Radar systems and methods utilizing composite waveforms for customization of resolution requirements

Abstract

System and methods are provided which involve a radar system that includes one or more signal generators configured for generating a composite radar waveform formed by combining different component waveforms; and a detector for detecting reflected signals from the composite waveform and determining velocity and distance measurements of a target relative to a host vehicle. Advantageously, the first and second component waveforms are selected such that the composite waveform is able to meet two different sets of resolution requirements with respect to at least one of: (i) the velocity measurement of the target vehicle relative to the host vehicle and (ii) the distance measurement of a target vehicle relative to the host vehicle. Notably, each of the different sets of resolution requirements is pre-selected based on a different type of detection scenario.

Claims

1. A radar system for a host vehicle comprising: one or more signal generators configured for generating a composite linear frequency modulated continuous waveform formed by combining different component waveforms; and a detector for detecting reflected signals from the composite waveform and determining velocity and distance measurements of a target relative to the host vehicle; wherein the first and second component waveforms are selected such that the composite waveform is able to simultaneously meet two different sets of resolution requirements with respect to at least one of: (i) the velocity measurement of the target vehicle relative to the host vehicle and (ii) the distance measurement of the target vehicle relative to the host vehicle; and wherein each of the different sets of resolution requirements is pre-selected for a different detection scenario.

2. The radar system of claim 1, wherein the first and second component waveforms each include a series of pulses.

3. The radar system of claim 2, wherein the first component waveform has a different pulse repetition pattern than the second pulse repetition frequency, wherein a first pulse repetition pattern of the first component waveform is selected to meet a first predetermined set of resolution requirements applicable for a first detection scenario and a second pulse repetition pattern of the second component waveform is selected to meet a second predetermined set of resolution requirements applicable for a second detection scenario.

4. The radar system of claim 3, wherein the first pulse repetition pattern is characterized by a different pulse repetition frequency than the second pulse repetition pattern.

5. The radar system of claim 2, wherein each component waveform includes a repeated super-pulse sequence (of a plurality N of super-pulses) with a pre-determined cycle time (Tc), each super-pulse cycle including an active phase with a time Ta<Tc and an inactive phase with a time Ti<Tc, wherein Ta+Ti=Tc, the active phase characterized by a sequence of a plurality of pulses (M pulses) each with a pulse duration Td.

6. The radar system of claim 5, wherein each component waveform is characterized by a different cycle time Tc for its repeated super-pulse sequence.

7. The radar system of claim 6, wherein the different cycle time Tc is characterized by a different active phase Ta as characterized by one or more of: (i) a different number of pulses per super-pulse or (ii) a different pulse duration Td.

8. The radar system of claim 6, wherein the different cycle time Tc is characterized by a different inactive phase Ti.

9. The radar system of claim 6, wherein each component waveform is characterized by one or more of: (i) a different low pulse frequencies Fl or (ii) a different high pulse frequency Fh.

10. The radar system of claim 5, wherein each pulse is further characterized by a change in frequency between low and high pulse frequencies (Fl and Fh).

11. The radar system of claim 2, wherein the pulses are coherent.

12. The radar system of claim 11, wherein the first and second component waveforms are each frequency modulated waveforms.

13. The radar system of claim 1, wherein the first and second component waveforms are each repetitive oscillation waveforms.

14. The radar system of claim 13, wherein oscillations in each of the first and component waveforms vary between low and high pulse frequencies (Fl and Fh).

15. The radar system of claim 14, wherein each component waveform is characterized by one or more of: (i) a different low pulse frequencies Fl or (ii) a different high pulse frequency Fh.

16. The radar system of claim 13, wherein the first and second component waveforms are each continuous waveforms.

17. The radar system of claim 1, wherein the first and second component waveforms are interleaved to form the composite waveform.

18. The radar system of claim 1, wherein the first and second component waveforms are alternated to form the composite waveform.

19. The radar system of claim 1, wherein a sampling of the composite waveform is constant.

20. The radar system of claim 1, wherein sampling of the composite waveform is variable for each of the component waveforms.

21. The radar system of claim 1, wherein the different sets of resolution requirements includes a first set of resolution requirements for a first scenario involving increased maximum distance resolution requirements and reduced velocity and range accuracy requirements and a second set of resolution requirements for a second scenario involving decreased maximum range requirements and increased velocity and range accuracy requirements.

22. The radar system of claim 21, wherein each of the different sets of resolution requirements is for a different scenario as characterized by target size, target identification, target speed, target position, target range, host vehicle speed and/or environment.

23. The system of claim 1, wherein the each set of resolution requirements includes different accuracy requirements for at least one of: (i) the velocity measurement of the target vehicle relative to the host vehicle and (ii) the distance measurement of the target vehicle relative to the host vehicle.

24. The system of claim 1, wherein the each set of resolution requirements includes different maximum, minimum or resolvable range requirements for at least one of: (i) the velocity measurement of the target vehicle relative to the host vehicle and (ii) the distance measurement of the target vehicle relative to the host vehicle.

25. The radar system of claim 1, further comprising a processor for ambiguity resolution, the processor configured to determine a true velocity for a target based determining corresponding Doppler bins that correctly reflect ambiguous velocity calculations for both component waveforms.

Description

BRIEF DESCRIPTION OF THE DRAWINGS

(1) The present disclosure is further described in the detailed description which follows, in reference to the noted plurality of drawings by way of non-limiting examples of embodiments of the present disclosure

(2) FIG. 1 depicts an investigative setup for demonstrating the impact of velocity ambiguity in the context of multiple target vehicles, according to the present disclosure.

(3) FIG. 2 depicts results from the investigative setup of FIG. 1, according to the present disclosure.

(4) FIG. 3 depicts a block diagram of an example automotive radar system, according to the present disclosure.

(5) FIG. 4 depicts an example waveform structure for use with the systems and methods described herein, according to the present disclosure.

(6) FIGS. 5 and 6 depict velocity ambiguity ranges for example component waveforms, according to the present disclosure.

(7) FIG. 7 depicts ambiguity resolution based on the example component waveforms of FIGS. 5 and 6, according to the present disclosure.

(8) FIG. 8 depicts an example search and dwell processing algorithm which may be used to implement the ambiguity resolution techniques described herein, accordingly the present disclosure.

DETAILED DESCRIPTION

(9) As noted above, the systems and methods described herein may relate to the operations of an automotive radar system. FIG. 3 includes a schematic block diagram of an example automotive radar system 10, including one or more radar sensor modules 12 for processing automotive radar signals, in accordance with some exemplary embodiments. Referring to FIG. 3 radar system 10 includes one or more radar modules 12, which process radar transmit and receive signals which are compatible with radar system 10 in the host vehicle. Radar sensor module 12 generates and transmits radar signals into the region of interest adjacent to the host vehicle that is being monitored by the radar system. Generation and transmission of signals is accomplished by RF signal generator 24, radar transmit circuitry 20 and transmit antenna 16. Radar transmit circuitry 20 generally includes any circuitry required to generate the signals transmitted via transmit antenna 16, such as signal shaping/timing circuitry, transmit trigger circuitry, RF switch circuitry, RF power amplifier circuitry, or any other appropriate transmit circuitry used by radar system 10 to generate the transmitted radar signal according to exemplary embodiments described in detail herein. In some embodiments, the RF signal transmit circuitry 20 may include an RF switch mechanism may rely on inputs from an RF oscillator included in RF signal generator 24. The RF signal transmit circuitry may further advantageously include pulse shaping circuitry, e.g., based on transmit antenna trigonometric calculations.

(10) Radar module 12 also receives returning radar signals at radar receive circuitry 22 via receive antenna 18. Radar receive circuitry 22 generally includes any circuitry required to process the signals received via receive antenna 18, such as RF low noise amplifier circuitry, signal shaping/timing circuitry, receive trigger circuitry, RF switch circuitry, or any other appropriate receive circuitry used by radar system 10. In some embodiments, radar receive circuitry 22 may also include a receiver antenna select module for selecting the receive antenna from a plurality of receive antennas. In some exemplary embodiments, the received signals processed by radar receive circuitry 22 are forwarded to phase shifter circuitry 26, which generates two signals having a predetermined phase difference. These two signals, referred to as an inphase (I) signal and a quadrature (Q) signal, are mixed with an RF signal from RF signal generator 24 by mixers 28 and 30, respectively, to generate I and Q intermediate frequency (IF) signals. In some embodiments mixing may further be based on pulse shaping of the RF signal from the RF signal generator 24 based on receive antenna trigonometric calculations. The resulting IF signals are further filtered as required by filtering circuitry 32 to generate filtered IF I and Q signals, labeled I and Q in FIG. 3. The IF I and Q signals are digitized by analog-to-digital converter circuitry (ADC) 34. These digitized I and Q IF signals are processed by a processor, such as a digital signal processor (DSP) 36. In some exemplary embodiments, the DSP 36 can perform all of the processing required to carry out the object detection and parameter determination, including object range, bearing and/or velocity determinations, performed by system 10. For example in some embodiments, the digital signals are used for retrieving an azimuth angle of possible targets by simultaneously sampling and analyzing phase and amplitude of the received signals. The analysis is generally performed by means of Fast Fourier Transform (FFT) processing.

(11) It will be understood that the system configuration illustrated in FIG. 3 is exemplary only and that other system configurations can be used to implement the embodiments described herein. For example, the ordering of filtering of the IF signal and analog-to-digital conversion may be different than the order illustrated in FIG. 3. The IF signal may be digitized before filtering, and then digital filtering may be carried out on the digitized signal(s). In other embodiments, the entire IF stage may be removed so that the RF signal is directly converted to DC for further digitizing and processing. The ADC is sampled on one channel only.

(12) Advantageously, the systems and methods described herein utilize a unique waveform model to facilitate ambiguity resolution. This waveform model is illustrated with respect to FIG. 4. In particular, the waveform model is a linear frequency modulated continuous waveform characterized by a repeated super-pulse sequence of N super-pulses (e.g., N=8 super-pulses). The super-pulses may be characterized by a pre-determined cycle time Tc for each super-pulse. Notably, the super-pulse cycle may typically include an active phase with a time Ta<Tc and an inactive phase with a time Ti<Tc (wherein Ta+Ti=Tc). As depicted, the active phase Ta may be characterized by a sequence of a plurality of M pulses (M=16 pulses) while the inactive phase Ti may be characterized by a pause between respective last and first sub-pulses of sequential super-pulses. Advantageously, each pulse in the plurality of M pulses may be characterized by a change in frequencies (e.g., a frequency ramp) between low and high pulse frequencies Fl and Fh and may include a pulse cycle Tp (Thus, Ta=MTp). While in example embodiments the pulses may be characterized by a uniform (fixed slope) frequency ramp, it is noted that the subject application is not limited to such embodiments. In particular, in alternative embodiments, the change in frequency may be disjointed, stepwise, or otherwise non-uniform provided that the frequency is modulated between the low and high frequencies. Thus, in some embodiments, the plurality of pulses may be implemented as a sawtooth wave or other periodic function. Furthermore, while in example embodiments the change in frequency may start at the low frequency Fl and end at the high frequency Fh, the subject application is not limited to such embodiments. Thus, for example, in alternative embodiments, the change in frequency may start at the high frequency Fh and end at the low frequency Fl, start and stop at a same frequency (e.g., at the low frequency Fl or at the high frequency Fh), or start and/or end at an intermediate frequency between the high and low frequencies.

(13) As described herein, to enable ambiguity resolution, a composite waveform may be formed by alternating between, interleaving or otherwise combining at least two different sets of super-pulses. For example, a first component waveform based on a first set of parameters of the waveform model may include a first set of super-pulses characterized by a first Tc while the second component waveform based on a second set of parameters of the waveform model may include a second set of super-pulses characterized by a second Tc different than the first Tc. In example embodiments, the first and second component waveforms may be characterized by the super-pulses of the first and second respective waveforms having one or more of (i) differing numbers of pulses M, (ii) differing pulse cycle Tp (iii) differing high pulse frequencies Fh (iv) differing low pulse frequencies and/or (iii) differing inactive phases Ti. In general, the goal is to combine component waveforms having different ambiguity ranges, e.g., for velocity. In some embodiments, such a combination may be a sequential alternating between super-pulses of each component waveform. For example, a super-pulse of a first component waveform may be followed by a super-pulse of a second component waveform. In other embodiments, the component waveforms may be interleaved, e.g., where individual pulses of a super-pulse of a second component waveform are synched between individual pulses of a super-pulse of a second component waveform. Notably, the subject application is not limited to such embodiments, however, and other ways of combining component waveforms may also be utilized. For example, in some embodiments, component waveforms may be multiplexed in any way, e.g., via time-division multiplexing, frequency-division multiplexing, polarization-division multiplexing, code-division multiplexing, etc.

(14) As described herein, the velocity ambiguity range may be provided by the following equation:

(15) $V_{\mathrm{amb}}=\frac{C}{T_c*\left(F_H+F_L\right)}$

(16) Note that, as described above, Tc>Ta=MTp. Thus, based on the forgoing equation, any change to Tc, Ta, M, Tp, Fh, Fl, or Ti will result in a different Vamb (note that in some embodiments, Tc may be changed by changing either Ta or Ti and Ta may be changed by changing M or Tp). Thus, relying on the above principles, the composite waveform may include a sequence of sets of super-pulses wherein at least two different Vamb values are reflected in the different sets of super-pulses (e.g., in different composite waveforms which are based on the same waveform model described herein).

(17) Also note that while, example embodiments described herein may be generally illustrated with respect to the use of two different sets of super-pulses, the present disclosure it not limited to such embodiments. Rather, as will be appreciated by a person of ordinary skill in the art, a composite waveform formed by alternating super-pulse sets from first and second composite waveforms may also alternate super-pulse sets from third, fourth fifth (etc.) component waveforms, each based on the waveform model described herein and each advantageously with a different ambiguity range for velocity.

(18) FIGS. 5 and 6 provide illustrate calculation of velocity ambiguity ranges for respective sets of super-pulses. In FIG. 5, the example component waveform is characterized by the following set of parameters (note that Tset represents the time cycle time for the entire super-pulse set):

(19) Parameter Set 1 T.sub.set=40.0 ms;

(20) T.sub.c=4.9 ms; T.sub.p=50.0 us; F.sub.L 76.0 GHz; F.sub.H=77.0 GHz; M=16; N=8;

(21) Using the above equations, the velocity ambiguity range (also known as the Doppler Nyquist) can be calculated as 0.4 m/s. This means that there is resulting ambiguity between Doppler bins that represent velocities 0.4 m/s apart. Thus, in FIG. 5 if the true target is the bins indicated by the solid arrow, the shaded bins to the right indicated by the dashed arrows represent Doppler bins that are difficult to distinguish in same cycle. Note that the fine Doppler bin in this example is 0.05 m/s (representing the smallest resolvable unit within the ambiguous range).

(22) Similar to the waveform in FIG. 5, the example component waveform in FIG. 6 is characterized by the following parameters (note that they are slightly different than the parameters in FIG. 5 with Tc having been reduced):

(23) Parameter Set 2 T.sub.set=40.0 ms;

(24) T.sub.c=3.268 ms; T.sub.p=50.0 us; F.sub.L=76.0 GHz; F.sub.H=77.0 GHz; M=16; N=8;

(25) Again, using the above equations, the velocity ambiguity range (also known as the Doppler Nyquist) can be calculated as 0.6 m/s. This means that there is resulting ambiguity between Doppler bins that represent velocities 0.6 m/s apart. Thus, in FIG. 6 if the true target is the bins indicated by the solid arrow, the shaded bins to the right indicated by the dashed arrow represent Doppler bins that are difficult to distinguish in the same cycle. Note that in the examples of FIG. 6, as in FIG. 5 the fine Doppler bin is 0.05 m/s (representing the smallest resolvable unit within the ambiguous range).

(26) As illustrated in FIG. 7, if above two set parameters (from the example component waveforms of FIGS. 5 and 6) are alternatively applied via a composite waveform, the false targets represented by the shaded bins indicated by the dashed arrows can be distinguished from the true target represented by the shaded bins indicated by the solid arrow based on differing corresponding bins of the other component waveform. Thus, by combining data from corresponding bins for the first and second component waveforms, there is no non-distinguishable bin. This combination of data can be implemented in a number of ways. In example embodiments, sampling for the previous several sets of super-pulses can buffered with combined results generated for every observe cycle. Then the true velocity can be determined, e.g., based on the corresponding Doppler bins that correctly reflect the ambiguous velocity for both component waveforms (e.g., the target would be implicated by an ambiguity velocity of 0.15 (+some multiple of 0.4 m/s) for the first component waveform and an ambiguity velocity of 0.55 (+some multiple of 0.6 m/s) for the second component waveform. There is only one combination of corresponding bins that meets this requirement (thereby resolving the ambiguity).

(27) Turing to FIG. 8, it is noted that the ambiguity resolution systems and methods described herein may advantageously be applied in the context of (e.g., as a module of) a larger search and dwell processing (SAD) processing algorithm which may be configured to maximize the performance with limited demand on calculation and memory resources. SAD is the choice balanced for performance and resource demanded. Comparing with the ordinary block processing, SAD has much low demand on RAM memory, with minimized performance change.

(28) The SAD algorithm is described in greater detail in PCT Publication No. WO2016188895 the contents of which are hereby incorporated herein. In the SAD algorithm IF signals are sampled at a certain predetermined sampling frequency fs and converted to digital signals. Notably, the sampling frequency fs may be provided by way of a sampling and timing signal produced by a sampling and timing arrangement and may be responsive to changing resolution requirements, e.g., depending on characteristics such as target size, target identification, target speed, target position, target range, host vehicle speed, environment (such as highway vs city vs parking lot), etc. A transceiver arrangement including a DSP may then be adapted for radar signal processing by means of a first FFT (Fast Fourier Transform) to convert the digital signals to a range domain, and a second FFT to combine the results from successive pulses into the Doppler domain thereby producing a two-dimensional spectrum of a Range-Doppler matrix 810. In general, the SAD algorithm involves, processing the Range-Doppler matrix via two parallel processing paths 820 and 830. The first processing path 820 is arranged to produce a periodically updated dwell list 840, and the second processing path 830 is arranged to collect and process data from the Range-Doppler matrix 810 in dependence of the present dwell list 840. By means of the dwell list 840, it is possible for the second processing path 830 to focus the processing efforts for true target detection where there is an increased possibility for presence of objects. The dwell list 840 comprises information where the probability of presence of objects exceeds a certain threshold. The SAD algorithm is described in greater detail in the sections which follow.

(29) At an initial step 810, Range-Doppler (RD) matrixes are prepared. As noted above, this function block basically contains the implementation of a two-dimensional Fourier transform of the input time domain matrix, where the produced matrix has a range dimension as well as a Doppler dimension. Notably, there is one RD matrix for each Tx-Rx combination of every super-pulse. For example, assuming 2 Tx channels an 4 Rx channels, this results in 8 Tx-Rx combinations and therefore 8 RD matrices. The range resolution for the RD matrix is based on:

(30) $R_{\mathrm{RES}}=\frac{N_S-1}{N_S}.Math.\frac{C_0}{2.Math.\mathrm{BW}}$
Wherein the bandwidth BW equals the frequency high (FH) minus the frequency low (FL) and where NS is the number of samples per Tx ramp. Velocity resolution, as noted above, is based on:

(31) $V_{\mathrm{RES}}=\frac{C}{T_c*\left(F_H+F_L\right)}$

(32) The first processing path 820 begins with search processing 850 where the primary task of the search engine is to identify those Range-Doppler (RD) cells which most likely contain energy of radar targets. For this purpose, the consistency of the magnitudes of the transformed RD-matrices is evaluated one at a time. Since the individual pulses cannot be saved, it is necessary to sequentially work through the process and get the most important information in compressed form. In example embodiments, the sequential probability ratio test (SPRT) provides the framework for search processing. As described in WO2016188895 search processing may include sub-steps of noise estimation, log-likelihood function (LLF) matrix alignment, probability ratio computation and updating the LLF matrix.

(33) In particular, the noise floor for all ranges is estimated in the Doppler dimension within the RD magnitude matrix. The median function is able to provide a relatively robust estimation. It is the nature of the Range-Doppler matrix that frequencies in the range dimension are changing as a function of time. The range-rate of the energy within a certain RD-cell can be derived from the cell index in the Doppler dimension. In order to keep track of the received energy the LLF matrix is altered with respect to this relation. As noted above, the search module is arranged to find Range-Doppler (RD) matrix elements which most likely contain energy of radar targets. For this purpose, the consistency of the magnitudes of the Range-Doppler matrix is evaluated one at a time. Since the individual pulses are not saved, it is necessary to sequentially work through the process and get the most important information in compressed form. This may for example be accomplished by combining the pulse blocks of one or more processing cycles with the aim to restore both the detection probability and the resolution using the SPRT algorithm. The ideal implementation of a sequential detection algorithm requires a decision to be made after every pulse block, where three decision outcomes are possible: No Target, Target Present, and No Decision. If the latter decision is made, then another pulse block is evaluated. The probability ratio computation step is arranged to perform sequential detection using the SPRT algorithm. In order to adapt this algorithm to radar sensors, a calculated signal magnitude, m, is assumed as based on the on-going collection of noisy radar signal observations, so far comprising N such observations. The function fo (m) is a probability density function for the calculated signal magnitude m, when an object is actually present at some specified magnitude, and the function fl (m) is the probability density function for the calculated signal magnitude m, when no such object is actually present. According to SPRT theory, the ratio, r, provides a measure of the relative probability of the object being present or not where:

(34) $r=\frac{f_0\left(m\right)}{f_1\left(m\right)}$
In updating the LLF matrix, a target signal detection decision is made. For example, where r>A it may be decided that a target is present, while were r<B it may be decided that a target is not present. Otherwise additional observation may be required.

(35) At the end of every radar signal processing cycle dwell-probe positioning is calculated (step 860). This includes identifying the loaded bins in the RD matrix (e.g., where r>A) and calculating dwell positions. In order to lose as little energy as possible during the dwelling process, it is necessary to put the measuring point to the optimum position. The calculation is basically the prediction of the target position using the current position, dwell duration, and the target speed represented by the Doppler index.

(36) The second processing path 830 involves applying dwelling processing 870 and results in target detection 880. In dwelling processing 870, complex data is collected from the transformed superpulse matrix. Notably, the values which are copied from the data matrix are reflected in the dwell list 840. In target detection, dwell data is transformed via a proper window function for each dwell signal. All dwell signals are also converted into the frequency domain. A peak detection subroutine may then be applied based on a magnitude signal created from the complex FFT output. In particular, a detection threshold may be applied taking into account a noise floor for each signal. Radar parameter estimation is then applied where parameters such as range, Doppler and bearing are calculated for each target. For this purpose, it is necessary to consider the orthogonal neighboring cells in the three-dimensional signal space of every target peak. Notably, with the magnitudes of the neighbors one can apply 3-point interpolation.

(37) The SAD algorithm can use improvement with respect to resolving speed ambiguity. For example, under one set of designed parameters, there is a speed ambiguity of 0.4 m/s. Which leads to an issue of distinguishing the 0.4 mps target from stationary target when these two targets being at same range (distance) and azimuth (velocity) bin. Thus the systems and methods described herein are intend to improve SAD speed measurement ambiguity performance.

(38) One function of the SAD algorithm stems from specific implementation with respect to automotive radar systems. In particular, in the context of automotive radar systems, low host (the vehicle with the detector) speed (for example speed of less than 15 m/s) reduces the range (distance) of interest. Indeed, at lower speeds there is no need to detect far away target objects (e.g., as far away as 150 meter). As a general rule a collision safety range can be applied which accounts for a time to impact. Thus, for example, a safety range of 3-5 seconds (e.g., 4 seconds) can be applied. At 20 m/s a safety range of 4 seconds means that the necessary distance/range for resolving is only 80 m. Thus, in general, detection range may be reduced based on the host speed. In the context, of a high speed environment (such as a highway environment) block processing may be avoided to save resources. Thus, the SAD algorithms may be employed to reduce memory demands. In contrast, for a low speed environment, fewer range bins are required to be resolved. Thus, block processing may be implemented to utilize the full potential of the calculation power and memory resource. For example, if the host speed is greater than 15 m/s the SAD algorithm may be used to allow for the longer detection distance. If the speed is lower than 10 m/s, then the block processing algorithm is used to cover only the 50 m detection range. Thus, in some embodiments, the SAD algorithm may be configured to selectively apply ambiguity resolution only where the host vehicle is in a predetermined range a predetermined velocity. In such embodiments, a lower velocity host vehicle may have a shorter required distance range for detection which may facilitate ambiguity resolution. Alternatively, ambiguity resolution may only be required where a target is within a predetermined range of the host vehicle. Again the predetermined range may be a dynamic range based on the velocity of the host vehicle.

(39) In example embodiments, range resolution has an important role in the SAD approach. The higher the range resolution the better the performance of the search algorithm checking the consistency of Doppler vs. range rate. Therefore, the strategy of the waveform design is different than for the conventional processing. Here we start with the maximum allowed transmit bandwidth and adjust the rest of radar parameter. In some embodiments, different resolution requirements may be achieved via different component waveforms (e.g., accounting for different transmit channels). It should also be noted that there are two ways to include a second transmit channel into the SAD algorithm. Firstly, one can interleave transmit channels (interleave pulses). Secondly, one can alternate channels (alternate super-pulses). The main advantage of the second approach over the first approach is that it requires only half of memory to store the entrance RD matrix. The biggest disadvantage is that the pulse range interval (PRI) of the super-pulses would double as fast as with the first approach.

(40) As noted herein, one advantage of the SAD algorithm is the ability to use target tracking between pulses to facilitate Doppler estimation. In particular, as a target's position changes velocity for a target can be estimated based on such a change in position between successive blocks (e.g., assuming a constant velocity). This velocity estimate can be utilized as a coarse prediction to help resolve the finer Doppler estimation with its limited Nyquist range. Notably, however, the SAD algorithm is somewhat reliant on being able to track individual targets. Thus, where multiple targets overlap (as shown in FIG. 2) it may be difficult to track target position and resolve Doppler measurements in that way. Thus, in some embodiments, ambiguity resolution processing, such as described herein may be applied to supplement the tracking approach of the SAD algorithm. For example, in some embodiments, ambiguity resolution may be applied to facilitate resolving Doppler measurements specifically in the case of overlapping targets. Otherwise processing power may be conserved.

(41) Another advantage of the SAD algorithm discussed herein is the ability to apply to utilize component waveforms and windowing/sampling processes which are advantageously designed to meet different resolution specifications. For example, in some embodiments, different resolution specifications may be required based on characteristics such as target size, target identification, target speed, target position, target range, host vehicle speed, environment (such as highway vs city vs parking lot), etc. Thus, it may be advantages to utilize different component waveforms which, when combined, are able to meet the resolution specifications for each active scenario. For example, in some embodiments, a composite waveform may include a first component waveform that is able to resolve out to a greater range but with a lower velocity resolution and a second waveform component which is able to resolve out to a shorter range but with greater velocity resolution. Notably, these two composite waveforms may also simultaneously be applied to helping facilitate ambiguity resolution. In some embodiments, windowing/sampling may be applied to control the number of resolvable bins based on active scenarios. Thus, for example, memory load may be reduced when coarser range resolution is ok (such as at high velocities). The waveform variation examples described below further illustrate these concepts. For example, in some embodiments, fewer range bins may be processed, e.g., for certain component waveforms and/or scenarios, thereby reducing processor and memory load. In other embodiments, the same ADC sampling rate may be used regardless. In further embodiments, there may be a reduced sampling number for each ramp. The range processing bin/cell number for example can be reduced by half, so the total sampling point for each ramp or FFT points will be reduced by half, and the ramp time will be reduced by half, again reducing processor and memory load. The forgoing examples are to illustrating of a general concept of waveform and/or sampling/windowing parameters taking into consideration balanced demands for different scenarios while allowing for efficient use of resources.

(42) Whereas many alterations and modifications of the disclosure will no doubt become apparent to a person of ordinary skill in the art after having read the foregoing description, it is to be understood that the particular embodiments shown and described by way of illustration are in no way intended to be considered limiting. Further, the subject matter has been described with reference to particular embodiments, but variations within the spirit and scope of the disclosure will occur to those skilled in the art. It is noted that the foregoing examples have been provided merely for the purpose of explanation and are in no way to be construed as limiting of the present disclosure.

(43) While the present inventive concept has been particularly shown and described with reference to exemplary embodiments thereof, it will be understood by those of ordinary skill in the art that various changes in form and details may be made therein without departing from the spirit and scope of the present inventive concept as defined by the following claims.