This example shows how to convert Cartesian to polar coordinates using a CORDIC vectoring kernel algorithm in MATLAB®. CORDIC-based algorithms are critical to many embedded applications, including motor controls, navigation, signal processing, and wireless communications.
CORDIC is an acronym for COordinate Rotation DIgital Computer. The Givens rotation-based CORDIC algorithm (see [1,2]) is one of the most hardware efficient algorithms because it only requires iterative shift-add operations. The CORDIC algorithm eliminates the need for explicit multipliers, and is suitable for calculating a variety of functions, such as sine, cosine, arcsine, arccosine, arctangent, vector magnitude, divide, square root, hyperbolic and logarithmic functions.
The fixed-point CORDIC algorithm requires the following operations:
1 table lookup per iteration
2 shifts per iteration
3 additions per iteration
CORDIC Kernel Algorithm Using the Vectoring Computation Mode
You can use a CORDIC vectoring computing mode algorithm to calculate
atan(y/x), compute cartesian-polar to cartesian conversions, and for other operations. In vectoring mode, the CORDIC rotator rotates the input vector towards the positive X-axis to minimize the
component of the residual vector. For each iteration, if the
coordinate of the residual vector is positive, the CORDIC rotator rotates clockwise (using a negative angle); otherwise, it rotates counter-clockwise (using a positive angle). Each rotation uses a progressively smaller angle value. If the angle accumulator is initialized to 0, at the end of the iterations, the accumulated rotation angle is the angle of the original input vector.
In vectoring mode, the CORDIC equations are:
is the angle accumulator
where if , and otherwise;
, and is the total number of iterations.
As approaches :
Typically is chosen to be a large-enough constant value. Thus, may be pre-computed.
Efficient MATLAB Implementation of a CORDIC Vectoring Kernel Algorithm
A MATLAB code implementation example of the CORDIC Vectoring Kernel algorithm follows (for the case of scalar
z). This same code can be used for both fixed-point and floating-point operation.
CORDIC Vectoring Kernel
function [x, y, z] = cordic_vectoring_kernel(x, y, z, inpLUT, n) % Perform CORDIC vectoring kernel algorithm for N iterations. xtmp = x; ytmp = y; for idx = 1:n if y < 0 x(:) = accumneg(x, ytmp); y(:) = accumpos(y, xtmp); z(:) = accumneg(z, inpLUT(idx)); else x(:) = accumpos(x, ytmp); y(:) = accumneg(y, xtmp); z(:) = accumpos(z, inpLUT(idx)); end xtmp = bitsra(x, idx); % bit-shift-right for multiply by 2^(-idx) ytmp = bitsra(y, idx); % bit-shift-right for multiply by 2^(-idx) end
CORDIC-Based Cartesian to Polar Conversion Using Normalized Input Units
Cartesian to Polar Computation Using the CORDIC Vectoring Kernel
The judicious choice of initial values allows the CORDIC kernel vectoring mode algorithm to directly compute the magnitude and angle .
The input accumulators are initialized to the input coordinate values:
The angle accumulator is initialized to zero:
After iterations, these initial values lead to the following outputs as approaches :
Other vectoring-kernel-based function approximations are possible via pre- and post-processing and using other initial conditions (see [1,2]).
Suppose that you have some measurements of Cartesian (X,Y) data, normalized to values between [-1, 1), that you want to convert into polar (magnitude, angle) coordinates. Also suppose that you have a 16-bit integer arithmetic unit that can perform add, subtract, shift, and memory operations. With such a device, you could implement the CORDIC vectoring kernel to efficiently compute magnitude and angle from the input (X,Y) coordinate values, without the use of multiplies or large lookup tables.
sumWL = 16; % CORDIC sum word length thNorm = -1.0:(2^-8):1.0; % Also using normalized [-1.0, 1.0] angle values theta = fi(thNorm, 1, sumWL); % Fixed-point angle values (best precision) z_NT = numerictype(theta); % Data type for Z xyCPNT = numerictype(1,16,15); % Using normalized X-Y range [-1.0, 1.0) thetaRadians = pi/2 .* thNorm; % real-world range [-pi/2 pi/2] angle values inXfix = fi(0.50 .* cos(thetaRadians), xyCPNT); % X coordinate values inYfix = fi(0.25 .* sin(thetaRadians), xyCPNT); % Y coordinate values niters = 13; % Number of CORDIC iterations inpLUT = fi(atan(2 .^ (-((0:(niters-1))'))) .* (2/pi), z_NT); % Normalized z_c2p = fi(zeros(size(theta)), z_NT); % Z array pre-allocation x_c2p = fi(zeros(size(theta)), xyCPNT); % X array pre-allocation y_c2p = fi(zeros(size(theta)), xyCPNT); % Y array pre-allocation for idx = 1:length(inXfix) % CORDIC vectoring kernel iterations [x_c2p(idx), y_c2p(idx), z_c2p(idx)] = ... fidemo.cordic_vectoring_kernel(... inXfix(idx), inYfix(idx), fi(0, z_NT), inpLUT, niters); end % Get the Real World Value (RWV) of the CORDIC outputs for comparison % and plot the error between the (magnitude, angle) values AnGain = prod(sqrt(1+2.^(-2*(0:(niters-1))))); % CORDIC gain x_c2p_RWV = (1/AnGain) .* double(x_c2p); % Magnitude (scaled by CORDIC gain) z_c2p_RWV = (pi/2) .* double(z_c2p); % Angles (in radian units) [thRWV,rRWV] = cart2pol(double(inXfix), double(inYfix)); % MATLAB reference magnitudeErr = rRWV - x_c2p_RWV; angleErr = thRWV - z_c2p_RWV; figure; subplot(411); plot(thNorm, x_c2p_RWV); axis([-1 1 0.25 0.5]); title('CORDIC Magnitude (X) Values'); subplot(412); plot(thNorm, magnitudeErr); title('Error between Magnitude Reference Values and X Values'); subplot(413); plot(thNorm, z_c2p_RWV); title('CORDIC Angle (Z) Values'); subplot(414); plot(thNorm, angleErr); title('Error between Angle Reference Values and Z Values');
Jack E. Volder, The CORDIC Trigonometric Computing Technique, IRE Transactions on Electronic Computers, Volume EC-8, September 1959, pp330-334.
Ray Andraka, A survey of CORDIC algorithm for FPGA based computers, Proceedings of the 1998 ACM/SIGDA sixth international symposium on Field programmable gate arrays, Feb. 22-24, 1998, pp191-200