chart illustrating a generic process200for MIMO sensing by monitoring spatial information variables derived from the channel matrix of the MIMO communication link, in accordance with an embodiment of the disclosure. The order in which some or all of the process blocks appear in process200should not be deemed limiting. Rather, one of ordinary skill in the art having the benefit of the present disclosure will understand that some of the process blocks may be executed in a variety of orders not illustrated, or even in parallel.

In a process block205, the channel matrix H for the MIMO communication link is generated. As described above, the MIMO communication link is made up of multiple spatial communication channels (illustrated inFIG. 1as dotted lines155). These spatial communication channels are physically offset or spatially separated. As object105passes through these spatially distinct communication channels, it perturbs the individual channels. These perturbations are reflected in real-time in each update of channel matrix H. The channel matrix H can then be analyzed to derive spatial information variables that describe or include spatial information about object105. Stated another way, the various different physical parts of object105will interact with the spatially separated communication channels in a manner that is related to those different physical parts of object105. Thus, once obtained, the channel matrix H is analyzed to derive spatial information variables that are responsive to the various interactions between the object105and the multiple spatially distinct communication channels (process block210). These spatial information variables may include one or more singular values from a matrix factorization (e.g., eigenvalues from SVD) of channel matrix H, a channel capacity C value calculated based upon the singular values, or values derived from various other filter or weighting algorithms applied to channel matrix H.

It should be appreciated that channel matrix H is continuously updated in real time. Thus, values for the spatial information variables are accumulated over time (process block215) as a series of temporally derived values to generate a signature or object signature. The object signature is a signature of object105as it moves within physical environment110.

FIGS. 3A,3B, and3C illustrate plots of various different signatures obtained from ten different tests using three different object types moving along various different trajectories within physical environment110. The demonstrative plots use singular values (y axis) from an SVD of the channel matrix H as the spatial information variable being used for MIMO remote sensing. The plots include singular value series of the first order305, second order310, third order315, and fourth order320. The singular values of the first order305illustrate distinctive signatures330, while the high order singular values series appear less distinctive. However, that is not to say that signatures are not embedded within the noise of those higher order signatures. Rather, appropriate filtering and more sensitive equipment would likely be able to extract useful spatial information from those singular values series as well.

Tests 1 through 4 all represent a van as object105moving within physical environment110. Tests 1 and 2 are of the van moving along trajectory160between MIMO TX115and MIMO RX120, while tests 3 and 4 are of the van moving along trajectory161behind MIMO RX120. Tests 5 through 8 all represent a single person as object105walking within physical environment110. Tests 5 and 6 are of the single person walking along trajectory162between MIMO TX115and MIMO RX120, while test 7 is of a person walking along trajectory163behind MIMO RX120. Tests 9 and 10 all represent a group of people as object105walking within physical environment110. Tests 9 and 10 are of the group of people walking along trajectory164between MIMO TX115and MIMO RX120.

As can been seen, the signatures330of the van moving between MIMO TX115and MIMO RX120are similar, the signatures330of the van moving behind MIMO RX120are similar, the signatures330of the individual person walking between MIMO TX115and MIMO RX120are similar, and the signatures330of the group of people walking between MIMO TX115and MIMO RX120are similar. Not only do like objects moving along like trajectories produce like signatures330, but different objects and different trajectories result in different signatures330. As such, these signatures not only provide spatial information related to the type of object105but also provide spatial information of the location and trajectory of object105. This spatial information, as represented in signatures330, can be used to remotely sense and identify object105using a MIMO communication link. Identifying object105may include merely sensing its presence within physical environment110, sensing its movement within physical environment110, or even determining what object105is (e.g., is object105a person, a group of people, a vehicle, a type of vehicle, etc.).

Returning to process200, a first type of identification may be achievable by thresholding signatures330(decision block220). Thresholding signatures330may include an amplitude deviation threshold that is triggered when the spatial information variable deviates (either up or down) from a baseline (e.g., trend line or average value) by a threshold amount. In one embodiment, the threshold deviation must additionally deviate from the baseline for a threshold period of time. If the threshold requirements are satisfied, then an identification alert is issued in process block225.

In some cases thresholding may not be used (decision block220), but rather the measured signatures are compared to a known signatures. In this case, once a sufficient number of spatial information variables have been accumulated to acquire a signature (decision block230), the measured signature is compared to a database of known signatures (process block235). The known signatures may represent previously measured signatures of known objects moving along known trajectories and cataloged for future reference. Various different types of comparison techniques may be applied; however, in one embodiment, a statistical correlation is used to identify matches with specified confidence intervals. If a statistical correlation to a known signature is found within the database that exceeds a threshold confidence interval, then object105is assumed to be identified (process block240).

FIG. 4is a flow chart illustrating a process400for MIMO sensing by monitoring channel capacity of the MIMO communication link, in accordance with an embodiment of the disclosure. Process400operates similar to process200, but includes additional procedures for deriving the channel capacity from the singular values. The order in which some or all of the process blocks appear in process400should not be deemed limiting. Rather, one of ordinary skill in the art having the benefit of the present disclosure will understand that some of the process blocks may be executed in a variety of orders not illustrated, or even in parallel.

In a process block405, the channel matrix H for the MIMO communication link is generated. Once obtained, an SVD is performed to factorize the channel matrix H and derive eigenvalues associated with the eigenchannels of the MIMO communication link (process block410). The eigenvalues are then inserted into equation (4) to determine the appropriate power levels for each eigenchannel using the water-filling technique. The channel power levels are then inserted into equation (3) to calculate the channel capacity C for a given transmit power level (process block415). In this embodiment, the channel capacity C is the spatial information variable of interest.

As the channel matrix H is continuously updated in real time, new eigenvalues are continuously derived and a continuous stream of channel capacity values calculated. These are accumulated over time (process block420) as a series of temporally derived spatial information variable values to generate a signature or object signature based on the channel capacity C. The object signature is a signature of object105as it moves within physical environment110.

If thresholding is performed (decision block425), then the channel capacity values are thresholding in process block430. Thresholding the channel capacity values or signature may include an amplitude deviation threshold that is triggered when the channel capacity deviates (either up or down) from a baseline (e.g., trend line or average value) by a threshold amount. In one embodiment, the threshold deviation must additionally deviate from the baseline for a threshold period of time. If the threshold requirements are satisfied, then an identification alert is issued in process block430.

In some cases thresholding may not be used (decision block425), but rather the measured signatures are compared to a known signatures. In this case, once a sufficient number of channel capacity values have been accumulated to acquire a signature (decision block435), the measured signature is compared to a database of known signatures (process block440). The known signatures may represent previously measured signatures of known objects moving along known trajectories and cataloged for future reference. Various different types of comparison techniques may be applied; however, in one embodiment, a statistical correlation is used to identify matches with specified confidence intervals. If a statistical correlation is found to exceed a threshold confidence interval, then object105is assumed to be identified (process block445).

The channel capacity described in equation (3), assumes that some level of channel state is known both at the transmitter and at the receiver. This capacity is not always achievable in a practical sense. However, this measure has the advantage for mixing different amounts of multipath spatial information together into a single variable through setting the average transmitted power level and maximizing equation (3) through water-filling as described by equation (4). By setting μ to a low level only one eigenchannel will be used. Correspondingly, by raising the average power level, an increasing number of eigenchannels will be used in the capacity expression thereby potentially including additional spatial information into the channel capacity value. This is akin to analyzing more than just the dominant singular value series305, but also analyzing the higher order singular value series310,315, and320. How many orders are included is selectable via appropriate control of average transmit power. Below a threshold transmit power, MIMO TX115substantially only powers the dominate eigenchannel; however, beyond the threshold level, the transmitter will start diverting power to the non-dominant eigenchannels (i.e., the higher order eigenchannels). For the non-dominant eigenchannels to have meaningful spatial information, the signal-to-noise-ratio (“SNR”) of those channels must be sufficiently high so that the useful spatial information is not drowned out by the background noise. Of course, a combination of channel capacity and singular values may be used together as spatial information variables to identify object105.

FIG. 5is a block diagram illustrating a demonstrative processing system500for implementing either of processing systems135or150and executing any or all of processes200or400. The illustrated embodiment of processing system500includes one or more processors (or central processing units)505, system memory510, nonvolatile (“NV”) memory515, a data storage unit (“DSU”)520, a communication interface525, and a chipset530. The illustrated processing system500may represent a variety of computing system including a desktop computer, a notebook computer, a workstation, a handheld computer, a server, a blade server, an intelligent wireless access point, or otherwise.

The elements of processing system500are interconnected as follows. Processor(s)505is communicatively coupled to system memory510, NV memory515, DSU520, and communication interface525, via chipset530to send and to receive instructions or data thereto/therefrom. In one embodiment, NV memory515is a flash memory device. In other embodiments, NV memory515includes any one of read only memory (“ROM”), programmable ROM, erasable programmable ROM, electrically erasable programmable ROM, or the like. In one embodiment, system memory510includes random access memory (“RAM”), such as dynamic RAM (“DRAM”), synchronous DRAM, (“SDRAM”), double data rate SDRAM (“DDR SDRAM”), static RAM (“SRAM”), or the like. DSU520represents any storage device for software data, applications, and/or operating systems, but will most typically be a nonvolatile storage device. DSU520may optionally include one or more of an integrated drive electronic (“IDE”) hard disk, an enhanced IDE (“EIDE”) hard disk, a redundant array of independent disks (“RAID”), a small computer system interface (“SCSI”) hard disk, or the like. Although DSU520is illustrated as internal to processing system500, DSU520may be externally coupled to processing system500. Communication interface525may couple processing system500to a network or communication link such that processing system500may communicate over the network/communication link with one or more other computing devices. Communication interface525may couple to a MIMO antenna array (e.g., wireless antenna array, optical antenna array, acoustical antenna array, etc.) to establish a MIMO communication link with a remote MIMO antenna array. Communication interface525may an Ethernet card, a Gigabit Ethernet card, Universal Serial Bus (“USB”) port, a wireless network interface card, a fiber optic interface, or the otherwise.

It should be appreciated that various other elements of processing system500have been excluded fromFIG. 5and this discussion for the purposes of clarity. For example, processing system500may further include a graphics card for rendering images to a screen, additional DSUs, other persistent data storage devices (e.g., tape drive), or the like. Chipset530may also include a system bus and various other data buses for interconnecting subcomponents, such as a memory controller hub and an input/output (“I/O”) controller hub, as well as, include data buses (e.g., peripheral component interconnect bus) for connecting peripheral devices to chipset530. Correspondingly, processing system500may operate without one or more of the elements illustrated. For example, processing system500need not include DSU520.