SPARSE MIMO PHASED ARRAY IMAGING RADAR

High-performance 4-D Sparse MEMO Phased Array imaging and object detection radars with substantially reduced hardware and processing specifications are presented for automotive, ariel, and other application spaces. The radar antennas have 2-D angular sparse array and MIMO (Multiple Input and Multiple Output) features that can be implemented with a variety of subarrays or Antenna in Packages (AiPs) greatly simplifying the system manufacturing and feasibility. The significantly reduced data processing requirements also become feasible with the sparse subarray architectures. Advanced signal processing algorithms are presented, when coupled with the sparse and MIMO features, allow improved 2-D angular resolution of objects, improved imaging, and low sidelobes allowing the resolution of weaker targets in the presence of stronger target reflections.

BACKGROUND

Imaging radar systems for automotive applications have many challenging and often conflicting requirements. Consequently, these applications result in difficult choices between high resolution, long range, fast update rates, low hardware complexity, size dimensions, low power, and cost. Phased arrays for example can meet long ranges by focusing the energy into pencil beams but has limitations in covering the desired field of view (FoV or FOV) in a reasonable time. Radars relying on multiple input-multiple output (MIMO) techniques have improved angular resolution and fast update rates, however since the transmit antenna radiates power over a wide FoV, the detection range of small RCS targets such as pedestrians is limited. For current radar technology, improved angular resolution of targets requires increased aperture size which leads to increased hardware complexity cost, processing, and power consumption.

DETAILED DESCRIPTION

The present invention provides methods and apparatuses for imaging and object detection radars having subarray antenna arrays arranged in a sparse array and MIMO configurations that improves sensor performance. This class of radars in the following is referred to as a Sparse MIMO Phased Array (SMPA) radar. To realize the object detection and imaging capabilities with the sparse array features, the invention implements advanced algorithms, such as detailed below.

As presented herein, a high performance four-dimensional (4-D) imaging and object detection radars are achieved that minimize requirement trade-offs using an SMPA radar. Hardware and processing complexity are reduced using sparse array features. Subarrays realized with antenna in packages (AIPs) reduce the manufacturing complexity and achieve longer ranges for small RCS targets and the MIMO features improve the 2-D angular resolution and accuracy with a single snapshot of data when coupled with the advanced signal processing that is presented. Angular resolution refers to the ability of the radar to distinguish and separate two targets at a same range same radial velocity relative to the radar. Accuracy refers to conformance of the radar measurements to the physical position, velocity and so forth.

For automotive applications, for example, one desires fast object detection and accurate understanding of the field of view (FoV) to enable real time decision-making, such as braking or taking avoidance action. Examples provided herein are primarily directed to a radar system implementation but are applicable in a variety of applications, scenarios and uses. As a non-automotive application of this invention, an aerial application, such as for drone and unmanned aircraft, landing requires an accurate knowledge of an expanded field of view. The present invention provides this expanded ariel view while reducing the hardware and weight of the antenna and radar unit.

While in the following, we will primarily be referring to radar designs with collocated transmit and receive antennas employing Frequency Modulated Continuous-Wave (FMCW) waveforms, the invention is not limited to collocated antennas or FMCW waveforms. Pulse waveforms, LFM waveforms and others in a collocated, a transmit-receive, or a bistatic architecture are applicable given the proper hardware and processing support. In addition, the following invention is applicable to any MIMO waveforms (e.g. Time Division Multiple Access (TDMA), Doppler Division Multiple Access (DDMA), Code Division Multiple Access (CDMA), Frequency Division Multiple Access (FDMA), Orthogonal-FDMA (OFDMA), and others) that provides sufficient orthogonality between transmit antenna signals.

The examples provided herein of the present inventions are constructed with antenna subarrays. For transmit and receive subarrays, phase shifters at the appropriate elements allow steering of the subarrays and the antenna beams. For the receive subarray, low noise amplifiers (LNA) improve the noise figure of the radar system. For the transmit (Tx) subarray, power amplifiers (PAS) at the appropriate elements improve the effective radiated power (EIRP). Transmit/Receive subarray architectures would of course include both LNAs and PAs as well as the capability to switch between transmit and receive signal paths. A desirable way to realize the subarrays is through packaged structures, referred to as an Antenna in Package (AIP), which position the antenna elements in close proximity to the active elements, namely, LNAs, PAs, and the phase shifters. The benefits of the AiP include lower transmission line loses due to the proximity and simplified manufacturing of the SMPA which consists of multiple AiPs as shown in the examples below. Note that while the active elements provided added capability, they are not necessarily required in the AiPs or subarrays depending on the SMPA design goals.

FIG.1illustrates a part of a radar system packaged in an antenna in package (AIP) structure100, according to example embodiments of the present inventions and subject technology. Examples of AIP structures are illustrated having various formations for various applications, size specifications, performance specifications and/or manufacture considerations. Structure100provides an antenna element structure104on a substrate102, wherein multiple individual antenna element structures form an antenna array structure. Structure120includes an antenna array124and a phase shifter integrated circuit (IC)126on a substrate122. The AIP structure120is encapsulated as a single unit or component for use in a radar or other electromagnetic device. The AIP120integrates radiating antenna elements, an antenna controller and transceiver into a single unit. In a top portion of the AIP120,124the phased-array antenna and126the antenna control components enclosed in a package. The controller for beamsteering the antenna array124is phase shifter circuit126. In the present embodiment, the phase shifter circuit126is an analog circuit of components, including variable gain amplifier and low noise amplifier, wherein the components are controlled to affect change in the beam direction from the antenna array124.

Also illustrated inFIG.1is AIP110, having multiple radiating elements forming an antenna array112configured in a (4×4) square and positioned on an upper surface of the AIP110. The spacing between radiating elements112is as given in the dimensions of structure100, d1in a first dimension and d2in a second dimension. The device110is an AIP having connectors on the side opposite the antenna elements112, which are in the form of a ball grid array114. The phase shifting circuit (not shown) is within the AIP110coupled to the connectors and the antenna array112.

Device130is similar to device110and is illustrated from a side perspective view, having a flip chip134with antenna array150on a top side and phase shifting circuits (not shown) coupled to the antenna array150and the connectors152. The flip chip134is enclosed in a package having substrates142and cover136. There are a variety of configurations and structures for AIP designs.

FIG.2illustrates various antenna arrays and subarrays, according to example embodiments of the present inventions and subject technology, specifically examples of various configurations of radiating elements forming AiPs/subarrays that can be used to realize an SMPA. AIPs or other package configurations may be used for the SMPA. In a first example, the AIP200includes 16 elements, with each element202positioned proximate to other elements. The Tx or receive (Rx) AiP200configures radiating or signal receiving elements202into the shape of a square. The Tx AiP200has a radiating or transmitting functionality when it is supplied with radio Frequency (RF) electromagnetic power from an active element, such as a transceiver or PA, which may be further amplified by PAs associated with the AiP before radiating from the elements202. The Rx AiP200has a receiving functionality for RF signals received by the AiP elements, which are then amplified by active elements such as an LNA allowing further signal processing. The AiP200, which may be an Rx or Tx AiP, is referred to as a (4×4) Tx or Rx subarray based on the number of elements in each dimension and the transmit or receive functionality of the AiP. The dimensions of the AiP defines the aperture of the antenna array of the AiP. For example, consider AiP210, which has a larger aperture made up of more elements; specifically, AiP210is made up of multiple (4×4) AiPs configured in a larger square and is referred to as an (8×8) AiP. The AiP210has elements212and subarrays214, which may also be referred to as sub-blocks. Another example of an AiP is a (16×8) array220made up of eight subarrays224having elements222. The subarray224is a (1×16) array including elements222. Each configuration and subarray definition determine the aperture and behavior of the antenna array. Similarly, subarrays within AiP220or AiP210may be defined in different shapes and configurations according to application, such as (2×16) and so forth. The difference in the various configurations and layouts result in different antenna apertures and behavior. In these various configurations, AiPs are configured in uniform patterns, such as rectangular. The present inventions have a reduced number of elements in the arrays, some arrays have uniform patterns and others are non-uniform.

WhileFIG.2provides specific configuration examples, once designed the subarray or blocks may be arranged in various configurations. The AiP provide a modular solution of building blocks that may be placed together to operate as an antenna. This modular approach extends to the phase shifting components as well, which may be configured to accommodate any of a variety of sizes. The flexibility of these designs provides expanded applications with small modification.

For most of the examples, (4×4) subarrays or AiPs are given as examples of basic units of an antenna array, however the invention is not limited to this size and could be smaller, such as smaller arrays of (2×2) or (3×3), or larger arrays, of (5×5) or (8×8) and may be rectangular, such as a. (1×8) or may be arranged in various configurations arranged with spacing to achieve a desired result, depending on the radar/antenna design goals and desired illumination of the FoV. The signal processing to achieve the high 2-D angular resolution discussed below, depends primarily on the phase center locations of the AiPs, where the phase center is defined as the apparent source point of radiation. The AiP size determines the subarray gain and the instantaneous coverage of the FoV. For example, a first (4×4) receive AiP with first phase centers and has a 3 dB beamwidth of ˜25 deg, may be organized as a (2×2) AiP with the same phase centers (or just activating a (2×2) part of the (4×4) AiP) would double the azimuth and elevation beamwidth. The advantage of the smaller size would be that fewer beam steers are required to cover the FoV and improve scan time at the expensive of the subarray antenna gain. Also, while λ/2 spacing is generally assumed between the AiPs elements, other spacings could be used, where A is the wavelength of transmission.

The example SMPA radar designs typically employ eight (8) physical receive AiPs, such as AiP200, and two (2) or three (3) physical transmit AiPs, such as AiP200, or subarrays. Additional or fewer receive and transmit AiPs could be used to obtain the desired angular resolution through aperture size or the desired sensitivity through EIRP or antenna gain. There are a variety of configurations and organizations to achieve desired results.

FIG.3illustrates a progression from a full array of elements to a sparse array configuration, then to positioning subarrays and finally to a virtual SMPA with MIMO capability, according to example embodiments of the present inventions and subject technology. The present imaging and object detection radar invention can be thought of as evolving or as a progression of steps from a full phased-array,300, with (32×32) elements302as inFIG.3, which is the receive antenna of a radar system. With over 1000 elements, such as element302, in the full array300(having physical dimensions z1×y1) having a corresponding aperture. Within the full receive array300, each element302has a transmission path including phase shifters and low noise amplifiers along the digital channel, and this requires a substantial amount of components and complexity, and therefore, such a system is not always feasible, cost effective or optimum. In addition, the computational requirements for ˜1000data channels incur substantial computational burden and may not be reasonably feasible for real-time operation. To simplify the full size receive array300, the design may include subarrays having a reduced capability and performance; however, even with such reduced performance, the complexity is still quite high for a full (32×32) or other large receive array. The (32×32) full receive array300in the present applications may be designed to radiate a pencil beam where numerous beam steers are used to cover the FoV corresponding to a low scan rate and/or high computational requirements. A reduction in the number of elements and/or subarrays results in reduced number of beam steering elements for each element and/or subarray. To provide the same radar behavior, angular resolution, and accuracy, while reducing the complexity and computation of the radar, the present inventions provide a virtual array using sparse array techniques.

The present inventions provide a sparse array to replicate the operation of the full receive array300. The process identifies locations312within a same physical dimension as in full receive array300. A radiating element302is positioned at each location312forming a subset configuration314. This is a sparse receive array310which avoids many of the phase-lag redundancies present in the full size receive array300. By using a sparse array configuration, such as sparse receive array310, the number of elements is reduced from 1000 elements, as in the full receive array300, to 8 elements in the sparse receive array310with corresponding reductions in the beam shift components, such as phase shifters, LNAs, reductions in digital channels and reductions in processing complexity. The sparse signals may be reconstructed from the 8 digital channels using Compressive Sensing, Iterative Adaptive Analysis (IAA) and other high-resolution algorithms presented hereinbelow. SMPA design goals consider the choice elements302to include in the 2-D sparse array310to obtain a faithful reconstruction to cover the FoV. The chosen elements302are represented as elements302′ and represent phase center locations for placement of subarrays or blocks. The sparse receive array310requires the presence of the signal phase lags between the proximate elements302′ similar to the signal phase-lags in the full receive array300between the elements302. The distance between elements302are y2and z2. The full receive array300has substantially more redundancy in the signal phase-lags which may be avoided to achieve acceptable 2-D angular resolution performance and side lobe level (SLL) performance with the sparse receive array310. Note, the sparse array process is applied to the receive array for clarity of understanding.

To further improve the signal power, the present inventions design a sparse receive array320by positioning (4×4) AiPs/subarrays322at the phase centers identified by elements302′ of the sparse receive array310. For example, at location312of sparse receive array310, a (4×4) subarray312′ is positioned. The receive AiP312′ couples the LNA signal outputs at each patch element322into a single output signal at the phase center of subarray312′ corresponding to location312of an element302′. The receive beams of the receive antenna array320includes 8 subarrays322organized at locations as the phase centers in sparse receive array310. The subarrays322are steered to a desired pointing direction to realize the improved antenna gain. Steering in the present inventions may be implemented with phase shifters in an AiP. Additional subarray beam steers may be used to cover the entire FoV. There are a variety of scanning and beam steering patterns that may be used to scan an entire 3-D FoV.

Another method to improve performance is to design MIMO capability to the sparse receive array320to enhance the 2-D angular resolution. As MIMO techniques provide signals from each of multiple transmit antenna arrays having discernable waveforms, the receive antenna array aperture is effectively expanded from the physical receive array320to include additional virtual arrays such as334according to the number of transmit antenna arrays. By appropriate placement of multiple transmit antennas (not shown), the increased aperture of a virtual SMPA330is obtained. Extension of the sparse array signal processing achieves the improved 2-D angular target resolution and accuracy. In this case, an additional Tx AiP (not shown) was placed to duplicate each physical receive AiPs322, in subset configuration324that effectively map to subset334. A subarray312′ is located at a position of element312, having 16 elements arranged in a (4×4) square, and the phase center326is located at the phase center of element312. From the transmitter, transmit signaling from multiple physical transmit arrays are provided with signaling differences, such as time, frequency or phase shifts, to identify the Tx AiP origin of each received signal at the SMPA330. Signals from the multiple transmit arrays are received at the subarrays322′ as if also received at a virtual subset334, and so forth. Implementing the MIMO process to allow corresponding virtual receive arrays334resulting in an effective doubling of the 8 physical receive arrays322with8receive virtual arrays334to form the larger virtual array, SMPA330. The phase centers of each of the arrays322are the same in the virtual arrays322′. As illustrated, the effective realizable size or aperture of SMPA330is (z1×2y2).

FIG.4illustrates an example of an SMPA radar virtual array configuration, according to example embodiments of the present inventions and subject technology, having expanded receive aperture of an SMPA physical receive402having 8 subarrays406configured in a sparse array format. SMPA402is positioned proximate to a transmit array404. Within the SMPA transmit array404are N transmit arrays408, which in the present inventions are Tx AiPs408. The SMPA radar physical arrays are illustrated as SMPA receive array402and the SMPA transmit array404, having transmit subarrays T1, where i=1, 2, . . . . N. This configuration behaves as an SMPA virtual array410having a larger aperture than the SMPA physical receive402, as illustrated where in addition there are (N−1) virtual arrays, identified as VR1, . . . . VRN−1. In this embodiment, the SMPA physical receive array402is a configuration of AiPs or subarrays406in a distributed manner. Rather than forming a geometric shape, such as a rectangle in theFIG.4examples, the receive array402has subarrays406are arranged as a stairstep or diagonal pattern. There are a variety of arrangements of receive arrays402and transmit AiPs408that may be implemented.

To realize the increased virtual sparse array aperture of the present examples, each Tx AiP408transmits an orthogonal MIMO waveform such as DDMA, TDMA, and CDMA. With these waveforms, the system may separate the target signal reflections resulting from each MIMO AiP transmitter408providing additional spatial information. With the known positions of the transmit and receive arrays, which in these examples are AiPs but may be implemented in other forms, the relative phase centers of each transmit and receive pair are arranged to form the SMPA virtual array410. With the increased aperture of the SMPA virtual array410, better angular resolution may be achieved.

Note that an SPMA radar as inFIG.4can be configured with the sparse array features of the receive antenna and a single transmitter T1. In this case, which may be advantageous for some applications, the MIMO virtual array features will not be present which would not increase the virtual aperture size. The native angular resolution of the physical receive antenna with low SLL can be achieved provided the advanced signal processing techniques for the sparse array signals detailed for this invention below are implemented.

Furthermore, an application of the antenna configurations of this invention may only involve the sparse array features of the receive antenna with no transmitters. In this case, the application functions as a receiver module capable of determining the AoA of signal sources external to the receive module. The angular resolution of the physical receive antenna can be achieved provided the advanced signal processing techniques for the sparse array signals detailed for this invention below are implemented.

For most of the SMPA radar designs discussed above and below, the receive subarrays406may be thought of as having a signal input from the patch elements in the AiP or subarray. These signals are combined after a phase shifter and LNA into a single output at the AiP phase center. For a (4×4) receive AiP, there are 16 signal inputs from each patch that are eventually combined into a single output. A receive AiP design could also have multiple outputs along with the multiple inputs, not necessarily one input per patch or radiating element, in an SMPA radar design. For the transmit AiPs408of the SMPA designs discussed, the situation is essentially reversed with one input signal that is split into multiple signal outputs that are each amplified and phase shifted with a PA and phase shifter, respectively, before being radiated at each AiP element. Here again the transmit AiP408for an SMPA radar design is not limited to a single input and the multiple outputs may not have a one-one correspondence to each transmit AiP element.

In designing SMPA radar systems, the configuration of AiPs or subarrays may be linear or non-linear, they may be in a single plane or in multiple planes, they may be equally spaced or variously spaced, they may be symmetric, uniform, non-uniform, or geometric in layout. The goal is to achieve the desired results of focus and control of the radar beam, increased FOV, reduced scan time, reduced processing time and reduced hardware, weight, and costs. The system may position receive antenna arrays in various configurations to focus the beam, expand the FOV, avoid redundancy, reduce data processing, and improve the accuracy of the radar system.

The AiPs or subarrays may be implemented in a radar unit, which is designed for imaging or to detect objects in the FOV of the vehicle. The receive and transmit AiPs are sized and positioned to meet specifications for the desired applications. In some examples of automotive or ariel applications, the radar unit is a front facing unit having a broad FOV in front of the vehicle. In other examples, the radar unit is positioned on a corner of the vehicle and requires a narrower FOV. The goal is to provide as much coverage for a vehicle as realizable with AiP configurations.

Before presenting the design steps for the 2D sparse array aspects of the invention, sparse linear arrays (SLAs) and uniform linear arrays (ULAs) are briefly compared inFIGS.5A,5B, and5Cto provide context for the SMPA radars.FIGS.5A,5B and5Cillustrate example arrays formed to reduce redundancy of radiating elements and to reduce the computational burden. The array designs based on compact uniform linear array (ULA) and minimum redundancy array (MRA) sparse array features as well as the associated array responses.

A ULA is a set of sensor elements equally spaced along a straight line and the design may be used to improve signal-to-noise ratio (SNR) of the transmitted signal and gain in a given direction. SLAs have non-uniform spacing between elements and are used to reduce the phase-lag redundancy between array elements and to reduce the computational burden. The array designs compared inFIG.5are based on a compact uniform linear array (ULA) and a Minimum Redundancy Array (MRA) sparse array with the same aperture size as the ULA. The compact array504is a ULA with half-wavelength (λ/2) spacing between each element508. The MRA502has element506spaced at non-uniform spacings between elements. The compact ULA504with λ/2 spacing between 7 elements508is shown along with MRA502with 4 elements506. While for a compact array λ/2 spacing may be chosen to satisfy the Nyquist sampling criteria, the MRA has only one pair of elements506with λ/2 spacing while the remaining pairs have larger multiples of λ/2. The difference co-array is determined by the spacings between all element pairs in the array. The difference co-array510corresponding to the compact ULA and difference co-array512corresponding to the MRA each have spacings between elements with maximum phase lags between elements of +3λ with no missing half-wavelength spacing differences or phase lags. Without missing phase lags and the same maximum phase lag, the MRA502can have a comparable angular resolution to the compact array504. For the compact difference co-array510, there are multiple phase lags that are the same while the MRA difference co-array512minimizes the redundancy of phase lags except for the zero-phase lag. Consequently, from the point of the corresponding difference co-arrays510, the compact array504contains a large amount of signal sampling redundancy that is not present in the MRA504.

As motivation for the use of sparse arrays for the SPMA radar, the array responses520of the compact array and MRA beams are shown when they are both steered to 0 degrees. The MRA pattern522has a comparable beamwidth to the compact array pattern524implying comparable angular resolution as should be expected for arrays with the same aperture length. Consequently, the sparse array features of an SPMA radar should not limit the achievable angular resolution.

Further inspection of the compact array and MRA array responses520indicates that while the beamwidths are comparable the SLL of the MRA pattern522are significantly poorer than the compact array pattern524. These poorer SLLs of the MRA, however, does not imply that a SMPA radar will also have poor SLL. With the appropriate signal processing as is shown below, the SPMA radar can achieve good sidelobe performance.

Another important factor that impacts sidelobe level perform is the amount of phase-lag redundancy present in the array. To increase the phase-lag redundancy and lower the SLL of an array aperture, one also considers non-redundant arrays (NRAs) which are similar to MRAs. The difference being that the NRAs may have redundancies at non-zero phase lags. Generally, an NRA attempts to approach the ideal MRA with a minimal number of phase lag redundancies while reducing the number of array elements compared to a compact array. NRAs would also be expected to achieve a better SLL than the MRA given the additional phase lag redundancies. Improving SLL by adding redundant phase lags will be an important consideration for the MIMO aspects of the SMPA radar design.

FIG.6illustrates a process for designing an SPMA radar as inFIG.4and in following examples with various MIMO aspects for 2-D Angle of Arrival (AoA) target location detection, according to example embodiments of the present inventions and subject technology. The flow chart600shows the steps for generating, and in some examples, optimizing the sparse array and incorporating MIMO features of an SMPA radar design with 2-D Angle of Arrival (AoA) capability. The radar system implementing an SMPA receive and transmit antenna is designed to scan a FoV having a desired area or volume. The specifications of the SPMA receive and transmit antenna are a function of the FoV dimensions and desired angular resolution. The process600includes selecting an aperture size, a subarray size, and configuration of subarrays and AiP structure. The following description is done by a first dimension and then another. In this example, the process starts with the y-dimension, although one could exchange the coordinates and start with the z-dimension first. First, the process selects the type of subarray,602, to cover the FoV as discussed forFIG.2. Selection of the subarray type and AiP correspond to the aperture size. The next step604involves choosing an NRA or other sparse array configuration for the physical AiPs or subarrays in the y-dimension and positioning the subarrays to have the desired phase centers. This step places the phase centers in a sparse array ULA configuration along y-dimension. At this point, the subarrays may physically overlap. The physical overlap of AiPs is removed in the next step606where an NRA is selected in the orthogonal z-dimension while the initial y-dimension of the phase centers remain unchanged. This process moves the subarray phase centers in the orthogonal dimension such that subarrays are non-overlapping and are in a sparse array configuration. The goal is to position the elements of the receive antenna array to have a variety of phase lags or shifts with minimal redundancy. The process determines if there are redundancies or gaps in the phase-lags as is discussed in later examples of SMPA radars. For the next step608, if there is sufficient redundancy, minimal phase-lag gaps and if desired MIMO features were added in612, processing stops and the SMPA is complete; else the process adjusts the subarray phase-center locations,610, which are further optimized for aperture size, phase-lag redundancy, and to minimize the phase-lag gaps.

Continuing with process600, when the MIMO configuration for the SMPA is being included, step612, the N MIMO transmit (Tx) AiPs are placed at the appropriate phase centers to achieve the desired virtual array aperture size and to increase the phase-lag redundancy. The physical aperture size may be increased up to N times when the Tx AiP are placed at integer multiples greater of the receive antenna dimensions. To improve SLL performance and minimize phase-lag gaps in the prior step, the Tx AiPs are placed at fractional values or fractional plus integer values of the physical receive subarray dimensions. Steps610and612as indicated in process600may be iterated until acceptable 2-D angular resolution and side-lobe level (SLL) performance is achieved. While the flow chart inFIG.6is specifically for planar arrays in the y-z plane, an extension of procedure for AiPs or subarrays on a non-planar surface or 3-D volume is straight forward.

Several examples of possible SMPAs using the above combination of subarrays, sparse arrays and MIMO configurations follow. InFIGS.7-10, several staircase subarray patterns are shown. InFIG.11, a sparse array pattern with an interleaved MIMO design and focused beam in elevation with monopulse AoA capability is shown. InFIG.12, it is shown that the SMPA is not limited to a specific sparse array pattern but may also be designed with a random selection of subarray phase centers. This random pattern may achieve reasonable 2-D angular resolution performance which further demonstrates the flexibility and diversity of the invention.

FIGS.7A and7Billustrate an example of a staircase configuration sparse array, referred to herein as “Staircase A,” and the phase-lag redundancy, according to example embodiments of the present inventions and subject technology. The first SMPA Staircase-A No-MIMO pattern700inFIG.7Aincludes (4×4) subarrays, such as subarray702, in a sparse configuration. In this example, the subarrays are implemented as AiPs. In the y-dimension, the phase centers are chosen for an NRA pattern with positions 1, 2, 4, 7, 11, 15, 19, 23 separated by units of λ/2. The positions are identified by triangular markers, such as marker704. The phase differences between phase centers may be referred to as the phase lag, phase difference or phase shift. For the orthogonal z-dimension, a similar non-redundant array with positions of 1, 5, 9, 13, 17, 18, 20, 23 was chosen. For the z-dimension, the difference co-array is essentially reversed from the y-dimension to prevent the physical overlap of the AiPs702. The difference co-array phase-lags710ofFIG.7Bare illustrated for the azimuth by the plot712and for the elevation by the plot714, each corresponding to AoA. The length of the horizontal line segment716, corresponding to a given phase lag, indicates the redundancy or the number of times the phase lag occurs for the sparse array. The x-axis of710is the index of a phase lag after the phase lags have been sorted in ascending order. Phase-lag gaps718, occur when there are no phase center difference that result in a phase lag between existing phase lags. For the azimuth plot712and elevation plot714there is a single gap greater than λ/2 which allows lower side lobe levels to be achieved with the advanced signal processing detailed later. When additional phase-lag gaps are present, the SLL performance is degraded.

FIGS.8A and8Billustrate an example of an SMPA radar based on a Staircase-A configuration having MIMO design aspects.FIGS.8A and8Billustrate an example of an SPMA radar in a staircase sparse array configuration having MIMO design aspects, wherein the SPMA radar is based on a Staircase-A configuration to form a virtual array, the virtual array having a physical Staircase-A array positioned proximate multiple transmit arrays and multiple virtual arrays, wherein the system operates as having an enhanced receive aperture for improved azimuth resolution along with plots of the array response.

The SMPA virtual array has a physical Staircase A array positioned proximate multiple transmit arrays and multiple virtual arrays, wherein the system operates as having an enhanced receive aperture for improved azimuth resolution along with plots of the resultant responses, according to example embodiments of the present inventions and subject technology.FIG.8Aillustrates the SMPA radar with a Staircase-A MIMO-A design where the MIMO features have been added to the Staircase-A sparse array similar to array700having configuration as inFIG.7A. The distance in the y-direction between array802and virtual array820is yA1; the distance between arrays820and830is yA2; and the distance across the array830is yA3. These distances are measured with respect to phase centers of the arrays802,820,830. The first distance yA1is from the phase center of AiP804to the phase center of AiP824. Note that the phase center of AiP806in this embodiment is aligned in the z-direction with AiP824; similarly, the phase center of AiP826is aligned with the phase center of AiP834. The second distance yA2is from the phase center of AiP824to the phase center of AiP834. The aperture in the y-dimension of the receive antenna array, SMPA virtual array800, is the sum of yA1, yA2, yA3plus the y-dimension of an AiP. The transmitter antenna, SMPA MIMO transmitters810, includes Tx AiPs spaced as a function of the array size dimensions in the y-dimension from the Tx AiP812in the y-dimension by half times AiP814and one times AiP816the physical receive size yA1. The effect is improved azimuth resolution due to the doubling of the azimuth virtual array aperture size with the virtual arrays820and830. SLL performance is also improved with the sub-multiple placement of the Tx AiP814in the y-dimension due to the resulting virtual array820.FIG.8Bplots the difference co-array phase lags840which has increased azimuth phase-lag redundancy842and fewer phase-lag gaps compared to plot712. The elevation phase lags844also show additional redundancy when compared to plot714. With increased redundancy and fewer gaps, better SLL performance can be achieved with an SMPA radar.

An SMPA radar can angularly resolve closely space objects when the range and Doppler measurements are unable to. This makes the SMPA radar highly desirable for imaging as well as object detection applications. The array response850shows that two closely spaced objects separated only by their 2-D angles are resolved as indicated by the peaks852and854. The peaks agree well with the true object positions indicated by the diamonds. The array response850also shows low SLLs is achieved for this SMPA radar.

FIGS.9A and9Billustrate an example of an SPMA radar in a Staircase-A configuration with multiple transmit arrays distributed in azimuth and elevation to provide corresponding virtual arrays for improved azimuth and elevation resolution and plots of the resultant responses, according to example embodiments of the present inventions and subject technology. An SMPA radar Staircase-A MIMO-B design900with improved azimuth and elevation resolution compared to the SMPA Staircase-A No-MIMO design700ofFIG.7illustrates the flexibility of the technology wherein SMPA900doubles the aperture, physical size in the z-dimension by separating the Tx AiP916from Tx AiPs912and914in the z-dimension by one-times the physical receive elevation size, de. Similarly, the aperture is doubled in the y-dimension by separating Tx AiPs912and914by one-times the SMPA physical receive azimuth size, da. With the SMPA virtual arrays920and930, both the azimuth and elevation resolutions are improved by a factor of 2. InFIG.9B, the difference co-array phase lags940shows both the azimuth942and elevation944redundancies are comparable. Plot950shows the peak side lobe level (PSLL) for a target placed throughout the FoV for the SMPA radar configuration inFIG.9A. The PSLL response950shows that peak sidelobes can be below 25 dB which allows better resolution of weak return signals from targets that are located close to other targets having strong return signal, radar echo.

FIGS.10A and10Billustrate examples of the SPMA radar in a Staircase-B configuration with multiple transmit arrays uniformly spaced and plots illustrating the resultant impact on the angular sidelobes and object detections, according to example embodiments of the present inventions.FIG.10Aillustrates the SMPA radar Staircase-B MIMO-A design1000. The Staircase-B pattern is based on an NRA that contains more phase-lag gaps than the Staircase-A pattern. The choice of on an NRA with more gaps will depend on the tolerance of the radar application to the higher sidelobes. Following the design process ofFIG.6, the y and z-dimension NRA phase centers of the 8 physical receive AiPs1020were chosen by offsetting AiPs by λ/2 between successive subarrays while also alternating between the y and z dimensions. The Tx AiPs1010are separated by one-half times the azimuth aperture, da, where the distance from AIP1012to AIP1014is (λ/2)*daand the distance from AIP1012to AIP1016is da. . . . The resulting SMPA virtual arrays1030and1040correspond to the placement of the Tx AiP1014and Tx AiP1016respectively.FIG.10Billustrates the difference co-array phase lags. Plot1050has several phase-lag gaps (e.g.1056and1058) in the azimuth1052and elevation1054dimensions. While the NRA choice for the Staircase-B pattern would be expected to improve the angular resolution slightly with a larger aperture, the resulting phase-lag gaps illustrated in plots1052and1054degrade the SLL performance as illustrated in plot1060. The AoAs of two targets are accurately determined from the peaks1062and1064. Also present is a high side-lobe1066that could be mistaken for a target unless additional prevention features are implemented such as preventing the Tx illumination of this high sidelobe location.

As mentioned previously, the invention is not limited to the (4×4) AiPs subarrays examples discussed above.FIGS.11A and11Billustrate an example of an SPMA radar incorporating ULA subarrays proximate a transmit array, and plots of the array response for detection of targets clustered close together, according to example embodiments of the present inventions and subject technology.FIG.11Aillustrates an SMPA radar design1100in an Elevation Monopulse configuration. The radar configuration and design1100uses ULA subarrays with the length along the z-axis. This choice allows a wide azimuth FoV (e.g.,)±60° while focusing the subarray beam in elevation. To cover the desired elevation FoV (e.g.,)±20°, the subarray phase shifters (not shown) steer the elevation beam with the appropriate angle step sizes. The specified sizes of the ULAs below are examples which would ultimately be influenced by the SMPA radar design goals. The receive antenna array1120of configuration1100are positioned lengthwise along the y-dimension, the receive antenna array1120is made up of subarrays1110. The physical receive antenna array1120employs (4×1) AiPs1110, which are separated by 21 in the y-dimension. Half of the physical receive antennas1120are separated and offset from the other half1130in the z-dimension, as illustrated, which allows the elevation AoA estimate of the targets.FIG.11Billustrates the configuration1100with virtual subarrays sandwiched between the physical subarrays1110.

The SPMA MIMO transmit antenna is configured lengthwise along the z-dimension. The Tx AiPs1140consists of 4 Tx AiPs or subarrays arranged in (32×1) columns1142. The λ/2 spacing in the y-dimension between the Tx AiPs1142; in the present embodiment, is designed for operation with the receive antenna arrays1100. When a MIMO orthogonal waveform is used, the radar antenna array configuration1100results in the SMPA virtual array1102. The 2λ spacing along the y-dimension of the physical receive arrays in1120and1130is filled in by the Tx AiPs1142. To determine an AoA of a target, the azimuth is determined by the virtual array phase centers along the y-dimension while elevation is determined in a monopulse-like fashion along the z-dimension. The difficulty is that the azimuth and elevation are coupled since the virtual array halves corresponding to physical subarrays1120and1130are offset along the y-dimension. Consequently, advanced signal processing such as IAA detailed inFIG.13or Compressive Sensing techniques are used to solve for both the azimuth and elevation AoA at once. The accuracy of the elevation estimate is determined by the phase-center distance in the z-dimension between1120and1130.FIG.13illustrates an Iterative Adaptive Algorithm (IAA) method to process radar signals received by an SMPA as described herein, according to example embodiments of the present inventions and subject technology (Sandy this seems redundant here?). Using IAA as detailed inFIG.13, the SMPA response resolves the two target signals1132. Elevation aliasing1134of the targets occurs since there are only 2 phase centers in the z-dimension and their spacing is substantially larger than λ/2 for minimum Nyquist sampling of the signals to prevent aliasing. The aliasing1134would not be identified as a target since it is outside the steered elevation beam of the Tx AiPs1140. Guard channels for elevation could also be added to a system to prevent detections that are illuminated by the Tx AiPs1140elevation side-lobes. To provide additional sidelobe protection, the receive subarrays could use a larger ULA subarray (e.g., 16×1 instead of 4×1) that is steered in elevation with the Tx and providing lower two-way PSLL. Also, one should note that while the transmit antennas were presented as 4 Tx AiPs with (32×1), these subarrays could also be implemented with eight (4×4) Tx AiPs that are stacked in a column along the z-dimension. The (32×1) Tx subarrays would be realized by controlling the phase shifters and PAs across the (4×4) AiPs to obtain the (32×1) column behavior.

An SMPA radar design has a great deal of flexibility in the placement of AiPs or subarrays and is not limited to the use of the staircase and ULA patterns presented. Good performance can also be achieved with non-specific patterns. In some embodiments, a randomly chosen array gives reasonably good performance as illustrated inFIG.12A.FIGS.12A and12Billustrate an example of an SPMA radar with random placement of the subarrays and plots of the resultant phase-lag redundancy and of the antenna response for detection of closely spaced targets, according to example embodiments of the present inventions and subject technology. Here, the (4×4) subarray (y,z) phase centers were randomly chosen such that the physical subarrays/AiPs1210do not overlap. The virtual array1200is formed with 3 Tx (4×4) AiPs placed along the y axis in a similar fashion to the previous MIMO-A placements. The difference co-array phase-lags1230, shows a single gap in the azimuth1232and a few gaps in the elevation dimension1234so one expects that the array response will have higher sidelobes in the array response for some target locations. The array response for two targets1232and1234are clearly identifiable above the sidelobes.

Another important aspect of the inventions is the sparse-MIMO placement of the subarrays/AiPs are the advanced signal processing algorithms to reconstruct the signal that an equivalently sized full array would have received so that the 2-D angular resolution and accuracy performance can be recovered. The classical Delay and Sum (DAS) algorithm for determining the AoA is not suited for sparse array signals and leads to poor performance with very high sidelobes. A large degree of phase-lag redundancy is required in the difference co-array for the DAS algorithm to achieve the low sidelobes with an appropriate weighting window. As stated previously, the SMPA sparse and MIMO array features have greatly reduced the hardware complexity but also it is worth emphasizing that the with the substantial reduction of the number of phase centers, the digital signal processing data load is also greatly reduced.

Advanced signal processing options for sparse array signals include various Compressive Sensing algorithms and IAA. Compressive Sensing algorithms can reconstruct the sparse array signal that have minimal redundancy of the difference co-array phase-lags. A strength of these algorithms is that the signal reconstruction can be done with a single snapshot of data which is an important feature for object detection and imaging radars where the target scene can change quickly. These algorithms include Orthogonal Matching Pursuit (OMP), Basis Pursuit Denoising (BPDN), Alternating Direction Method of Multipliers (ADDM), to name a few as well as others that may be developed in the future. The basic approach of CS algorithms for sparse arrays is to first define a dictionary of steering vectors that covers the FoV to the desired granularity of potential target locations. One then searches through the dictionary for the combination of steering vectors that best fit the array signal data. For example, the BPDN best estimate of the steering vector combinations s for a sparse array data set of Y is obtained by optimizing the following criteria

s^=argminss1;subject⁢to⁢12⁢Y-Θ⁢s2<ϵwhere s is a vector that contains the amplitude of each dictionary steering vector, θ is a matrix that contains the dictionary steering vectors, e is a small value, and ∥ ∥1and ∥ ∥2are the l1and l2norms respectively. Some challenges for using CS techniques for an SMPA radar system include reducing computation time for real-time system operation, choosing an appropriate value for parameters such as, and the estimate accuracies for target locations that are in between steering vectors or grid points.

The Iterative Adaptive Algorithm appears to be a better choice than CS algorithms for determining the 2-D AoA of an SMPA radar system. The algorithm is a non-parametric iterative algorithm based on a weighted least squares for target localization. IAA requires a single snapshot as does CS, resolves close targets relative to the sparse array aperture size, is more robust than CS for off-grid target locations, provides low SLLs as discussed above for SMPA radar systems, and converges after a few iterations. While the algorithm requires less computation time than the CS algorithms mentioned above, computation time is significant and requires sufficient processing capability for real-time applications. There are also implementations of IAA that can reduce the computation time by an order of magnitude or more by taking advantage of the Hermitian and Toeplitz properties of the covariance matrix.

FIG.13illustrates an iterative adaptive algorithm (IAA) for determining an AoA for the SMPA. The process1300captures and stores a sequence of observations in the first steps,1302,1304. The steering vectors or spatial frequency vectors fN(ωk) are defined to cover the FoV of the SMPA radar with the desired granularity where N is the number of virtual phase centers in the SMPA radar and 0≤k≤K−1 where K is the number of steering vectors,1306. The process initializes the covariance matrix RNto the N×N identiy matrix,1308. The formulas to calculate spectral power |α(ωk)|2of each steering vector fN(ωk) and covariance matrix, RN, are given,1310. When the stopping criteria is met1312, the process defines spectral power in the direction of steering function to cover FoV,1314. Else the process returns to step1310. The 2-D AoA is then determined by finding the peaks of the spectral power or array response |α(ωk)|2.

FIG.14illustrates an aerial application for use of an antenna array using an SMPA radar, according to example embodiments of the present inventions and subject technology. An aircraft1400includes radar modules1410,1412, each having a specified FOV, range and aperture. In the case of radar module1410, an SMPA would provide imaging capability of the FoV to ensure safe Vertical Take-Off and Landing (VTOL). Similarly, for a forward-looking radar module1412, the imaging and object detection capability of an SMPA radar could provide avoidance of other aircraft, delivery drones, building structures and other obstructions. The radar module1412is further detailed having sensor control1416coupled to SMPA radar1418. Received information from object detections is processed by detection module1414. The radar module1412has a FoV1420. The operational architecture of a radar module employing an SMPA radar, and the methods and apparatuses of the inventions described herein are illustrated inFIG.15.

In landing operation, the aircraft1400, such as a helicopter, plane, UAV, drone and so forth, requires a detailed understanding of the landing area FoV1420. The aerial vehicle may land on non-conventional surfaces as well as on landing strip and prepared or known landing areas. Additionally, there may be a variety of obstacles that may impact the landing. There are two radar units1410,1412illustrated on aerial vehicle1400. The FoV1420of the radar unit1412is the area within which objects are to be detected. For example, trunks1446, trees and bushes1448, people1432, rocks1444and so forth. In military settings, such as scene1430, the soldiers are moving and close together, requiring the radar to operate rapidly to respond. In addition, on landing the aerial vehicle1400often stirs up dust1450which interferes with visibility in the landing area.

Continuing withFIG.14, the radar unit1412includes a detection module1414, sensor control1416and SMPA radar1418. Sensor control1416includes beamforming and beamsteering controls for the arrays of SMPA1418. The sensor control also controls and configures the transmission signals of the various radiating elements according to desired aperture of the receive array. The sensor control1416and the SMPA1418create a virtual array having a larger aperture than the physical receive array of SMPA1418. The detection module1414responds to received signals to classify and/or identify the detected object. This hybrid radar unit1412incorporates analog beam steering by phase control and digital processing of received signals. The virtual array may take a variety of forms. In some embodiments, the sensor control1416directs signals to subsets of the full physical receive arrays to adjust the aperture.

FIG.15illustrates a radar architecture incorporating SMPA techniques, according to example embodiments of the present inventions and subject technology. The radar system1500includes a radar controller and interface module1514providing control of transmit operation, interpretation of received information and interface to outside the system1500, wherein module1514is coupled to multi-mode function module1512. The electromagnetic control module1510includes a beam shaping module1520, phase control module1522and modulation module, DDMA1524. The electromagnetic control module1510is coupled to hybrid control modules1508, having analog resolution module1530and digital processing module1532. The system1500also includes a radar hardware portion1502coupled to a distribution controller,1504. Radar hardware includes transmit and receive antenna array configurations, which in the present inventions implement SMPA and MIMO. Received signals are processed through virtual array processing modules1540, super resolution module1542, digital signal processing (DSP) unit1544, and an AI classifier module1546.

An imaging and object detection radar module according to an example embodiment, referred to as an SPMA radar, includes a transceiver adapted to generate transmit signals to a transmit antenna array, the array arranged as multiple subarrays, each adapted to transmit electromagnetic signals having different transmission parameters. The radar having a receive antenna array made up of multiple subarrays configured in a sparse formation. The receive subarrays are configured in a stairstep type pattern, where the subarrays are sparse in orthogonal dimensions, to have no overlapping phase centers. Other embodiments may implement different shapes, formations, and configuration, such as a random arrangement. Arrangements may include subarray separations between phase centers that are multiple or sub-multiple of the receive antenna array aperture dimensions. Each subarray is made up of radiating elements organized into a shape. In the examples provided herein, the subarrays were in rectangular shapes, however, alternate embodiments may implement different shapes and configurations to separate the phase centers of the receive subarrays sufficiently to distinguish the transmit signals from each other by transmit parameters. There may be a uniform or a non-uniform spacing between subarrays. The configuration of receive antenna subarrays and the configuration of transmit antenna array, which may also be multiple subarrays, may be configured using a method to form linear NRAs or sparse arrays and used to optimize the sparse and MIMO configuration of subarrays to achieve optimal 2-D angle of arrival, phase-lag redundancy, phase-lag gaps, and sidelobe levels for the array response. The configuration chosen determines the abilities of the radar module. The sparse array techniques enable reduced receive processing by reducing redundancy of physical elements and transmitting distinct phase signals from each transmit subarray. This reduces the size and complexity of the radar module. These apparatuses and methods may be used in other applications as well.

In some embodiments, the receive subarray elements are coupled to analog components to modify received signals. This may include low noise amplifiers (LNAs) for signal amplification and phase shifters for beam steering. Transmit subarray elements are also coupled to analog components, such as in the transmission signal feed, wherein the signals are amplified by power amplifiers (PAs) and beam steered by phase shifters prior to transmission. The transmit antenna subarrays and the receive antenna subarrays may be positioned such that the radar module behaves as a sparse MIMO virtual array.

In some embodiments, a radar module has receive antenna subarrays packaged as an AiP having radiating elements, LNAs and phase shifters. Alternate embodiments may implement a variety of methods for beam steering. In some embodiments, the AiP has substrate material sandwiched between the antenna arrays on one side and the beam steering and control on the opposite side. The transmission signals may have modulations such as frequency modulated continuous wave (FMCW), time division multiple access, frequency division multiple access, orthogonal frequency multiple access, code division multiple access, Doppler division multiple access or other modulation protocol.

In some embodiments, a radar module includes a receive processing module adapted to receive transmission signals from the plurality of transmit antenna subarrays and adapted to distinguish the transmission signals as a function of transmission parameters. The receive processing module may employ signal processing with FFT or other algorithms to determine object range and Doppler velocity. The receive processing module may employ advanced sparse-array signal processing such as IAA or CS to determine the 2-D angle of arrival of objects for an SPMA radar module.