Patent Application
20240118404
The present disclosure relates to an apparatus and method for determining the directions of arrival angles for each of a plurality of targets in a dataset indicative of radar signals received at an antenna array. In particular, it relates to the use of Deterministic Maximum Likelihood techniques. The disclosure also relates to a radar system, such as a frequency-modulated-continuous-wave, FMCW, radar system configured to perform said method.
Deterministic Maximum-Likelihood (DML) Direction-of-Arrival (DoA) estimation is a technique for determination of the directions of arrival angles of component radar signals reflected from each of a plurality of targets in radar signals received at a plurality of antenna elements. The antenna elements may be part of a FMCW radar.
According to a first aspect of the present disclosure there is provided an apparatus comprising a processor configured to:
In one or more embodiments, predetermined threshold λ is based on the candidate number of targets.
In one or more embodiments, the objective function is formulated such that the evaluation of the objective function for the first candidate number of targets K comprises a function of two or more sub-functions, and
In one or more embodiments, the processor is configured to, if the branch metric function is less than the predetermined threshold λ, not evaluate the objective function for the second candidate number of targets K+1, wherein the evaluation is based on the first candidate number of targets, K, of the second candidate number of targets, K+1, having the respective first candidate direction-of-arrival angles and the remaining target of the second candidate number of targets, K+1, the remaining target comprising the additional-target having the additional-target-first-candidate direction-of-arrival angle and any other direction-of-arrival angle in the search space.
Thus, in one or more examples, the search for additional targets in combination with the first candidate direction-of-arrival angles is terminated.
In one or more embodiments, the branch metric function is also a function of the additional-target-first-candidate direction-of-arrival angle (the angle represented by aK+1), comprising the angle of said one more target than the first candidate number of targets,
In one or more embodiments, when the branch metric function is less than the predetermined threshold λ and the processor has evaluated the objective function for the first candidate number of targets K, wherein the evaluation is based on at least one of the first candidate number of targets K having a different, second candidate direction-of-arrival angle;
In one or more embodiments, the processor is configured to, as part of said evaluation of the objective function for the first candidate number of targets K and the first evaluation of the branch metric function:
In one or more embodiments, the processor is additionally configured to, if the branch metric function is greater than the predetermined threshold λ:
for use in a subsequent calculation of the branch metric function; and
In one or more embodiments, the processor is configured to output a final set of direction-of-arrival angles comprises the direction-of-arrival angles that resulted in the one of the maximal and minimal evaluation of the objective function.
In one or more examples, the evaluation of the objective function comprises the repeated evaluation of the objective function for a plurality of candidate matrices based on matrix A that each include different direction-of-arrival-angle vectors over the respective search space, wherein said set of direction of arrival angles are derived from one of the candidate matrices of the plurality of candidate matrices that provides one of a maximum and a minimum evaluation of the objective function over the search space.
In one or more embodiments, the apparatus comprises a frequency-modulated-continuous-wave, FMCW, radar system.
In one or more embodiments, the input dataset comprises a dataset that has been Range-Doppler processed such that the input dataset is representative of radar signals that have reflected from targets at a predetermined range of distances from the antenna elements and a predetermined range of radial velocities relative to the antenna elements.
In one or more examples, such a determination of the objective function comprises determining an objective function based on a beam forming method, as will be familiar to those skilled in the art.
In one or more examples, one of:
According to a second aspect of the disclosure we provide a method for evaluating an objective function, the method, performed by a processor, comprising:
In one or more embodiments, the objective function is formulated such that the evaluation of the objective function for the first candidate number of targets K comprises a function of two or more sub-functions, and
In one or more embodiments, the processor is configured to, if the branch metric function is less than the predetermined threshold λ, not evaluate the objective function for the second candidate number of targets K+1, wherein the evaluation is based on the first candidate number of targets, K, of the second candidate number of targets, K+1, having the respective first candidate direction-of-arrival angles and the remaining target of the second candidate number of targets, K+1, the remaining target comprising the additional-target having the additional-target-first-candidate direction-of-arrival angle and any other direction-of-arrival angle in the search space.
According to a third aspect of the disclosure we provide a non-transitory computer readable medium comprising computer program code configured to, when executed, perform the method of the second aspect.
While the disclosure is amenable to various modifications and alternative forms, specifics thereof have been shown by way of example in the drawings and will be described in detail. It should be understood, however, that other embodiments, beyond the particular embodiments described, are possible as well. All modifications, equivalents, and alternative embodiments falling within the spirit and scope of the appended claims are covered as well.
The above discussion is not intended to represent every example embodiment or every implementation within the scope of the current or future Claim sets. The figures and Detailed Description that follow also exemplify various example embodiments. Various example embodiments may be more completely understood in consideration of the following Detailed Description in connection with the accompanying Drawings.
One or more embodiments will now be described by way of example only with reference to the accompanying drawings in which:
The antenna array 103 comprises a plurality of antenna elements 107-111. One or more of the antenna elements may be configured to transmit radar signals, which may comprise a FMCW chirp 112, that will reflect from the targets 105, 106. Two or more of the antenna elements 107-111 may be configured to receive the reflected radar signals 104A, 104B from the targets 105, 106.
Radar systems, such as FMCW among others, has many applications and may be used in the automotive field to detect targets in the neighbourhood of the vehicle with the objective to make driving safer and more comfortable. Distance to the target(s) 105, 106 and the relative velocity of the target(s) can be estimated. The use of several antenna elements 107-111 to transmit and receive radar signals allows for the direction in which this target is present to be determined and it is typically represented as an angle relative to a direction of the antenna elements.
The reflected radar signal 104A from the first target 105 has a direction of arrival angle of θ1 at the antenna elements 107-111. The reflected radar signal 104B from the second target 106 has a direction of arrival angle θ2 at the antenna elements 107-111. However, the radar signals 104 as received by the antenna elements 107-111 comprises a combination of the signals 104A and 104B and noise. It will also be appreciated that the direction of arrival angle may represent the angle of arrival of the reflected radar signals 104A, 104B in one or both of an azimuth angle and an elevation angle.
It is necessary to processes the received radar signals to determine, optionally, the number of targets (if not known or otherwise determined) and the direction of arrival angles θk of K targets, which in this example comprise θ1 and θ2.
Deterministic Maximum-Likelihood (DML) Direction-of-Arrival (DoA) estimation is a known process for determining the most likely (including likely) angles from which the radar signals 104A, 104B are received to create the observed combination of radar signals 104 as received by the antenna elements 107-111.
The transmitted radar signals are reflected by the target(s) 105, 106 and received by the receive antenna elements of the radar system 100 and, depending on the direction of arrival angle of the reflected wave(s) θ1 and θ2, different pathlengths between a transmit antenna element(s), targets 105, 106 and receive antenna elements are realised, leading to phase differences in the received radar signals between the antenna elements. Analysis of these phase differences is carried out to estimate the direction of arrival angle of the target(s).
Direction of arrival angle estimation based on data from the antenna array 103 is an important matter for radar systems 100. If the radar signals received originate from one target 105, the signal strength at the antenna elements 107-111 is substantially identical but due to path length differences between antenna elements 107-111 and target 105 the phase of the radar signal will be different and is a function of the direction of arrival angle.
When multiple targets 105, 106 are reflecting, a linear combination of these signals will be received. Because of the linear combination, both the amplitude and the phase per antenna element 107-111 will vary and has to be used to estimate the DoA angles of the targets 105, 106.
In practice the number of targets 105, 106 is unknown and has to be estimated as well. In one or more examples, data from the antenna array 103 can be pre-processed to analyse the space in which the targets are located.
Using radar signals, such as FMCW radar signals, one can use the known technique of Range-Doppler processing to quantize the received signal in Range and Doppler shift (i.e. frequency change). For each Range-Doppler combination for which one has detected energy (above a threshold), one can carry out the DoA estimation. The Range-Doppler pre-processing separates targets on the basis of their distance from the antenna array 103 and their velocity (Doppler) and therefore the number of targets per Range-Doppler bin is expected to be low. The properties of the FMCW signal determine how fine the radar scene is quantized in Range and Doppler. With an appropriate designed radar system it is reasonable to assume that having one target present in the radar data of one Range-Doppler bin is more likely than having two present in the radar data of one Range-Doppler bin, and 2 targets more likely than 3 targets etc. In known examples, therefore, it is typical for the algorithm used to solve the DoA problem to start with searching first for only one target over the range of DoA angles, then for all the combinations of DoA angles for two targets, then for all the combinations of DoA angles three targets, etc. When each of these searches indicates how well the found candidate DoA's match with the received radar data signals then one can stop searching for more targets if the match with the received signal is sufficiently close (e.g. above a threshold level of confidence). Noise in the radar system is a reason why an exact match is unlikely to happen. Since noise power is estimated in radar systems, a threshold may be derived to evaluate the match.
Deterministic Maximum Likelihood DoA estimation is a technique that for a given number of targets can determine what the most likely DoA angles are and what their match is to the received radar signal. In one or more examples, the DML algorithm may be configured to find the DoA angles that maximizes the match with the received radar data. In case a K-target search with DML finds a match that is too poor (e.g. below a threshold level of a match) on the basis of the known noise properties, then one proceeds with a (K+1)-target search with DML. λ DML search for (K+1) targets is more complex than a search for K targets. Therefore, in a practical implementation one has to stop after a certain K because of limitations in computing resources to search for more targets. Moreover, system imperfections (amplitude and phase distortions, noise) also limits the number of targets one successfully can estimate. In one or more examples a practical value for K is therefore from 1 to 2 or 1 to 3 or 1 to 4 or 1 to 5 potential targets.
DoA estimation may be carried out for each Range-Doppler bin for which sufficient energy is detected. In a rich radar scene this means that DoA estimation may have to be carried out many times within a system cycle. For that reason it is important that the corresponding complexity of the DoA estimation process is low.
DoA estimation starts with the radar signals received at the antenna elements or, more particularly, an input dataset representing the reflected radar signals received at the antenna elements 107-111. The input dataset can be represented collectively with an N-dimensional vector x=(x1, . . . , xN)T, which is often called a snapshot, and wherein T stands for transpose, such that x is a column vector. The number of antenna elements is N. During a system cycle, radar signals received by the apparatus 101 may comprise data representative of the received signals at each of the antenna elements 107-111. In one or more examples, during a system cycle, radar signals received by the apparatus may comprise input datasets or snapshots extracted from one or more Range-Doppler bins. In one or more examples, DoA estimation may be carried out only for those Range-Doppler bins that contain radar signals having an energy above a certain threshold. Thus, the following process can be performed on the input dataset whether or not Range-Doppler processing has been performed.
A signal received from a target at DoA angle θ1 will result in a response at the antenna elements 107-111. That response has constant amplitude and a phase relation between the antenna elements that is specific for the DoA angle θ1 and the relative positions of the antenna elements 107-111. The response can be denoted with a vector: a1=a(θ1). When at least two antenna elements have a distance ≤λ/2, and the DoA angle θ may be between −90 and 90 degrees, any two single target responses will be different and therefore the DoA angle of a single target response can be unambiguously determined. For multiple, say K targets, the antenna response will be a linear combination of K single target responses, i.e.
x=Σ
K
k=1
s
k
a
k
+n,
where n represents additive noise, and sk represent the complex amplitude of the targets and x represents an input dataset representing the radar signals received at the antenna elements 107-111, and ak (comprises a vector (known in that art as a beamsteering vector) and comprises a function of the DoA, wherein
a
T
n=(ej2π(d
and (d1, . . . ,dN) are the relative positions of the antenna elements or virtual antenna elements. The vector an carries the relative phase behaviour among the antenna elements due to pathlength difference of a planar wave originating from an angle θn. Put another way, a beamsteering vector represents the expected response at the antenna elements of a single target or one of a plurality of targets with unit amplitude and comprises a function of Direction-of-Arrival angle.
The processing performed by the apparatus is based on the aforementioned input dataset. The input dataset may be from a Range-Doppler bin if the optional Range-Doppler processing is performed.
λ general description of DML-DoA determination will be provided followed by embodiments of the example embodiment apparatus 101.
Thus, to summarise, an input dataset or snapshot is represented with a vector x. The snapshot represents the amplitude and phase of the reflected radar signals received at the N antenna elements 107-111. If Range-Doppler processing is applied, the snapshot may comprise the reflected radar signals received at the N antenna elements extracted from a Range-Doppler bin, as will be familiar to those skilled in the art. Furthermore, we assume that K targets are present in a snapshot (e.g. K targets are contained in one Range-Doppler bin) with respective, possible DoA angles θ1, θ2, . . . , θK. The K targets can be represented with a beamsteering matrix A consisting of K beamsteering vectors (a1, . . . , aK). As mentioned above, a beamsteering vector is the expected response of single target with unit amplitude and is given by:
a
T
n=(ej2π(d
where (d1, . . . ,dN) are the relative positions of the antenna elements (which may be real antenna elements or a combination of real and virtual antenna elements, as will be familiar to those skilled in the art).
A signal amplitude vector s represents the complex amplitudes of the individual targets. Furthermore we assume that the snapshot is corrupted by antenna noise and that the noise is identical and independent distributed zero-mean Gaussian with variance σ2. Hence, x=As+n. Because of the Gaussian noise assumption, one can write the joint probability density function of the snapshot as
The Maximum-Likelihood (ML) estimate of s and A can be found by minimizing (mathematically, the maximization can be solved by differentiating log(p(x)) with respect to s and by requiring equality to zero)
Q=|x−As|
2
To solve this equation, that is find the K most likely DoA angles to the targets and their complex amplitude, a bootstrapping approach may be followed.
Assuming that A is known, that is to propose a candidate set of beamsteering vectors represented as A, one can find s by minimizing Q in the least square sense:
s
=(AHA)−1AHx
This result is substituted back into the expression for Q. Hence, as will be familiar to those skilled in the art, after some mathematical formatting one obtains
Q=x
H(I−πA)x, where πA=A(AHA)−1AH
This matrix πA is called the projection onto the column space of A. Since, the beam-steering matrix A is a function of the K beamsteering vectors that represent the candidate DoA angles θ=θ1, θ2, . . . , θK, the DML problem can now be formulated as the search for a K-dimensional vector θ
θDML=arg minθQ
An equivalent notation for Q is using the trace operator (Tr( )) and therefore Q is given by:
Q=x
H(I−πA)x=Tr((I−πA)xHx)
This form enables the generalization to estimating the DoA angles using multiple snapshots.
By using the sample covariance matrix {circumflex over (R)}=E[xHx] one obtains
Q=Tr((I−πA){circumflex over (R)})
Instead of minimizing Q, the DML criterion can also be formulated as a maximizing criterion:
In these expressions the DML objective function is equal to:
f(πA,x)=xHπAx=Tr(πAxHx)
or in case of multiple snapshots:
f(πA,{circumflex over (R)})=Tr(πA{circumflex over (R)}))
The use of the matrix πA leads to a compact notation for the DML criterion.
As preparation for the DML algorithm one may start with the definition of an e.g. 1-Dimensional search space (although the principle can be extended to 2-Dimensional search spaces) that has a granularity (i.e. points in the search space) that reflects the resolution and/or the wanted accuracy.
One can select Nθ discrete points in the range [−π/2, π/2]. As will be understood, if the desired field-of-view is smaller one can select accordingly a smaller search space, such as with fewer discrete points over the narrow angular range. Thus, each point in the search space corresponds, such as by way of a function, to a direction-of-arrival angle.
With such a search space or grid of points and the search for K possible targets, the search space has size
E.g. with the search for K=2 targets and a grid of size Nθ=256 the number of discrete points to be investigated is equal to NK=½Nθ(Nθ−1)˜215=32 k.
We now provide a coarse outline of the DML algorithm.
Coarse DML Algorithm Outline
In this for-loop the lines Select, Calculate and Evaluate can be considered an “inner loop”, which is performed for each target. Note that in this for-loop the search for K targets, making it a K-dimensional search, is not shown for clarity. That is, here it is shown as a 1-dimensional loop, but in a more general case, there are K nested loops. Each loop represents one dimension of the K-dimensional search space.
As the algorithm progresses, different combinations of Direction-of-Arrival angles are input to the beamsteering vectors and the objective function evaluated. Thus, as each candidate set of beamsteering vectors as represented by the beam-steering matrix is selected, one has to keep track of the maximum value (or minimum depending on how the objective function is formulated) of the DML objective function. Thus, each index n is uniquely associated with a set of candidate beamsteering vectors and therefore also with a set of K candidate DoA angles for each of the possible targets. The index n for which the DML objective function is maximal (or above a threshold) determines the Maximum-Likelihood estimate of the DoA of the K targets. Likewise, depending on how the objective function is formulated the index n for which the DML objective function is minimal (or below a threshold) may determine the Maximum-Likelihood estimate of the DoA of the K targets.
As will be appreciated by those skilled in the art, the evaluation of the objective function may involve performing a correlation with the beamsteering vectors, as will be described in more detail below. In general, for a non-uniform linear array the correlation with all beamsteering vectors on the grid may be carried out using a zero-padded FFT of size Nθ.
In one or more examples zero-value insertion may be performed to pad the input dataset to the predetermined number of entries by adding zero-valued entries in the input dataset at positions after the data indicative of the radar signals. Thus, the padding may be performed because we want to evaluate the objective function at Nθ direction-of-arrival angles. Using FFT for performing a correlation, one can do this efficiently by extending the input dataset (e.g. vector) x using zero-valued entries at the locations {20,21, . . . , Nθ−1}×λ/2.
FFT can be used to obtain a correlation that forms part of the assessment of the objective function. It has the advantage that when a non-uniform grid of DoA angles is tested, there is a more dense grid for DoA's around θ=0 and a less dense grid for DoA's around θ=±π/2. In
Embodiments of the processes performed by the apparatus 100 will now be described.
Recall that the DML objective function is equal to:
f(πA,x)=xHπAx, with πA=A(AHA)−1AH
Hence, one can also write the DML objective function as:
f(πA,x)=xHA(AHA)−1AHx=yH(AHA)−1y, where y=AHx.
The interpretation of y=AHx is that y is the correlation of the snapshot with K beamsteering vectors. With this interpretation, the following steps can be defined to reduce the complexity of the inner-loop (that is the Select, Calculate, Evaluate steps recited above):
As an example we like to illustrate the presented idea by looking to the dual target case.
The beamsteering matrix A consists now of 2 beamsteering vectors A=akan). In the core of the DML algorithm an inverse of the matrix AHA has to be calculated. As suggested above in Step 3, this inverse can be determined symbolically and we call this matrix B.
The value ak,n is the in-product between 2 beamsteering vectors and can be calculated once for a specific array and stored in a Look up-Table (LUT). In general such a LUT will have size Nθ2, but if the correlation step is performed using FFT then the ak,n is a function of (k-n) and therefore the size of the LUT is linear with Nθ The size of the table has a gross length of 2Nθ and by exploiting symmetries one can reduce its size to Nθ/2.
Example
We will now describe a recursive process which may be performed by the apparatus 101.
The K=1 branches in layer 501 represent the calculation of the beamforming spectrum, i.e. the DML search for a single target on search grid of points of size Nθ. The Nθ nodes represent the Nθ values of the beamforming spectrum, which can be used to find the most likely target in the case that one target is present in the input dataset representing the amplitude and phase of reflected radar signals. The next layer 502 (K=2) consists of
nodes and therefore represents the DML objective function evaluated on all possible combinations of two possible DoA angles. In prior-art DML search, the search for K=1, K=2, K=3 is sequentially carried out, with only re-use of the beamforming results as determined for K=2. Hence, the lines between second layer 502 (K=2) and the third layer 503 (K=3) is not something that is present in state-of the art DML solutions.
The proposed algorithm performed by the apparatus 101 is based on a recursive formulation of the DML objective function. It will be appreciated that an objective function may be represented in many different ways depending on the algebraic formulations used. Thus, we provide below an example formulation that may be processed recursively but other formulations are possible.
Assume that the DML objective function is calculated for a set of DoA angles [θ1, θ2, . . . , θK] (K targets) and now you want to calculate the DML objective for an extra target θK+1. The K targets can be associated with K beamsteering vectors A=[a1 . . . aK] and the DML objective function can be calculated with f(πA,x)=xHA(AHA)−1AHx=yH(AHA)−1y=yHBy, where y=AHx and B=(AHA)−1.
Thus, the objective function f(πA,x) comprises a function of two sub-functions y=AHx and B=(AHA)−1.
Assume one has determined B and wants to calculate the DML objective function for an extra target θK+1 which is associated with a beamsteering vector aK+1. The set of beamsteering vectors can be represented with a matrix Ã=[A aK+1] and now we have to calculate {tilde over (B)}=(ÃHÃ)−1. One can show that:
where c=B AHaK+1, and
The DML objective function can now be calculated with f(πÃ,x)={tilde over (y)}H{tilde over (B)}{tilde over (y)}, where {tilde over (y)}H=[yHy*K+1] and yK+1=aHK+1x(this is the correlation of the received snapshot x with a (K+1)-th beamsteering vector aK+1).
By substituting former results we get
{tilde over (y)}
H
{tilde over (B)}{tilde over (y)}=y
H
B y+d(|z|2−2Re{zyK+1}+|yK+1|2), (2)
where z=yHc.
In one or more examples, yHB y may be termed a Path Metric. In one or more examples, d(|z|2−2Re{zyK+1}+|yK+1|2) may be termed a Branch Metric function. In one or more examples, {tilde over (y)}H{tilde over (B)}{tilde over (y)} may be considered to be the Path Metric of an extended path.
Thus, the branch metric function is formulated such that it comprises a function of the sub-function y (and yK+1) in combination with the additional-target beamsteering vector aK+1. The additional-target beamsteering vector represents the expected response at the plurality of antenna elements of the additional-target at a proposed additional-target-first-candidate direction-of-arrival angle. The proposed additional-target-first-candidate direction-of-arrival angle may be determined by a particular search strategy or may be a predetermined angle.
It will be appreciated that this is not the only formulation of the objective function that is suitable for evaluation in a recursive manner. However, in one or more examples, this formulation or those similar to it may provide for efficient calculation of the objective function in a recursive manner.
Above we have shown how to get a recursive way of calculating the DML objective function. With such a method one can keep track of the most likely set of DoA angles as the evaluations of the objective function represented by the nodes of the tree 500 are performed. In one or more examples, we provide a strategy for traversing the tree and determining when the algorithm is finished. It should be remembered that we do not a-priori know the depth of the tree to be searched, i.e. we do not know how many targets are present in the snapshot. Therefore in one or more examples, we provide a method to:
In one or more examples, it has been found that sequential decoding can be used. Sequential decoding is an approximate decoding technique for codes with a large state space. In one or more examples, when the DML search uses a sequential decoding algorithm there is then a good trade-off between performance and complexity. However, rather than being a Maximum Likelihood search it becomes an estimate.
In one or more examples, the branch metric function, d(|z|2−2Re{zyK+1}+|yK+1|2), mentioned above is compared to a predetermined threshold λ, which may be termed a path extension penalty. In some examples, this may be advantageous because the DoA angle estimation problem does not have a unique solution and for that reason we favour solutions with few targets over solutions with many targets.
The DoA angle estimation problem can be translated to the search of K-targets that minimizes:
Q
opt
∥x−As∥
2
+λ∥s∥
0 (3)
This optimization criterium consists of 2 parts: the first part is the Mean-Square-Error (∥x−As∥2) and the second part (λ∥s∥0) may be considered a sparsity promoting penalty. In convex optimization, usually a I1- or I2-norm is used to favour few targets over many targets. The I0-norm is more difficult to track because its non-convex behaviour, but for our DoA search algorithm it can serve as path penalty. The value for λ should be positive in order to penalize non-sparse solution. The exact value for λ is a trade-off between performance and computational complexity.
The objective function as formulated in (3) is a Figure-of-merit (FOM) number, i.e. the higher the better. However, in other implementations, the objective function may be formulated such that lower the FOM the better. Nevertheless, in the present example in the search for a high FOM, the path penalty λ should be subtracted. Since, ∥s∥0 is the number of non-zero entries in s and therefore equal to the hypothesized number of targets we find that with every path extension a path penalty value λ should be subtracted from the total metric.
Thus in one or more examples we define a branch metric (which includes the branch metric function) as:
d(|z|2−2Re{zyK+1}+|yK+1|2)−λ (4)
recalling that z=yHc and c=B AHaK+1, and
As a consequence, in one or more examples, if
d(|z|2−2Re{zyK+1}+|yK+1|2)<λ
there is no increase in the FOM in the determination of the path metric of the extended path and therefore extending the path has no benefit and should not be carried out.
The search tree can be pruned at that node and path search should continue with one or more different DoA angles. At a certain moment, all path extensions are explored and the algorithm completes with returning the set of DoA angles that can be associated with the node with the highest FOM.
Thus, to summarise, the apparatus 101, comprising the processor 102 which may be associated with any required memory, is configured to:
The search over the possible DoA angles of the search space for each of the candidate number of targets is now performed by the processor being configured to:
The branch metric function is then evaluated based on a second candidate number of targets (K+1) comprising one more target than the first candidate number of targets. The branch metric function may comprise a function of the respective first candidate direction-of-arrival angles of the first candidate number of targets and an additional-target at an additional-target-first-candidate direction-of-arrival angle. The branch metric function may be considered a prediction that the addition of an additional-target to the first candidate number of targets will lead to a greater figure of merit. In particular, the branch metric function may represent the change to the evaluation of the objective function caused by the addition of the additional-target.
If the branch metric function is greater than the predetermined threshold λ, then the processor is configured to move to the deeper layer in the tree, that is the layer with one more target considered. The processor 102 then evaluates the objective function for the second candidate number of targets, that is K+1 targets. The evaluation is based on the original K targets maintaining their respective first candidate direction-of-arrival angles and with the addition of the further, K+1th, target having an additional-target-first-candidate direction-of-arrival angle.
However, if the branch metric function is less than the predetermined threshold λ, then the processor is configured to evaluate the objective function for the first candidate number of targets K, that is not proceed to a deeper layer of the tree 500. Instead, the evaluation is based on at least one of the first candidate number of targets K being given a different, second candidate direction-of-arrival angle. In other examples, the objective function is evaluated with more than one of the K targets, or all of the K targets having a different, second candidate direction-of-arrival angle. In this instance, the search progresses to a different node in the same layer e.g. layer 502 in the tree 500.
One can imagine that the larger the predetermined threshold or “path penalty”, the less nodes in the search tree 500 will be visited. This has been tested with the number of targets per snapshot being four and the search tree also confined to a depth of four targets. Setting the predetermined threshold to zero, λ=0, a full search of every node in the tree is achieved. For a discrete search grid of points of size 256, 1.8×108 nodes have to be analyzed to find the most likely set of four DoA angles. By increasing, for example, the path penalty to λ=3, the average number of visited nodes (over 10000 experiments) decreases to 1.4×107. In another example test scenario, the number of targets per snapshot is three and the search tree is confined to a depth of three. Full search requires visiting of 2.8×106 nodes, while with λ=3, the average number of visited nodes decreases to 3.1×105. It will be appreciated that setting of the predetermined threshold represents a trade-off. Thus, increasing the predetermined threshold will reduce the computational complexity but the accuracy of the estimated DoA angle relative to the true DoA angle may decrease and the probability of not detecting a target (mis-detection probability) may increase.
In one or more further examples, instead of the FOM-metric example explained above, one can also carry out the DoA search with a metric based on equation (3).
In one or more examples, the root of the tree one starts with a metric M0=∥x∥2 and with each increase in the candidate number of targets (or “path extension”) adds a branch metric λ−d(|z|2−2Re{zyK+1}+|yK+1|2). With such an approach the metric should decrease with every path extension.
For the recursive calculation of B one starts with B=1/N (N being the number of antennas) at layer 501, where K=1. This can be used for calculating B for K=2, 3, . . . etc. targets.
In one or more examples, the process followed when the candidate number of targets is increased (a so called “path extension part”) may be summarized as follows:
The part B AHaK+1 is available from previous calculation, aHK+1A can also be read from the LUT and aHK+1aK+1 is equal to N.
Step 601 comprises the start of the algorithm. The depth in the tree starts at 1, i.e. a proposal that there is one target. Evaluation of the objective function may be performed, or a correlation, to determine the possibility of one target causing the input dataset.
Step 602 comprises the processor being configured to next consider the extension of the depth. i.e. to K=2. As mentioned previously, this may be assessed by determination of a branch metric.
Step 603 represents the comparing of the branch metric to the predetermined threshold.
Step 604 shows the branch metric being greater than the threshold which determinative in the algorithm making further evaluations of the objective function with the additional target. Step 604 also shows the recalculation of the path variables.
Steps 602, 603 and 604 are shown in more detail in the box 605. Thus, step 603 comprises the more detailed steps 606 and 607. Step 606 comprises calculating A, B and y and the path metric.
Step 607 shows calculating c, d, z and the branch metric.
Step 608 shows calculating of Ã, {tilde over (B)} and y for the greater number of candidate number of targets.
Step 609 comprises a step after step 608 and represents the depth in the tree being increased by one, that is with an additional target being considered.
Step 610 is arrived at from step 609 or step 603 (if the branch metric is less than the threshold). Step 610 comprises the processor 102 determining if there are any other combinations of candidate angles to evaluate at the current depth. If yes, they are evaluated and the method proceeds to step 602. If not, the method proceeds to step 611 in which the method proposes there is only less target, that is decreasing the current depth in the tree by one target.
The evaluation of the objective function at different candidate angles can now be performed.
Step 612 shows a determination of whether the search of the tree has completed, i.e. arrived at depth=0. If not, the method proceeds to step 610. If so, the method proceeds to step 613 in which the candidate DoA angles (and candidate number of targets) that resulted in the greatest (or smallest, if formulated such) evaluation of the objective function are output.
Thus, to summarise, in
To limit the number of calculations it is convenient to have a Look Up Table (LUT) available containing the values
These values depend on the used antenna array and the chosen search grid. This LUT can be an 1-Dimensional table when the DoA angles θ to search for are chosen on grid that is uniform in sin(θ). Furthermore, the correlation of the received snapshot x with all possible beamsteering vectors can be carried out prior to the search in order to avoid recalculating these values multiple times during the search, i.e. the values yk=aHkx for each possible beamsteering vector ak, (and therefore DoA angle) can be calculated before the search starts (e.g. with use of FFT) and stored in a LUT such that they can be retrieved when a path extension has to be processed.
It will be appreciated that one or more alternative embodiments are possible. For example, the branch metric and predetermined threshold may be configured to perform a full tree search up to a certain depth (i.e. a predetermined maximum number of targets=Kmax).
The apparatus may be configured to keeping track of best paths for different depths in the tree 500, not only the best overall path. Thus, the best evaluation of the objective function for the various depths may be determined. Sequential search with maximum depth. Because of antenna imperfections it becomes difficult to reliably estimate DoA angles beyond a number of targets (e.g. K=3 or 4).
The predetermined threshold λ is in principle a value that depends on the Signal to Noise Ratio and is fixed. In other examples, the predetermined threshold A may be based on the candidate number of targets being considered, or, put another way, the current depth in tree 500. At increasing depth it becomes more difficult to differentiate between proper and false path extension. Therefore it might be beneficial to apply different path penalty values at different depths of the tree.
Along with the path variables one can calculate for each node also a pseudo-spectrum. This can be done recursively as well. The pseudo-spectrum is the set of complex amplitudes that can be associated with the DoA angles that are hypothesized in a node, i.e. along with estimated DoA angles they provide an estimate of the complex amplitudes of the targets. The pseudo-spectrum is given by s=B y. Hence, the pseudo-spectrum update after successful path extension is {tilde over (s)}={tilde over (B)} {tilde over (y)}, which after substitution gives
Computational Complexity
The computational complexity of a recursive formulation of DML as proposed in this disclosure is as follows. First we have the unconditional path extension exploration from a K-target node to a K+1-target node, i.e. the branch metric calculation. This involves 4 steps
and form together a K-dimensional vector. This vector is multiplied with the K×K matrix B. Complexity is K2 complex multiplications and K(K−1) complex additions.
In the present algorithm, the path extension is explored further when the branch metric is >0. Hence there are unconditional calculations (branch metric) and conditional calculations. If the branch metric is >0, the following calculations are carried out
Using the property that {tilde over (B)} is Hermitian, the total complexity is K(K−1)/2 multiplications, (K−1)(K+2)/2 scalings and K(K−1)/2 complex additions
Summarizing we have per (K+1)-target node:
This is a worst case calculation assuming that both the conditional and unconditional calculations are carried out and counting real multiplications and real additions as complex operations. One can translate the computations to a K-target node by substituting K−1 in the expressions
The complexity of the direct method consists of 2 parts
Summarizing we have per K-target node evaluation:
The method and processor described herein can be easily extended for a 2D antenna array by deriving the beam-forming spectrum in the same way but in 2-dimensions.
The instructions and/or flowchart steps in the above figures can be executed in any order, unless a specific order is explicitly stated. Also, those skilled in the art will recognize that while one example set of instructions/method has been discussed, the material in this specification can be combined in a variety of ways to yield other examples as well, and are to be understood within a context provided by this detailed description.
In some example embodiments the set of instructions/method steps described above are implemented as functional and software instructions embodied as a set of executable instructions which are effected on a computer or machine which is programmed with and controlled by said executable instructions. Such instructions are loaded for execution on a processor (such as one or more CPUs). The term processor includes microprocessors, microcontrollers, processor modules or subsystems (including one or more microprocessors or microcontrollers), or other control or computing devices. λ processor can refer to a single component or to plural components.
In other examples, the set of instructions/methods illustrated herein and data and instructions associated therewith are stored in respective storage devices, which are implemented as one or more non-transient machine or computer-readable or computer-usable storage media or mediums. Such computer-readable or computer usable storage medium or media is (are) considered to be part of an article (or article of manufacture). An article or article of manufacture can refer to any manufactured single component or multiple components. The non-transient machine or computer usable media or mediums as defined herein excludes signals, but such media or mediums may be capable of receiving and processing information from signals and/or other transient mediums.
Example embodiments of the material discussed in this specification can be implemented in whole or in part through network, computer, or data based devices and/or services. These may include cloud, internet, intranet, mobile, desktop, processor, look-up table, microcontroller, consumer equipment, infrastructure, or other enabling devices and services. As may be used herein and in the claims, the following non-exclusive definitions are provided.
In one example, one or more instructions or steps discussed herein are automated. The terms automated or automatically (and like variations thereof) mean controlled operation of an apparatus, system, and/or process using computers and/or mechanical/electrical devices without the necessity of human intervention, observation, effort and/or decision.
It will be appreciated that any components said to be coupled may be coupled or connected either directly or indirectly. In the case of indirect coupling, additional components may be located between the two components that are said to be coupled.
In this specification, example embodiments have been presented in terms of a selected set of details. However, a person of ordinary skill in the art would understand that many other example embodiments may be practiced which include a different selected set of these details. It is intended that the following claims cover all possible example embodiments.
| Number | Date | Country | Kind |
|---|---|---|---|
| 22198193.9 | Sep 2022 | EP | regional |
