Patent ID: 12210084

DETAILED DESCRIPTION

As required, detailed embodiments are disclosed herein; however, it is to be understood that the disclosed embodiments are merely exemplary and may be embodied in various and alternative forms. The figures are not necessarily to scale; some features may be exaggerated or minimized to show details of particular components. Therefore, specific structural and functional details disclosed herein are not to be interpreted as limiting, but merely as a representative basis for teaching one skilled in the art to variously employ the present embodiments.

With the ongoing advancements in wireless technologies, people now use any number of connected and personalized services. As the number of wireless systems and services increases, manufacturers have begun to leverage such pre-existing systems and services in a different way than what was originally contemplated. For instance, manufacturers have begun to leverage radio frequency (RF) transceivers (such as WiFi) to track moving humans through walls and behind closed doors. The use of WiFi to track human movement is much different than the original use-case of a way to communicate data between electronic devices. By leveraging a pre-existing system beyond an intended application, manufacturers have been able to reduce the need for extra hardware which in turn reduces cost, space and/or provide increased power savings.

In automotive applications, key fobs have become more common for certain functions. For instance, when a user is located within the vicinity of a vehicle, the key fob may be used to automatically unlock doors. Or when a user is located within the vehicle, the key fob may allow the user to start the vehicle using a push button. To perform these functions, a key fob will wirelessly communicate and perform an authentication process. Currently, automotive manufacturers may rely on two types of radio frequency (RF) technologies. For passive entry systems (PES) and comfort entry go (CEG) applications, a low frequency (LF) technology may be used for key fob proximity and localization. For remote keyless entry, ultra-high frequency (UHF) technology may be employed. However, traditional LF and UHF technologies may not be adequate for additional leveraging. For instance, traditional LF and UHF technologies may not be capable of performing detection of users within a vehicle. As a result, additional systems may be required to perform such functionality. Also, LF and UHF systems have been known to be subjected to security breaches including “relay” attacks. There exists a need to provide a single system that can operate to provide more than just vehicle access and starting capabilities and to provide a system that can perform such functionality with improved security.

As disclosed by U.S. patent application Ser. No. 16/042,397, which is incorporated herein by reference, an Ultra-Wideband (UWB) system is disclosed and operable to perform certain automotive functions such as vehicular access (i.e., keyless entry). UWB technology may be preferred over LF and UHF technology because it may provide more robust functionality and improved security capabilities. It is contemplated that a UWB system may also be capable of providing increased context awareness, safety, and security applications.

FIG.1illustrates a UWB system100that may include nodes110-136located at various locations around a vehicle102. The number and location of nodes110-136may depend on the desired accuracy, application, performance, and/or the make and model of the vehicle102. For instance, the UWB system100may include one or more nodes112that are able to monitor a sensing zone within and around the vehicle102. Placement may allow the UWB system100to use information received by nodes110-124to perform features internal to the vehicle102and nodes126-136to perform features external to the vehicle102. For instance, based on information received from nodes126-136the UWB system100may detect a user is within the vicinity of vehicle102and subsequently unlock the doors of vehicle102. If there exists a stored user profile, UWB system100may be operable to automatically adjust the vehicle seats, adjust the rear-view mirrors, activate the rear-view camera, adjust the HVAC system to a desired vehicle cabin temperature, or activate the in-cabin infotainment system.

Similarly, nodes110-124(i.e., internal nodes) may be used to start the vehicle102when the UWB system100determines target portable device106is within the vehicle102. UWB system100may also be operable to perform the following functions: (1) detecting the state of the vehicle102(e.g., whether the vehicle102is unoccupied or occupied; or whether a door, window, or trunk is open); (2) monitoring the vital signs of an occupant within the vehicle102(e.g., heart rate or breathing rate); (3) determining the occupancy of the vehicle102(i.e., count the number of people in the vehicle102); (4) detecting human movement or activity near the vehicle; (5) detecting the occupancy when a driver/passenger approaches (or leaves) the vehicle102; and (6) detecting an intrusion in the vehicle102while ensuring complete privacy.

Nodes110-136may include a processor, memory, and a transceiver unit. The memory may be configured to store program instructions that, when executed by the processor, enable the nodes110-136to perform various operations described elsewhere herein, including localization of a target portable device106(e.g., a key fob, smart phone, or smart watch). The memory may be of any type of device capable of storing information accessible by the processor, such as write-capable memories, read-only memories, or other computer-readable mediums. Additionally, it will be recognized by those of ordinary skill in the art that a “processor” may include hardware systems, hardware mechanisms or hardware components that processes data, signals or other information. The processor may include a system with a central processing unit, multiple processing units, dedicated circuitry for achieving functionality, or other systems.

The nodes110-136may use an ultra-wideband transceiver configured to communicate with the target portable device106. But nodes110-136may also include transceivers configured for communication with other electronic devices, including the ability to send communication signals and receive communication signals. The transceiver included within nodes110-136may comprise multiple ultra-wideband transceivers and/or multiple ultra-wideband antennas arranged in an array. The nodes110-136may also allow wireless or wired communication between the nodes110-136and/or communication with one or more control modules located within vehicle (e.g., ECU, HVAC system, security system) or external to the vehicle102. The control module may also include a processor and memory that is operable to receive, store, and transmit information between the control module and the nodes110-136. The control module may also be operable to control various systems (e.g., HVAC system) within the vehicle102based on the information received from the nodes110-136.

Nodes110-136may be operable as a transceiver for sending and receiving a UWB message. One or more of nodes110-136may periodically transmit (or blink) a UWB message. One or more nodes110-136may perform the UWB-based sensing of car states using the channel impulse response (CIR) computed by a given receiver. For instance,FIG.2illustrates node110transmitting a UWB message that may be received by node118. As illustrated, UWB message may be reflected at various points (shown by τ1-τ5) around the vehicle102.

Graph210illustrates the CIR that may be computed by node118based on the reflected UWB message. As shown, the CIR for τ1 may have the greatest amplitude and the least amount of time delay because it was not reflected at any point within vehicle102. Conversely, the CIR for τ5 may have one of the smallest amplitudes and the largest delays because it was reflected by a rear point (e.g., the trunk) of the vehicle102before being received by node118.

FIG.3illustrates an exemplary UWB packet (message) that may be transmitted by nodes110-136. The preamble of UWB packet may include a synchronization header that may be of a 64, 1024 or 4096 symbol-length known preamble sequence followed by an 8 or 64 symbol-length start of frame delimiter (SFD). A 19-bit physical header (PHR) may follow the SFD and include information for successful packet decoding such as the length and the data rate of the following data payload. The UWB symbol (Ts) may be comprised of multiple narrow pulses, and the pulses generated by the preamble may be used to compute the CIR when received by nodes110-136. The exemplary UWB symbol shown includes a chipping sequence 11, 0, 2, 31 and the four exemplary pulses illustrate a polarity of +1, −1, −1, and +1. The transmitted UWB symbol may be represented by the following equation:
s(t)=√{square root over (Ep)}*Σj=0Nf−1bjω(t−jTf−cjTc)  (Equation 1)

Where ω(t) denotes the UWB pulse of duration Tp; Tfmay be the duration of a given frame (i.e., a symbol that may be divided into Nfframes); bj∈{−1, +1} denotes the polarity code; cjdenotes the hopping sequence; Tcis the chip duration; and, Eprepresents the energy of the symbol. The hopping sequence cjmay also be part of a set {1,2 . . . Nh} where Nhis the number of hopping slots (i.e., the hopping code may determine the location of the pulse within the Nh, slots of the frame).

As described with respect toFIG.2, the UWB messages may travel wirelessly across multiple paths before being received by a given node. The signal received at a given node (e.g., node122) for a given UWB message that is reflected by any number of different paths may be represented by the following equation:
s(t)=√{square root over (Ep)}*Σj=0Nf−1Σl=1Lαlbjω(t−τl−jTf−cjTc)  (Equation 2)

Where α1and τ1refer to the complex attenuation and time of flight of the 1thpath. A UWB receiver included within nodes110-136may leverage the periodic auto-correlation property of the known preamble sequence illustrated byFIG.3to compute the CIR. In other words, the UWB receiver may correlate the received signal with the known preamble sequence to compute a channel impulse (CIR) which is represented by the following equation:
h(t)=Σl=1Lαkδ(t−τl)  Equation 3

Where δ(·) represents the Dirac delta function. Nodes110-136may use this computed CIR to identify a car state by leveraging the intuition that the different states of the car may affect the CIR differently.

FIG.4Ais a flow diagram400of the UWB system100operating during a calibration or initial setup process.FIG.4Bis a flow diagram430of the UWB system operating during a runtime process. The similarities and differences between the calibration process illustrated by flow diagram400and the runtime process illustrated by flow diagram430will be discussed below.

At steps410and432, UWB system100may select one of nodes110-136to operate as a transmitter. For flow diagram430, the link selection (i.e., node selection) may be predetermined based on the calibration process performed by flow diagram400.

With reference to flow diagram400, the transmitting node may be selected based on connectivity (UWB packet deliver rate) and the strength of the received packet and location of the nodes110-136. In other words, step410may determine which of the nodes110-136are operable to communicate with each other.

For instance,FIG.5Ais a graph500illustrating the connectivity matrix (i.e., blink delivery rate) of the nodes110-136. The vertical axis illustrates the transmission (TX) of a given UWB packet from the nodes110-136. The horizontal axis illustrates the reception (RX) of transmitted UWB packet at the nodes110-136. For the connectivity matrix illustrated, the UWB system100may have each node110-136send numerous blinks sequentially (e.g., 14,000 blinks), and the UWB system100may then compute the blink delivery rate at the nodes110-136. The connectivity matrix illustrates a “1” for a strong UWB packet delivery ratio and a “0” for a weak UWB packet delivery ratio. As shown, node124located in the rear of the vehicle102(e.g., the trunk) may have a low (weak) delivery ratio with respect to nodes126and128located at the front end of the vehicle102.

FIG.5Bis another graph510illustrating the average received signal strength (RSS) as determined at the nodes110-136. The vertical axis illustrates the transmission (TX) of a given UWB packet from the nodes110-136. The horizontal axis illustrates the reception (RX) of transmitted UWB packet at nodes110-136. It may also be understood that a less negative RSS number indicates a higher signal strength. As shown byFIG.5B, a UWB packet transmitted by node124located in the rear of vehicle102would have a low signal strength when received at nodes126or128. However, a signal transmitted by node112would have a high signal strength when received by node110.FIG.5Billustrates that nodes110-124(i.e., interior nodes) may have a high RSS because there is no significant attenuating object to impede the transmitted signal, such as the metal frame of vehicle102.FIG.5Balso illustrates that nodes126-136(i.e., exterior nodes) may have a lower RSS because the transmitted signal may be impeded by the metal frame of the vehicle102.

Using the data gathered from the connectivity test, the calibration process may establish node110as the transmitting node (i.e., tag) and the remaining nodes112-136as receivers (i.e., slave nodes). Node110may be selected as the transmitting node because: (1) node110can communicate with nodes112-136at a reasonably high power; and (2) node110may create a symmetric sensing region in the vehicle102.

During the runtime process (i.e.,FIG.4b), the UWB system100may store the results of the connectivity tests so that node110is selected as the transmitting node (i.e., tag) and the remaining nodes112-136are selected as receivers (i.e., slave nodes). Or the UWB system100may be preprogrammed so that node110is established as the transmitting node (i.e., tag) and the remaining nodes112-136are established as receivers (i.e., slave nodes).

Because UWB system100may use a single transmitting node (e.g., node110may be considered a “tag”) a total of N nodes (e.g., nodes110-136) may be available while N−1 links are treated as sensors (e.g., nodes112-136are sensors or “slaves” because node110is considered the “tag”). It is contemplated that the N internal nodes may be made transmitters, however, in a round-robin manner. UWB system100may then use N(N−1)/2 links as sensors. By operating in a round-robin manner, the average power draw may be reduced because operating nodes110-136as transmitters less current will be drawn than operating nodes112-136as receivers alone. Also, operating in a round-robin manner may also improve the robustness of UWB system100. Further, a subset of nodes can be made transmitters communicating with respective disjoint (mutually exclusive) subset of receivers, simultaneously with the transmitter on different frequency band.

At steps412and440, the resolution of the CIR computed by the receiver of nodes110-136may be increased by interpolating and upsampling in the frequency domain to aid in accurate alignment and feature extraction. Again, node110may transmit a blink that will have an associated CIR when received by nodes112-136. For a UWB bandwidth of 1 GHz, the CIR tap may be 1 nanosecond apart. The resolution of the CIR may be increased by an upsampling process where the UWB system100can make the received response emulate the original analog waveform. By performing an upsampling process the UWB system100may be able to more accurately align the CIR received by a given node (e.g., node110).FIG.6is a graph600illustrating how the upsampling process that may performed on a raw CIR waveform610to generate an upsampled CIR waveform612. During the calibration process (i.e.,FIG.4a), step412may undergo several iterations of testing to improve the upsampling algorithm employed. At step440, UWB system100will operate based on the predefined upsampling algorithm established during step412.

It is contemplated that the algorithm employed during the upsampling process (i.e., steps412and440) may include a Fast Fourier Transformation (FFT) algorithm that operates on the time-domain CIR y. The upsampling process (i.e., steps412and440) may also zero-pad the frequency domain signal by factor of N*(K−1), where N may be the number of taps in the CIR and K may be the upsampling factor. The upsampling process (i.e., steps412and440) may also obtain the upsampled CIR ŷ by taking the inverse-FFT of the upsampled frequency domain signal.

At steps414and442, a CIR alignment process may be employed to randomly shift the CIR. Steps414and442may align the CIRs received by nodes110-136by pivoting the first (direct) path at a reference tap. For instance,FIG.7Aillustrates five separate CIRs that may be computed when received for example by node110. As illustrated, the five CIRs are misaligned when computed by node110because there may not exist synchronization between the receiver of node110and the transmitter of node (e.g., node126) where the five CIRs had originated. It is therefore contemplated that CIR alignment may be necessary because the receiver of node110and transmitter of node126are not synchronized with respect to one another. The same misalignment may occur between the receiver and transmitter of all the nodes110-136. It is therefore contemplated that synchronization between nodes110-136may be accomplished using a reference clock, although this may not be desirable as extensive wiring throughout the vehicle102to connect the nodes110-136would be required.

It is contemplated that physical wiring may be avoided if the nodes110-136perform alignment by identifying an “event” that may typically occur in all CIRs independent of the environment. The UWB system100may then be able to shift the location (i.e., tap) of that “event” to a reference pivot tap. In other words, the CIR may be shifted differently with the degree of a CIR shift depending on the tap corresponding to the arrival of the first (direct) path. Once the UWB system100has performed shifting of the CIR, the first path of the CIR computed by a given node110-136may now occur at the pivot. It is contemplated that the first path may not be the strongest path and that nodes126,128,134, and136may be selected as the first path, but selection of nodes126,128,134, and136as the first path may require further attenuation.FIG.7Ashows how the five CIRs received by node110may be aligned after the shifting process has occurred.

It is contemplated that the tap location may correspond to the arrival of the first-path (i.e., First Path Index (FPidx)). This first-path alignment may benefit from upsampling as the FPidxmay occur at a much finer resolution of (e.g., 15.625 picoseconds) as compared to the raw CIR tap resolution (e.g., 1 nanosecond). The “lag” between any two CIRs received by a node (e.g., node110) may correspond to the difference between their first-path indices. The UWB system100may obtain the aligned CIR ŷt(t) using the following equation:
ŷt(t)=ŷt(t+Δ)  Equation 4

Where t refers to the tap and Δ=F*Pidx−Pivot.

With reference to flow diagram400, the calibration process may include step416where a set of potential receivers for nodes110-136may be narrowed to those nodes that are more robust to location changes. In other words, upon aligning the CIRs received by a given node (e.g., node110) with respect to the other nodes (e.g., nodes112-136), the calibration process may be used to determine which if any other node may also operate as a receiver. For instance,FIG.8illustrates a graph800where vehicle102was parked at four different locations. In this example, vehicle102was parked at two different locations within an indoor garage, an outdoor location with another vehicle parked next to vehicle102, and an outdoor location with no obstructions located next to vehicle102. In this example, the calibration process may be used to select node110for transmission and nodes112-136for reception. As shown byFIG.8, the calibration process may be used to establish that nodes110-124(internal nodes) have a much higher correlation than nodes126-136(external nodes). In this example, the calibration process would have been used to establish that the internal nodes be more robust to location changes than the external nodes of vehicle102. It is contemplated that if nodes110-136are placed in a different location or a different size/type vehicle is used, the calibration process may establish using a different set of nodes for reception and transmission.

With reference to step416, the average Pearson correlation coefficient may be computed between the CIRs located in free-space and the remaining locations for nodes112-124(i.e., internal nodes) and nodes126-136(i.e., external nodes). The correlation coefficient R between two CIRs x and y of duration t taps may be determined using the following equation:

Rx⁢y=∑i=1t⁢(xi-x)¯⁢(yi-y)¯∑i=1t⁢(xi-x)¯2*∑i=1t⁢(yi-y)_2(Equation⁢5)

Where xi, yirefer to the CIR amplitude in the ithtap of CIRs x and y respectively, and x, y refer to the sample mean of two CIRs.

Once UWB system100has selected the transceivers of interest and converted the CIRs to a desired format, UWB system100may identify a state of interest for vehicle102. UWB system100may determine changes in a vehicle state (e.g., door open, window open, trunk open, or person inside) alter the multi-path reflections inside the vehicle102, which may then be observed in the CIR. For instance, opening the driver-side door of vehicle102may eliminate (or create) reflections that may not exist if the door is closed. UWB system100may detect these changes in reflections to infer the vehicle state. UWB system100may perform this state inference using the combination of Steps418and420.

At steps418and444, the K-most likely states may be identified by correlating the CIRs observed by the nodes with a reference corpus. During the calibration process, step418may include a training phase where a training dataset of CIRs may be applied to improve the decision algorithm. During the runtime process, step444would not be provided a training dataset but would instead operate on received CIR. Having pruned the state space, steps420and446may be used to extract features from the CIR (i.e., to generate a multipath profile) that may then be used to identify a given vehicle state.

At steps418and446, the algorithm may infer that some states may be captured better by some nodes (e.g., node110) while other nodes (e.g., nodes112-136) may infer a CIR equivalent to “empty.” The algorithm may be operable to allow the nodes110-126to vote on a state-based correlation to an observed CIR (by the nodes) due to a transmitted blink. The algorithm may also use the results of the vote to compute the likelihood of being in the of the possible states. The algorithm employed by steps418and446may also fuse (e.g., using a vector summing function) the likelihood estimates from all the nodes, to obtain the top-K most likely states. It is contemplated that as part of step418the algorithm may be refined using a training phase and testing phase. Step446may then employ the algorithm refined by step418.

FIG.9illustrates a training phase910and testing phase912that may be employed by step418. It is contemplated that the training phase910may be used during the calibration process illustrated by flow diagram400. The testing phase912, however, may be employed during the calibration process illustrated by flow diagram400and the runtime process illustrated by flow diagram430.

During the training phase910, UWB system100may set R={R110, R112, . . . ,Rn}. This vector may be higher or lower depending on the number (n) nodes deployed within the vehicle102. For instance, step418may establish n as correlating to the final node (i.e., R136) employed within vehicle102.

Step418may also establish a vehicle state of interest set (S). This vehicle state of interest set may be represented as S={S1, S2, . . . , Sn). For instance, the set may include a vehicle empty (S1), front door open (S2), front window open (S3), rear door open (S4), rear window open (S5), trunk open (S6), a person situated in the front seat (S7), or a person situated in the rear seat (S8). The vehicle state of interest set is not limited to these examples, and the set may include more or less states depending on the application.

As shown byFIG.9, the training CIRs920(e.g., CIRSS′1, CIRSS′2, CIRSS′m) for the simulated car state may then be provided. For instance, CIRS1′1may correlate to a vehicle empty state S1. Step418may proceed to operate on the CIR of state Sx(Sx∈S) for the receiver nodes Rn. As shown, step418may also include a set of corpus reference CIRs922for the states by the nodes110-136. The set of corpus reference CIRs922may be represented as Ci={Cis1, CiS2, . . . , CiSs} where i<n. Step418May Correlate a Given CIR with Other CIRs in its Corpus (Ci) and compute the mean correlation with the state. For instance, corpus CIRS1may correlate to the state where the front door of the vehicle102is open.

Step418may further operate to generate a likelihood matrix LMi(where i<n), of dimensions s×s for each of the n nodes (i.e., for nodes110-136). Step418may be operable to select and store a state of maximum correlation (Smaxx). Step418may repeat training phase910to generate different CIRs of state Sxby the node Ri, resulting in a maximum likelihood vector924(Mx=[Smax1,Smax2, . . . , Smaxx,m]). Step418may also compute a row of likelihood matrix which may be represented as follows:

Sy∈S,where⁢P⁡(Sx|Sy)=#⁢of⁢occurrencess⁢of⁢Sy⁢in⁢Mxm

Step418may be operable to repeat training phase910for each state and for every node110-136included within vehicle102. Upon completing training phase910, a likelihood matrix having a dimension of s×s is generated.

Once the training phase910is complete, step418may then employ a testing phase912to evaluate the machine learning algorithm established during training phase910. It is contemplated that during step418, the testing phase912may be employed to allow further modifications to the algorithm. For instance, step418may provide simulated CIRs to evaluate and further improve the machine learning algorithm. During the runtime process (i.e.,FIG.4B) step446may also employ the testing phase912, however, a receiving node (e.g., node110) will operate on CIRs received by the other nodes (e.g., nodes112-136).

It is therefore contemplated that the testing phase912process may be employed by step418or step446. It is contemplated that node110may first receive either simulated CIRs (i.e., step418) or node110may receive CIRs from one or more of the other nodes112-136located within vehicle102. The received CIRs may then be correlated with the corpus reference CIRs922. A state of maximum correlation (Smax) is then selected using the corpus reference CIRs922. A first likelihood vector926may then be generated using a column from the maximum likelihood matrix924.

Steps418and446will then repeat the testing phase912process for the nodes (as shown by box916) included within vehicle102to generate a second likelihood vector928. Once the testing phase912has been completed for each node, steps418and446will fuse (shown by vector summation930) all the generated likelihood vectors together (i.e., first likelihood vector926and second likelihood vector928) to generate a top-K vector932. It is contemplated that the top-K vector932may be the probability value of being in each state according to every node summed.

It is also contemplated that the machine learning algorithms established during the training phase910and testing phase912may be provided to a remote storage system (e.g., cloud storage). The machine learning algorithms may then be provided to other vehicles to perform vehicle state identification. It is also contemplated that vehicle102may likewise access and download machine learning algorithms stored on a remote storage system.

Steps420and448may then operate to extract features from the received CIRs of each node110-136. It is contemplated that a multipath profile may be generated at steps420and448to identify a car state from the K-shortlisted states. Steps420and448may be operable to identify a car state because a given CIR may be representative of how the environment impacts the transmitted signal. Steps420and448may be operable to determine that the CIR peaks look different when the state of the car changes because the CIR peaks represent the reflections from the environment. Steps420and448may then perform a peak-driven feature extraction to build a multi-path profile.

It is contemplated that steps420and448may operate to determine the peak-based features based on the position and amplitude of the CIRs. For instance,FIG.10illustrates a CIR that may be part of the Top-K vector932generated in step418. Steps420and448may operate to determine the peak-based features for CIR by calculating: (1) the ratio of the power (amplitude) of the first peak; (2) the ratio of the power of the top peaks; (3) relative tap distance between the first peaks; (4) relative tap distance between the top peaks; (5) power of the maximum valued peak (Pmax); and (6) position of the maximum valued peak (pos(Pmax)).

Steps420and448may operate to determine the ratio of amplitude power as follows: (P1/P2,P1/P3, . . . ,P1/Pk), where Pk′ refers to the kthpeak ordered by power. The ratio of power of the top peaks may also be determined as follows: (P2′/P1′, P3′/P1′, . . . Pk′/P1′), where Pk′ refers to the kthpeak ordered by power. Steps420and448may also be operable to determine the relative tap distance between the first peaks as follows: (pos(P2)−pos(P1), pos(P3)−pos(P1), . . . ,pos(Pk−pos(P1)), where pos(Pk) refers to the tap of the kthpeak ordered by location. The relative tap distance between the top peaks may be determined as follows: (pos(P′p) pos(P1)), where pos(P′k) refers to the tap of the kthpeak sorted by power.

During the training phase910, step418may reduce the correlation values obtained when a given CIR was correlated with the corpus to a single max value. Steps420and448may then be operable such that these correlation values may be used as features. Steps420and448may be operable to set ci1, ci2, . . . ,cisas the mean correlation value obtained by node Riby correlating the test CIR with elements of the corpus cis1, cis2, ciss. Since the correlation values may not be in the same scale, UWB system100may compute a relative correlation vector as follows: [ci1−ci1, ci2−ci1, . . . cis−ci1]. The computed relative correlation vector may be a measure of change relative to a reference state. In other words, the relative correlation vector may be the empty state. Steps420and448may then be operable to compute the features for each node110-136within the vehicle102to generate the multi-path profile.

Steps422and452may then use the multi-path profile to determine (i.e., predict) a vehicle state. It is contemplated that the resulting element feature vector may be further processed using a known classification algorithm (e.g., the Random Forest Classifier) to identify the state of the vehicle from the K short-listed states. It should be noted that a given RF signal (i.e., CIR) may be reflected by numerous types of mobile activity (e.g. by humans, robots, animals) and immobile objects situated in the environment (e.g., a house, vehicle, street). UWB system100may be operable to receive the reflected signals using nodes110-136and infer a given vehicle state.

FIG.11illustrates several examples of how the UWB system100may determine a vehicle state using the process illustrated by flow diagram400and430. For instance,FIG.11Aillustrates node110,130, and134may transmit and receive CIR. In this example, UWB system100may determine that vehicle102is operating in an initial static state where no obstructions exists because the CIR is operating under a line of sight (LOS) condition. In other words, UWB system100may determine that the CIR response times received by nodes110,130, and134indicate there is no obstruction.

In the example illustrated byFIG.11B, nodes,110,130, and134are again transmitting and receiving CIR. In this example, however, an obstruction (e.g., a driver) may be situated in the front-driver seat. UWB system100may determine based on the reflected CIR1212and1214that there is a person situated in the front driver seat.

In the example illustrated byFIGS.11C, nodes,110,130, and134are again transmitting and receiving CIR. In this example, UWB system100may determine that there is an obstacle1214located near vehicle102based on the CIR data.

FIG.12is a graph1200illustrating the root-mean-square energy for CIR that may be transmitted by node110and received by node134during various operating states.FIG.12again illustrates examples of how the UWB system100may determine a vehicle state using the process illustrated by flow diagram400and430.

With reference toFIG.12, CIR between 0-1,000 may be used by UWB system100to determine the vehicle102is operating in a static state with no obstructions. For CIR between 1,000-2,000, the UWB system100may determine a person may be located approximately 5 centimeters outside the vehicle102. For CIR between 2,500-3,500, the UWB system100may determine a person may be located approximately 100 centimeters outside the vehicle102. For CIR between 3,500-4,500, the UWB system100may determine a front door of the vehicle102is open. And for CIR between 4,500-5,500, the UWB system100may determine a person is situated within the vehicle102.

The processes, methods, or algorithms disclosed herein can be deliverable to/implemented by a processing device, controller, or computer, which can include any existing programmable electronic control unit or dedicated electronic control unit. Similarly, the processes, methods, or algorithms can be stored as data, logic, and instructions executable by a controller or computer in many forms including, but not limited to, information permanently stored on non-writable storage media such as ROM devices and information alterably stored on writeable storage media such as floppy disks, magnetic tapes, CDs, RAM devices, and other magnetic and optical media. The processes, methods, or algorithms can also be implemented in a software executable object. Alternatively, the processes, methods, or algorithms can be embodied in whole or in part using suitable hardware components, such as Application Specific Integrated Circuits (ASICs), Field-Programmable Gate Arrays (FPGAs), state machines, controllers or other hardware components or devices, or a combination of hardware, software and firmware components.

While exemplary embodiments are described above, it is not intended that these embodiments describe all possible forms of the invention. Rather, the words used in the specification are words of description rather than limitation, and it is understood that various changes may be made without departing from the spirit and scope of the invention. Additionally, the features of various implementing embodiments may be combined to form further embodiments of the invention.